Author: Arushi Pandey

GPT-3 Accelerates AI Progress, but the Path to AGI is Going to Be Bumpy | Blog

OpenAI recently released the third generation of Generative Pretrained Transformer or GPT-3, the largest neuro-linguistic programming (NLP) model ever built. It’s fundamentally a language model, a machine learning model that can look at part of a sentence and predict the next word. It’s been pre-trained on 175 billion parameters in an unsupervised manner and can be further fine-tuned to perform specific tasks. OpenAI is an AI research organization founded in 2015 by Elon Musk, Sam Altman, and other luminaries. It describes its mission as: to discover and enact the path to safe Artificial General Intelligence (AGI).

GPT-3 is breaking the internet

There’s been a lot of talk around the power, capabilities, and potential use cases of GPT-3 in the AI community. As the largest language model developed to date, it has the potential to advance AI as a domain. People have developed all sorts of uses – from mimicking Shakespeare, to writing prose, to designing web pages. It primarily stood out due to:

  1. Foraying into AGI. The language model isn’t trained to perform a specific task such as sentence completion or translation, which is normally the case with ANI, the most prevalent form of AI we have seen. Rather, GPT-3 can perform multiple tasks such as answering trivia questions, translating common languages, and solving anagrams, to name a few, in a manner that is indistinguishable from a human.
  1. Advancing the zero-shot/few-shot learning mechanism in model training. This mechanism is a setup in machine learning wherein the model predicts the answer only from the task description in natural language and/or maybe a few examples, implying that the algorithm can showcase accuracy without being extensively trained for a particular task. This capability opens the possibilities of building lean AI models that aren’t as data-intensive and don’t require humongous task-specific datasets for training.

So, this seems nifty – what next?

In addition to the flurry of standard NLP use cases that have been in existence for a while, which GPT-3 has advanced drastically, GPT-3 also has the potential to intercept the more technical and creative domains, which will lead to the democratization of such skills by making these capabilities available to non-technical people and putting business users in control, primarily by:

  • Furthering no-code/low-code by making code generation possible from natural language input. This is a step toward the eventual democratization of AI, making it accessible to a broader group of business users and has the potential to redefine job roles and the skill sets required to perform them.
  • Generating simple layouts and web templates to full-blown UI designs, using simple natural language input, potentially creating disruption in the design sphere. 
  • Shortening AI timelines to market. Automated Machine Learning (AutoML) creates machine learning architectures with limited human input. The confluence of GPT-3 and AutoML has the potential to drastically reduce the time it takes to bring AI solutions to production. It will take significantly less time and human intervention to train a system and build a solution, thereby reducing the amount of time needed to deploy an AI solution in the market.

GPT-3 is great, but we’re not in Space Odyssey yet

The massive language model is not without pitfalls. Its principal shortcoming is that, while it’s good with natural language tasks, is has no semantic understanding of the text. It is, by virtue of its training, just trying to complete a given sentence, no matter what the sentence means.

The second roadblock to mainstream adoption of the model is the fact that it’s riddled with societal biases in gender, race, and religion. This is because the model is trained on the internet, which brings its own set of challenges given the discourse around fake news and the post-truth world. Even OpenAI admits that its API models exhibit biases, and those can often be seen in the generated text. These biases need to be corrected before the model can be deployed in any real-world scenario.

These challenges certainly must be addressed before it can be deployed for actual, enterprise-grade use. That said, GPT-3 will potentially traverse the same trajectory that computer vision made at the start of the decade to eventually become ubiquitous in our lives.

What are your thoughts about GPT-3? Please share with us at [email protected] and [email protected].

Smartphones and 5G are the Keys to AR/VR Success | Blog

A Goldman Sachs Research report published in January 2016 stated that venture capitalists had pumped US$3.5 billion into the augmented reality (AR)/virtual reality (VR) industry in the previous two years and that AR and VR have the potential to become the next big computing platform.

But a recent PwC MoneyTree report stated that funding for augmented and virtual reality startups plunged by 46 percent to US$809.9 million in 2018, as compared to 2017. Indeed, multiple startups in the space shut down in 2019 because they haven’t been able to materialize their claims and have been unsuccessful in making the technology economically viable to the masses.

