Amid surging investment in artificial intelligence over the past few years and continuing concern about the implications of the technology, the White House announced on Tuesday that it intends to hold a series of workshops and form an interagency working group to examine the benefits and risks of AI. In a blog post, Ed Felten, Deputy US Chief Technology Officer, framed the issue in a way that excludes speculative scenarios presenting AI as a threat to humanity, a concern raised by the likes of Stephen Hawking and Elon Musk. While worries about runaway malevolent AI are often raised in public discussions of the technology, real AI research is more mundane, as in Google's effort to improve the conversational capabilities of its software by feeding it romance novels. "Today's AI is confined to narrow, specific tasks, and isn't anything like the general, adaptable intelligence that humans exhibit," said Felten. "Despite this, AI's influence on the world is growing. The rate of progress we have seen will have broad implications for fields ranging from healthcare to image- and voice-recognition."

San Francisco (AFP) - A US judge rejected a request by Facebook to toss out a civil suit accusing it of violating privacy with face-recognition software to help "tag" people in pictures. A lawsuit filed by three Illinois residents under the auspices of the state's Biometric Information Privacy Act can proceed, US District Court Judge James Donato said. "The court accepts as true plaintiffs' allegations that Facebook's face recognition technology involves a scan of face geometry that was done without plaintiffs' consent," he said in the ruling. It appeared that legislators in Illinois passed the act to address emerging biometric technology such as Facebook face-recognition software at issue in the case, according to the judge. Facebook had argued in a motion to dismiss that analyzing uploaded photographs did not qualify as biometric data and that the Illinois law did not apply.

As a result, users are forced to wade through pages and pages of listings to find the exact item they're looking for. That could push a lot of customers to rivals like Amazon.com, Inc., which is why eBay has finally done something about it. On Thursday, eBay announced that it's agreed to acquire Swedish software make Expertmaker, which specializes in AI and machine learning systems that can help to analyze and organize massive sets of data. It's just what the doctor ordered for eBay, which hosts an average of 900 million listings at any one time. Expertmaker has actually been working with eBay since 2010, most recently helping out with a "structured data" project aimed at making eBay's site less cluttered than it currently is.

The first commercial spam message was sent in 1994--at least that's the general consensus. Lawrence Canter and Margaret Siegel had a program written that would post a copy of an advertisement for their law firm's green card lottery paperwork service to every Usenet news group -- about 6,000 of them. Because of the way the messages were posted, Usenet clients couldn't filter out duplicate copies, and users saw a copy of the same message in every group. At the time, commercial use of internet resources was rare (it had only recently become legal) and access to Usenet was expensive. Users considered these commercial-seeming messages to be crass--not only did they take up their time, but they also cost them money.

This video will get you up and running with your first AI composer in just 10 lines of Python. This is'a' way to generate music, it's not necessarily the absolute best way. In a future video, I'll discuss how to easily use cloud GPU computing. Much more to come so please subscribe, like, and comment.

Earlier this year, we used DataRobot to test a large number of preprocessing, imputation and classifier combinations to predict out-of-sample performance. In this blog post, I'll take some time to first explain the results from a unique data set assembled from strategies run on Quantopian. From these results, it became clear that while the Sharpe ratio of a backtest was a very weak predictor of the future performance of a trading strategy, we could instead use DataRobot to train a classifier on a variety of features to predict out-of-sample performance with much higher accuracy. Backtesting is ubiquitous in algorithmic trading. Quants run backtests to assess the merit of a strategy, academics publish papers showing phenomenal backtest results, and asset allocators at hedge funds take backtests into account when deciding where to deploy capital and who to hire.

The scikit-learn library is the premiere library for machine learning in Python. The online documentation is quite good but sometimes can feel fragmented or limited by narrow examples. In this post you will discover the book Scikit-Learn Cookbook by Trent Hauck that provides a desktop reference to supplement the online documentation and help you get started with scikit-learn quickly. The Scikit-Learn Cookbook is a focused book written by Trent Hauck and published by Packt Publishing. Over 50 recipes to incorporate scikit-learn into every step of the data science pipeline, from feature extraction to model building and model evaluation.

The classic guide for entrepreneurs preparing a pitch is Sequoia's Business Plan Template. This post aims to be a mere addendum to that in the age of machine learning. Why do investors spend so much time focusing on'differentiation'? The job of an investor is to allocate money to its best use. Investors shouldn't allocate money to a company unless it is crystal clear that the company is the best one to solve a particularly valuable problem.

I first met Pepper in 2014, a human-shaped robot at a mobile store of Akihabara district in Tokyo. Although our conversation quickly reached some limits by the fact that he (it?) could only speak Japanese at the time, I sympathized with what his creator Aldebaran (a French company now part of the Softbank Japanese conglomerate) defines as a genuine day-to-day companion, whose number one quality is his ability to perceive emotions and adjust his behavior to your mood based on your voice, face expression and words you use. To-date, 10,000 Pepper robots have been sold mostly to Japanese homes. One third of them are used as an attraction to surprise customers and inform them. Nestlé is planning on equipping more than 1,000 Nescafé sales outlets in Japan.

Google artificial intelligence project DeepMind is building software to trawl through millions of patient records from three NHS hospitals to detect early signs of kidney disease. The project raises deep questions not only about data protection but about the ethics of artificial intelligence. But these are not the obvious questions about the ethics of autonomous, intelligent computers. Computer programs can now do some things that it once seemed only human beings could do, such as playing an excellent game of Go. But even the smartest computer cannot make ethical choices, because it has no purpose of its own in life.