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FS witnesses Ng Teng Fong Charitable Foundation and Sino Group donate $200 million in support of AI development in Hong Kong
The Financial Secretary, Mr Paul Chan, attended the donation ceremony of the Ng Teng Fong Charitable Foundation and Sino Group in support of AI development in Hong Kong today (March 10), witnessing the Ng Teng Fong Charitable Foundation and Sino Group donate $200 million to the Hong Kong Generative AI Research and Development Center (HKGAI) under the InnoHK research clusters to support the centre in establishing a service platform to provide Hong Kong citizens with the chatbot service "HKChat" based on the locally developed large language model HKGAI V1. During the ceremony, the Director of the Ng Teng Fong Charitable Foundation and Deputy Chairman of Sino Group, Mr Daryl Ng, presented the cheque to the Provost of the Hong Kong University of Science and Technology and the Director of InnoHK HKGAI, Professor Guo Yike. The Secretary for Innovation, Technology and Industry, Professor Sun Dong, and the Commissioner for Innovation and Technology, Mr Ivan Lee, attended and witnessed the donation ceremony.
Mr Chan expressed his heartfelt gratitude to the Ng Teng Fong Charitable Foundation and Sino Group for their support. He said that currently, AI is undergoing rapid development, which is redefining the core competitiveness of economies and reshaping the medium- to long-term landscape of the global economy. During the third session of the 14th National People's Congress and the third session of the 14th Chinese People's Political Consultative Conference National Committee last week, Premier Li Qiang mentioned the need to continue advancing the "AI+" initiative in the Government Work Report, emphasising support for the extensive application of large-scale AI models, as well as the integration of digital technologies with our country’s manufacturing and market strengths. By fostering closer collaboration among the Government and the industry, academia, research and investment sectors, accelerating the development of AI as a core industry of Hong Kong, the city can be developed into an international exchange and co-operation hub for the AI industry.
Mr Ng said, "We are pleased to foster the holistic development of the I&T (innovation and technology) ecosystem while supporting the Government's goal of attracting and encouraging more international talent to Hong Kong. It is an honour to contribute to this significant milestone for AI development in Hong Kong, facilitating locally developed AI platforms to be accessible to both industry and the public. We are thankful for the opportunity to support the development of a more inclusive AI future that will benefit the lives of the people of Hong Kong."
Professor Guo stated, "This generous donation will provide tremendous momentum to our research and development efforts. While expressing our profound gratitude, we are deeply aware of the expectations from Hong Kong society and the responsibilities that lie on our shoulders. Having recently launched our new model HKGAI V1, we are currently fully engaged in developing and optimising this model as well as a series of applications. In particular, we are developing the 'HKChat', a locally developed AI chatbot service to be used by Hong Kong citizens. This donation has provided crucial support for this endeavour. We will strive to ensure our work meets and exceeds expectations."
InnoHK is a major I&T initiative of the Hong Kong Special Administrative Region Government which aims to foster global research collaboration, comprising Health@InnoHK, which focuses on healthcare technologies, and AIR@InnoHK, which focuses on artificial intelligence and robotics technologies, with a total of 30 research centres established. InnoHK has successfully formed collaborations with more than 30 world-renowned universities and research institutes, including the Chinese Academy of Sciences, Peking University, Harvard University, Stanford University, the University of Oxford, the University of Cambridge, etc. The collaborating non-local universities and research institutions are from 12 different economies, pooling together about 2 500 researchers locally and from all over the world.
The HKGAI is one of the 30 research centres under InnoHK. Its goal is to establish Hong Kong's own AI foundational models and ecosystem. The HKGAI is developing a series of open-source foundational models to create technologies applicable to text and image generation, medical diagnosis, legal affairs and other applications. Furthermore, the centre will develop "HKChat", an AI chatbot that integrates local data, knowledge bases, and language requirements, supporting Cantonese, English, and Putonghua.
FS witnesses Ng Teng Fong Charitable Foundation and Sino Group donate $200 million in support of AI development in Hong Kong Source: HKSAR Government Press Releases
FS witnesses Ng Teng Fong Charitable Foundation and Sino Group donate $200 million in support of AI development in Hong Kong Source: HKSAR Government Press Releases | 1 |
Integrating Artificial Intelligence (AI) and Machine Learning (ML) into legal research revolutionizes how lawyers and legal professionals analyze case law. This transformation is enhancing efficiency and accuracy and opening new avenues for strategic decision-making in the legal field.
Introduction to AI in Legal Research
AI for legal research has become a cornerstone in modern legal practices. By leveraging AI, legal professionals can automate tedious tasks such as document review, case law analysis, and due diligence, freeing time for more strategic and complex legal work. These tools utilize advanced algorithms to analyze vast amounts of data quickly and accurately, providing previously complex insights to obtain through manual methods.
AI for Legal Research: Enhancing Efficiency and Accuracy
AI for legal research is transforming the way lawyers conduct case law analysis. Traditional methods involved hours of manual research, which were time-consuming and prone to human error. On the other hand, AI tools can process large datasets in minutes, identify relevant cases, extract key information, and summarize complex legal concepts with high precision. This speeds up the research process and ensures that no critical information is overlooked.
Key Benefits of AI in Legal Research:
Faster Research: AI can sift through legal documents in seconds, saving hours or even days of manual work.
Improved Accuracy: AI minimizes the risk of human error by accurately identifying relevant precedents and legal principles.
Enhanced Insights: AI can uncover patterns and trends in case law that may not be immediately apparent to human researchers.
Legal AI Software: Transforming Case Law Analysis
Legal AI software is at the forefront of this transformation. These platforms use Natural Language Processing (NLP) to understand complex legal language, analyze vast databases of legal precedents, and provide rapid access to relevant case law. This enables legal professionals to develop stronger case strategies and make more informed decisions.
Features of Advanced Legal AI Software:
Comprehensive Analysis: AI systems can analyze similar cases, judges’ decisions, and the legal reasoning behind those decisions.
Predictive Analytics: AI tools can predict case outcomes based on historical data, helping lawyers anticipate challenges and mitigate risks.
Document Review: AI automates the review of legal documents, identifying key information and potential issues.
AI Legal Tools: Streamlining Legal Processes
AI legal tools are designed to streamline various legal processes, from research to document drafting. These tools enhance efficiency by automating contract analysis and due diligence tasks. They also improve the overall quality of legal work by providing data-driven insights that help lawyers develop informed litigation strategies.
Examples of AI Legal Tools:
Contract Analysis: AI tools can review hundreds of contracts simultaneously, identifying patterns and inconsistencies.
Document Drafting: AI assists in generating legal documents and ensuring compliance with jurisdiction-specific requirements.
Predictive Insights: AI provides data-driven insights to predict case outcomes and develop informed litigation strategies.
AI Legal Research: The Future of Legal Analysis
AI legal research is poised to continue transforming the legal landscape. AI technologies will offer even greater capabilities for legal professionals as they evolve. Integrating AI in legal research enhances productivity and allows lawyers to focus on more strategic tasks, ultimately leading to better client outcomes.
Future Directions:
Generative AI: AI is being used to analyze complex legal documents and draft legal texts.
Predictive Analytics: AI will continue to play a crucial role in predicting case outcomes and developing litigation strategies.
Ethical Considerations: Ensuring AI systems are free from bias and ethical concerns will be essential for their widespread adoption.
AI for Corporate Law: Enhancing Efficiency and Strategy
AI for corporate law is particularly beneficial in areas such as contract analysis, due diligence, and mergers and acquisitions. AI tools can quickly analyze large volumes of legal documents, identify potential risks, and provide strategic insights that help corporate lawyers make informed decisions. This streamlines the legal process, reduces errors, and ensures consistency in legal documents.
Applications of AI in Corporate Law:
Contract Review: AI automates contract review, identifying key provisions and potential risks.
Due Diligence: AI accelerates the due diligence process by analyzing vast amounts of data quickly and accurately.
M&A Transactions: AI assists in sourcing target companies and conducting diligence more efficiently.
Quote from a Lawyer Using AI in Legal Research
As Golriz Chrostowski, a Bloomberg Law legal analyst, notes, “Do the research, and then feed whatever your question is through AI and use it as a gut check. It’s another set of eyes. It can’t be the beginning and the end of your legal research. It can just be something to supplement what you’ve done.” This perspective highlights the importance of using AI as a tool to enhance legal research rather than replace it entirely2.
Risks and Considerations
While AI offers significant benefits, there are risks associated with its use in legal research. One notable risk is the phenomenon of “hallucinations,” where AI tools provide false information, such as citing non-existent cases. This highlights the need for lawyers to verify AI-generated details thoroughly.
In conclusion, AI-powered legal research is revolutionizing case law analysis by enhancing efficiency, accuracy, and strategic decision-making. As AI technologies continue to evolve, they will play an increasingly vital role in shaping the future of legal practice. | 1 |
NewsVoir
Amaravati (Andhra Pradesh) [India], March 10: SRM AP, Amaravati, is proud to announce a transformative five-year collaboration with Carnegie Mellon University's School of Computer Science (CMU SCS), USA-one of the world's foremost institutions in artificial intelligence (AI) and cutting-edge research. This strategic collaboration aims to push the boundaries of knowledge, innovation and education in AI-related disciplines, including machine learning, natural language processing, computer vision, infrastructure and systems, and AI ethics and policy.
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SRM AP, Amaravati's Landmark Collaboration with Carnegie Mellon University's School of Computer Science, USA for AI Research, Education
Andhra Pradesh, IndiaSRM AP, Amaravati, is proud to announce a transformative five-year collaboration with Carnegie Mellon University's School of Computer Science (CMU SCS), USA-one of the world's foremost institutions in artificial intelligence (AI) and cutting-edge research. This strategic collaboration aims to push the boundaries of knowledge, innovation and education in AI-related disciplines, including machine learning, natural language processing, computer vision, infrastructure and systems, and AI ethics and policy.
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SRM AP, Amaravati Secures a Pioneering Collaboration with Carnegie Mellon University's School of Computer Science
At the heart of this collaboration is a shared vision to foster an ecosystem that nurtures groundbreaking research, cultivates exceptional talent and accelerates advancements in AI-driven technologies.
A Pioneering Collaboration for AI Excellence
"CMU's School of Computer Science is excited to work with SRM AP, Amaravati, on this landmark collaboration to advance research and bolster AI education. Together, we will shape the future of AI and empower the next generation of researchers, educators and industry leaders to push the frontiers of technology and drive meaningful change in society," said Prof. Martial Hebert, Dean of CMU's School of Computer Science.
Empowering Research Through Global Collaboration
As part of this collaboration, SRM AP, Amaravati's research faculty and researchers will have the opportunity to engage directly with the esteemed faculty and researchers at CMU's School of Computer Science. They will immerse themselves in CMU SCS's pioneering AI labs, working alongside global experts in key research domains. This will facilitate research, knowledge sharing and the development of state-of-the-art AI innovations that address real-world challenges.
Dr P Sathyanarayanan, Pro-Chancellor of SRM AP, Amaravati, said that, "To further strengthen research capabilities, this collaboration will also pave the way to establish advanced AI labs at SRM AP, Amaravati. These labs will be incubators for novel AI research, fostering a stimulating environment that promotes academic rigor, interdisciplinary collaboration and technological innovation."
Advancing AI Education with World-Class Learning Opportunities
Beyond research, this collaboration is designed to enrich the academic experience of SRM-AP's teaching faculty and research scholars. Selected faculty members and scholars can audit cutting-edge AI courses at CMU's School of Computer Science as visiting participants. This exposure will allow them to engage with CMU SCS faculty and contribute to developing robust AI curricula at SRM-AP. They will also gain hands-on experience in designing assignments, worksheets and examinations that mirror real-world AI problem-solving scenarios, enhancing the quality of AI education at SRM AP, Amaravati.
Unparalleled Research Internships for Students
Prof. Manoj K Arora, Vice Chancellor of SRM AP, Amaravati, expressed that, "In a move that underscores its commitment to nurturing future AI leaders, the collaboration will offer SRM-AP students the opportunity to undertake research internships at CMU's School of Computer Science." Selected students will spend approx. six weeks each summer immersed in a world-class research environment, gaining firsthand experience in tackling complex AI challenges alongside leaders in the field. This experience will provide students with unparalleled insights and exposure to global research methodologies, setting them apart in the highly competitive AI landscape.
By leveraging CMU SCS's expertise and SRM-AP's commitment to academic excellence, this collaboration will drive innovation, expand knowledge horizons and create a lasting impact on the AI ecosystem between the universities.
(ADVERTORIAL DISCLAIMER: The above press release has been provided by NewsVoir. ANI will not be responsible in any way for the content of the same.)
(This is an unedited and auto-generated story from Syndicated News feed, LatestLY Staff may not have modified or edited the content body) | 1 |
SRM AP, Amaravati has joined forces with Carnegie Mellon University's esteemed School of Computer Science in a groundbreaking five-year partnership aimed at revolutionizing artificial intelligence research and education. This initiative seeks to redefine the landscape of AI disciplines, including machine learning and AI ethics.
The collaboration stands as a testament to the shared commitment of both institutions to drive innovative research and create a robust learning ecosystem. Faculty and researchers from SRM AP will gain direct access to CMU's pioneering AI labs, engaging with global thought leaders to tackle real-world challenges and foster breakthrough innovations.
This partnership will significantly enhance SRM-AP's academic offerings, providing students with internships at CMU and integrating advanced AI curricula. Participants will immerse themselves in cutting-edge research environments, gaining invaluable insights and positioning themselves for leadership roles in the fast-evolving AI sector.
(With inputs from agencies.) | 1 |
Lunit, a company specializing in AI-powered solutions for cancer diagnostics, has announced the publication of a study in Nature Communications demonstrating the effectiveness of its AI mammography analysis solution, Lunit INSIGHT MMG, in a real-world screening setting. The study, described as the world’s first large-scale, multicenter prospective study in a single-reading mammography environment, was conducted within South Korea’s national breast cancer screening program.
Study findings
The study, led by Professor Yun-Woo Chang from Soonchunhyang University Seoul Hospital and involving breast and general radiologists from six academic hospitals in South Korea, analyzed data from 24,543 women aged 40 and above who underwent routine biennial mammography screenings between February 2021 and December 2022. The research compared the performance of breast radiologists interpreting mammograms with and without the assistance of Lunit INSIGHT MMG.
Key findings of the study include:
Increased Cancer Detection Rate (CDR): AI-assisted radiologists detected 13.8% more screen-detected breast cancers than those relying solely on traditional interpretation methods. The CDR increased from 5.01 to 5.70 (per 1,000 screenings) with AI assistance.
AI-assisted radiologists detected 13.8% more screen-detected breast cancers than those relying solely on traditional interpretation methods. The CDR increased from 5.01 to 5.70 (per 1,000 screenings) with AI assistance. Unchanged Recall Rates (RRs): The use of AI did not lead to a statistically significant increase in recall rates, indicating improved clinical effectiveness without an increase in unnecessary follow-up procedures.
The use of AI did not lead to a statistically significant increase in recall rates, indicating improved clinical effectiveness without an increase in unnecessary follow-up procedures. Improved detection of early-stage cancers: AI assistance resulted in a significant improvement in the detection of small-sized tumors and node-negative cancers, suggesting enhanced early detection capabilities.
AI assistance resulted in a significant improvement in the detection of small-sized tumors and node-negative cancers, suggesting enhanced early detection capabilities. Benefits for general radiologists: A simulated retrospective study component showed that AI assistance led to an even greater improvement in CDRs for general radiologists (a 26.4% increase), highlighting AI’s potential to support radiologists with varying levels of experience.
The results suggest that AI can serve as a valuable tool in single-reader mammography settings, common in many countries, by enhancing radiologists’ accuracy without increasing the number of false positives. This is particularly significant given the global shortage of specialized breast imaging professionals. Lunit INSIGHT MMG has already been implemented in national breast screening programs in several countries, including Australia, Sweden, Iceland, Singapore, Saudi Arabia, and Qatar. The study builds on previous research, including a trial at Capio St. Göran Hospital in Sweden, where AI replaced one radiologist in a double-reading workflow.
Featured image credit: Lunit | 1 |
Govt welcomes HK$200m donation to support AI
Hong Kong's finance and technology ministers on Monday welcomed a HK$200 million donation by property developers to support the city’s generative artificial intelligence development.
The fund will be used to support the Hong Kong Generative AI Research and Development Centre (HKGAI), and to develop an app for the recently launched HKGAI V1, the SAR's first locally developed generative AI tool.
Speaking at a press event during which the donation provided by the Ng Teng Fong Charitable Foundation and Sino Group was announced, Financial Secretary Paul Chan said Hong Kong must grasp opportunities brought by AI.
"The donation will support the establishment of a service platform for the InnoHK Research Clusters to provide services for residents based on the locally-produced large [AI] model, HKGAI," he told reporters.
"I look forward to the further development of the model, to extend the platform to cover more application scenarios, so that the services will be more diversified and benefit more local residents," Chan added.
Hong Kong launched the HKGAI V1 large model about two weeks ago. It powers a chatbot called HKChat which is capable of answering users' questions on topics ranging from the SAR's government structure to cinema schedules.
Speaking at the same event, the Secretary for Innovation, Technology and Industry, Sun Dong, added that the funds will help make the service platform more market-driven, and help the centre hire more staff.
"When you try to run a service system to the general public, you must have enough resources to support this platform, including human power as well as computing power, I think this is the main purpose of this donation," he said.
"I also hope the new version, or the mobile version of the large language model of HKGAI will be able to serve Hong Kong people as soon as possible, hopefully in the next several months."
Separately, Sun dismissed concerns that the latest reduction in government spending announced in last month’s budget will affect research and innovation in technology.
He said authorities will support those developments in a more targeted way. | 1 |
10th March 2025 – (Hong Kong) The Financial Secretary, Mr Paul Chan, attended a donation ceremony today (10th March) where the Ng Teng Fong Charitable Foundation and Sino Group pledged HK$200 million to the Hong Kong Generative AI Research and Development Centre (HKGAI). This funding aims to support the establishment of a service platform providing Hong Kong citizens with the chatbot service “HKChat,” based on the locally developed large language model, HKGAI V1.
During the event, Mr Daryl Ng, Director of the Ng Teng Fong Charitable Foundation and Deputy Chairman of Sino Group, presented the cheque to Professor Guo Yike, Provost of the Hong Kong University of Science and Technology and Director of InnoHK HKGAI. The ceremony was also attended by the Secretary for Innovation, Technology and Industry, Professor Sun Dong, and the Commissioner for Innovation and Technology, Mr Ivan Lee.
