I don't think anyone was sure just what Microsoft was thinking when it put its AI chatbot called Tay on twitter with no protections in place. Tay is back and this time Microsoft thinks it has things in hand. The thing with Tay was that it learned from the chats it had with other users and could be forced to repeat what users tweeted at it back as if the chatbot had the same opinions. Anyone who has ever seen twitter or used the platform could have told you that wouldn't end well, and end well it didn't . Tay turned into a racist pro-Hitler fan before Microsoft realized its mistake and turned Tay off.

The bot was designed to learn by talking with real people on Twitter and the messaging apps Kik and GroupMe. And, after less than a day on Twitter, the bot had itself started spouting racist, sexist, anti-Semitic comments. "Tay" went from "humans are super cool" to full nazi in 24 hrs and I'm not at all concerned about the future of AI pic.twitter.com/xuGi1u9S1A Now, you might wonder why Microsoft would unleash a bot upon the world that was so unhinged. The AI chatbot Tay is a machine learning project, designed for human engagement.

The bot was designed to learn by talking with real people on Twitter and the messaging apps Kik and GroupMe. But the well-intentioned experiment quickly descended into chaos, racial epithets, and Nazi rhetoric. Tay started out by asserting that "humans are super cool." But the humans it encountered really weren't so cool. And, after less than a day on Twitter, the bot had itself started spouting racist, sexist, anti-Semitic comments.

Standard Chartered is moving heavily into radical new technologies that could one day see robots providing bespoke wealth advice and artificial intelligence answering customer questions. The emerging markets-focused British bank has set up a new lab called the eXellerator in Singapore in an attempt to bring theoretical ideas from Silicon Valley to life. Chief executive Bill Winters has put the centre at the heart of a 1.5bn commitment to improving computing and IT systems. Some of the ideas are also urgently needed cost-saving initiatives. The bank has hired thousands of additional compliance officers in the past three years and last year hiked annual compliance spending by an extra 1bn in an effort to stop workers breaking laws and regulations, in the wake of expensive scandals including the breaking of US sanctions against Iran.

"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]

Latency to end-users and regulatory requirements push large companies to build data centers all around the world. The resulting data is "born" geographically distributed. On the other hand, many machine learning applications require a global view of such data in order to achieve the best results. These types of applications form a new class of learning problems, which we call Geo-Distributed Machine Learning (GDML). Such applications need to cope with: 1) scarce and expensive cross-data center bandwidth, and 2) growing privacy concerns that are pushing for stricter data sovereignty regulations. Current solutions to learning from geo-distributed data sources revolve around the idea of first centralizing the data in one data center, and then training locally. As machine learning algorithms are communication-intensive, the cost of centralizing the data is thought to be offset by the lower cost of intra-data center communication during training. In this work, we show that the current centralized practice can be far from optimal, and propose a system for doing geo-distributed training. Furthermore, we argue that the geo-distributed approach is structurally more amenable to dealing with regulatory constraints, as raw data never leaves the source data center. Our empirical evaluation on three real datasets confirms the general validity of our approach, and shows that GDML is not only possible but also advisable in many scenarios.

Microsoft has created a new chat bot to "learn" from the internet… but she picked up a lot of bad habits. The tech company announced the launch of Tay this week, an artificial intelligence bot that is learning to talk like millennials by analysing conversations on Twitter, Facebook and the internet. The company's optimistic techies explained: "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. "Tay is designed to engage and entertain people where they connect with each other online through casual and playful conversation. The more you chat with Tay the smarter she gets."

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Microsoft launched an artificial intelligence chat "bot" last week, thinking this would be a way to show off its AI programming skills. Unfortunately, within 24 hours Tay had turned from a naive young AI into a rude, anti-Semitic pro-Nazi jerk. Also: Taliban kills 76 Christians in Pakistan on Easter; LGBTQ activists upset at North Carolina; Disney, Marvel and the NFL bully Georgia; CIA-armed Syrian rebels shooting at Pentagon-armed rebels; more evidence for Planet 9; and Mexicans celebrate Easter by burning Donald Trump in effigy.

So he and his business partner, Dan Roth, decided to create a program that would help lawyers manage electronic documents for litigation. Their idea led them to purchase an e-discovery application. By 2000, Leib and his partner launched their own creation, Discovery Cracker. "We saw a gap in the marketplace," Leib says. Lawyers need tools to keep up with it." Instead of wading through piles of paper, lawyers now deal with terabytes of data and hundreds of thousands of documents. E-discovery, legal research and document review are more sophisticated due to the abundance of data. So while working as chief strategy officer at kCura in Chicago, Leib saw a need again in the market. "For years, lawyers have been stuck with antiquated tools that focus primarily … on Boolean search. Better tools are needed to truly understand data." "What is the future of the industry?