It’s not just startups that are throwing in the towel on their investments. For example, a dwindling user base drove Google to shut down its Jump VR platform in June 2019, and Facebook-owned Oculus is closing its Rooms and Spaces services at the end of this month.

And the startups cited above sunk nearly $550 million in investments when they shuttered their doors.

So, what’s going wrong?

Problems with present-day AR/VR

New technologies, particularly those for the consumer market, invariably need hype to succeed. But, despite all the buzz around how AR/VR can change the way consumers interact with commercial and non-commercial entities (like healthcare providers and educational institutions), multiple problems are getting in the way of mass adoption.

  1. Cumbersome hardware: Despite 2-3 generational improvements, the hardware for these technologies remains bulky and difficult to set-up or use. More research is needed to bring advanced optics and computation of head-mounted displays (HMD) to a usable level
  2. High cost: Nearly all the standalone AR HMDs cost over $1000, and those for VR are over $150. At these price points, the vast majority of purchasers are technology enthusiasts and novelty buyers
  3. Poor content: While the premise of buying an HMD is to consume and interact with content in an engaging way, the flood of poorly designed experiences hardly makes the case for purchasing it, even for those who can afford it
  4. Selling an idea, instead of a product: This is perhaps the biggest reason for the slew of closures in recent months. While AR and VR both have compelling use cases, the entrepreneurs and enterprising providing the products and platforms promised the sky and underdelivered on expectations.

So, what should enterprises do to change the narrative behind and fate of AR/VR?

Here are our recommendations.

Focus on developing smartphone-based AR

AR adoption is far outpacing VR adoption, not only because it adds to users’ reality rather than replacing it, but also because smartphones make its cost much lower for consumers. Indeed, smartphone-based AR has gone mainstream in the retail and gaming spaces; examples are IKEA, Nike, Nintendo, and Sephora, all of which have deployed applications for interactive experiences. The buzz will stay alive, and the uptake will continue to grow as an ever-increasing number of developers incorporate AR elements into their applications.

Embrace 5G with open arms

Fifth-generation (5G) wireless promises to bring high bandwidth and reliable low latency in data communications. Along with the proliferation of edge computing, 5G will help move processing-intensive tasks closer to the edge of the network and content closer to the user. In the near future, telecom operators could provide dedicated network slices for AR/VR applications, greatly reducing network latency. By enabling faster processing and increased proximity to content, 5G will boost the overall user experience. And this will lead to increased adoption.

But, before going all in, enterprises should partner with communication service providers to test 5G PoCs for AR/VR. Doing so will help them better prepare for scaled adoption as HMDs become less cumbersome.

By placing hype before substance, AR/AV providers created the current low-growth environment. We believe that focusing on smartphone- and 5G-based AR/VR will increase both investor confidence and customer adoption.

What is your view on the AR/VR space and the emergence of 5G as a savior? Please share with us at [email protected], [email protected], and [email protected].

Digital Experience Platforms: An Idea Whose Time Has Come | Blog

In today’s increasingly competitive environment, enterprises need to package their offerings with superior and memorable experiences to remain relevant. They need to streamline their efforts to deliver a unified and seamless digital experience to stakeholders. While they’ve attempted to achieve this with point solutions such as CRM platforms, campaign management tools, and other experience management solutions, their disjointed and incompatible portfolios have often created more problems than solutions.

Enter the Digital Experience Platform (DXP)

In response to an obvious need, vendors including Adobe, IBM, Oracle, and Salesforce have created a digital experience platform or DXP. We define a DXP as a comprehensive suite of solutions enabling enterprises to deliver a content-rich, stakeholder-driven digital experience (DX), encompassing all digital touchpoints.

Its main function is to digitally enable the three pillars or modules of DX – content management, brand engagement, and digital e-commerce – so enterprises can create business value through a well-structured and unified experience.

The Digital Experience Platform (DXP)

  • Content management: A DXP offers various services across the content management lifecycle, such as dynamic templates for designers, a library of frequently used content, and widgets and tools for reviewing and publishing content to multiple platforms, which help enterprises effectively and centrally manage the content they publish.
  • Brand engagement: A DXP unlocks numerous aspects of brand engagement across functions including marketing, advertising, sales, and experience management. With capabilities like end-to-end campaign automation and drag-and-drop tools to design customer journey maps, a DXP enables experience-as-a-service for enterprises.
  • Digital e-commerce: A DXP activates different facets of digital e-commerce with solutions like AI-enabled merchandising, visual merchandising, automated management and maintenance of product data, and central dashboards to manage all websites.

