Source: Google AI

In recent years, the AI field has made tremendous progress in developing AI systems that can learn from massive amounts of carefully labeled data.

This paradigm of supervised learning has a proven track record for training specialist models that perform extremely well on the task they were trained to do. Unfortunately, there’s a limit to how far the field of AI can go with supervised learning alone.

Supervised learning is a bottleneck for building more intelligent generalist models that can do multiple tasks and acquire new skills without massive amounts of labeled data. Practically speaking, it’s impossible to label everything…

Time series analytics is a statistical method that interacts with time series information or pattern recognition. Time series information means the information is usable in a variety of discrete cycles or periods.

Time Series Data: The values a variable uses at various periods are observed.

Assume you had to examine a one-year time series of regular closing market values for a specific share You will receive a list of all the daily closing for the stock on each day during the previous year and organize them in sequential sequence. This is the stock’s one-year regular closing price period series.

Forecasting of Time Series Data:




American Express? A financial services provider was facing challenges in customer retention. Managing the customer churn was crucial to them. So, in 2017, they experimented with predictive analytics using machine learning and developed a forecasting model for potential churn. It is believed that the model identified 24% of Australian accounts that will close within the next four months!

In a data-driven world, data analytics & Machine Learning in the banking & financial services sector has the potential to play a crucial role. …

Source: Unsplash

Artificial intelligence (AI) uses personified knowledge and learns from the solutions it produces to address not only specific but also complex problems.

At present, the pharmaceutical industry is facing challenges in sustaining its drug development programs because of increased R&D costs and reduced efficiency.

Source: Medium

The major issue arises from the essence that machine learning algorithms were not primarily developed to deal with eloquent and dynamic adversaries, and therefore, in concept, the entire security level can be breached by leveraging the relevant security issues of mastering algorithms through meticulous tampering of raw data.

Here Adversarial learning can come into the picture!!

Adversarial learning is a novel research area that mainly lies in the convergence of machine learning and cybersecurity. It focuses on allowing the secure implementation of machine learning approaches in adverse circumstances such as spam filtering data protection and biometric identification.

What is Adversarial Machine Learning?

Adversarial machine learning…

Source: Towards Data Science

The Cloud Native Computing Foundation (CNCF) found that in 2019 the vast majority of respondents — 84 percent — were running containers in production. That was up roughly 15 percent (or 11 percentage points, from 73) from the previous year. Production container usage was at just 23 percent when CNCF first did its survey in March 2016.

As a newbie in “Machine Learning”, it’s always an exciting journey that starts from data cleaning, exploratory data analysis, feature engineering, and it usually ends at model training and deploying it through the flask. …

Source: DataCamp

Transfer Learning will be the next driver of Machine Learning success. — Andrew Ng

What is Transfer Learning?

The general idea of transfer learning is to use knowledge learned from tasks for which a lot of labeled data is available in settings where only a little labeled data is available. Creating labeled data is expensive, so optimally leveraging existing datasets is key.

In a traditional machine learning model, the primary goal is to generalize to unseen data based on patterns learned from the training data. With transfer learning, you attempt to kickstart this generalization process by starting from patterns that have been learned for…

Source: Twitter

It was reported in 2020 that about 222 per 10,000 children in the United States have autism spectrum disorder, one of the highest diagnosis rates in the world. Autism spectrum disorder is more prevalent in boys than girls. In the U.S., about 3.63 percent of boys between 3 to 17 years have autism spectrum disorder equivalent to 1.25 percent of children.

What is Autism?

Autism Spectrum Disorder (ASD) is a range of neurodevelopmental disorders mainly distinguished by decreased cognitive interaction and speech deficiencies.

Symptoms of Autism:

Symptoms can include excessive concentration on one object, unresponsiveness, lack of comprehension of verbal norms (such as voice tone or…

“Netflix has a global algorithm which is helpful in the recommendations to all the users and the company claims that the combined effect of personalization and recommendations is worth $1 Billion per year!”

Well, the above statistics are not much surprising as we know what potential the recommendation systems are having these days! More surprising are, the use-cases of Artificial Intelligence (AI)!

AI has paved its way into the film-making industry now!

Recently, a group of researchers presented their research in “The Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing” forming a deep learning model to…

Source : Towards Data Science

According to David Kenny, General Manager, IBM Watson — the most advanced cognitive computing framework, “AI can only be as smart as the people teaching it.” The same is not true for the latest cognitive revolution.

What is Cognitive Computing?

Cognitive computing is the use of computerized models to simulate the human thought process in complex situations where the answers may be ambiguous and uncertain. The phrase is closely associated with IBM’s cognitive computer system, Watson. Cognitive computing overlaps with AI and involves many of the same underlying technologies to power cognitive applications, including expert systems, neural networks, robotics and virtual reality (VR).

How Cognitive Computing works?


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