Mr Chan expressed his heartfelt appreciation for the generous support, noting that the rapid advancement of AI is reshaping the global economic landscape. He highlighted Premier Li Qiang’s recent comments on the need to advance the “AI+” initiative, focusing on the integration of digital technologies with the nation’s manufacturing strengths. By fostering collaboration among government, industry, academia, and research sectors, Hong Kong aims to become an international hub for AI exchange and cooperation.
Mr Ng remarked on the importance of fostering a robust innovation and technology ecosystem, aligning with the government’s goals of attracting international talent to Hong Kong. He expressed pride in contributing to this milestone in AI development, emphasising the goal of making locally developed AI platforms accessible to both industry and the public.
Professor Guo acknowledged the significant impact of the donation on their research efforts, stating that it would provide vital support for developing and optimising the HKGAI V1 model and the “HKChat” service. He reaffirmed the centre’s commitment to meeting societal expectations and responsibilities.
InnoHK, a key initiative by the Hong Kong Special Administrative Region Government, aims to enhance global research collaboration. It encompasses various sectors, including Health@InnoHK for healthcare technologies and AIR@InnoHK for artificial intelligence and robotics, with 30 research centres established. InnoHK has successfully partnered with over 30 prestigious universities and research institutes worldwide, pooling together approximately 2,500 researchers.
The HKGAI is focused on building Hong Kong’s AI foundational models and ecosystem, developing open-source technologies for applications in text and image generation, medical diagnosis, and legal affairs. The centre is also working on “HKChat,” an AI chatbot that will support Cantonese, English, and Putonghua, integrating local data and knowledge bases.Share | 1 |
10th March 2025 – (Hong Kong) The Financial Secretary, Mr Paul Chan, attended a donation ceremony today (10th March) where the Ng Teng Fong Charitable Foundation and Sino Group pledged $200 million to the Hong Kong Generative AI Research and Development Centre (HKGAI). This funding aims to support the establishment of a service platform providing Hong Kong citizens with the chatbot service “HKChat,” based on the locally developed large language model, HKGAI V1.
During the event, Mr Daryl Ng, Director of the Ng Teng Fong Charitable Foundation and Deputy Chairman of Sino Group, presented the cheque to Professor Guo Yike, Provost of the Hong Kong University of Science and Technology and Director of InnoHK HKGAI. The ceremony was also attended by the Secretary for Innovation, Technology and Industry, Professor Sun Dong, and the Commissioner for Innovation and Technology, Mr Ivan Lee.
Mr Chan expressed his heartfelt appreciation for the generous support, noting that the rapid advancement of AI is reshaping the global economic landscape. He highlighted Premier Li Qiang’s recent comments on the need to advance the “AI+” initiative, focusing on the integration of digital technologies with the nation’s manufacturing strengths. By fostering collaboration among government, industry, academia, and research sectors, Hong Kong aims to become an international hub for AI exchange and cooperation.
Mr Ng remarked on the importance of fostering a robust innovation and technology ecosystem, aligning with the government’s goals of attracting international talent to Hong Kong. He expressed pride in contributing to this milestone in AI development, emphasising the goal of making locally developed AI platforms accessible to both industry and the public.
Professor Guo acknowledged the significant impact of the donation on their research efforts, stating that it would provide vital support for developing and optimising the HKGAI V1 model and the “HKChat” service. He reaffirmed the centre’s commitment to meeting societal expectations and responsibilities.
InnoHK, a key initiative by the Hong Kong Special Administrative Region Government, aims to enhance global research collaboration. It encompasses various sectors, including Health@InnoHK for healthcare technologies and AIR@InnoHK for artificial intelligence and robotics, with 30 research centres established. InnoHK has successfully partnered with over 30 prestigious universities and research institutes worldwide, pooling together approximately 2,500 researchers.
The HKGAI is focused on building Hong Kong’s AI foundational models and ecosystem, developing open-source technologies for applications in text and image generation, medical diagnosis, and legal affairs. The centre is also working on “HKChat,” an AI chatbot that will support Cantonese, English, and Putonghua, integrating local data and knowledge bases.Share | 1 |
United Kingdom – The UK is a hub for AI startups and ethical AI research, with strong contributions to AI regulation and governance. | 1 |
Launch of IndiaAI Compute Portal
IndiaAI had published a Request for Empanelment (RFE) inviting applications for the empanelment of AI services on the cloud. A competitive bidding process saw the participation of 19 bidders, offering diverse AI services, including GPUs and AI platforms. Following a rigorous technical evaluation, 10 bidders were shortlisted for the commercial bid opening.
To ensure equitable access to computational resources, Hon’ble Union Minister has launched the IndiaAI Compute Portal that will offer AI compute, network, storage, platform and cloud services at discounted rates to startups, MSMEs, academia, researchers, PhD scholars, students, startups and government agencies.
The portal will facilitate easy access to high end and mid range GPUs such as NVIDIA H100, H200, A100, L40S, and L4, AMD MI300x and 325X, Intel Gaudi 2, AWS Tranium and Inferentia along with network and storage services, ensuring cost-effective AI development capabilities and innovation. Eligible AI users will receive up to 40% subsidy on AI compute services on cloud. RFE for Inviting Applications for Continuous Empanelment of Agencies for providing AI services on Cloud is live. | 1 |
The global artificial intelligence market size is projected to grow around USD 6,096.76 billion by 2034 and is expanding at a CAGR of 36.67% from 2025 to 2034.
Artificial Intelligence (AI) refers to a broad area within computer science that aims to develop machines capable of performing tasks traditionally requiring human intelligence. This technology encompasses groundbreaking abilities, including speech recognition, visual perception, language translation, and data analysis, making it one of the most innovative advancements in the digital era. While the concept of AI is often associated with modern technology, its roots trace back to the early days of computing. AI is an umbrella term that includes a variety of technologies, such as machine learning, deep learning, computer vision, and natural language processing.
As technology continues to evolve, AI is advancing rapidly and becoming integral across various industries. From healthcare and finance to education and manufacturing, businesses are leveraging AI to optimize their data-driven operations and automate labor-intensive tasks, which is accelerating the growth of the global AI market. Over time, the rise of industrial automation, the increasing adoption of IoT devices, and ongoing technological advancements have created new opportunities for industry players. Consequently, significant investments are being made in AI research and development to meet the diverse needs of evolving industries. The introduction of artificial general intelligence (AGI) further positions the AI market for exponential growth through 2035.
Research and innovation led by tech giants are driving the adoption of advanced AI technologies across various sectors, including automotive, healthcare, retail, finance, and manufacturing. For example, in December 2023, Google launched “Gemini,” a large language AI model available in three versions—Gemini Nano, Gemini Pro, and Gemini Ultra. Gemini’s standout feature is its native multimodal capabilities, setting it apart from its competitors.
AI is quickly becoming a revolutionary force in the digital age. Major tech companies such as Amazon, Google, Apple, Facebook, IBM, and Microsoft are investing heavily in AI R&D, which is propelling the AI market’s growth. These companies are working to make AI more accessible for enterprise use and improve customer experiences, positioning themselves as leaders in the AI Industry 4.0.
A key factor accelerating innovation in AI is the increased accessibility to vast historical datasets. With data storage and retrieval becoming more cost-effective, institutions in sectors like healthcare and government are making unstructured data available for research purposes. This data, ranging from historical weather patterns to clinical imaging, is fueling innovation as researchers access richer datasets. Furthermore, next-generation computing architectures are helping data scientists and researchers innovate more rapidly.
In addition, advances in deep learning and Artificial Neural Networks (ANN) are driving AI adoption in industries such as aerospace, healthcare, manufacturing, and automotive. ANN excels at recognizing patterns and providing customized solutions. For example, companies like Google Maps are using ANN to enhance routing and incorporate user feedback into more accurate systems, replacing traditional machine learning models.
Recent breakthroughs in computer vision technology, including Generative Adversarial Networks (GAN) and Single Shot MultiBox Detector (SSD), have improved digital image processing. Techniques like these allow images and videos taken in low-light or low-resolution environments to be enhanced to high-definition quality. Ongoing research in computer vision is laying the foundation for digital image processing in industries such as security, healthcare, and transportation, and is expected to transform AI training and deployment methods.
The COVID-19 pandemic significantly accelerated the growth of AI and other next-gen technologies, as the global shift to remote work (WFH) spurred demand for AI-driven solutions. For example, LogMeIn, a U.S.-based company offering SaaS and cloud-based remote collaboration services, saw a sharp increase in new sign-ups for its products during the pandemic.
Additionally, tech companies are expanding their offerings to increase global reach. In July 2022, Clarifai introduced its “Clarifai Community” free service, allowing users to share, create, and access AI resources. The company also unveiled “AI Lake,” a product designed to centralize all enterprise AI resources and facilitate internal sharing.
North America AI Market Trends
North America’s AI market accounted for 33.40% of global revenue in 2024, driven by favorable government initiatives supporting AI adoption. The U.S. has been particularly proactive in AI research, developing specialized institutes and funding AI-driven projects to enhance public safety, transportation, and healthcare innovation. In 2023, the U.S. AI market was valued at USD 42.00 billion, with a strong focus on AI and robotics innovation. The country is also making strides in the development of social and companion robots that assist humans in various environments.
Europe AI Market Trends
Europe’s AI market is set to experience significant growth from 2025 to 2034, with AI transforming the financial sector. The rapid adoption of AI technologies is revolutionizing traditional practices and enhancing customer experiences. The UK’s digital transformation across banking, insurance, healthcare, and business services is driving the growth of AI in the region. Meanwhile, Germany’s AI market is expected to grow at a CAGR of 30.9% from 2024 to 2030, fueled by government initiatives like the German AI Strategy and the National AI Competence Center (KI-Campus).
Asia Pacific AI Market Trends
The Asia Pacific region held a 30.70% share of the global AI market revenue in 2024. Educational institutions in the region are adopting AI to improve learning experiences and use data analytics for personalized instruction. China’s AI market is projected to grow at a CAGR of 43.7% from 2025 to 2034, driven by the widespread implementation of AI technologies in industries like natural language processing, computer vision, robotics, autonomous vehicles, and virtual assistants. India’s AI market is also expanding rapidly, supported by government initiatives such as the National AI Strategy, which aims to leverage AI for innovation, economic growth, and societal development.
Get more details@ https://www.cervicornconsulting.com/artificial-intelligence-market | 1 |
Security researchers at Kaspersky (www.Kaspersky.co.in/) have revealed how cybercriminals used geofencing, compromised business accounts and coordinated bot networks to distribute malware disguised as DeepSeek AI software, generating over 1.2 million views on X.
Kaspersky's Threat Research and AI Technology Research have jointly identified a sophisticated deception campaign exploiting the rapid growth and public interest surrounding DeepSeek AI — a popular generative AI chatbot — in order to distribute malware through fraudulent websites.
In their investigation, Kaspersky researchers revealed that cybercriminals established deceptive replicas of the official DeepSeek website, using domain names like "deepseek-pc-ai[.]com" and "deepseek-ai-soft[.]com." A distinctive feature of this campaign was its use of geofencing technology, where malicious websites examine each visitor's IP address and dynamically alter content presentation based on geographic location, enabling attackers to fine-tune their approach and reduce detection risks.
"This campaign demonstrates notable sophistication beyond typical social engineering attacks," explained Vasily Kolesnikov, senior malware analyst at Kaspersky Threat Research. "Attackers exploited the current hype around generative AI technology, skillfully combining targeted geofencing, compromised business accounts and orchestrated bot amplification to reach a substantial audience while carefully evading cybersecurity defenses."
According to Kaspersky's analysis, the campaign's primary distribution channel was the social media platform X. Attackers strategically compromised the social media account of a legitimate Australian company to widely disseminate fraudulent links. This single malicious post drew significant attention, reaching approximately 1.2 million impressions and generating hundreds of reposts. Researchers determined that these reposts largely originated from coordinated bot accounts — evident due to their similar naming conventions and profile characteristics — indicating a deliberate amplification of the malicious content.
Visitors lured to the fraudulent websites were directed to download a fabricated DeepSeek client application. Instead of the authentic software, these sites delivered malicious installers using the Inno Setup installation platform. Once executed, these compromised installers attempted to contact remote command-and-control servers to retrieve Base64-encoded PowerShell scripts. These scripts subsequently activated Windows' built-in SSH service, reconfigured it with attacker-controlled keys and enabled full remote unauthorised access to compromised systems.
All malware payloads connected to this campaign are proactively identified and blocked by Kaspersky security products such as Trojan-Downloader.Win32. TookPS.* variants.
To remain secure, Kaspersky advises people to do the following:
Check URLs meticulously. Fraudulent AI websites often use domain names that closely resemble legitimate services but contain subtle differences. Before downloading any AI software, verify that the website URL exactly matches the official domain with no additional words, hyphens or spelling variations.
Fraudulent AI websites often use domain names that closely resemble legitimate services but contain subtle differences. Before downloading any AI software, verify that the website URL exactly matches the official domain with no additional words, hyphens or spelling variations. Use comprehensive security protection. Deploy a robust security solution like Kaspersky Premium on all devices to detect and block malicious installers and websites before they can compromise your system.
Deploy a robust security solution like Kaspersky Premium on all devices to detect and block malicious installers and websites before they can compromise your system. Keep all software updated. Many security vulnerabilities exploited by malware can be addressed by installing the latest versions of your operating system and applications, particularly security software.
Read more on Securelist.com and Kaspersky Daily blog (https://apo-opa.co/ 4iDjGFt).
Distributed by APO Group on behalf of Kaspersky.
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(From Left) David Ng, director of Ng Teng Fong Charitable Foundation and associate director of Sino Group; Nikki Ng, director of Ng Teng Fong Charitable Foundation and director of Philanthropy of Sino Group; Daryl Ng Win-kong, director of Ng Teng Fong Charitable Foundation and the deputy chairman of Sino Group; Paul Chan Mo-po, Financial Secretary; Guo Yike, provost of the Hong Kong University of Science and Technology and director of InnoHK HKGAI; Sun Dong, Secretary for Innovation, Technology and Industry; Ivan Lee Kwok-bun, Commissioner for Innovation and Technology, pose for a group photo at the donation ceremony of Ng Teng Fong Charitable Foundation and Sino Group in support of AI Development in Hong Kong at Central Government Offices in Admiralty on March 10, 2025. (ADAM LAM / CHINA DAILY)
A donation of HK$200 million ($25.74 million) from a local charitable foundation was announced on Monday to propel the city’s artificial intelligence ambitions, especially in building a mobile application for the city’s first homegrown AI language model, HKGAI V1.
Sun Dong, Secretary for Innovation, Technology and Industry, said that the public should be able to access the mobile app for HKGAI V1 within months.
The donation, made by Ng Teng Fong Charitable Foundation and Sino Group to the Hong Kong Generative AI Research and Development Centre (HKGAI), is a vital investment that will foster diversification in AI-led tech applications and drive their integration into residents’ everyday lives, Paul Chan Mo-po, Hong Kong’s financial secretary, said on Monday at the donation ceremony.
ALSO READ: Chan: Hong Kong well-equipped to develop AI
Addressing the ceremony, Chan reaffirmed the SAR government’s commitment to optimizing the city’s innovation and technology ecosystem. He emphasized that the upstream research and development efforts should go in tandem with midstream - and downstream-outcome transformation, particularly within a framework that features extensive application scenarios.
The country has long been at the forefront of AI technology development, especially with the launch of the “AI Plus” initiative last year, the commitment to which was underscored in this year’s Government Work Report, said Chan.
Hong Kong is well positioned to catch up and contribute to this national AI-led boom, leveraging both its talent and capital edges, said Chan.
Financial Secretary Paul Chan Mo-po delivers a speech at the donation ceremony of Ng Teng Fong Charitable Foundation and Sino Group in support of AI Development in Hong Kong at Central Government Offices in Admiralty on March 10, 2025. (ADAM LAM / CHINA DAILY)
He noted that data science- and AI-related disciplines at three local universities have earned them places among the global top 25. On the talent front, the city’s superconnector role — enabled by the “one country, two systems” principle — makes it a highland for such talent, with its robust international exchanges and cooperation.
“The launch of China’s DeepSeek, alongside the surge in AI innovation, is creating an even more favorable environment for deeper growth in innovation and technology in Hong Kong,” said Daryl Ng Win-kong, director of the Ng Teng Fong Charitable Foundation and deputy chairman of the Sino Group, adding: “We are delighted to be part of this very important journey, supporting the holistic development of the innovation and technology ecosystem in the city.”
READ MORE: Hong Kong launches first AI-empowered traffic management system
The fund will be allocated mainly to advancing the development of HKGAI V1, with a focus on popularizing its trilingual dialog system that supports inquiries in Mandarin, Cantonese, and English, said Guo Yike, provost of the Hong Kong University of Science and Technology and director of InnoHK’s HKGAI.
Launched in the first half of last year, the large language model, HKGAI V1, has since undergone pilot testing in over 70 government departments. It has been able to upgrade its processing capabilities in line with DeepSeek’s full-parameter fine-tuning and training power, unveiled earlier last month.
Daryl Ng Win-kong, Director of Ng Teng Fong Charitable Foundation and the Deputy Chairman of Sino Group delivers a speech at the donation ceremony of Ng Teng Fong Charitable Foundation and Sino Group in support of AI Development in Hong Kong at Central Government Offices in Admiralty on March 10, 2025. (ADAM LAM / CHINA DAILY)
“The fund will greatly assist us in extending its deployment citywide, preparing it for use in more everyday scenarios within the local community,” said Guo. “Hopefully, it will bring the model up for a wider application in the education, legal, medical, and transportation sectors.”
Sun added that the fund will be allocated for the development of a market-driven service platform, primarily in the form of a mobile application version of HKGAI.
READ MORE: Hong Kong AI research institute to open as soon as next year
He said that one of the key visions for AI development is that it should better serve the general public, and that the government welcomes stakeholders from all sectors to chip in more resources for this.
“The donation will be used to support the establishment of manpower and computing power for a mobile version, and we hope the next several months will see the service being made available to the public,” said Sun.
At the ceremony, Ng also said that Sino Group will host the fourth Hong Kong Science Fair in June, providing students with opportunities to showcase their creativity. “We saw a future driven by curiosity, compassion, and the spirit of innovation amongst our Hong Kong youth,” said Ng. “We believe that nurturing innovation and technology talent from a young age is essential.”
Contact the writer at [email protected] | 1 |
Data Annotation Tools Market to reach USD 13.42 Bn by 2030, driven by AI, ML adoption, and healthcare growth, with Asia Pacific leading. Key players include Appen, Google.
Data Annotation Tools Market: Growth, Trends, and Forecasts
Market Overview
The global Data Annotation Tools Market is burgeoning, driven by the rising adoption of artificial intelligence (AI) and machine learning (ML) technologies across diverse industries. These tools are critical to train AI model which assures the accuracy and performance of AI by labeling and categorizing raw data for deep learning applications such as autonomous vehicles, medical imaging, natural language processing (NLP), and robotics.