In addition, a DXP has tools to help deliver a data-driven experience across the customer experience value chain by enabling functions such as sales, marketing, merchandising, and content publishing via different modules.

Beyond the basics

Most of the DXPs in today’s market provide the same basic services. But the leading DXP providers also provide ancillary, value-add services on top. Some of the most popular are omnichannel services, API-integration, and tools for improved developer experience.

Per our recently released research report, BigTech Battle: Digital Experience Platforms (DXP) Assessment – Rise of the Digital Experience Platform, the leading players are adding more functionality to the DXP to enhance its features and functionality. For instance, they are helping make the development process less technical with the help of services such as What You See Is What You Get (WYSIWIG) interfaces, drag and drop functionality, and templates to create new experiences. This significantly reduces the creative team’s dependency on the technical team and improves the overall efficiency of the experience delivered. The top providers also have tools for end-to-end omnichannel customer journey mapping and enable the use of “win scores” to prioritize sales opportunities and probability metrics to measure the experience delivered.

These players are also using technology to enhance the functionality of the different solutions they offer, such as AI for content creation, event-based automation (cart abandonment), and advanced analytics solutions.

Simply put, a DXP is a more efficient way for an enterprise to manage its DX. In today’s increasingly competitive market, enterprises need to leverage a platform-based approach to deliver a compelling and sticky experience.

For more insight on the DXP market and a detailed analysis of current vendors, please read our report: BigTech Battle: Digital Experience Platforms (DXP) Assessment – Rise of the Digital Experience Platform.

Please share your experiences with the digital experience platform and the overall experience ecosystem with us at [email protected] and [email protected].

Fragmented DevOps = Minimal Value | Blog

Enterprises are increasingly embracing DevOps to enhance their business performance by accelerating their software time-to-market. In principle, DevOps covers the entire spectrum of Software Development Life Cycle, SDLC, activities from design through operation. But, in practice, only ~ 20 percent of enterprises are leveraging DevOps end-to-end, according to our recent research, DevOps Services PEAK Matrix™ Assessment and Market Trends 2019 – Siloed DevOps is No DevOps!

That means the remaining ~ 80 percent that are taking a siloed approach to DevOps are missing out on the many types of values it can deliver.

Types of DevOps fragmentation

Instead of adopting DevOps in its intended end-to-end fashion, many enterprises in different verticals and at different stages of maturity are tailoring it to focus on siloed, distinct portions of the SDLC. The most common types of fragmentation are: 1) Apps DevOps, applying DevOps principles only across the application development cycle; 2) Test Ops, using DevOps principles in testing; and 3) Infra Ops, applying DevOps principles only to infrastructure.

Why fragmentation delivers minimal value

Pocketed adoption makes it tough to realize the full value that DevOps can deliver. The primary reason is bottlenecks. First, workflow throughout the SDLC is impeded when DevOps principles and automation are only applied to certain phases of it. Second, lack of end-to-end adoption makes it more difficult for enterprises to gain a full view of their applications portfolio, spot bottlenecks, incorporate stakeholder feedback in real-time, and make the entire process more efficient.

Additionally, when DevOps is used in a siloed manner, it focuses primarily on increasing the technical efficiency of processes. This means that DevOps’ ability to support the enterprise’s broader business-oriented objectives is severely restricted.

Finally, fragmented DevOps adoption creates a disintegrated culture in which teams work independently of each other, in turn leading to further conflicts, dependencies, and stretched timelines. All this, of course, defeats DevOps’ main purpose.

Moving to end-to-end DevOps adoption

To successfully adopt DevOps end-to-end, enterprises should place automation, culture, and infrastructure at the heart of their strategy.

  • Automation: Automating various elements of the SDLC is extremely beneficial; doing so helps reduce implementation timelines and increase team productivity by standardizing processes and diminishing the scope of errors
  • Culture: A collaborative culture is essential to a successful DevOps implementation as it involves the development, operations, and business teams working together in an iterative fashion to achieve cross-team and business-oriented KPIs
  • Infrastructure: Increasing adoption of cloud-native technologies like as infra-as-code, microservices, serverless, and containers helps maintain configuration consistency in deployment, eventually leads to an increase in developer productivity, and saves on cloud computing costs.

