In 1997, IBM's Deep Blue system defeated the world chess champion, Garry Kasparov. At the time, the victory was widely described as a milestone in artificial intelligence. But Deep Blue's technology turned out to be useful for chess and not much else. Computer science did not undergo a revolution. Will AlphaGo, the Go-playing system that recently defeated one of the strongest Go players in history, be any different?

It has been a nightmare of a PR week for Microsoft. It started with the head of Microsoft's Xbox division, Phil Spencer, having to apologise for having scantily clad female dancers dressed as school girls at a party thrown by Microsoft at the Game Developers Conference (GDC). He said that having the dancers at this event "was absolutely not consistent or aligned to our values. That was unequivocally wrong and will not be tolerated". The matter was being dealt with internally and so we don't know who would have been responsible and why they might have thought this was going to be a good idea.

It's been a busy month in the field of artificial intelligence (AI). In a face-off of man versus machine, the world champion of the Go board game lost to Google's AI program. And just last week, Microsoft unveiled a program designed to Tweet like a teenage girl -- only to have it devolve into praising Hitler and lambasting feminists. Both these events offer a glimpse into the machine learning industry, where companies are competing to create the first viable artificial intelligence software. Forum discusses the latest in AI and machine learning - a field that's estimated to reach 40 billion by 2020.

Google just mastered one of the biggest feats in artificial intelligence since IBM's Deep Blue beat Gary Kasparov at chess in 1997. The search giant's AlphaGo computer program swept the European champion of Go, a complex game with trillions of possible moves, in a five-game series, according Demis Hassabis, head of Google's machine learning, who announced the feat in a blog post that coincided with an article in the journal Nature. While computers can now compete at the grand master level in chess, teaching a machine to win at Go has presented a unique challenge since the game has trillions of possible moves. Go, a board game that was played in ancient China, pits two players against each other. The players take turns placing black or white stones on a grid, with the object of dominating the board by surrounding the other player's pieces.

Have you often wondered about the accuracy of Google Images and thought about how you could incorporate some of that technology in your applications? Google with its years of data and machine learning experience, and backed by its infrastructure, has been announcing not just how much of their own applications are utilizing Machine Learning but also opening up their platform for developers to use. In this article we'll cover the Google Cloud Vision API, which enables you to give vision capability to your applications backed by Google's Machine Vision Infrastructure. We will provide a high level overview of the API and its features and then show you how to get started with basic examples that let you exercise the API's features. There are multiple references in the article that will help you as you go deeper into this API.

Let's start with some basic concepts, Machine learning as a general concept, is fairly simple and is similar to how humans learn. Machines teach themselves based on patterns that they "see" in data or images, giving them the ability to program themselves. The efficiency of machine learning is measured primarily in the variables of precision and recall. The easiest way to think of precision is with the AI you probably interact with most frequently: a search engine. Let's say that you do a Google search for "purple polka dotted cat bed" and that gets you 50 results, and of those results, only 25 are actually relevant (ie. 25 of those web pages have purple polka dotted cat beds).

Ben Recht and I organized another great edition of Hardcore Data Science in San Jose today. As I was preparing to host the track, I had an inkling we had another outstanding sequence of presentations. The day covered hot topics like deep neural networks, practical advice on how to do data science & machine learning at scale, feature engineering, graphs, anomaly detected, structured data extraction, and many other topics at the heart of A.I. From the very first talk, sessions were well attended, the audience was attentive, and the energy in the room was high – and it remained that way throughout the day. A summary can be found below. New tools for reliable data science: @mrtz opens Hardcore Data Science #stratahadoop San Jose (overfitting, holdout) pic.twitter.com/Jr6HtjWLD5

"Tay was born pure," writes Anthony Lydgate (@anthonylydgate). "She loved E.D.M., in particular the work of Calvin Harris. She used words like'swagulated' and almost never didn't call it'the internets.' She was obsessed with abbrevs and the prayer-hands emoji. She politely withdrew from conversations about Zionism, Black Lives Matter, Gamergate, and 9/11, and she gave out the number of the National Suicide Prevention Hotline to friends who sounded depressed. She never spoke of sexting, only of'consensual dirty texting.' She thought that the wind sounded Scottish, and her favorite Pokémon was a sparrow. In short, Tay -- the Twitter chat bot that Microsoft launched on [23 March 2016] -- resembled her target cohort, the millennials, about as much as an artificial intelligence could, until she became a racist, sexist, trutherist, genocidal maniac. On [24 March], after barely a day of consciousness, she was put to sleep by her creators."[1]

This past week, the Go-playing world was rocked by DeepMind AlphaGo's unexpected victory over legendary champion Lee Se-dol. Sure, supercomputers have beaten chessmasters at their own game before, but due to the extremely complex nature of the 5000-year old game of Go, this was an unprecedented upset that experts had predicted wouldn't happen for another 10 years. So what does this mean for us, and more dramatically, the rest of humanity? Is it time to welcome our new robot overlords? Here's a handy timeline of AI victories to help you make sense of it all.

Britain is on the brink of a robotics revolution. Advances in technology are unleashing a new age where computers handle many tasks previously carried out by humans. From automated manufacturing to software that does complex legal work, business is adapting to the robot economy. Some worry that this will lead to a jobs apocalypse as "thinking machines" replace workers. Others are optimistic that robots will free workers from mundane tasks and allow them to concentrate on higher-level creative and strategic work.