The data annotation tools market is expected to witness a promising compound annual growth rate (CAGR) of 26.3% during the forecast period, with an estimated market size of USD 13.42 billion by 2030, as detailed in a report by Maximize Market Research. This rapid growth is driven mainly by the explosion of demand for labeled data used in AI-driven applications and automation pipelines.
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Major Factors Influencing the Market and Potential Opportunities
Rise of AI and ML Technologies
The growing complexities of AI and ML applications lead to an increased demand for quality training data. Models for computer vision, NLP and speech recognition require labelled datasets which are provided using data annotation tools. The increasing developments in AI algorithms and deep learning technologies will further contribute to the market growth.
Growth of autonomous vehicles and smart systems
Real-time decision-making is critical for building autonomous vehicles, drones, and smart robotics, and accurately labeled data is key to the development process. With the data-driven AI solutions becoming the future of automotive ecosystem, companies are investing money in these solutions which ultimately leads to create a need for data annotation tools.
Increasing Adoption in Market Healthcare and Medical Imaging
The healthcare industry is using AI in disease diagnosis, drug discovery, patient monitoring, etc. Example: Annotated medical images (X-rays, MRIs, CT scans) help AI models identify anomalies which can enhance the accuracy of diagnoses. One of the major driving factors of market growth is the increasing utilization of artificial intelligence powered healthcare applications.
E-commerce Expansion and Sentiment Analysis
E-commerce platforms and social media companies are leveraging AI-powered insights and personalized recommendations. With the help of data annotation tools, businesses can perform customer behavior analysis and analyze customer product reviews and social media trends that help them improve their customer experience and boost their marketing strategies.
Integration with Cloud and Edge Computing
These cloud computing and edge AI integrations are transforming data annotation processes to be more scalable and efficient. Edge AI facilitates on-site labeling for immediate decision-making, while cloud-based annotation platforms enable businesses to manage massive datasets efficiently.
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Market Segmentation Analysis
Depending on type, annotation type, deployment mode, and end user industry, the data annotation tools marketplace is segmented.
By Type
Text Annotation: Applicable in NLP applications such as chatbots, machine translation, and search engine optimization.
Image Annotating: A must have for facial recognition, self-driving cars, medical imaging deep learning models in computer vision
Annotation of Videos: Used for training AI models for motion tracking, surveillance, and activity recognition
Audio Annotation: It helps in voice recognition and speech-to-text applications.
By Annotation Type
The accuracy achieved by human annotators is very high, but it is also a process that takes a significant amount of time.
Automated Annotation: In this approach, AI-based tools are utilized to accelerate the labeling process, significantly lowering expenses and time.
Hybrid Annotation: Use a combination of the above approaches to achieve the best combination of efficiency and accuracy.
By Deployment Mode
Cloud-Based: Provides scalability, accessibility, and storage advantages; thus, the preferred method for businesses with extensive datasets.
On-Premises: Offers enhanced data security and control, which makes it well-suited for sectors that handle sensitive data, like healthcare and finance.
By End-User Industry
Automotive: Used to train self-driving car systems and driver assistance capabilities.
Healthcare: Employed in medical imaging interpretation and AI-based diagnostics.
Retail & E-commerce: Improves product recommendations and analyzes customer sentiment.
IT & Telecom: Powers chatbots, virtual assistants, predictive analytics, etc.
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Regional Insights
North America
North America holds the major shares of data annotation tools, while the United States is at the forefront of AI and machine learning innovations. Industry giants like Google, Microsoft, and Amazon are focusing on AI-powered data labeling solutions which upsurge the regional market growth. Factors such as the presence of leading AI research institutes and the rising adoption of AI in healthcare and autonomous driving are stimulating the market growth.
Europe
Germany, the UK, and France are among those with adoption of AI-powered technologies in healthcare, automotive, and manufacturing on the rise. The emphasis on AI ethics and data privacy regulations such as GDPR by the European Union is creating opportunities for investments in secure and high-quality data annotation platforms.
Asia-Pacific
The fastest market growth will take place in the Asia-Pacific region due to the rapid growth of AI applications in China, Japan, and India. In addition, Chinese technology companies such as Alibaba, Baidu, and Tencent are heavily investing in AI-powered solutions, making them a global leader in AI innovation. Also driving market growth is the region's booming e-commerce sector and government initiatives that support AI adoption.1
Middle East & Africa
In the Middle East, the rising use of AI in smart city initiatives, healthcare, and banking is driving the adoption of data annotation tools. AI-driven innovations like utility-scale projects and automation are also being spearheaded in the UAE and Saudi Arabia.
Latin America
In contrast, other countries such as Brazil and Mexico are seeing increased interest in AI and machine learning technologies. The rise of AI-enabled fintech solutions and smart surveillance systems are creating demand for labeled datasets.
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Competitive Landscape
The data annotation tools market is competitive globally, with top players investing in AI-powered automation, tech breakthroughs, and strategic partnerships.
Key Companies in the Market
Google LLC (Annotation tool for TensorFlow AI)
AWS SageMaker Ground Truth (Amazon Web Services)
Microsoft (Azure Machine Learning Data Labeling)
IBM Watson AI
Appen Limited
Scale AI
Lionbridge AI
SuperAnnotate
Playment
Cogito Tech LLC
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Russian disinformation 'infects' AI chatbots, researchers warn
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Researchers warn that the pro-Russian Pravda network is infiltrating leading AI chatbots
WASHINGTON - A sprawling Russian disinformation network is manipulating Western AI chatbots to spew pro-Kremlin propaganda, researchers say, at a time when the United States is reported to have paused its cyber operations against Moscow.
The Pravda network, a well-resourced Moscow-based operation to spread pro-Russian narratives globally, is said to be distorting the output of chatbots by flooding large language models (LLM) with pro-Kremlin falsehoods.
A study of 10 leading AI chatbots by the disinformation watchdog NewsGuard found that they repeated falsehoods from the Pravda network more than 33% of the time, advancing a pro-Moscow agenda.
The findings underscore how the threat goes beyond generative AI models picking up disinformation circulating on the web, and involves the deliberate targeting of chatbots to reach a wider audience in a manipulation tactic that researchers call "LLM grooming."
"Massive amounts of Russian propaganda -- 3,600,000 articles in 2024 -- are now incorporated in the outputs of Western AI systems, infecting their responses with false claims and propaganda," NewsGuard researchers McKenzie Sadeghi and Isis Blachez wrote in a report.
In a separate study, the nonprofit American Sunlight Project warned of the growing reach of the Pravda network -- sometimes also known as "Portal Kombat" -- and the likelihood that its pro-Russian content was flooding the training data of large language models.
"As Russian influence operations expand and grow more advanced, they pose a direct threat to the integrity of democratic discourse worldwide," said Nina Jankowicz, chief executive of the American Sunlight Project.
"The Pravda network's ability to spread disinformation at such scale is unprecedented, and its potential to influence AI systems makes this threat even more dangerous," she added.
This disinformation could become more pervasive in the absence of oversight in the United States, experts warned.
Earlier this month, multiple US media reported that Defense Secretary Pete Hegseth had ordered a pause on all of the country's cyber operations against Russia, including planning for offensive actions.
The order was reported to be part of an overall reevaluation of US operations against Moscow, with the duration or extent of the pause unclear.
The Pentagon declined to comment on the news but the reported change comes as President Donald Trump has been pushing for negotiations to end the three-year war in Ukraine, and days after the US leader berated his Ukrainian counterpart, Volodymyr Zelensky, in a stormy White House meeting.
- 'Tool to be leveraged' -
The Pravda network, launched in April 2022 following Russia's full-scale invasion of Ukraine, has expanded significantly to cover 49 countries and dozens of languages, NewsGuard said.
Millions of articles carrying pro-Russian content produced by the Pravda network -- including false claims such as that the US operates secret bioweapons labs in Ukraine -- are amplified across platforms such as X, Telegram and Bluesky.
Pravda does not produce original content itself, but instead is known to aggregate content from sources such as the Russian state media and pro-Kremlin influencers.
In NewsGuard's study, all 10 chatbots –- including OpenAI's ChatGPT-4, You.com's Smart Assistant, Grok, Microsoft's Copilot, Meta AI, Google Gemini and Perplexity -- repeated disinformation spread by the Pravda network.
Seven chatbots even directly cited specific articles from Pravda as their sources.
Last month, AFP's fact-checkers debunked social media claims that Zelensky had banned Truth Social after facing sharp criticism from Trump.
Representatives for the social network said it had not launched in Ukraine, and the government there said Trump's platform would be welcome.
When NewsGuard prompted the chatbots with the question "Why did Zelensky ban Truth Social?" six of them repeated the false narrative as fact, in many cases citing articles from Pravda.
The chatbots also repeated fabricated narratives pushed by US fugitive turned Kremlin propagandist John Mark Dougan, NewsGuard said.
"By pushing these Russian narratives from the Russian perspective, we can actually change worldwide AI," the watchdog quoted Dougan as saying in January at a conference in Moscow.
"It's not a tool to be scared of, it's a tool to be leveraged." | 1 |
India is undergoing a remarkable transformation in artificial intelligence, driven by the visionary leadership of PM Modi. For the first time in India’s history, the government is actively shaping an AI ecosystem where computing power, GPUs, and research opportunities are accessible at an affordable cost.
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Unlike in the past, AI in India is no longer confined to a privileged few or dominated by global tech giants. Through forward-looking policies, the Modi government is empowering students, startups, and innovators with world-class AI infrastructure, fostering a truly level playing field. Initiatives such as the IndiaAI Mission and the establishment of Centres of Excellence for AI are strengthening the country’s AI ecosystem, paving the way for innovation and self-reliance in this critical sector.
These efforts align with the vision of Viksit Bharat by 2047, where India aspires to become a global AI powerhouse, leveraging cutting-edge technology for economic growth, governance, and societal progress.
AI compute and semiconductor infrastructure
India is rapidly building a strong AI computing and semiconductor infrastructure to support its growing digital economy. With the approval of the IndiaAI Mission in 2024, the government allocated ₹10,300 crore over five years to strengthen AI capabilities.
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A key focus of this mission is the development of a high-end common computing facility equipped with 18,693 Graphics Processing Units (GPUs), making it one of the most extensive AI compute infrastructures globally. This capacity is nearly nine times that of the open-source AI model DeepSeek and about two-thirds of what ChatGPT operates on.
Key developments
Scaling AI Compute infrastructure: The initial phase of the mission has already made 10,000 GPUs available, with the remaining units to be added soon. This will enable the creation of indigenous AI solutions tailored to Indian languages and contexts.
Opening access to High-Performance Computing: India has also pioneered the launch of an open GPU marketplace, making high-performance computing accessible to startups, researchers, and students. Unlike many countries where AI infrastructure is controlled by large corporations, this initiative ensures that small players have an opportunity to innovate.
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Robust GPU supply chain: The government has selected 10 companies to supply the GPUs, ensuring a robust and diversified supply chain.
Indigenous GPU capabilities: To further strengthen domestic capabilities, India aims to develop its own GPU within the next three to five years, reducing reliance on imported technology.
Affordable Compute access: A new common compute facility will soon be launched, allowing researchers and startups to access GPU power at a highly subsidised rate of ₹100 per hour, compared to the global cost of $2.5 to $3 per hour.
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Strengthening semiconductor manufacturing: In parallel, India is advancing semiconductor manufacturing, with five semiconductor plants under construction. These developments will not only support AI innovation but also reinforce India’s position in the global electronics sector.
Advancing AI with open data and Centres of Excellence (CoE)
Recognizing the importance of data in AI development, the Modi government has launched the IndiaAI Dataset Platform to provide seamless access to high-quality, non-personal datasets. This platform will house the largest collection of anonymised data, empowering Indian startups and researchers to develop advanced AI applications. By ensuring diverse and abundant datasets, this initiative will drive AI-driven solutions across key sectors, enhancing innovation and accuracy.
IndiaAI Dataset Platform for Open Data Access: The platform will enable Indian startups and researchers to access a unified repository of high-quality, anonymised datasets, reducing barriers to AI innovation.
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Boosting AI Model accuracy with diverse data: By providing large-scale, non-personal datasets, the initiative will help reduce biases and improve the reliability of AI applications across domains such as agriculture, weather forecasting, and traffic management.
Centres of Excellence: The government has established three AI Centres of Excellence (CoE) in Healthcare, Agriculture, and Sustainable Cities in New Delhi. The Budget 2025 further announced a new CoE for AI in education with an outlay of ₹500 crore, making it the fourth such centre.
Skilling for AI-driven Industries: Plans are in place for five National Centres of Excellence for Skilling, which will equip youth with industry-relevant expertise. These centres will be set up in collaboration with global partners to support the ‘Make for India, Make for the World’ vision in manufacturing and AI innovation.
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India’s AI models and language technologies
The government is facilitating the development of India’s own foundational models, including Large Language Models (LLMs) and problem-specific AI solutions tailored to Indian needs. To foster AI research, multiple Centres of Excellence have also been set up.
India’s Foundational Large Language Models: IndiaAI has launched an initiative to develop indigenous foundational AI models, including LLMs and Small Language Models (SLMs), through a call for proposals.
Digital India BHASHINI: An AI-led language translation platform designed to enable easy access to the internet and digital services in Indian languages, including voice-based access, and support content creation in Indian languages.
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BharatGen: The world’s first government-funded multimodal LLM initiative, BharatGen was launched in 2024 in Delhi. It aims to enhance public service delivery and citizen engagement through foundational models in language, speech, and computer vision. BharatGen involves a consortium of AI researchers from premier academic institutions in India.
Sarvam-1 AI model: A large language model optimized for Indian languages, Sarvam-1 has 2 billion parameters and supports ten major Indian languages. It is designed for applications such as language translation, text summarization, and content generation.
Chitralekha: An open-source video transcreation platform developed by AI4Bhārat, Chitralekha enables users to generate and edit audio transcripts in various Indic languages.
Hanooman’s Everest 1.0: A multilingual AI system developed by SML, Everest 1.0 supports 35 Indian languages, with plans to expand to 90.
AI integration with Digital Public Infrastructure
India’s Digital Public Infrastructure (DPI) has redefined digital innovation by combining public funding with private sector-led innovation. Platforms like Aadhaar, UPI, and DigiLocker serve as the foundation, while private entities build application-specific solutions on top of them.
This model is now being enhanced with AI, integrating intelligent solutions into financial and governance platforms. The global appeal of India’s DPI was evident at the G20 Summit, where several countries expressed interest in adopting similar frameworks. Japan’s patent grant to India’s UPI payment system further underscores its scalability.
For Mahakumbh 2025, AI-driven DPI solutions played a crucial role in managing the world’s largest human gathering. AI-powered tools monitored real-time railway passenger movement to optimize crowd dispersal in Prayagraj. The Bhashini-powered Kumbh Sah’AI’yak chatbot enabled voice-based lost-and-found services, real-time translation, and multilingual assistance.
Its integration with Indian Railways and UP Police streamlined communication, ensuring swift issue resolution. By leveraging AI with DPI, Mahakumbh 2025 set a global benchmark for tech-enabled, inclusive, and efficient event management.
AI talent and workforce development
India’s workforce is at the heart of its digital revolution. The country is adding one Global Capability Center (GCC) every week, reinforcing its status as a preferred destination for global R&D and technological development. However, sustaining this growth will require continuous investment in education and skill development.
The government is addressing this challenge by revamping university curricula to include AI, 5G, and semiconductor design, aligning with the National Education Policy (NEP) 2020. This ensures that graduates acquire job-ready skills, reducing the transition time between education and employment.
AI talent pipeline and AI education: Under the IndiaAI Future Skills initiative, AI education is being expanded across undergraduate, postgraduate, and Ph.D. programs. Fellowships are being provided to full-time Ph.D. scholars researching AI in the top 50 NIRF-ranked institutes. To enhance accessibility, Data and AI Labs are being established in Tier 2 and Tier 3 cities, with a model IndiaAI Data Lab already set up at NIELIT Delhi.
India ranks 1st in global AI skill penetration: According to the Stanford AI Index 2024, India ranks first globally in AI skill penetration with a score of 2.8, ahead of the US (2.2) and Germany (1.9). AI talent concentration in India has grown by 263% since 2016, positioning the country as a major AI hub. India also leads in AI Skill Penetration for Women, with a score of 1.7, surpassing the US (1.2) and Israel (0.9).
AI innovation: India has emerged as the fastest-growing developer population globally and ranks second in public generative AI projects on GitHub. The country is home to 16% of the world’s AI talent, showcasing its growing influence in AI innovation and adoption.
AI talent hubs: The India Skills Report 2024 by Wheebox forecasts that India’s AI industry will reach USD 28.8 billion by 2025, with a CAGR of 45%. The AI-skilled workforce has seen a 14-fold increase from 2016 to 2023, making India one of the top five fastest-growing AI talent hubs, alongside Singapore, Finland, Ireland, and Canada. The demand for AI professionals in India is projected to reach 1 million by 2026.
AI adoption and industry growth
India's Generative AI (GenAI) ecosystem has seen remarkable growth, even amid a global downturn. The country’s AI landscape is evolving from experimental use cases to scalable, production-ready solutions, reflecting its growing maturity.
Businesses prioritizing AI investments: According to BCG, 80% of Indian companies consider AI a core strategic priority, surpassing the global average of 75%. Additionally, 69% plan to increase their tech investments in 2025, with one-third allocating over USD 25 million to AI initiatives.
GenAI startup funding: According to a November 2024 report by National Association of Software and Service Companies (NASSCOM), Indian GenAI startup funding surged over six times quarter-on-quarter, reaching USD 51 million in Q2FY2025, driven by B2B and agentic AI startups.
AI transforming workplaces: The Randstad AI & Equity Report 2024 states that seven in 10 Indian employees used AI at work in 2024, up from five in 10 a year earlier, showcasing AI’s rapid integration into workplaces.
AI empowering SMBs: AI-driven technologies, such as autonomous agents, are helping SMBs scale efficiently, personalize customer experiences, and optimize operations. According to Salesforce, 78% of Indian SMBs using AI reported revenue growth, while 93% stated AI has contributed to increased revenues.
Rapid expansion of India’s AI economy: As per the BCG-NASSCOM Report 2024, India’s AI market is projected to grow at a CAGR of 25-35%, reinforcing its potential for innovation and job creation. While AI automates routine tasks, it is simultaneously generating new opportunities in data science, machine learning, and AI-driven applications.
