Machine learning applications on natural language are an extremely important tool in the data scientist's toolbox. Use cases can include auto-detecting the language of a website, detecting spam in your spam filter, or auto-completing search queries. When you're working with text data, an important use case is text classification, where the data scientist is tasked with creating an algorithm that can figure out what a bit of text is all about (what is the tagline) based on what is written in the document. This can be used in a myriad of examples we see everyday, tagging things such as blog articles, app descriptions, and reviews. In many cases traditional text classification can be difficult to scale, because as the order of the taxonomy count increases, the amount of training required increases as well.

A company that helps users to create their own websites now knows what kind of sites their 80 million users are building without pestering them with repeated questions. Wix, a Tel Aviv-based Web development company, is using machine learning on Google's cloud platform to learn more about its users so it can help them find the images they need to build interesting and useful websites. That's just the beginning of how machine learning will be used in the cloud, according to industry analysts who say machine learning will be the biggest thing that's ever hit the cloud. David Zuckerman, head of developer experience for Wix, said machine learning in the cloud will be a boon to companies that don't have a major research division. "The cloud has brought this technology to everyone," he said.

Machine learning algorithms can warn investors when a particular stock is going to fall by predicting likely instances of insider trading (when information that's not in the public domain is capitalised upon by people in the know). This can be done by analysing previous occasions when company insiders did apparently well-timed trades in their own stocks, and recognising these patterns. This might seem deceptively simple, but it isn't, explains Tom Doris, CEO of OTAS Technologies, a London-based market analytics and machine learning trading system. While company executives are required to file details of transactions in their company's stock, most insider trades are few and far between: a needle in a haystack. Doris told IBTimes UK: "We look at all of the insiders, the directors of companies, and we see all of their historical transactions in their own stock. If you are the chief financial officer of Vodafone, any time you buy or sell Vodafone stock, you're obliged under regulation to file details of those transactions. So that would include the amount that you bought or sold, when you did it and what price you got and the reason, if any, for the transaction. There is an enormous database of all of these transactions for all of the world's listed stocks and we go and we basically back-test all of the insiders and we find the ones that are apparently good at timing their own stocks."

The MIT Technology Review team is hard at work on our popular EmTech conference series for 2016. This May, our fourth annual EmTech Digital program will explore the growing implications of artificial intelligence on our connected environments at home, at work, and on the go. AI already plays an active role in our day-to-day lives. Intelligent systems power search, social media, and your smartphone and track your health and finances. At EmTech Digital, our program will explore how AI technologies are already transforming industries globally, as well as what we can expect to experience in the next wave of innovation.

Tay inexplicably added the "repeat after me" phrase to the parroted content on at least some tweets, implying that users should repeat what the chatbot said.Quickly realizing its teenage bot had been radicalized into a genocidal, Nazi-loving, Donald Trump supporter, Microsoft shut Tay down. According to Tay's "about" page linked to the Twitter profile, "Tay is an artificial intelligent chat bot developed by Microsoft's Technology and Research and Bing teams to experiment with and conduct research on conversational understanding". Unfortunately, Microsoft continues, within the first 24 hours of coming online, they became aware of a coordinated effort by some users to abuse Tay's commenting skills to have it respond in inappropriate ways. Apple Temporarily Pulls iOS 9.3 Update for Older iOS Devices It will then click on "All my devices" and select the device before clicking "Delete Account" and restart the terminal again. The video below (and its comments) will give you some idea about what to expect if you're coming from iOS 8 to iOS 9.3.

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Volume of studies The amount of captured and stored medical images is increasing. As the quantity of images has gone up, so too has the amount of time it takes radiologists to review the data. Artificial intelligence in healthcare can take some of this work away from radiologists by processing images and scanning medical studies to quickly detect patterns or abnormalities that could be missed by the naked eye. The artificial intelligence system could then pass the results of its review to a radiologist for confirmation. Reporting and classification Natural language processing is another technology radiologists can use to assist them with documentation and reporting.

Artificial Intelligence continues to surprise and shock us. Last week, we saw that AI can be better than us at playing Go. This week, Microsoft's chatbot TayTweets showed us that machine learning can even produce AI's that are better at hate speech than we are. Microsoft promptly hit the killswitch on that one. This year's edition of TNW Conference in Amsterdam includes some of the biggest names in tech.

With AI Scry, you'll never have to wonder how your iPhone would describe the world around you if it was capable of autonomous thinking. Available for iOS, AI Scry is a new app that generates automatic descriptions of whatever appears in front of your phone's camera. Get your company on stage at TNW Europe. Created by Oakland-based art/technology studio Disc Cactus (or as it's stylized), the app aims to showcase the merits and weaknesses of machine learning technologies in a fun and entertaining manner. One of the developers who worked on the project, Sam Kronik, says that to give your phone a mind of its own it uses the open-sourced neural network Neural Talk introduced by Stanford scientist Andrej Karpathy.

This solution presents an example of using machine learning with financial time series on Google Cloud Platform. Time series are an essential part of financial analysis. Today, you have more data at your disposal than ever, more sources of data, and more frequent delivery of that data. New sources include new exchanges, social media outlets, and news sources. The frequency of delivery has increased from tens of messages per second 10 years ago, to hundreds of thousands of messages per second today.