The internet is a bright place, made dark by internet trolls. To help with this issue, a recent Kaggle competition has provided a large number of internet comments, labelled with whether or not they're toxic. The ultimate goal of this competition is to build a model that can detect (and possibly sensor) these toxic comments. While I hope to be an altruistic person, I'm actually more interested in using the free, large, and hand-labeled text data set to compare LSTM powered architectures and deep learning heuristics. So, I guess I get to hunt trolls while providing a casestudy in text modeling.

Circa 1997, the reigning world chess champion Garry Kasparov was against an unknown opponent. Garry was not playing a human. He was playing the game with IBM's behemoth supercomputer, Deep Blue. Garry had beaten the opponent in the last few games. However, the game played on 11th May 1997 game was different.

Our digital world and data are growing faster today than any time in the past?even faster than our computing power. Deep learning helps us quickly make sense of immense data, and offers users the best AI-powered products and experiences. To continually improve user experience, our challenge, then, is to quickly improve our deep learning models...

I am more than happy to see that after the full hype period where everybody was talking about AI and machine learning as the solution for all the problems of the world (with the sky as the only limit), intelligent and honest persons/experts are publishing more and more articles where the things are described as they are and the expectations are set in a correct way. It took more than expected for this to happen but it was inevitable and only a matter of time: as Lincoln said "You can fool all the people some of the time, and some of the people all the time, but you cannot fool all the people all the time". Out of the many articles that have been published recently, let me link this one that is clear, short and that can be understood by nearly everyone. There are some statements that are very important to highlight because they explain very clearly that ML techniques can be useful (very useful in a few specific cases) but are much more limited and simple than many like to think and that they still require a huge amount of work and perspiration (sorry but also here there are no free lunches:-). Despite evocative names like "artificial intelligence," "machine learning" and "neural networks," such technologies have little to do with human thought or intelligence.

Mayank Vatsa has created a software tool to help people spot a sculpted selfie, a tampered profile picture on Facebook, or a manipulated photograph on a matrimonial website - a tool superior to human eyes. Vatsa, associate professor of computer science at the Indraprastha Institute of Information Technology, New Delhi, is using artificial intelligence technology, called deep learning, to detect retouching or digital alteration of facial images. Computer science researchers view deep learning as an advance of machine learning that uses software to model high-level abstract information hidden in large volumes of data. Deep learning algorithms, which use data to look for patterns, have already been shown to outperform humans in tasks such as image, text, and voice recognition. "Deep learning has the ability to take in huge amounts of data - in fact, it needs the huge amounts of data to function at all - and make sense of it," said a computer vision expert who has been involved in deep learning techniques for automatic predictions of human facial expressions.

Google AI has made a breakthrough: successfully predicting cardiovascular problems such as heart attacks and strokes simply from images of the retina, with no blood draws or other tests necessary. This is a big step forward scientifically, Google AI officials said, because it is not imitating an existing diagnostic but rather using machine learning to uncover a surprising new way to predict these problems. What's more, the new system shows what parts of the eye image lead to successful predictions, giving researchers new leads into what causes cardiovascular disease. The results of the Google AI research have been published in an article entitled "Prediction of Cardiovascular Risk Factors from Retinal Fundus Photographs via Deep Learning" in Nature Biomedical Engineering. "Using deep learning algorithms trained on data from 284,335 patients, we were able to predict CV risk factors from retinal images with surprisingly high accuracy for patients from two independent data sets of 12,026 and 999 patients," Lily Peng, MD, product manager and a lead on these efforts within Google AI, wrote in the Google AI official blog.

Artificial Intelligence (AI) has the peculiar ability to simultaneously amaze, enthrall, leave us gasping and intimidate. The possibilities of AI are innumerable and they easily surpass our most artistically fecund imaginations. What all we read in science fiction novels or saw in movies like'The Matrix' could someday materialize into reality. Bill Gates, the founder of Microsoft, recently said that'AI can be our friend' and is good for the society. From decision-making to computing to robotics to vehicles and even cosmetics, AI has left its mark everywhere and it will usher in the grandest social engineering experiment in the history of the world.

How scarce is AI talent in India? As Artificial Intelligence starts dominating the market, India does stare at a significant talent shortage just like the global market where AI has taken over low-skilled IT jobs. A LinkedIn report on the Digital Workforce Future states that India is like a testing ground for some of the most exciting applications of AI such as Microsoft's application that helps farmers in predicting crop yields in the agriculture sector, to Google's Deep Learning application that detects diabetes-related eye diseases in health care.

Mental and substance use disorders are the leading cause of years lived with disability worldwide and in 2010 accounted for 7.4% of years of productive life lost due to disability1. Natural language processing of electronic health records (EHRs) is increasingly being used to study mental illness2 and risk behaviours in much closer detail than previously3. However, narrative notes are written by clinicians who record those positive findings and relevant negatives that guide their subsequent diagnosis and treatment plan for the patient4. Although EHRs allow clinicians to synthesise disparate facts making them interpretable by other clinicians, they do not "paint a full picture" of the patient experience of a mental health problem, particularly as patients may answer interview questions in a manner that they perceive will be viewed favourably by their clinician. Moreover, as patient records are only written based on meetings with their healthcare provider, critical changes in patient behaviour and wellbeing may not be recognised either immediately or at all due to a time delay in reporting, thus preventing certain real time interventions.

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