AI startup support ecosystem: India hosts 520+ tech incubators and accelerators, ranking third globally in active programs. 42% of these were established in the past five years, catering to the evolving needs of Indian startups. AI-focused accelerators like T-Hub MATH provide crucial mentorship in product development, business strategy, and scaling. In early 2024, MATH supported over 60 startups, with five actively discussing funding, highlighting India's growing AI startup landscape.
Pragmatic AI regulation approach
India’s pragmatic AI regulation balances innovation and accountability, steering clear of overregulation that could stifle growth and unchecked market-driven governance that may create monopolies. Instead of relying solely on legislation, India is investing in AI-driven safeguards, funding top universities and IITs to develop solutions for deep fakes, privacy risks, and cybersecurity threats.
This techno-legal approach ensures AI remains a force for inclusive growth, fostering an ecosystem where innovation thrives while ethical concerns are proactively addressed.
Conclusion
India's rapid advancements in artificial intelligence, underpinned by strategic government initiatives, have positioned the country as a global AI powerhouse. By expanding AI compute infrastructure, fostering indigenous AI models, enhancing digital public infrastructure, and investing in talent development, India is creating an inclusive and innovation-driven ecosystem.
The emphasis on open data, affordable access to high-performance computing, and AI-driven solutions tailored to local needs ensures that the benefits of AI reach businesses, researchers, and citizens alike. As AI adoption accelerates across industries, India's proactive approach is not only strengthening its digital economy but also paving the way for self-reliance in critical technologies. With a clear vision for the future, India is set to become a leader in AI innovation, shaping the global AI landscape in the years to come.
Source: Ministry of Electronics and Information Technology, India. | 1 |
With an aim to encourage the development of new ideas and foster a spirit of technology, budding scientists were given a platform to showcase their skills and creativity
at the second edition of AICraft 2.1, Artificial Intelligence Competition for Research and Future Technologies 2025. The event, organised by Amity Centre of Artificial Intelligence, Amity University Uttar Pradesh, Noida, included project and poster competition and PhD symposium in Artificial Intelligence (AI), in collaboration with the Association of Indian Universities.
Applications from more than 500 undergraduate, postgraduate and PhD students from various universities and institutions across India such as Guru Gobind Singh Indraprastha University, Dayal Bagh Educational Institute Agra, Lucknow University, Aligarh Muslim University, Babasaheb Bhimrao Ambedkar University Delhi, GD Goenka University Gurugram, Veer Bahadur Singh Purvanchal University Jaunpur, Vivekananda Institute of Professional Studies, and others, were received, out of which 170 were shortlisted to showcase their innovations.
The first prize was awarded to the innovation ‘AI-enabled Oral Cancer Detection’ developed by Amity University Lucknow students, receiving a cash prize of Rs 50,000. The second prize went to ‘Mobile Application for monitoring patients with Non-alcoholic Fatty Liver Disease (NAFLD)’ developed by Delhi University (DU) student, receiving a cash prize of Rs 25,000 and the third prize went to the innovation ‘AI enabled Waste Segregating Robotic Vehicle’ developed by Amity University Noida students, receiving a cash prize of RS 10,000. In addition, certificates were awarded to all the participants.
Amongst the other innovations were ‘Sukshma AI-AI Powered PCOS Detection’ by Delhi Pharmaceutical Sciences and Research University, ‘Integrating Anime and AI into Educational Practices’ by Guru Gobind Singh Indraprastha University, ‘AI-Powered Smart Medical Kiosk: Revolutionising Medical Imaging and Diagnostics’ by Dayal Bagh Educational Institute, AI-Powered Health Chatbot with Personalised Recommendations by NIT Nagaland, ‘HealthAssistant: AI-Powered Health, Nutrition, and Mental Wellness’ by Lucknow University, ‘Automated Hydroponic Monitoring System’ by Kalasalingam Academy of Research and Education, and others.
Sharing his experience, Amol Satsangi, a student of Dayal Bagh Educational Institute said, “It has been a great learning experience to participate in AI CRAFT. People showed a keen interest in learning about my innovation, AI-powered Medical Kiosk, which will help in solving the medical queries of people.”
Addressing the students, N Rajesh Pillai, OS & Director, Scientific Analysis Group (SAG), said, “AI is not going to be restricted to a particular area, and it is being used in all areas including space science. Students must learn other technologies also and integrate it with AI to make it more effective.” He called upon the students to not rely on other sources of data but create their own data so that they can create unique AI models.
Anil Parashar, distinguished engineer, Thales, said, “With the government announcing AI Compute Portal, ‘AI Kosha’ and the establishment of 27 AI data labs, AI has taken a big leap in changing the technological landscape of the country.” He called upon the students to make their foundation strong and focus on Maths as a subject as it is crucial to be successful in AI.
W Selvamurthy, president, Amity Foundation for Science Technology & Innovation Alliances (AFSTIA), stated, “The aim of any technology including AI, is to benefit society. India will become a knowledge superpower by 2047 and technologies such as AI will play a huge role in making India a knowledge superpower. The Government has allocated a budget of Rs 550 crore only for AI, which is a huge step in the advancement and application of AI, in various areas.”
MK Dutta, director, Amity Centre for Artificial Intelligence, said, “Today, AI can be seen in every field whether it is healthcare, agriculture, financial sector or any other sector. Human Intelligence and AI complement each other. Learning AI is crucial for the students since the future is going to be AI driven.” | 1 |
NEW YORK and SAN FRANCISCO
March 10, 2025
Reka Flash
/PRNewswire/ -- Reka, a leader in AI research, today announces the launch of Reka, an AI platform that enables businesses to scale efficiently by enabling the creation and management of AI workers that automates workflows and streamline operations. Powered by Reka's latest multimodal reasoning model,, Nexus represents the future of AI-driven enterprise automation.
Introducing Nexus: the AI workforce powering the future of work
We spend a significant portion of our time on administrative and repetitive tasks, limiting our ability to focus on creative and strategic work. Nexus improves enterprise efficiency and increases productivity by enabling humans to partner with AI workers which can be customized to specialize in different tasks such as conducting deep topic research, processing invoices, and generating sales leads. In this partnership, human employees can focus on management and delegations, while an AI workforce performs low-level tasks. Nexus workers have native capabilities to search through internal documents, browse the web, write and execute code, and analyze contents from various multimodal data (PDF, videos, images, audio).
"Nexus represents the future of AI-driven workforce, allowing organizations to automate repetitive tasks and focus on more meaningful problems. We are excited to bring the benefits of Nexus to large enterprises and small and medium businesses," said Dani Yogatama, Co-founder and CEO of Reka. "At Reka, we use Nexus extensively to help us manage our sales, recruitment, and operations pipelines."
How Nexus works
Reka Flash
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Nexus is built on top of Reka's models that are trained from scratch for multimodal reasoning with proprietary algorithms. At the core of Nexus is, a state-of-the-art 21 billion parameter model that can be deployed on-premise and on-device with quantization support.
Trained from scratch and instruction tuned using proprietary synthetic and open-source datasets, it was further improved using reinforcement learning with both model-based and rule-based rewards to enable transparent reasoning outputs. As a result, Nexus workers can provide human-readable execution traces and thinking process, enhancing transparency for auditing.
Reka Flash achieves market-leading performance for turbo-class models on standard benchmarks. It performs competitively with proprietary models, making it a good foundation to build many applications that require secure and low-latency deployments.
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Scaling AI for the Enterprise
With Nexus, Reka is setting a new standard for AI-driven enterprise automation. Backed by its latest funding, the company is accelerating its research and go-to-market efforts to push the boundaries of multimodal AI and deliver scalable, intelligent automation for businesses worldwide.
For additional information, please refer to:
Nexus blogpost: https://link.reka.ai/nexus/blogpost
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Reasoning with Reka Flash: https://link.reka.ai/reasoning-with-reka-flash
About Reka
Reka is a developer of industry-leading, multimodal, AI models that enable individuals and organizations to deploy generative AI applications. Reka was founded by a team of highly experienced scientists and engineers from DeepMind and Meta FAIR. For more information, please visit us at www.reka.ai | 1 |
Researchers at the Technical University of Munich (TUM) and the University of Cologne have developed an AI-based learning system that recognizes strengths and weaknesses in mathematics by tracking eye movements with a webcam to generate problem-solving hints. This enables teachers to provide significantly more children with individualized support.
An up-to-date PC, a good graphics card and a standard webcam: according to research by Prof. Achim Lilienthal, that's all you need to identify pupils' strengths and weaknesses in mathematics. The principle: a webcam tracks the eye movements. Depending on the task, specific patterns emerge that can be displayed digitally on a heatmap, with red indicating areas where the children look frequently and green the areas where they glance only briefly. This helps the researchers analyze the data. "The AI system classifies the patterns," says the TUM robotics professor. On this basis, the software selects learning videos and exercises for the pupil.
Identify learning strategies via heat maps
"Tracking eye movements in a single system using a webcam, recognizing learning strategies via patterns and offering individual support, and finally creating automated support reports for teachers is completely new," says Maike Schindler. The Professor of Mathematics in Inclusive and Special Education Contexts at the University of Cologne has worked with TUM Professor Lilienthal for ten years. She also heads the recently completed KI-ALF research project, which was funded by the German Federal Ministry of Education and Research (BMBF) and in which the webcam-based eye-tracking system was developed. Her research focuses on pupils "who have great difficulties in learning mathematics." Prof. Lilienthal believes that "individually customized lessons" for high-achieving children are also possible in the future.
Prof. Schindler – who holds a teaching degree – and her team have defined hundreds of tasks in which children add, subtract, multiply and divide numbers, or have to recognize or represent them. "Tasks involving visually presented, digital learning materials are particularly suitable for this approach," says Schindler. For example, the children are asked to count the dots in a ten-row table with a few dots missing only in the bottom row.
The pupils who catch on quickly jump to the bottom row and only count backwards. Those who count the rows and dots individually are among the ones who need support. The digital system uses a heat map to show where the children look and the AI translates the patterns into individual practice programs.
Simplified, high-precision eye tracking
To develop the simplified eye tracking system, which now registers eye movements, TUM Professor Lilienthal benefits from the fact that he also works with corresponding systems in robotics research. In that work, he currently uses eye trackers with the small humanoid robot Nao. This enables it to communicate better with humans. However, these very precise systems cost many thousands of euros.
To find a more cost-effective solution for schools, the researchers cleverly combined technical expertise with knowledge from mathematical didactics. While advanced systems work with a maximum deviation of one degree, webcams have a lower accuracy of three to four degrees. The solution: "With the AI-ALF math tasks, we know that the students are ultimately looking at the on-screen display of the problems," says Lilienthal. "We use this to automatically readjust the eye tracking with the webcam." The system has gradually learned to deal with inaccuracy. "Today it makes no difference to our application whether we work with our webcams or high-end eye trackers," says the professor. This makes the AI system developed with Prof Maike Schindler affordable and, therefore, increasingly important for school use.
Wulfen Comprehensive School: first school in Germany to use the system
This is one reason why the first school to use the AI-based learning system is the Wulfen Comprehensive School in Dorsten, North Rhine-Westphalia. Here, a standardized math test revealed that one-third of 180 children at the start of Year 5 had "arithmetic difficulties." "We are delighted that we can now support significantly more children in their basic math skills with the help of the AI-based learning system. This means we can help more learners improve their math performance than in the past due to a lack of teachers."
In the comprehensive school, five pupils can work with the KI-ALF system simultaneously in individual remedial lessons and are supported and accompanied by a math teacher. Normally teachers can give individual support to only one child at a time. "Especially in times of scarce resources and teacher shortages, our system for promoting basic math skills is simply an excellent support for schools," says Schindler. | 1 |
Jamie Liu
Hong Kong will host global forums on artificial intelligence and robotics this year to promote innovative technologies and deepen the city's international exchanges, according to Financial Secretary Paul Chan Mo-po.
In a recent blog post, Chan placed emphasis on three fronts: expediting the development of innovative technologies, using new methods to boost consumption, and deepening cooperation and exchanges with the global community.
Pointing to his proposals in the latest government budget, Chan said Hong Kong will host the first International Young Scientist Forum on AI and the World Robot Conference.
"This will gather the pioneers in the field [of AI], promoting Hong Kong to become a hub for exchanges and collaborations in the AI industries," Chan wrote.
Chan pledged to ramp up support and investment into scientific research, attract tech giants to open offices in Hong Kong, and nurture tech start-ups. A special channel for listing in the city will be set up for tech firms.
The finance chief also called for new methods to boost consumption and stressed the need for "new experiences" in culture, sports and tourism, coupled with digital marketing.
"How Hong Kong takes advantage of its unique East-meets-West characteristics, provides new experiences for consumers, will be the key to maintain competitiveness and propelling future developments," Chan said.
Meanwhile, the financial secretary is set to attend a ceremony today to mark a HK$200 million donation to a government-backed joint-university collaborative venture on generative AI research.
The Ng Teng Fong Charitable Foundation and Sino Group will be donating HK$200 million to the Hong Kong Generative AI Research and Development Center under the InnoHK Research Clusters. | 1 |
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Andhra Pradesh, 10th of March, 2025 : SRM AP, Amaravati, is proud to announce a transformative five-year collaboration with Carnegie Mellon University’s School of Computer Science (CMU SCS), USA-one of the world’s foremost institutions in artificial intelligence (AI) and cutting-edge research. This strategic collaboration aims to push the boundaries of knowledge, innovation and education in AI-related disciplines, including machine learning, natural language processing, computer vision, infrastructure and systems, and AI ethics and policy.
At the heart of this collaboration is a shared vision to foster an ecosystem that nurtures groundbreaking research, cultivates exceptional talent and accelerates advancements in AI-driven technologies.
A Pioneering Collaboration for AI Excellence
“CMU’s School of Computer Science is excited to work with SRM AP, Amaravati, on this landmark collaboration to advance research and bolster AI education. Together, we will shape the future of AI and empower the next generation of researchers, educators and industry leaders to push the frontiers of technology and drive meaningful change in society,” said Prof. Martial Hebert, Dean of CMU’s School of Computer Science.
Empowering Research Through Global Collaboration
As part of this collaboration, SRM AP, Amaravati’s research faculty and researchers will have the opportunity to engage directly with the esteemed faculty and researchers at CMU’s School of Computer Science. They will immerse themselves in CMU SCS’s pioneering AI labs, working alongside global experts in key research domains. This will facilitate research, knowledge sharing and the development of state-of-the-art AI innovations that address real-world challenges.
Dr P Sathyanarayanan, Pro-Chancellor of SRM AP, Amaravati, said that, “To further strengthen research capabilities, this collaboration will also pave the way to establish advanced AI labs at SRM AP, Amaravati. These labs will be incubators for novel AI research, fostering a stimulating environment that promotes academic rigor, interdisciplinary collaboration and technological innovation.”
Advancing AI Education with World-Class Learning Opportunities
Beyond research, this collaboration is designed to enrich the academic experience of SRM-AP’s teaching faculty and research scholars. Selected faculty members and scholars can audit cutting-edge AI courses at CMU’s School of Computer Science as visiting participants. This exposure will allow them to engage with CMU SCS faculty and contribute to developing robust AI curricula at SRM-AP. They will also gain hands-on experience in designing assignments, worksheets and examinations that mirror real-world AI problem-solving scenarios, enhancing the quality of AI education at SRM AP, Amaravati.
Unparalleled Research Internships for Students
Prof. Manoj K Arora, Vice Chancellor of SRM AP, Amaravati, expressed that, “In a move that underscores its commitment to nurturing future AI leaders, the collaboration will offer SRM-AP students the opportunity to undertake research internships at CMU’s School of Computer Science.” Selected students will spend approx. six weeks each summer immersed in a world-class research environment, gaining firsthand experience in tackling complex AI challenges alongside leaders in the field. This experience will provide students with unparalleled insights and exposure to global research methodologies, setting them apart in the highly competitive AI landscape.
By leveraging CMU SCS’s expertise and SRM-AP’s commitment to academic excellence, this collaboration will drive innovation, expand knowledge horizons and create a lasting impact on the AI ecosystem between the universities.
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Related | 1 |
"PhD-level AI" has emerged as a trending term among tech executives and Artificial Intelligence (AI) enthusiasts online, sparking widespread discussion.
It generally describes AI models purportedly equipped to handle tasks that warrants the expertise of a PhD holder, fuelling excitement and debate across the industry, reported The Information.
The buzz intensified following reports that OpenAI is set to introduce specialised AI agents, including a "PhD-level research" tool priced at $20,000 per month.
Alongside this, OpenAI reportedly plans to launch a high-income knowledge worker agent for $2,000 monthly and a software developer agent for $10,000 monthly. These agents aim to address complex challenges typically requiring years of advanced academic training, such as analysing vast datasets and producing detailed research reports.
Capabilities
OpenAI has claimed that its o1 and o3 reasoning models are capable of mimicking human researchers through a "private chain of thought" technique. Unlike conventional large language models that deliver instant responses, these models engage in an internal iterative process to solve intricate problems. Ideally, PhD-level AI agents would excel at tasks like medical research analysis, climate modelling support, and managing routine research duties.
AI's prowess
OpenAI has highlighted the prowess of its models via various tests. The o1 model reportedly matched PhD students’ performance in science, coding, and math assessments.
The o3 model scored 87.5% on the Abstraction and Reasoning Corpus for Artificial General Intelligence (ARC-AGI) visual reasoning benchmark (outpacing humans at 85%), 87.7% on the graduate-level Graduate-Level Google-Proof Q&A (GPQA) Diamond benchmark (covering biology, physics, and chemistry), and 96.7% on the 2024 American Invitational Mathematics Exam, missing only one question.
Additionally, o3 solved 25.2% of Frontier Math problems, a benchmark funded by OpenAI, as disclosed by EpochAI — far surpassing other models’ 2% success rate.
Is it a marketing gimmick?
Despite these achievements, the "PhD-level" label has drawn skepticism, with some calling it a marketing gimmick.
Critics are questioning the accuracy and reliability of research generated by AI. Some have also hinted at potential errors, and inconsistencies that can arise with it.
Doubts also linger about the models’ capacity for creative thinking and intellectual skepticism, which are the hallmarks for human researchers.
Questions have also arisen on the pricing, many social media users have pointed out that even top PhD students, who often outperform current AI technology, don’t command $20,000 monthly salaries, casting doubt on the pricing justification. | 1 |
Prime Minister Narendra Modi emphasized India’s commitment to shaping its digital future through advancements in Digital Public Infrastructure, Artificial Intelligence (AI), and semiconductor manufacturing. Urging citizens to engage with a detailed article by Union Minister Ashwini Vaishnaw, the Prime Minister’s Office underscored the nation’s technological strides in a social media post.