DevOps has the ability to deliver significant value to enterprises. But implementing it in a siloed manner quickly dilutes a lot of that potential value. To realize all DevOps’ benefits, enterprises should implement it end-to-end, invest in automation, robust and modular infrastructure, and tools and technologies to ensure agility, and develop a culture that helps them improve cross-team collaboration.

What has been your experience in your DevOps adoption journey? Please share with us at [email protected] and [email protected].

Spotlight on Salesforce’s Acquisition of Tableau | Blog

On June 10, 2019, Salesforce announced an agreement to acquire Tableau, a leading interactive data visualization company, for US$15.7 billion in an all-stock deal. Here’s our take on it.

Strategic Intent behind the Deal

The announcement is a masterful move to aid Salesforce’s hyper growth agenda to become a US$28 billion company in three years’ time. In the past 15 months, Salesforce has accelerated the data pivot through its acquisitions of Mulesoft in March 2018 and now Tableau, for a combined value of $22.2 billion.

Given its ambitious topline growth goals, Salesforce has hedged its bet against a pure cloud play. Tableau, which is not a cloud company, runs most of its products on-premise, with over one-third deployments in the cloud. However, last year, Tableau announced that its products will also be available on hyperscalers’ cloud platforms (AWS, Microsoft Azure, and GCP.) Addressing the ubiquity of data in a modern enterprise and recognizing the transition in software consumption pattern, Salesforce is taking an “anytime, anywhere” analytics approach to cater to enterprise’s hybrid cloud-first mandate.

In addition, Tableau’s strong performance against rivals including IBM Cognos, MicroStrategy, Oracle BI, and QlikView makes a strong case for the acquisition, given Salesforce’s big bet on its Customer 360 initiative and its broader foray into empowering clients with data analytics and visualization capabilities.

Enhancing the Data Analytics and Experience Pivot

Salesforce, a veteran in the CRM space, is repositioning itself as a digital experience (DX) platform, wherein it intends to become a one-stop, end-to-end solution for enterprises’ DX needs. It has been making strategic acquisitions over the years to plug in the gaps in its DX platform portfolio to achieve this goal.

SFDC Acquisition blog DX image

Because Tableau and Salesforce’s in-house analytics tool, Einstein Analytics, can easily interoperate, the company will be able to sell a well-packaged data analytics offering. Tableau’s niche capabilities in data analytics will not only deliver an improved data management solution but will also help enterprises form data-intensive strategies and optimize the overall stakeholder experience. And, the acquisition gives Salesforce new up- and cross-sell opportunities, as enterprises will be able to purchase CRM and business intelligence (BI) capabilities from a single vendor.

Gaining a Full View of Enterprise Data

Looking at the timeline of Salesforce’s acquisitions, we see a strategic shift from targeting digital marketing and commerce space toward enhancing enterprise data lifecycle management. Since 2018, Salesforce’s top deals have been to expand its coverage in the data and analytics space. Undoubtedly, the move has given Salesforce a shot in the arm when it comes to showcasing its capabilities across the data management value chain. Tableau sits atop of its acquisitions, plugging in multiple outside data sources and offering an easy to use UI for data visualization.

SFDC Acquisition blog CRM image

Indeed, Salesforce’s acquisition of Tableau is a strategic next step after its 2018 acquisition of MuleSoft. While Salesforce leveraged Mulesoft to create a “Salesforce Integration Cloud” that allows different cloud applications to connect via APIs, Tableau can help it gain deeper insights in this data, in turn driving enterprises toward data-driven decision making.

Data Orchestration Meets Cognitive

We give a thumbs up to this deal, particularly for what it means to the market going forward. Why?

The move fits well with Salesforce’s agenda to move into machine learning-driven analytics. Essentially, it will now have a strong BI tool, underpinned by AI, that will democratize enterprise access to next-generation data modeling and analytics capabilities.  A Tableau-integrated Salesforce Einstein Analytics offering should be able to deliver an intelligent, intuitive analytics and data visualization platform that leverages enterprise-wide data to help enterprise customers, employees, and partners with well-curated insights.

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