AI: The next stage of human-machine collaboration.
Some AI-based services and tasks today are relatively trivial. Explainable AI won’t replace people but will complement and support them so they can make better, faster, more accurate and more consistent decisions.

In brief

  • Use cases for Explainable AI include detecting abnormal travel expenses and assessing the driving style, based on Accenture Labs research.
A technology revolution with people at its heart



Machine learning

Machine learning software development services involve creating self-learning algorithms that can minimize errors and maximize accuracy with time. Systems powered by ML analyze data and learn new things from them which results in fast and reliable insights delivered without any human intervention.


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Business Benefits of Machine Learning/Artificial Intelligence.

Spam detection was one of the earliest problems solved by ML. Few years ago email providers made use of rule-based techniques to filter out spam. However, with the advent of ML, spam filters are making new rules using brain-like neural networks to eliminate spam mails. The neural networks recognize phishing messages and junk mail by evaluating the rules across a huge network of computers.

ML helps enterprises in multiple ways to promote their products better and make accurate sales forecasts. ML offers huge advantages to sales and marketing sector, with the major ones being  Massive Data Consumption from Unlimited Sources Rapid Analysis Prediction and Processing Interpret Past Customer Behaviors

In healthcare industry, ML helps in easy identification of high-risk patients, make near perfect diagnoses, recommend best possible medicines, and predict readmissions. These are predominantly based on the available datasets of anonymous patient records as well as the symptoms exhibited by them.

Data duplication and inaccuracy are the major issues confronted by organizations wanting to automate their data entry process. Well, this situation can be significantly improved by predictive modeling and machine learning algorithms. With this, machines can perform time-intensive data entry tasks, leaving your skilled resources free to focus on other value-adding duties.

ML also has a significant impact on the finance sector. Some of the common machine learning benefits in Finance include portfolio management, algorithmic trading, loan underwriting and most importantly fraud detection. In addition, according to a report on ‘The Future of Underwriting’ published by Ernst and Young, ML facilitates continual data assessments for detecting and analyzing anomalies and nuances. This helps in improving the precision of financial models and rules

Product recommendation is an important aspect of any sales and marketing strategy including upselling and cross-selling. ML models will analyze the purchase history of a customer and based on that they identify those products from your product inventory in which a customer is interested in. The algorithm will identify hidden patterns among the items and will then group similar products into clusters. This process is known as unsupervised learning, which is a specific type of ML algorithm.

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