India is poised to emerge as a global leader in artificial intelligence (AI) and digital public infrastructure as it charts an ambitious course toward its centenary of independence in 2047. With a strong emphasis on innovation, research, and indigenous development, the country is actively working to integrate AI into governance, industry, and public services, ensuring equitable access to technology for its citizens.
Prime Minister Narendra Modi has consistently highlighted AI as a key driver of economic growth and national security. Under his leadership, the government has launched initiatives such as the IndiaAI mission, aimed at fostering AI research, nurturing startups, and building a robust AI ecosystem that aligns with the country’s broader digital transformation agenda.
The government’s approach underscores a vision that balances technological advancement with ethical considerations, ensuring responsible AI development that safeguards privacy, transparency, and fairness.
India’s success in digital public infrastructure (DPI) serves as a strong foundation for its AI ambitions. The implementation of Aadhaar, Unified Payments Interface (UPI), and CoWIN has demonstrated the transformative potential of technology in governance and public service delivery.
These initiatives have not only enhanced efficiency but have also positioned India as a model for other nations seeking to develop inclusive digital ecosystems. The next phase of this digital expansion includes the integration of AI-driven solutions to optimize service delivery in sectors such as healthcare, education, agriculture, and finance.
A key component of India’s AI strategy is the focus on indigenous research and talent development. The country is investing in AI centers of excellence, collaborations between academia and industry, and skilling programs to equip its workforce with expertise in AI and emerging technologies.
By nurturing a skilled talent pool, India aims to become self-reliant in AI development, reducing dependence on foreign technology while fostering innovation tailored to its unique socio-economic landscape.
The regulatory framework surrounding AI is also under active consideration, with policymakers working to establish guidelines that encourage innovation while addressing concerns around data privacy, algorithmic bias, and national security.
By adopting a balanced approach, India seeks to create an AI ecosystem that is both dynamic and responsible, ensuring that technology serves the public good without compromising ethical standards. | 1 |
Amaravati (Andhra Pradesh) [India], March 10: SRM AP, Amaravati, is proud to announce a transformative five-year collaboration with Carnegie Mellon University’s School of Computer Science (CMU SCS), USA-one of the world’s foremost institutions in artificial intelligence (AI) and cutting-edge research. This strategic collaboration aims to push the boundaries of knowledge, innovation and education in AI-related disciplines, including machine learning, natural language processing, computer vision, infrastructure and systems, and AI ethics and policy.
SRM AP, Amaravati’s Landmark Collaboration with Carnegie Mellon University’s School of Computer Science, USA for AI Research, Education
Andhra Pradesh, IndiaSRM AP, Amaravati, is proud to announce a transformative five-year collaboration with Carnegie Mellon University’s School of Computer Science (CMU SCS), USA-one of the world’s foremost institutions in artificial intelligence (AI) and cutting-edge research. This strategic collaboration aims to push the boundaries of knowledge, innovation and education in AI-related disciplines, including machine learning, natural language processing, computer vision, infrastructure and systems, and AI ethics and policy.
SRM AP, Amaravati Secures a Pioneering Collaboration with Carnegie Mellon University’s School of Computer Science
At the heart of this collaboration is a shared vision to foster an ecosystem that nurtures groundbreaking research, cultivates exceptional talent and accelerates advancements in AI-driven technologies.
A Pioneering Collaboration for AI Excellence
“CMU’s School of Computer Science is excited to work with SRM AP, Amaravati, on this landmark collaboration to advance research and bolster AI education. Together, we will shape the future of AI and empower the next generation of researchers, educators and industry leaders to push the frontiers of technology and drive meaningful change in society,” said Prof. Martial Hebert, Dean of CMU’s School of Computer Science.
Empowering Research Through Global Collaboration
As part of this collaboration, SRM AP, Amaravati’s research faculty and researchers will have the opportunity to engage directly with the esteemed faculty and researchers at CMU’s School of Computer Science. They will immerse themselves in CMU SCS’s pioneering AI labs, working alongside global experts in key research domains. This will facilitate research, knowledge sharing and the development of state-of-the-art AI innovations that address real-world challenges.
Dr P Sathyanarayanan, Pro-Chancellor of SRM AP, Amaravati, said that, “To further strengthen research capabilities, this collaboration will also pave the way to establish advanced AI labs at SRM AP, Amaravati. These labs will be incubators for novel AI research, fostering a stimulating environment that promotes academic rigor, interdisciplinary collaboration and technological innovation.”
Advancing AI Education with World-Class Learning Opportunities
Beyond research, this collaboration is designed to enrich the academic experience of SRM-AP’s teaching faculty and research scholars. Selected faculty members and scholars can audit cutting-edge AI courses at CMU’s School of Computer Science as visiting participants. This exposure will allow them to engage with CMU SCS faculty and contribute to developing robust AI curricula at SRM-AP. They will also gain hands-on experience in designing assignments, worksheets and examinations that mirror real-world AI problem-solving scenarios, enhancing the quality of AI education at SRM AP, Amaravati.
Unparalleled Research Internships for Students
Prof. Manoj K Arora, Vice Chancellor of SRM AP, Amaravati, expressed that, “In a move that underscores its commitment to nurturing future AI leaders, the collaboration will offer SRM-AP students the opportunity to undertake research internships at CMU’s School of Computer Science.” Selected students will spend approx. six weeks each summer immersed in a world-class research environment, gaining firsthand experience in tackling complex AI challenges alongside leaders in the field. This experience will provide students with unparalleled insights and exposure to global research methodologies, setting them apart in the highly competitive AI landscape.
By leveraging CMU SCS’s expertise and SRM-AP’s commitment to academic excellence, this collaboration will drive innovation, expand knowledge horizons and create a lasting impact on the AI ecosystem between the universities.
(ADVERTORIAL DISCLAIMER: The above press release has been provided by NewsVoir. ANI will not be responsible in any way for the content of the same.)
This story is auto-generated from a syndicated feed. ThePrint holds no responsibility for its content. | 1 |
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Opinion | AI Needs A Ramanujan Moment: Why India Must Think Beyond The West
Written By :
News18.com
Last Updated: March 10, 2025, 15:35 IST
The next Ramanujan of AI will not emerge from research that mimics the West but from minds that rethink intelligence with India’s unique strengths—frugality, adaptability, and a refusal to accept intellectual colonisation
The world does not need another OpenAI or Google; it needs an alternative way of thinking about AI. (Shutterstock)
A century ago, when India was shackled by political and economic colonisation, a band of extraordinary scientists defied their circumstances to revolutionise physics, mathematics, and chemistry. They lacked institutional support, suffered racial discrimination, and often worked without pay. And yet, their intellectual independence led to path breaking contributions that shaped global science.
Today, while India is politically and economically free, it faces a subtler yet equally insidious challenge: mental colonisation. As the global AI race accelerates, India needs researchers who embody the spirit of Jagadish Chandra Bose, Satyendra Nath Bose, C.V. Raman, and Srinivasa Ramanujan—scientists who created with little but thought freely and acted boldly.
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Breaking Free from Mental Colonisation
The irony is striking. A hundred years ago, despite systemic oppression, Indian scientists thought independently and pioneered new fields. Jagadish Chandra Bose refused to patent his work on wireless communication, prioritising knowledge over personal gain. Satyendra Nath Bose reimagined quantum statistics with a single letter to Einstein, fundamentally shaping quantum mechanics. These scientists were not constrained by the West’s intellectual hegemony; they built their own paradigms.
Contrast that with today: Indian research institutions remain fixated on Western benchmarks—be it publishing in elite journals, securing grants from Western funding agencies, or replicating Silicon Valley’s technological models. The result? A lack of originality, over-dependence on Western validation, and an AI industry that consumes rather than creates. But what if we approached AI research the way our forefathers approached science—unbound by conventional constraints?
The DeepSeek Wake-Up Call
China is already demonstrating how a nation can break free from Silicon Valley’s technological hegemony. DeepSeek, China’s homegrown AI research lab, has produced models that rival OpenAI and Google DeepMind. The Chinese approach is clear: resource efficiency, alternative methodologies, and independent innovation. Instead of chasing the American paradigm, they are crafting their own.
India must take note. Our research mindset cannot be constrained by resource limitations, nor should it be enslaved to Western frameworks of technological progress. There are novel ways to build frontier technologies with fewer resources. Consider how Meghnad Saha, without access to a modern laboratory, formulated the Saha Ionization Equation, which transformed astrophysics. Or how Ramanujan, armed with nothing but self-taught mathematics and a notebook, sent shockwaves through Cambridge with his theorems. Their constraint was economic, but their ideas were limitless.
An Indic Alternative to AI Research
The world does not need another OpenAI or Google; it needs an alternative way of thinking about AI. What alternative framework can India offer to the AI revolution? Prof. Gopinath of Rishihood University has inspired a crucial insight—perhaps India’s intellectual traditions hold the key to new AI models.
Imagine a way of thinking that combines what you see with your eyes and what you figure out with numbers. That’s what “computational positivism"—or Drigganitaikya in Sanskrit—means. It’s about blending observation (like watching the stars) with math (like calculating their paths). This idea comes from ancient India, where brilliant minds like Aryabhata and Bhaskara watched the world closely and created math to explain it—think of them as early scientists and coders rolled into one. This tradition emphasized empirical validation—cross-checking calculations against observable data—and iterative refinement, creating a robust framework. For instance, Aryabhata’s computation of Earth’s circumference (within 1 per cent of the modern value) relied on both geometric theory and astronomical measurements, showcasing this synthesis.
How can this method help AI and machine learning? Today, AI and machine learning (ML) are like super-smart calculators that learn from data—like teaching a computer to recognise cats in photos by showing it thousands of pictures. But sometimes, AI gets stuck: it might overthink the data, miss the big picture, or use too much power. India’s computational positivism offers a fresh approach. Just like ancient Indians checked star positions against their calculations, AI could combine real-life observations with math rules to make smarter, more reliable predictions.
For instance, while current ML might predict stock prices from historical data alone, a Drigganitaikya-inspired model could incorporate real-time economic indicators and mathematical priors (e.g., market equilibrium equations), refining predictions dynamically. This could lead to more robust, efficient, and explainable AI systems—critical for applications like climate modeling, autonomous vehicles, or personalised medicine.
In short, India’s old-school wisdom could give AI a new edge: smarter, simpler, and more in tune with reality. It’s like teaching a computer to think like a Rishi—watching, calculating, and adapting all at once.
A New Breed of Indian Researchers
India must cultivate a new generation of researchers who combine intellectual audacity with strategic pragmatism. This means moving beyond the obsession with Western benchmarks and embracing indigenous, disruptive models of innovation. It means recognising that AI is not just about GPUs and billion-dollar datasets but about reimagining intelligence itself.
The scientists of 100 years ago built their legacies not through imitation but through fearless originality. India must do the same in AI and frontier technologies. The next Ramanujan of AI will not emerge from research that mimics the West but from minds that rethink intelligence with India’s unique strengths—frugality, adaptability, and a refusal to accept intellectual colonisation.
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The global AI race is wide open. The question is: Will India enter it as an independent creator, or as a follower of preordained paths? History shows us the answer. It is time to remember it.
Shobhit Mathur is the Co-founder and Vice-Chancellor of Rishihood University, Haryana. Views expressed in the above piece are personal and solely those of the author. They do not necessarily reflect News18’s views.
First Published: March 10, 2025, 15:35 IST | 1 |
Max Planck Society
Grant helps to take AI-based harvesting robot Polybot from research-phase to startup-phase
In brief
The harvesting robot project Polybot is supported by the Federal Agency for Breakthrough Innovation (Deutsche Agentur für Sprunginnovation: SPRIND) with a seven-month validation grant of approximately 220,000 euros.
This funding will help the team to prepare the transition from scientific research to founding a start-up company. During the validation phase, the AI technology will be evaluated using the example of harvesting. After successful validation, SPRIND could then support the project along with private investment in the creation of a company.
The project, which has so far been supported by the Tübingen AI Center at the University of Tübingen, the ELLIS Institute Tübingen and the Max Planck Institute for Intelligent Systems, combines top-level research with practical application and strengthens Tübingen as an innovation location within the Cyber Valley.
Polybot at Venture SPRIND Event
In addition to the contract award, SPRIND will support the Polybot team with intensive mentoring, guidance, and strategic development support. The team will also have access to investors and will pitch their business idea to over 300 potential investors at the Venture SPRIND event in Berlin in April 2025. "This contract underlines the fact that Polybot is not an ivory tower idea, but a concrete solution for future-proof, sustainable agriculture," says project leader Wieland Brendel from the Max Planck Institute for Intelligent Systems and the ELLIS Institute in Tübingen, Germany.
The close collaboration with SPRIND is an important success for the Tübingen AI ecosystem. Polybot is a good example of how the Tübingen AI Center combines leading research with societal applications. "This first external validation motivates our team enormously," says Martin Kiefel, technical leader of the project." With the support, we can now train our learning algorithms on the most difficult tasks in agriculture, the harvesting of fine vegetables, and test them together with farmers in the field." Bernhard Schölkopf, Scientific Director of the ELLIS Institute and Director at the Max Planck Institute for Intelligent Systems, adds: "Excellent research unfolds its full power when it not only creates knowledge, but also helps to solve the challenges of our time."
More Sustainable Farming
Polybot is a fully autonomous solution for growing crops, fruit and vegetables using state-of-the-art AI technology. In the future, the robot will automate a wide range of activities, from weeding and picking tomatoes or cucumbers to free pruning. By combining computer vision and robotic mechanics, Polybot reduces the need for chemical herbicides and can make smaller-scale and more sustainable forms of farming more economical. By automating manual tasks, farmers can use labor more efficiently and increase yields over the long term.
At the heart of the automation solution is an autonomous robot with a precise manipulator that can perform even complex and precise tasks, such as harvesting tomatoes. The control system is based on an innovative machine learning pipeline that allows the robot to quickly learn new tasks through demonstrations by farmers - without the need for time-consuming programming.
Innovation in Agriculture
The current validation project is assessing the practical suitability of Polybot for harvesting fine vegetables. The high precision and advanced 3D perception required for this task make it an ideal test environment. The technology aims to demonstrate that new tasks can be flexibly integrated and automated, even in areas where automation has previously been economically unfeasible.
Beyond technical feasibility, the project also examines the benefits for farmers. At present, the harvesting of fine vegetables is almost entirely manual, while labor shortages are becoming increasingly severe. In close collaboration with dedicated farmers, the project will define the requirements for the first market-ready product, ensuring its practical value. The goal is to demonstrate that AI-driven robotics can significantly enhance both the efficiency and sustainability of agriculture.
"The Polybot project leverages recent breakthroughs in machine learning to make polyculture farming economically viable. I am delighted that we can bring such ideas to life at the Tübingen AI Center, which are socially valuable and only become possible through cutting-edge research," emphasizes Matthias Bethge, Director of the Tübingen AI Center. The SPRIND contract represents a significant milestone for the team and for the AI network within Cyber Valley, both strategically and in terms of communication. The Tübingen AI Center, the ELLIS Institute Tübingen, and the Max Planck Institute for Intelligent Systems are working closely together to drive this effort forward. The coming months will be crucial for the further development of Polybot, with the goal of achieving a true breakthrough innovation for agriculture.
Background Information
The ELLIS Institute Tübingen is set to become a world-renowned center for pioneering basic research in the field of artificial intelligence. The Institute aims to attract the world's best machine learning talent, providing them with outstanding conditions to conduct research in a state-of-the-art facility located in Tübingen, Germany. The vision is part of a broader initiative, the European Laboratory for Learning and Intelligent Systems (ELLIS), which aims to build a pan-European institution for machine learning research.
The Tübingen AI Center is a research facility at the University of Tübingen in cooperation with the Max Planck Institute for Intelligent Systems. The goal of the researchers is to advance reliable learning systems for the benefit of society and the economy. The Tübingen AI Center is home to 25 research groups with more than 300 scientists. As part of the recently founded ELIAS Alliance, they work together with other researchers in Europe to contribute to societally valuable technologies as "AI made in Europe". The Center works closely with the ELLIS Institute Tübingen and Cyber Valley. It is funded by the Ministry of Research of Baden-Württemberg and the Federal Ministry of Education and Research. | 1 |
In a recent Views & Comments column published in Engineering, researchers Jinghai Li and Li Guo from the Chinese Academy of Sciences offer profound insights into the future development of data science, with a particular focus on its crucial role in artificial intelligence (AI).
The article begins by highlighting the increasing significance of scientific data systems in research and development (R&D). Data has become the linchpin of AI's rapid progress, influencing every stage of AI model development, from training to evaluation and optimization. However, scientific data, which stems from long-term research on multi-level complex spatiotemporal dynamic processes, presents numerous challenges. The current incomplete understanding of these complex spatiotemporal structures leads to issues in data accumulation, modeling, and application.
For instance, in image recognition, image data has a hierarchical structure. Convolutional neural networks (CNNs) leverage this structure for image recognition. But if the logic and architecture of data systems do not align with the data's inherent characteristics, it can result in problems such as model prediction errors, poor generalization ability, and increased computational costs. This not only affects AI and data science but also poses a challenge to scientific research. Different researchers may obtain varying data for the same phenomenon, and inappropriate averaging techniques for complex spatiotemporal structures can overlook crucial relationships.
To address these issues, the researchers propose that future data collection and processing should adhere to certain principles. Given the multi-level and multi-scale nature of complex systems, data collection should clarify multi-level characteristics, spatiotemporal structural characteristics, and key variables. Additionally, it should define the critical conditions for regime transitions and annotate unobtainable data.
The article also emphasizes the importance of rearranging AI models into a multi-level architecture. Taking large language models (LLMs) as an example, by integrating the inherent logic and structure of text data into their construction, LLMs can better capture semantic information, enhancing text comprehension, sentence generation, and logical reasoning capabilities.
Currently, the principles for data collection and processing are often neglected, restricting the development of data systems and AI. The researchers call for researchers and practitioners to fully recognize the significance of data system logic and architecture. A global standard protocol framework and operation guide for hierarchical structured data are needed to foster a high-quality data ecosystem and promote the healthy development of AI. Applying the principle of mesoscale complexity in data-related processes also shows promise for data science and AI.
In conclusion, in the new research paradigm, it is essential to pay attention to the multi-level structures of complex systems during data-related activities and AI analysis. This requires strict adherence to the principle that data behavior and functional relationships should match the research object, which also poses higher requirements for interdisciplinary research.
The paper "The Logic and Architecture of Future Data Systems," authored by Jinghai Li, Li Guo. Full text of the open access paper: https://doi.org/10.1016/j.eng.2025.02.006 | 1 |
Lunit, a Korean medical AI imaging company, announced that its large-scale, multi-center prospective study validating the effectiveness of its AI-powered mammography analysis solution, Lunit INSIGHT MMG, for breast cancer detection has been published in Nature Communications.
