When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level. Reinforcement learning has recently become popular for doing all of that and more. Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn't been until recently that we've been able to observe first hand the amazing results that are possible. In 2016 we saw Google's AlphaGo beat the world Champion in Go.

Chintu, the robot, slowly sat down on the floor, with both hands resting on its knees. Then, on command, it stood up, using one hand for support. The 58-centimetre-tall robot, manufactured by Softbank Robotics of France and owned by Maharashtra Institute of Technology (MIT), Pune, was one of the attractions of IBM Cloud Forum, a jamboree of companies using IBM's cloud and machine learning (ML) solutions in the last week of May in Mumbai. Alongside Chintu were its guardians -- Astitva Shah and Krishnamohan M, final-year engineering students from MIT, Pune. The duo have been working on a project to develop Chintu as an assistant for elderly people who are living alone.

Machine learning is a growing field, used in everything from the basics of anti-spam functions to the complexities of self-driving cars. As this is a constantly adapting technology, companies seeking to take advantage of the system for functions like analytics may have trouble finding the best place to begin. So what is the first step for a tech department that wants to start using machine learning to improve its data analytics? Even if you rely on outside expertise, it is important to understand what machine learning can and can't do with data. Stanford, Caltech and others offer online classes on Coursera that are very good.

Gradient boosting is a technique attracting attention for its prediction speed and accuracy, especially with large and complex data. As evidenced in the chart below showing the rapid growth of Google searches for xgboost (the best gradient boosting R package). From data science competitions to machine learning solutions for business, gradient boosting has produced best-in-class results. In this blog post I describe what it is and how to use it in Displayr. Gradient boosting is a type of boosting.

The theatrical release of James Cameron's sci-fi film Terminator 2, featuring Arnold Schwarzenegger as a cyborg with a computer brain, had a crucial scene deleted. The scene, part of the extended release of the movie, shows young John Connor and his mother opening up the head of the cyborg to switch its computer brain from "read only to "learning" mode. The cyborg (Schwarzenegger) then picks up human values and mannerisms as the movie progresses. For movie buffs, the deleted scene is worth seeing for special effects and also to catch a glimpse of Linda Hamilton (playing John's mother Sarah Connor) with her twin sister Leslie playing her image in a mirror. In the theatrical release, where the scene is omitted, the cyborg just tells John that its brain is a "neural-net processor, a learning computer", without mentioning any on/off options. That was back in 1991. Today, in 2017, a learning computer is much more of a reality. While artificial intelligence (AI) and machine learning (ML) concepts have been around since the 1940s and 1950s (See ABC of AI, ML and Deep Learning), the availability of huge amounts of data is making the difference now. A "learning computer" does not need to travel back in time -- like in the movie -- and many are solving real problems in India. For example, in healthcare, ML is helping oncologists sift through huge amounts of cancer cases and suggesting preferred treatment; in education it is predicting who might drop out of school; and in fashion it is forecasting colours that can dominate the next season. Retail, transportation and financial services have adopted ML in different forms. The "learning switch" is turned on in India. "Every large organisation was sitting on data.

A new program called Project Icarus is teaching kids how to build drones, while at the same time helping them grow in STEM fields, which focus in science, technology, engineering and mathematics. Project Icarus, launched by the Texas-based Flynoceros Racing Drones, is a workshop that not only allows kids to build drones, but also teaches them how to fix and fly them. This is different from drone kits bought at stores that don't show users how repair them. Seven-year-old Ava builds her drone at a Project Icarus workshop. Although parents can buy a drone kit at a retail store, those products are sometimes cheap and can break easily after crashing.

This course is about the fundamental concepts of machine learning, focusing on neural networks, SVM and decision trees. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example or we may construct algorithms that can have a very very good guess about stock prices movement in the market. In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together. The first chapter is about regression: very easy yet very powerful and widely used machine learning technique.

AI-MATHS is trying to get into college. Unbeknownst to students sitting for China's grueling college entrance exams, a robot was vying for a spot in one of the country's most prestigious universities. But it doesn't pose much threat yet. Called AI-MATHS, the robot completed one version of a two-hour Maths paper in 22 minutes and scored 105 points out of the maximum score of 150, reports the state-run Xinhua. AI-MATHS scored 100 points in another version of the paper which it completed in 10 minutes.

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Apple chief executive Tim Cook implored Massachusetts Institute of Technology grads to make sure the power of technology doesn't overshadow human decency in his commencement address yesterday. "If we are ever going to solve some of the hardest problems facing the world today, everything from cancer to climate change to inequality, then technology will help us do it," Cook said. While speaking to more than 2,000 MIT graduates and their families, Cook warned that many of the technological advances that are just over the horizon will be only as helpful and beneficial as the people who create them. "I'm not worried about artificial intelligence giving computers the ability to think like humans," Cook said. That is what we need you to help us guard against."