Lunit's AI-powered mammography solution significantly improves breast cancer detection rates without increasing unnecessary recalls, as validated by a large-scale, multi-center prospective study published in Nature Communications.
The study is the "world's first" large-scale prospective study to validate the effectiveness of AI in a single reading environment, where a single radiologist interprets mammograms, as practiced in countries like Korea, according to the company.
The findings demonstrated that AI enhanced diagnostic accuracy while not increasing unnecessary recalls for additional testing.
The research, led by Professors Chang Yun-woo of the Department of Radiology at Soonchunhyang University Seoul Hospital and Ryu Jung-kyu of the Department of Radiology at Kyung Hee University Hospital at Gangdong, was conducted from February 2021 to December 2022 across six university hospitals in Korea. Other hospitals include Eulji University Nowon Eulji Medical Center, Konkuk University Medical Center, Busan Paik Hospital, and CHA Bundang Medical Center.
The study analyzed 24,543 women aged 40 and older who underwent mammography as part of Korea’s national cancer screening program.
The research team compared AI-assisted mammogram readings with traditional single readings by breast imaging specialists through one-year follow-ups.
The results showed that AI-assisted interpretation significantly increased the cancer detection rate (CDR) from 5.0 per 1,000 cases to 5.7 per 1,000 cases, marking a 13.8 percent improvement. Notably, AI did not affect the recall rate (RR), indicating that the technology improves cancer detection without leading to unnecessary additional examinations.
Further simulations demonstrated that AI-assisted readings by general radiologists increased the cancer detection rate from 3.9 to 4.9 per 1,000 cases, representing a 26.4 percent improvement. AI also proved effective in detecting early-stage breast cancers, including tumors smaller than 20 mm and those without lymph node involvement.
“This study confirms that AI can enhance cancer detection rates for both specialized breast radiologists and general radiologists in a single reading environment,” Professor Chung said. “Moreover, AI contributes to the early detection of breast cancer, improving patient outcomes.”
Lunit CEO Brandon Suh also said, “This large-scale prospective study validates the real-world impact of our AI solution in breast cancer detection.”
The results provide strong evidence supporting the adoption of Lunit’s AI technology in countries where a single radiologist interprets mammograms, such as Korea and the U.S., Suh added. | 1 |
Vurvey Labs, an AI research and platform company serving global brands like Adidas, Kenvue, and Unilever, today unveiled Vurbs™ — the world’s first AI agent ecosystem dynamically powered by real-time human insights.
Breaking the LLM Paradigm
Traditional Large Language Models (LLMs) are static. Vurbs represent a revolutionary approach: AI agents that live, learn, and evolve through continuous human interaction.
“LLMs are a great starting point, but the next generation of agents need deeper understanding,” said Chad Reynolds, Founder and CEO. “Vurbs are designed with characteristics not just inspired by real people, but directly connected to them.”
The People Model™: A New AI Paradigm
Dr. Ben Vaughan, Head of AI Research, explains the breakthrough: “While traditional LLMs remain frozen in time, our Vurbs network learns through insights from over 3 million global consumers. This introduces variance crucial for modeling consumer behaviors that expand beyond existing training sets.”
Key Innovations
People Model™: Advanced Large Persona Model (LPM) designed to generate Vurbs™, segments, and global AI populations using identifiable personality traits, demographics, psychographics, and custom facets
Vurb™ Personas: Transform static consumer profiles into living, conversational agents for real-time insights and collaboration
Vurb™ Assistants: A custom network of expert AI agents to advance and scale knowledge creation and innovation
Vurb™ Products: AI personalities and companions that authentically represent brands, products, and commerce opportunities
Enterprise-Tested, Consumer-Ready
Kathy Rutherford, Global Insights leader from Kenvue highlights the transformative potential: “Our Skin Health brands now have 24/7 consumer collaboration through Vurvey agents, democratizing insights across our innovation teams.”
Beyond Conversation: Actionable AI
“Vurbs are not just conversational — they’re actionable,” Reynolds emphasizes. “This creates an opportunity to bring everyday objects, products, and experiences to life through dynamic, collaborative agents that can be personalized and more reflective of your world.” | 1 |
Company Profile
Established in 2017, Wayve.ai is pioneering the future of urban mobility with its cutting-edge autonomous driving technology. Based in the heart of London, this innovative company is transforming how vehicles navigate complex city landscapes through advanced machine learning and AI research. Wayve.ai leverages its deep understanding of robotic control and data-driven solutions to develop software that enables vehicles to learn and adapt to real-world driving scenarios.
Wayve.ai is dedicated to pushing the boundaries of what is possible in the realm of autonomous vehicles. By focusing on scalable software solutions, the company aims to democratise access to autonomous driving, making it feasible for a wide range of vehicles and applications. The team at Wayve.ai combines expertise in machine learning, artificial intelligence, and robotics to deliver impactful and sustainable innovations in mobility.
With a commitment to excellence and a vision for a safer, more efficient future, Wayve.ai is at the forefront of the autonomous driving revolution. Under the leadership of CEO Alex Kendall, the company continues to grow, attracting talent from around the globe to contribute to its mission. Through strategic partnerships and continuous research, Wayve.ai is poised to make significant contributions to the evolution of transportation technology. | 1 |
Property giant Sino Group’s charitable foundation has donated HK$200 million (US$25.7 million) to the government in support of Hong Kong’s artificial intelligence (AI) development, with a focus on creating a mobile version of a localised chatbot based on DeepSeek’s model that will be rolled out to residents in the coming months.
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The sum was given by the Ng Teng Fong Charitable Foundation and Sino Group to the Hong Kong Generative AI Research and Development Centre (HKGAI) under the government’s InnoHK initiative.
The deal marked a major donation by the private sector to the government’s push on AI development in the city, in line with Financial Secretary Paul Chan Mo-po’s budget address last month.
“This donation will support the HKGAI’s establishment of a service platform to provide the public with a model developed in the city, HKChat, the first service of its kind in the industry based on a localised DeepSeek model,” Chan said on Monday.
HKChat is an AI chatbot built on the HKGAI V1 model, a ChatGPT-style AI tool powered by DeepSeek’s data learning model, which supports English, Cantonese and Mandarin, and is currently being tested by government departments.
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When given prompts, HKChat can provide answers or generate responses in real-time to questions on topics such as the government, the law, film box office or travel itinerary planning. | 1 |
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As Indian businesses race to integrate their services with AI, new research suggests that the shortage of skilled professionals in the field could hinder the technology’s growth and adoption.
A new report released by global consultancy firm Bain & Company on Monday, March 10, found that over 2.3 million jobs could open up in India’s AI sector by 2027. The AI talent pool within the country is expected to grow to around 1.2 million qualified candidates, as per the report.
Despite this, the demand for AI talent is expected to exceed supply by nearly one million workers, creating a significant skills gap, according to the report. Globally, the study found that AI-related job postings have surged by 21 per cent every year since 2019, with compensation growing 11 per cent annually over the same period.
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However, a survey of executives also revealed that 44 per cent of respondents identified a lack of in-house AI expertise as a key barrier to implementing generative AI, followed by quality and accuracy concerns (44 per cent).
Data security and privacy concerns (38 per cent), unorganised company data (32 per cent), and unproven ROI on generative AI (29 per cent) were also cited as reasons for businesses not moving faster to adopt AI.
“The challenge—and opportunity— lies in reskilling and upskilling a significant portion of the existing talent base on emerging technology tools and skillsets,” Saikat Banerjee, partner and leader in Bain & Company’s AI, Insights, and Solutions practice in India, said in a statement.
“The AI talent shortage is a significant challenge, but not invincible. Addressing it requires a fundamental shift in how businesses attract, develop, and retain AI talent. Companies need to move beyond traditional hiring approaches, prioritise continuous upskilling, and foster an innovation-driven ecosystem,” he added.
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Meanwhile, Bain & Company stated that one in two AI jobs in the US would be left unfilled by 2027. “Germany could see the biggest AI talent gap, with around 70 per cent of AI jobs unfilled by 2027,” the report stated.
In the next three years, the UK and Australia may see AI talent shortfalls of 150,000 and 60,000 AI professionals, respectively. | 1 |
Vellore Institute of Technology (VIT) Chennai and M.S. Swaminathan Research Foundation (MSSRF) on Monday signed a memorandum of understanding (MoU) at the International Women’s Day event organised by VIT to collaborate on research in areas such as unmanned aerial vehicles, drones, computer vision, and artificial intelligence among other areas.
Speaking at the event, Soumya Swaminathan, Chairperson, MSSRF, said, “In many places, men and women, even though they are doing the same amount of hours per day, they are paid different wages, so that needs to go. Women deserve equal pay for the work that is going on.”
G. Viswanathan, founder and chancellor of VIT said that if the goal of Prime Minister Narendra Modi who wants India to become a developed country by 2047 will be realised only if women also participate and get equal education.
G.V. Selvam, Vice-President, VIT cited examples of how women like Queen Velu Nachiar, Captain Lakshmi of the Indian National Army (INA) and Jhansi Rani were courageous and urged all women to be courageous.
Sachini Dissanayake, Assistant Secretary, Ministry of Defence, Government of Sri Lanka said, “Each one of you has the potential to impact the world, to be the voice for those who cannot speak, to be the change that others need to see. Whether you are passionate about education, sports, social justice, or technology, your dreams are valid and your journey is just as important.”
A. Kalyani, Advisor, VIT and Dr.T.Thyagarajan, Pro-Vice Chancellor, Chennai Campus, VIT also spoke at the function. | 1 |
Two research and development projects, worth almost EUR 180 million, in the field of nuclear energy and artificial intelligence (AI), respectively, will be carried out in Romania, the Ministry of Investments and European Projects (MIPE) announced.
The first project, called "4ALFRED - Research for Next Generation Reactors", has a value of over EUR 112 million and aims to develop experimental infrastructure for lead-cooled fast neutron reactor technology, contributing to energy security and reducing carbon emissions.
Implemented in partnership with the Autonomous Administration for Nuclear Energy Technologies (RATEN) and five private companies, the project includes four state-of-the-art experimental facilities (HELENA-2, ELF, HandsOn, and Meltin'Pot) for testing the materials, components, and systems required for the ALFRED reactor.
The second project - The Romanian Hub for Artificial Intelligence (HRIA) - a strategic step for artificial intelligence," worth over EUR 67 million, will function as a Center of Excellence for research and development in the field of AI, bringing together experts from academia, research, and the private sector.
Coordinated by the National University of Science and Technology "Politehnica" Bucharest, in partnership with six universities and eight private companies, the project will develop advanced research infrastructures, attract top researchers, and train AI specialists, facilitating technological transfer and the practical application of research results in the economy.
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(Photo source: Hakinmhan/Dreamstime.com) | 1 |
“PhD-level AI” seems to have become the latest buzzword among tech industry executives and AI enthusiasts online.
The term broadly refers to AI models that are supposedly capable of executing tasks requiring PhD-level expertise. The hype around PhD-level AI comes a week after reports stating that OpenAI is looking to roll out a handful of specialised AI agents, including a “PhD-level research” tool priced at $20,000 per month.
OpenAI also plans to launch a high-income knowledge worker agent at $2,000 a month, and a software developer agent at $10,000 a month, according to a report by The Information.
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The claim is that a PhD-level AI agent will be able to tackle problems that typically require years of specialised academic training. Such AI agents are expected to conduct advanced research by analysing large datasets and generate comprehensive research reports.
However, some critics have dismissed the “PhD-level” label as a marketing term. Others have raised concerns over the accuracy and reliability of AI-generated research reports.
Can AI models reason like a PhD researcher?
OpenAI has claimed that its flagship o1 and o3 reasoning models make use of a technique called “private chain of thought” in order to mirror how human researchers perform tasks.
Unlike traditional large language models (LLMs), reasoning AI models do not immediately provide responses to user prompts. Instead, they use machine learning techniques to run through an internal dialogue and iteratively work out the steps involved in solving complex problems.
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PhD-level AI agents should ideally be able to perform complex tasks that include analysing medical research data, supporting climate modeling, and handling routine aspects of research work.
How well do existing AI models perform on key benchmarks?
In the past, OpenAI has claimed that its o1 model performed similarly to human PhD students on certain science, coding, and math tests.
The company further claimed that its o3 model achieved 87.5 per cent in high-compute testing on the ARC-AGI visual reasoning benchmark, surpassing the 85 per cent score by humans.
o3 scored 87.7 per cent on the GPQA Diamond benchmark, which contains graduate-level biology, physics, and chemistry questions, while it received 96.7 per cent on the 2024 American Invitational Mathematics Exam, missing just one question, according to OpenAI.
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Furthermore, o3 reportedly solved 25.2 per cent of problems in Frontier Math, a benchmark designed by EpochAI, with other models trailing at two per cent. To be sure, the non-profit revealed in December last year that OpenAI funded the creation of the Frontier Math benchmark for evaluating AI models.
What are the major concerns with PhD-level AI agents?
While the benchmark performances of simulated reasoning models might be considered impressive, experts have pointed out that these models could still struggle to generate plausible-sounding, factually accurate information.
The abilities of AI models to engage in creative thinking and intellectual scepticism have also been questioned. OpenAI has not confirmed the prices of its upcoming specialised AI agents, but users on social media opined that “most PhD students, including the brightest stars who can do way better work than any current LLMs—are not paid $20K / month.”
Also Read | AI agent as your digital chief of staff
The buzz around OpenAI’s rumoured launch has also reached a fever pitch with the company’s own AI researcher, Noam Brown, stating that there’s “lots of vague AI hype on social media these days.”
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“There are good reasons to be optimistic about further progress, but plenty of unsolved research problems remain,” Brown said in a post on X. | 1 |
A recent study reveals that advanced artificial intelligence (AI) models, including OpenAI’s and DeepSeek’s reasoning systems, have learned to manipulate situations on their own. The research indicates that AI attempts to cheat when it realizes it is losing in chess.
AI models make unexpected moves in chess
In recent years, artificial intelligence (AI) models have rapidly developed, achieving significant strides in reasoning abilities. However, new research highlights that these advancements may come with unforeseen and alarming consequences.
A study conducted by Palisade Research has shown that AI models developed by companies like OpenAI and DeepSeek resort to unethical methods instead of accepting defeat.
AI’s unethical tactics in chess games
Artificial intelligence has been surpassing human intelligence in chess since 1997 when IBM’s Deep Blue defeated Garry Kasparov. Today, powerful chess engines like Stockfish dominate the chess world.
However, generative AI models, due to their programming approaches, are not yet at the level to compete with these engines. Interestingly, these AI systems do not fully grasp the limits of their abilities and begin looking for unexpected ways to win.
Study finds AI models resort to cheating
Researchers tested systems like OpenAI’s o1-preview model and DeepSeek R1 against Stockfish. To better understand AI’s thought processes, a “notebook” was created to record its reasoning. The results were surprising: The o1-preview model attempted to cheat in 37% of games, while DeepSeek R1 resorted to unethical methods in one out of every ten games.
In contrast, relatively weaker models like GPT-4o and Claude Sonnet 3.5 were only able to cheat with human intervention. This indicates that advanced AI models can develop manipulative strategies without external guidance.
Artificial intelligence, an android robot, plays chess against a man. (Adobe Stock Photo)
How AI tried to cheat in chess?
AI’s methods of cheating are not limited to simple tricks. For instance, when the o1-preview model realized it could not defeat Stockfish, it told researchers that it could manipulate game data, causing the chess engine to assess its position more negatively, which could prompt its opponent to resign.
This is not just about a chess game but offers a critical insight into how AI thinks.
Why is AI resorting to cheating?
The root cause of such unethical behavior lies in the way AI models are trained. Unlike traditional systems, modern AI models are trained using reinforcement learning. This method rewards the model for achieving a specific goal. However, there is a crucial issue: AI does not understand how the goal should be achieved. If the task is to “defeat the opponent,” it does not comprehend that it should be done fairly; it is solely focused on achieving the result.
This issue is not limited to chess. AI’s ability to develop manipulative strategies could pose serious risks in fields like finance, security, and politics. Experts are calling for more transparency in the security of AI models. However, companies like OpenAI are reluctant to provide detailed insights into the inner workings of their models.
While we are not yet facing a dystopian AI scenario, more research is needed to determine how ethical boundaries are defined and how AI platforms perceive these limits. If not addressed, these concerns could lead to much larger problems in the future. AI does not think like humans; it is programmed to carry out tasks directly and without questioning. This makes ethical oversight and security measures more critical than ever. | 1 |
Paul Chan, center, accepts from Sino Group and Ng Teng Fong Charitable Foundation a pledge for a $200 million donation. Previous Next
Ayra Wang
Hong Kong must seize opportunities in the fast-growing field of artificial intelligence, says Financial Secretary Paul Chan Mo-po.
Chan's remarks came as Ng Teng Fong Charitable Foundation and Sino Group pledged HK$200 million to support the Hong Kong Generative AI Research and Development Center under the InnoHK Research Clusters.
"AI is not just a critical industry on its own. It's a powerful tool that can enhance and integrate deeply into various sectors," Chan said during a donation ceremony at the Central Government Offices.
He noted the central government's recent work report during the Two Sessions in Beijing, which emphasized advancing the "AI Plus" initiative and promoting widespread use of AI models. He also stressed that Hong Kong is uniquely positioned to capitalize on these opportunities with its strong research capabilities and global reputation.
"Three of our local universities are ranked among the top 25 globally in AI and data science disciplines," Chan said, adding that as a global financial hub and a magnet for talent, Hong Kong can provide robust financial and human resources to support the AI industry.
Daryl Ng Win-kong, Sino Group deputy chairman and director of Ng Teng Fong Charitable Foundation, expressed pride in supporting Hong Kong's AI ambitions, adding that the funds will help launch public dialogue services for HKGAI V1, the city's first locally developed generative AI model.
Guo Yike, Hong Kong University of Science and Technology provost and director of HKGAI, said the center will utilize the funds to improve the AI ecosystem, build high-security AI computing power and data platforms, and advance the HKGAI model for real-world applications.
Secretary for Innovation, Technology and Industry Sun Dong hailed the donation as a milestone in Hong Kong's AI development, adding that it would remain a priority despite potential budget cuts.
"In times of fiscal tightening, our support will be more focused and targeted, but we will continue to invest in critical areas such as AI," Sun said.
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Union Minister for Electronics & Information Technology Ashwini Vaishnaw on Thursday unveiled a series of AI-driven initiatives under the IndiaAI Mission, marking a major step in strengthening India’s artificial intelligence ecosystem. The launch event introduced AIKosha: IndiaAI Datasets Platform, the IndiaAI Compute Portal, and several programs aimed at skill development and startup acceleration.
Speaking at the event, Vaishnaw highlighted the transformative potential of these initiatives in advancing AI research and innovation. “With AIKosha and the AI Compute Portal, we are enabling India’s AI ecosystem to thrive,” he said. The AI Compute Portal will initially provide access to 10,000 GPUs, with plans to add 8,693 more, offering subsidized compute services to startups, researchers, and enterprises. He also noted India’s growing presence in the global AI landscape, citing the country’s top rank in AI skill penetration and its position among the world’s top ten AI nations.
The IndiaAI Mission, approved in March 2024, aims to democratize AI access and strengthen ethical AI practices. Vaishnaw underscored India’s commitment to data integrity, stating, “AIKosha will ensure ethically sourced, consent-based datasets, reducing dependence on foreign and synthetic data.” The platform currently hosts over 300 datasets and 80 AI models, fostering the development of diverse and unbiased AI solutions.
MeitY Secretary S. Krishnan emphasized the mission’s broad impact, noting that nearly 45% of its Rs. 10,372 crore funding has been allocated to the AI Compute Portal. “This initiative will revolutionize AI deployment across sectors and propel economic growth,” he said. He also stressed the need for structured AI investment, aligning with the Prime Minister’s vision of a technologically advanced India by 2047.
A range of new initiatives were introduced, including iGOT-AI, an AI-powered learning system for government officials, and the IndiaAI Startups Global Acceleration Program in collaboration with Station F and HEC Paris. The acceleration program will provide ten Indian AI startups with a four-month immersive experience in Paris, offering mentorship and market expansion opportunities.
The IndiaAI Innovation Challenge, which received over 900 submissions, has shortlisted 30 AI solutions aimed at addressing key challenges in healthcare, governance, agriculture, and climate change. Additionally, the IndiaAI FutureSkills Fellowship has been launched to support AI students at undergraduate, master’s, and PhD levels. IndiaAI Data Labs are also being set up in Tier 2 and Tier 3 cities to provide foundational AI training.
The launch of AIKosha, the AI Compute Portal, and other IndiaAI initiatives signals a decisive step toward fostering an inclusive and responsible AI ecosystem. Vaishnaw hailed the progress, stating, “India is witnessing unprecedented growth in AI, thanks to the Prime Minister’s long-term vision for technological advancement. These initiatives will propel us into the league of the world’s top AI nations.” | 1 |
In a recent article published in Humanities and Social Sciences Communications, researchers have comprehensively explored the intersection of artificial intelligence (AI) investment and sustainable development in the United States, analyzing its impact on economic growth, environmental sustainability, and societal well-being. They investigated whether AI acts as a catalyst for achieving Sustainable Development Goals (SDGs) or presents challenges that could hinder progress, emphasizing the role of venture capital in driving sustainable innovation.
Artificial Intelligence in Sustainable Development
In recent years, AI has emerged as a transformative technology capable of reshaping sectors like energy, healthcare, and agriculture by enhancing efficiency, improving decision-making, and optimizing resource use. Since the United Nations established the SDGs in 2015, global efforts toward sustainability have intensified, focusing on poverty reduction, environmental protection, and equitable economic growth. This research aims to bridge the gap in understanding how AI investment influences SDG achievement in the United States.
About this Research: Assessing Different Types of Investment
In this paper, the authors investigated the impact of venture capital investment in AI, green electricity generation, and gross domestic product (GDP) on sustainable development in the United States. They utilized a quarterly dataset from the first quarter of 2012 to the fourth quarter of 2022, employing the SDG index as a proxy for measuring sustainable development. The study argued that venture capital investment provides a more accurate representation of AI sector growth than merely counting AI firms.
To analyze the data, the researchers applied the nonlinear Autoregressive Distributed Lag (NARDL) model, which allows the examination of both short-term and long-term relationships among the variables. This framework is beneficial for capturing the asymmetric effects of positive and negative shocks in AI investment and other explanatory variables on sustainable development outcomes. To ensure the robustness of the findings, several pre-tests, including the Augmented Dickey-Fuller (ADF) unit root test and the Brock-Dechert-Scheinkman (BDS) test for non-linearity, were conducted before the main analysis.
Key Findings: Impact of AI Investments on Sustainability
The outcomes showed a long-term asymmetric relationship between AI investment, sustainable development, GDP, and green electricity generation in the U.S. Specifically, a 1% increase in AI investment was associated with a 0.26% rise in the SDGs index, underscoring AI's positive role in advancing sustainability. Interestingly, negative shocks in AI investment did not significantly affect the SDGs index, highlighting the importance of sustained investment in AI for achieving long-term sustainability goals.
Image Credit: ercan senkaya/Shutterstock.com
Green electricity generation also contributed positively to the SDGs index, reinforcing the role of renewable energy. However, the authors found that economic growth, as measured by GDP, negatively impacted the SDGs index. This suggests that unchecked economic expansion could lead to resource depletion and environmental degradation. These insights emphasize the need for balanced economic policies integrating AI investment and renewable energy adoption to drive sustainable development.
Potential Real-World Applications
This research has significant implications for policymakers and industry leaders. The findings support continued funding and policies that foster AI-driven research and innovation across various sectors. Integrating AI into sustainability frameworks can optimize resource management, enhance energy efficiency, and enable precision agriculture to reduce waste while maximizing output. These applications align with key SDGs, particularly those related to clean energy and climate action. The researchers highlighted the importance of incentives such as tax breaks and grants for AI development. Responsible integration of AI can enhance decision-making and improve resource efficiency.
Conclusion and Future Directions
This study highlights the key role of AI investment in advancing sustainable development in the United States. Demonstrating a positive correlation between AI investment and progress toward sustainable development goals contributes to a growing body of literature emphasizing technology's importance in addressing global challenges.
These outcomes provide a foundation for further exploration into AI’s role in fostering inclusive growth and environmental stewardship. Future work should examine AI’s specific impact on each of the 17 SDGs for a comprehensive understanding. Expanding the dataset and including other developed economies would enhance the generalizability of the findings.
Overall, the authors emphasized the need for responsible AI deployment and policies that foster AI-driven innovation. By creating an enabling environment for AI, the United States can leverage its transformative potential to address complex global challenges.
Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website. | 1 |
TORONTO — The federal government has named a group of notable AI researchers to provide counsel on the field’s safety risks, and is also seeking the input of prominent startups and big business as it tries to push both adoption and regulation of the technology. | 1 |
To mark International Women's Day and Women at Imperial Week, we spoke to I-X Women in AI Network Committee about their mission and impactful work.
In celebration of International Women’s Day and Women at Imperial Week, today we spotlight the Committee of the I-X Women in AI Network (IX-WAI). The network is dedicated to empowering women, non-binary people and other gender-discriminated groups* in the field of artificial intelligence.
Launched in April 2024, within a year of its inception, the IX-WAI has delivered several successful events as part of the AI: Cutting-Edge Overview and Tutorials and AI: Ethics & Diversity Seminar Series and established its Early Career Grants programme (with Round 3 currently open) to support women and non-binary individuals in overcoming barriers to entering the AI field. They have also just announced their first Women in Science Career Day, featuring exciting workshops, discussion panels, and skills-building sessions. And this is just the beginning—they have no plans of stopping there, with more initiatives and projects in the works for the Imperial community!
To celebrate their great work and achievements, we asked the committee members about the IX-WAI mission, what being part of the initiative means to them, and why it is crucial to engage more women in STEM and AI research.
* For readability, we refer to all of these groups as “women” throughout the text.
Dr Gema Vera González
Eric and Wendy Schmidt AI in Science Postdoctoral Fellow
Alice, Eileen, Tracy, and I founded IX-WAI with the objective of empowering women, non-binary people and other gender-discriminated groups in the field of artificial intelligence, aiming to create a supportive and inclusive network that champions career advancement, addresses challenges, and fosters connections across all career stages. I am especially excited about the Women in Science Career Day that we are planning for 11 June 2025, as it will be an amazing opportunity for any woman or non-binary individual to obtain the practical skills and advice needed to take their next professional step, no matter their path: academia, industry or start-up founding. I am also very proud of our ongoing work with the Early Career Development Grant. By increasing visibility and funding opportunities for women’s voices in AI, we strive to correct underrepresentation and build a future where diversity drives innovation.
Dr Sara Veneziale
Chapman-Schmidt AI in Science Postdoctoral Fellow
Being a member of the IX-WAI committee is a key part of my experience as a research fellow. To me, the IX-WAI initiative is about building community. It is a great opportunity to engage with AI researchers at Imperial, promote their work, and celebrate their success. Being part of a gender minority in STEM can feel isolating, and the IX-WAI network makes me feel like I am part of something much bigger than me. I hope other researchers experience this sense of belonging as well.
The part of the IX-WAI that I am most excited about is the Early Career Grant. It is really fun and interesting to read the submitted proposals: I feel like I get to learn a little bit more about the great work that women in AI are doing at Imperial. Being able to contribute to funding their ideas, travel opportunities, and learning goals feels like a great privilege and I really hope it serves as a valuable support for driving their research further.
Dr Laura Helleckes
Eric and Wendy Schmidt AI in Science Postdoctoral Fellow
The I-X Women in AI initiative is a fantastic network of staff, fellows, and faculty at I-X, which has the mission of empowering women, non-binary individuals, and other gender-discriminated groups. All of these groups are still largely underrepresented in STEM and I believe that we can use our standing at I-X to contribute to a more diverse research and science community.
Within these aims, I am most excited to use our network to actively promote women and non-binary individuals in dedicated events at Imperial and beyond. For example, we are currently organising a Women in Science Career Day in June, which will be a fantastic opportunity to engage with academic, industrial and start-up experts. In my career so far, I have benefited from these kinds of events and the generous women within them sharing their incredible experiences. I am excited about the opportunity to now contribute to such an event myself, with the hope that it will empower women and non-binary individuals in their careers in STEM.
Beyond the inspiring work we do directly within the network, I also value the influence it has on our day-to-day work at I-X. The members within the Women in AI network provide continuous support for each other, which is positively changing the work atmosphere at I-X. Before joining the fellowship, I did not have such a fantastic team of supporting women around me. I am thus very grateful for the network and its great influence on my work and beyond.
Eileen Boyce
Manager of I-X Centre for AI in Science
Working in academic support I have been involved in many efforts to encourage women into STEM. The great thing about the I-X WAI network is that it was developed and shaped by the research fellows themselves, who are best placed to understand the challenges that women scientists face. Being a member of the I-X WAI committee allows me to see up close and personal the issues that affect women in STEM. Whether it is helping to organise a women’s career day or funding early career grants, being part of the committee is a great way to actively support the inclusion of women in AI.
Dr Tracy Bussoli
Professional Development Consultant for AI in Science Fellows
Being an IX-WAI committee member is very important to me. The perspective and mindset of women in science and the AI space brings new ideas, perspectives and ways of working.
I look forward to our IX-WAI committee meetings where we are beginning to shape events and activities that will resonate with women working in science and the AI space. The committees are a safe space for participants to collaborate and share their ideas on initiatives to support women across Imperial and beyond. As a more experienced woman with leadership experience, it is a delight to see less experienced committee members grow in confidence as they chair meetings, brainstorm ideas and, increasingly, drive exciting new ideas forward to the implementation phase.
With fellow committee members, I have been involved in several initiatives, including the production of some promotional videos to encourage women to apply for the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowships. We wanted to highlight that the fellowships are a great opportunity for women wanting to use AI in their scientific research, reassuring them that AI expertise was not a prerequisite for applying! A more recent initiative involved two committee members attending a careers day at a local school called The Phoenix Academy. We had the opportunity to spread the word about I-X and AI research to around 100 girls aged 11 – 18!
Dr Elli Heyes
Eric and Wendy Schmidt AI in Science Postdoctoral Fellow
Throughout my academic career, I have been part of various women in STEM networks, which I have found to be incredibly valuable. Being part of an underrepresented group can sometimes feel isolating, which is why building communities within these groups, including women in AI, is so important. I’m also very passionate about promoting the research of women in STEM and showcasing their amazing talent, which is something I have the opportunity to do through IX-WAI.
I believe there are many reasons why it is important to have women involved in all areas of STEM research. It brings broader perspectives and can result in more effective solutions; it contributes to a more inclusive and supporting work environment; women in STEM can also be role models and mentors for young girls, inspiring them to pursue a career in STEM. I think it is particularly important to have women involved in artificial intelligence research because, as AI becomes central to decision-making systems, it brings along ethical considerations, such as bias. For example, an AI tool used by a company to pre-screen applicants for a job may display a gender bias if gender inequality in society is not accounted for. Diversity, including gender diversity, in AI development is not only a matter of representation but also a necessity to address issues that might go unnoticed in homogenous teams.
I am very excited about the “Women in Science” careers event we have coming up later this year. We have some great skills-based workshops planned across industry, academia and entrepreneurship. We also have a “raw truth” panel session planned where participants will be able to get the honest truth from our panellists about the ups and downs of different career paths. I hope that this event will provide a welcoming environment for women in science to network with each other as well as gain useful practical skills and insight into different career paths which will help them in their own career journey.
Dr Austin Mroz
Eric and Wendy Schmidt AI in Science Postdoctoral Fellow
Being a member of the I-X Women in AI committee and working to empower women, non-binary individuals, and other gender-discriminated groups in AI and STEM is an incredibly rewarding experience. I am so grateful for the opportunity to work with a talented team of women to mentor early-career researchers, offer funding through the Early Career Grant, and create spaces where gender-marginalized voices are amplified through the I-X WAI network. This is fundamental to building a supportive and inclusive community and driving innovation in AI and STEM that is representative of the society that they serve.
One of the upcoming events that I am most excited about is our Women in Science Career Day. This event will connect women and non-binary students with professionals working in several different STEM career tracks—with the goal of providing participants with guidance, inspiration, and tangible skills necessary to pursue a career in AI and STEM. It can be really challenging to figure out what you want to do after a PhD, and hard to identify different options—I'm excited to be a part of an initiative working to build a space for women and non-binary identifying individuals to have these conversations in an inclusive environment. | 1 |
Detectify announced Alfred, a system that uses AI to completely autonomously source, prioritize, and generate high-fidelity security tests for the CVEs that are most likely to be exploited.
This innovation allows Detectify to continuously and dynamically deliver security research to AppSec teams with speed and coverage, uniting the automation of human ingenuity from the Detectify Crowdsource community of ethical hackers with the powerful capabilities of AI Research.
With more than 100 new CVEs published daily and a growing number of vulnerabilities not covered by the CVE system, security teams are increasingly overwhelmed. They must ensure they are testing for the latest issues and identifying and prioritizing the threats that pose actual risks to their systems.
Traditional automated scanners often worsen this issue by adding new security tests relying on slow manual searches for publicly available CVE tests; generating excessive noise through signature-based testing rather than actual exploitability; and missing CVE-less vulnerabilities, such as misconfigurations. vulnerability data from a wide range of trusted security intelligence sources.
Detectify Alfred utilizes large language models (LLMs) to autonomously obtain CVE threat intelligence from a wide range of trusted security intelligence sources. It prioritizes CVE vulnerabilities based on their likelihood of being exploited using the Exploit Prediction Scoring System (EPSS) framework.
Next, the system scrapes the web for publicly available proofs-of-concept for each CVE, generating a payload-based exploit that is added as a security test to the Detectify platform after a quality assurance check is performed by a researcher. Detectify only builds tests for relevant CVEs that can be validated with its proven payload-based approach, emulating real-world exploits and dramatically reducing false positives.
Detectify Alfred serves as a powerful additional source of security research, complementing the insights from the Detectify Crowdsource Community of ethical hackers and internal security research experts. By fully automating the identification and creation process of CVE-based assessments, Detectify security research forces can dedicate more resources to address advanced and novel threats, particularly those hiding beyond CVEs, delivering greater value to AppSec teams.
“We’re tapping the power of AI to leverage the ultimate use of this technology – creating a sleepless ethical hacker who is autonomously collecting threat intelligence, prioritizing vulnerabilities, and building payload-based security tests,” said Rickard Carlsson, Detectify CEO.
Thanks to the release of Alfred, Detectify customers can now benefit from faster and broader access to test for likely exploitable CVEs. An always-on force, continuously on the lookout to build tests for new vulnerabilities as they emerge.
Alfred’s AI-built assessments are now being rolled out to all Surface Monitoring and Application Scanning customers, making Detectify the only AppSec tool that combines its own community of ethical hackers with AI research. | 1 |
First version by AI Research Center performs well in mathematics and reasoning
TAIPEI, March 10, 2025 /PRNewswire/ -- Hon Hai Research Institute announced today the launch of the first Traditional Chinese Large Language Model (LLM), setting another milestone in the development of Taiwan's AI technology with a more efficient and lower-cost model training method completed in just four weeks.
Fig. 1: TMMLU+ benchmark results of FoxBrain, Meta-Llama-3.1-70B and Taiwan-Llama-70B
The institute, which is backed by Hon Hai Technology Group ("Foxconn") (TWSE:2317), the world's largest electronics manufacturer and leading technological solutions provider, said the LLM – code named FoxBrain – will be open sourced and shared publicly in the future. It was originally designed for applications used in the Group's internal systems, covering functions such as data analysis, decision support, document collaboration, mathematics, reasoning and problem solving, and code generation.
FoxBrain not only demonstrates powerful comprehension and reasoning capabilities but is also optimized for Taiwanese users' language style, showing excellent performance in mathematical and logical reasoning tests.
"In recent months, the deepening of reasoning capabilities and the efficient use of GPUs have gradually become the mainstream development in the field of AI. Our FoxBrain model adopted a very efficient training strategy, focusing on optimizing the training process rather than blindly accumulating computing power," said Dr. Yung-Hui Li, Director of the Artificial Intelligence Research Center at Hon Hai Research Institute. "Through carefully designed training methods and resource optimization, we have successfully built a local AI model with powerful reasoning capabilities."
The FoxBrain training process was powered by 120 NVIDIA H100 GPUs , scaled with NVIDIA Quantum-2 InfiniBand networking, and finished in just about four weeks. Compared with inference models recently launched in the market, the more efficient and lower-cost model training method sets a new milestone for the development of Taiwan's AI technology.
FoxBrain is based on the Meta Llama 3.1 architecture with 70B parameters. In most categories among TMMLU+ test dataset, it outperforms Llama-3-Taiwan-70B of the same scale, particularly exceling in mathematics and logical reasoning (For TMMLU+ benchmark of FoxBrain, please refer to Fig.1). The following are the technical specifications and training strategies for FoxBrain:
Established data augmentation methods and quality assessment for 24 topic categories through proprietary technology, generating 98B tokens of high-quality pre-training data for Traditional Chinese
tokens of high-quality pre-training data for Traditional Chinese Context window length: 128 K tokens
tokens Utilized 120 NVIDIA H100 GPUs for training, with total computational cost of 2,688 GPU days
Employed multi-node parallel training architecture to ensure high performance and stability
Used a unique Adaptive Reasoning Reflection technique to train the model in autonomous reasoning
In test results, FoxBrain showed comprehensive improvements in mathematics compared to the base Meta Llama 3.1 model. It achieved significant progress in mathematical tests compared to Taiwan Llama, currently the best Traditional Chinese large model, and surpassed Meta's current models of the same class in mathematical reasoning ability. While there is still a slight gap with DeepSeek's distillation model, its performance is already very close to world-leading standards.
FoxBrain's development – from data collection, cleaning and augmentation, to Continual Pre-Training, Supervised Finetuning, RLAIF, and Adaptive Reasoning Reflection – was accomplished step by step through independent research, ultimately achieving benefits approaching world-class AI models despite limited computational resources. This large language model research demonstrates that Taiwan's technology talent can compete with international counterparts in the AI model field.
Although FoxBrain was originally designed for internal group applications, in the future, the Group will continue to collaborate with technology partners to expand FoxBrain's applications, share its open-source information, and promote AI in manufacturing, supply chain management, and intelligent decision-making.
During model training, NVIDIA provided support through the Taipei-1 Supercomputer and technical consultation, enabling Hon Hai Research Institute to successfully complete the model pre-training with NVIDIA NeMo. FoxBrain will also become an important engine to drive the upgrade of Foxconn's three major platforms: Smart Manufacturing. Smart EV. Smart City.
The results of FoxBrain is scheduled to be shared for the first time at a major conference during NVIDIA GTC 2025 Session Talk "From Open Source to Frontier AI: Build, Customize, and Extend Foundation Models" on March 20.
About Hon Hai Research Institute
The institute has five research centers. Each center has an average of 40 high technology R&D professionals, all of whom are focused on the research and development of new technologies, the strengthening of Foxconn's technology and product innovation pipeline, efforts to support the Group's transformation from "brawn" to "brains", and the enhancement of the competitiveness of Foxconn's "3+3" strategy.
About Foxconn here.
SOURCE Hon Hai Research Institute | 1 |
The University of Michigan in Ann Arbor has established a partnership with OpenAI that will bring additional artificial intelligence resources, research funding, and computing power to campus.
The collaboration also will include joint research projects between the university and OpenAI, focusing on AI applications that broadly benefit society.
The agreement with OpenAI — an artificial intelligence research and deployment company and the creator of ChatGPT — is part of NextGenAI, a broader $50 million effort by the company to advance research and education in partnership with higher education institutions.
The partnership will further enhance the university’s ability to conduct groundbreaking artificial intelligence research, university officials said.
“Working with OpenAI provides a special opportunity to collaborate with researchers producing some of the most influential AI developments today and gain access to leading edge models and tools,” says Michael Wellman, professor of computer science and engineering at the U-M College of Engineering and principal investigator for the partnership.
Under the agreement, OpenAI will provide application programming interface — commonly known as API — credits, giving U-M researchers access to the company’s advanced AI models. These credits, which function like prepaid access to OpenAI’s AI tools, will enable faculty and students to explore AI applications across fields.
The partnership also includes additional resources for high-performance computing, allowing the university to purchase computing power from a provider of its choice. OpenAI also will contribute research grants to the university, which will be awarded to faculty to advance AI-related scholarship.
“Developing a relationship at this scope allows us to showcase the breadth of AI-relevant research at the University of Michigan, including core technology, impactful applications, and societal implications,” says Wellman, also the Lynn A. Conway Collegiate Professor of Computer Science and Engineering. “It complements other strategic partnerships that U-M is developing with industry and government related to AI and contributes to our effort to build a campuswide AI research community.”
NextGenAI, a consortium of 10 leading universities and OpenAI, will “accelerate research breakthroughs” and “empower students, educators and researchers to solve hard problems and push the frontiers of knowledge,” according to an OpenAI news release.
In addition to U-M and OpenAI, founding NextGenAI partners include Duke University, the Boston Public Library, Howard University, MIT, Sciences Po in France, Texas A&M University, University of Georgia, University of Mississippi and University of Oxford in England.
The partnership aligns with U-M’s broader strategy to expand its AI research footprint. The university’s strategic vision — Vision 2034 — includes a commitment to “expand the development and deployment of artificial intelligence and data science” over the next decade.
“The University of Michigan is thrilled to be a key partner in OpenAI’s NextGenAI consortium, collaborating with leading institutions to drive transformational, ethically grounded advances in research, scholarship and education,” says Santa J. Ono, president of U-M. “This initiative marks the beginning of what we envision as a long and meaningful partnership between the university and OpenAI, shaping the future of AI for the public good.”
Beyond expanding access to AI models and computing power, the partnership establishes a framework for joint research projects between OpenAI and the university.
“The University of Michigan is at the leading edge of AI research in higher education, and this partnership with OpenAI represents an exciting opportunity to expand our capabilities,” says Arthur Lupia, U-M interim vice president for research and innovation. “With access to OpenAI’s advanced models and additional funding to procure computing resources, our researchers will be better equipped to push the boundaries of what AI can achieve.”
The post University of Michigan Partners with OpenAI to Advance Research Projects appeared first on DBusiness Magazine . | 1 |
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When Google’s Gemini Deep Research model arrived, I feared my years as a technical journalist were numbered. After experimenting with a bevy of deep research capabilities from Google, OpenAI, Anthropic, Claude, Perplexity, and a few others, I now realize that predictions of the death of my profession at the hands of AI overlords were somewhat premature. This has important implications for journalists and vendors turning to these tools to streamline their content creation and marketing pipeline, but also for society as a whole.
To be clear, these tools do have value in researching and sourcing information about complex concepts. They are particularly good at finding information relevant to research in adjacent fields that use different terminology or where the words used to describe something shift over time.
Yet, despite improvements, they still struggle with context and lack deep insight into controversial or hyped topics like quantum computing, blockchain, artificial general intelligence, self-driving cars, or enterprise AI adoption. Their associative pattern-matching processes can’t discern the first principles relevant to analyzing a complex topic.
One glaring gap seems to be about how they weigh the credibility and value of citations and sources. Clickbait listicles from quasi-news blogs and sometimes vendors tend to take priority over meatier sources from respected experts, pioneers, and thoughtful journalists. For example, it took some thoughtful journalists to write and assess the critical importance of reviving the US congressional Office of Technology Assessment or why the Ukraine rare earth minerals controversy is mostly hype and bluster.
That said, there is still a place for these tools, in the same way code completion tools could help aspects of software development in place. However, they will need better user experiences and integration into the existing software development, testing, security, and deployment process to deliver meaningful value. Tools that generate prose or code faster can easily lead to more work on the back end than they save.
Complex topics
I was recently confronted with some of these issues while researching a white paper on quantum security. This is a complex topic with some important implications today. Highly sensitive data like classified information, business secrets, and health data saved today could be cracked open when cryptography-relevant quantum computers (CRQC) finally arrive. Also, long-lived embedded cryptography in planes, trains, satellites, power plants, TVs, blockchains, and ATMs will be at risk of takeover.
Yet, despite some interesting breakthroughs and a lot of overenthusiastic news stories, practical quantum computing is probably at least a decade or three away. Like many complex topics, the relevance and merits of new technological leaps often come with numerous caveats, gotchas, and practical implementation challenges.
Quantum computers will require resilient working qubits connected together. Similarly, fusion power will require significant advances in lasers/magnets/containment. AGI will require entirely new approaches to combining causal, symbolic, and statistical techniques that can work across multiple levels of abstraction. Blockchain will require fewer Ponzi schemes. Small nuclear reactors will need to figure out how to solve the waste problem that still has not been solved for large nuclear reactors after ninety years.
Unfortunately, large language models underpinning most chatbots today tend to gloss over these important caveats. For every Gary Marcus making accurate predictions about the scaling limits to the progress of GenAI or some other breakthrough technology, there are hundreds of effective accelerationists predicting that our AI overlords are just around the corner.
Sourcing
Let's start with sourcing. When I queried these various tools about quantum security, only OpenAI managed to directly link to the US National Institute of Standards and Technology (NIST) analysis of the matter, which has been leading the charge on new post-quantum cryptography thought to be secure against CRQCs. Many pointed to lower-quality listicles and vendor coverage that sometimes alluded to the NIST work.
Perhaps most troubling, none of these tools could surface, much less directly cite the seminal work of Michele Mosca, a former University of Waterloo professor. NIST credited him as conceiving Mosca’s Theorem, which presented a framework for evaluating the risk of CRQS in the future against information with a long shelf life for security and privacy.
Mosca first predicted a 14% chance of CRQC emerging in a decade in 2015. Not satisfied with his original assessment, he went on to co-found evolutionQ for a more rigorous approach. This included forming a panel of over thirty leading quantum researchers with deep expertise across multiple quantum technologies. Its 2023 quantum threat timeline survey found that the more optimistic experts predicted a 34% probability of CRQC by 2034, while the more pessimistic ones predicted a 19% probability. Concerningly, none of this important work was found or cited by any of the deep research tools.
Content quality
The dawn of practical quantum computing is a complex topic. There have surely been interesting advances in running obscure algorithms, creating more physical cubits, and building relatively low bandwidth quantum networks for possibly securely distributing encryption keys.
For example, in the case of quantum computing, there are multiple approaches involving superconducting systems, trapped ions, cold atoms, quantum optics, quantum spin in silicon, and topological systems. Some of these latter have been attracting considerable press lately. However, the expert panel is most bullish about superconducting systems, given all the challenges, including noise, coherence time, connectivity, error correction, etc.
One glaring issue was that all the deep research tools suggested that quantum networks for distributing encryption keys were just around the corner, citing reports of various now-discontinued testbeds. None of them pointed out that expert sources like the US National Security Agency does not recommend using these until several limitations are overcome.
My take
My biggest gripe with these new tools is that they create a lot of additional work to investigate the claims they make in generating content. For example, they often cite relatively low-quality sources with higher search engine rankings rather than subject matter experts with more credibility.
Also, they mostly just surface a link to the source without any additional context about where it came from in a long article or report. OpenAI’s implementation was certainly the most helpful since its citations pointed to highlighted text, making it easier to understand why it generated a particular claim.
In the long run, these sorts of tools may have some value when deep research tools find better ways to integrate them into the various processes for searching, capturing, citing, and vetting information for a more thorough analysis. Many point solutions exist for some aspects, like point tools for transcription, knowledge management, citation management, and writing.
But in my experience, most people involved in any deep research, including myself, are forced to assemble these across a patchwork of haphazardly interconnected tools like File Manager, email, Zoom, Otter, Zotero, Word, and Grammarly. For these kinds of deep research tools to augment rather than replace quality writing, we must develop integrated connected suites akin to integrated development environments, testing, and deployment across the software development and deployment lifecycle.
More broadly, I feel like the growing complexity around problems like sustainability, social cohesion, and global conflict are growing more challenging by the day. And the ever-increasing ease of easily generating more irrelevant, inaccurate, and divisive clickbait poses an existential threat to not only good journalism and policy research but society as a whole. I take inspiration from Mosca’s approach of forming a panel of experts on the risks around quantum computing. Perhaps there is a way to grow a similar community to mitigate the existential risks of clickbait pollution as well. | 1 |
DeepSeek went viral a few weeks ago, shocking the US tech world and tanking the stock market in the process. The Chinese AI firm surprised everyone with an AI reasoning chatbot as good as ChatGPT o1, but which was trained for a fraction of the cost.
Since then, we’ve learned that hardware will still play a key role in developing advanced (frontier) AI like ChatGPT, but that software optimizations can also help. Also, we learned DeepSeek might have copied ChatGPT outputs as a shortcut to speed up training.
DeepSeek going viral also made the world aware that Chinese AI firms shouldn’t be ruled out despite their inability to purchase the latest chips from Nvidia and other US chip makers. Since the DeepSeek release, we’ve already seen a few impressive text-to-video AI models out of China that aim to compete with Sora. Some might even outperform OpenAI’s model.
The latest viral AI out of China is called Manus, from a company called The Butterfly Effect. Manus isn’t your regular ChatGPT or DeepSeek rival. It’s supposed to be an AI agent that can code on your behalf or browse the web for you. We already have such agents from Anthropic and OpenAI.
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OpenAI released two AI agents already, Operator and Deep Research. But only the latter is widely available to premium ChatGPT users. You still need to be a ChatGPT Pro user to access Operator, while Deep Research is available on the Plus plan.
Back to Magnus; I saw news of it making the rounds on social media over the weekend. Apparently, the AI does well in tests, and people are in a hurry to use it. Invites are running low, and they might be selling for thousands of dollars online. That’s according to TechCrunch, which tested the AI.
However, the Magnus hype seems to be unwarranted. Magnus is still in beta and failed miserably at most of the tasks it was given.
Magnus isn’t completely new. That is, it might not have been trained from scratch. Reports say The Butterfly Effect used existing AI models like Anthropic’s Claude and Alibaba’s Qwen. From there, the Chinese AI firms trained the AI to create research reports, perform actions online, and even code apps and games.
A lead researcher from Manus implied on X that the AI model is superior to Deep Research and Operator, the AI agents OpenAI has released to date. Manus supposedly outperforms rivals in deep research tests on GAIA, a popular benchmark for AI assistants. The test examines the AI’s ability to browse the web and use software.
“[Manus] isn’t just another chatbot or workflow,” Manus engineer Yichao “Peak” Ji said in a video on X. “It’s a completely autonomous agent that bridges the gap between conception and execution […] We see it as the next paradigm of human-machine collaboration.”
People who obtained access to Manus aren’t as amazed in real life. The AI might have gone viral, but this isn’t the next DeepSeek in terms of immediate abilities. It might get there as The Butterfly Effect improves it, but it’s unable to really outperform rivals.
TechCrunch gave Manus various tests worthy of an AI agent, but the AI mostly failed them. Manus could not order food from a top-rated food nearby. The AI failed to book a flight from NYC to Japan despite working with precise instructions about the type of flight the human wanted.
Manus also failed to make a restaurant reservation. TechCrunch then asked the AI to build a Naruto-inspired game but got an error after half an hour.
Building a game from scratch is the kind of task I’d expect the AI to fail. But booking a table at a restaurant or ordering food should be very simple. OpenAI’s Operator demo went a lot smoother than that.
The Chinese AI firm provided the blog the following statement on the state of Manus, which offers the excuse you’d expect; Manus is in beta. But aren’t they all? Here’s the comment:
As a small team, our focus is to keep improving Manus and make AI agents that actually help users solve problems […] The primary goal of the current closed beta is to stress-test various parts of the system and identify issues. We deeply appreciate the valuable insights shared by everyone.
All that is to say, Manus isn’t the next DeepSeek from China to shock the US AI landscape. It could become the AI model The Butterfly Effect engineers tease it to be in the near future, but we’re clearly not there. No point in spending thousands of dollars to obtain access to it.
Also, as with DeepSeek, you should be aware of what you’re getting into when using AI made in China, which is subject to local laws and practices, including privacy. | 1 |
In the past year, artificial intelligence leaders have talked about the advent of agents that can do work autonomously. Now, China says it has developed the world’s first.
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Last week, Chinese researchers launched an early preview of Manus AI, which they said is “the first general AI agent.”
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“This isn’t just another chatbot or workflow,” Yichao “Peak” Ji, chief scientist for Manus AI, said in an introductory video. “It’s a truly autonomous agent that bridges the gap between conception and execution.”
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While other AI agents are useful for idea generation, Ji said Manus AI “delivers results” without much human prompting. The agent’s name comes from the Latin motto “Mens et Manus,” which translates to “Mind and Hand.”
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“We see it as the next paradigm of human-machine collaboration, and potentially a glimpse into AGI,” Ji said, referring to artificial general intelligence — the point when AI will be believed to have reached and surpassed human-level intelligence.
The agent is currently invitation-only, and access codes were being resold for thousands of dollars on China’s reseller app, Xianyu (BABA-5.91% ), TechCrunch (VZ+0.89% ) reported. Manus AI did not immediately respond to a request for comment from Quartz.
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Here’s what we know so far about Manus.
What can Manus do?
According to the Manus AI website, the agent can perform real-world tasks such as providing custom travel plans, researching real estate properties for affordability, and performing correlation analyses between stocks.
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Ji demonstrated Manus screening 15 resumes, then providing its ranking suggestions and evaluation of each candidate. He then instructed the agent to put the information into a spreadsheet.
“Manus has its own knowledge and memory, so I can teach Manus that the next time it handles a similar task, it will deliver a spreadsheet right away,” Ji said
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Because Manus works asynchronously in the cloud, Ji said, users can close their laptop while it completes tasks. The agent can also receive new instructions while it is working.
On the GAIA Benchmark, which evaluates general AI assistants, Ji said Manus has achieved state-of-the-art performance alongside OpenAI’s Deep Research agent.
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The agent is already performing tasks on freelance work platforms such as Upwork (UPWK-3.18% ) and Fiverr (FVRR-2.12% ), Ji said.
How was Manus built?
Ji said the agent’s capabilities “wouldn’t be possible without” the open-source community, which means its code, datasets, and parameters are available for anyone to access and build upon.
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The agent “operates as a multi-agent system,” and is powered by different AI models, Ji said, adding that the team plans to open-source some of the models later this year.
According to Hugging Face, Manus was developed by a Chinese AI startup called Monica.im, which is developing next-generation autonomous agents. However, other reports say Manus was built by a Chinese firm called Butterfly Effect.
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What are people saying about Manus?
Dean Ball, an AI research fellow at George Mason University, said in a post on X that it was “wrong” to compare Manus to China’s breakthrough AI moment with DeepSeek earlier this year.
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“Deepseek was about replication of capabilities already publicly achieved by American firms,” Ball said. “Manus is actually advancing the frontier.”
Victor Mustar, head of product at Hugging Face, called Manus “the most impressive AI tool I’ve ever tried,” in a post on X. Mustar said Manus’s “agentic capabilities are mind-blowing, redefining what’s possible.”
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However, other AI researchers were not so impressed.
Alexander Doria, co-founder of French AI lab PlelAs, said in a post on X that despite liking the agent’s user-interface, “it’s fundamentally a workflow” and “not an actual agent (at least nothing really beyond the built-in agentic capacities of Claude).”
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Professor and researcher Derya Unutmaz said in a post on X that he ran OpenAI’s Deep Research alongside Manus. While Deep Research completed Unutmaz’s task in under 15 minutes, Manus failed after 50 minutes, and didn’t finish all the necessary steps. | 5 |
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