Growing concerns about how artificial intelligence (AI) makes decisions has inspired U.S. researchers to make computers explain their "thinking." "Computers are going to become increasingly important parts of our lives, if they aren't already, and the automation is just going to improve over time, so it's increasingly important to know why these complicated systems are making the decisions that they are," assistant professor of computer science at the University of California Irvine, Sameer Singh, told CTV's Your Morning on Tuesday. Singh explained that, in almost every application of machine learning and AI, there are cases where the computers do something completely unexpected. "Sometimes it's a good thing, it's doing something much smarter than we realize," he said. Such was the case with the Microsoft AI chatbot, Tay, which became racist in less than a day. Another high-profile incident occurred in 2015, when Google's photo app mistakenly labelled a black couple as gorillas.

Facebook is challenging a court order preventing it from telling users about secret US government requests for their private account information, according to court documents. The company says the order threatens freedom of speech. The search warrants were accompanied by a non-disclosure order prohibiting Facebook from informing the users about the requests before it actually complied with them. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

Artificial intelligence (AI) in the banking sphere has gained significant interest in recent months. Combined with the noise around regtech (regulatory technology), both are starting to gain real traction and are actually beginning to be put to practical use. It might even be the turning point for the evolution of wider information-technology (IT) transformation for which the industry has been crying out to deliver sustainable, lower operating costs and smarter businesses. The tsunami of new regulations since the financial crisis has been the single biggest headache for banks to deal with. Already stressed IT systems have come under pressure, as have already bloated cost bases through the huge volumes of new people required to implement and monitor ongoing compliance.

Los Angeles County's most prominent start-up investor just got bigger. Upfront Ventures closed June with the announcement of a $400-million investment fund that it plans to spend on dozens of start-ups in the next couple of years. It's believed to be Los Angeles County's largest-ever venture capital fund by raw number, though Upfront Ventures' $390-million investment fund in 2000 comes out far on top when adjusted for inflation. Still, it beats the $280 million that Upfront Ventures picked up at the end of 2014. Mark Suster, managing partner at the firm, declined to provide specifics about the returns that prior funds have generated.

Businesses that put in the effort to create an artificially intelligent business may see amazing returns at first -- but there are good reasons to expect those to diminish. It would be very, very helpful to know what the future holds for artificial intelligence in business. Unfortunately, it is also very, very hard to predict. With this topic, our extrapolation heuristics may not work well. We tend to extrapolate linearly, expecting the pace of past progress to continue unchanged.

Math is a tool that doesn't discriminate. There's no bias in it; the numbers either add up or they don't. Algorithms depend on math, but they're data driven -- sometimes the information being fed into one is incorrect or doesn't represent the actual goals of the algorithm. Cathy O'Neil, the author of Weapons of Math Destruction, cautions us against trusting the data being fed into our judicial systems: And what ProPublica found was the compass model, which is one version of a recidivism model, made mistakes by sending people to prison longer, that kind of mistake, twice as often for African-American defendants as for white defendants, at least in Broward County Florida. There's another kind of mistake you can make which is: you look like you're not coming back, you look low-risk but you actually do come back that kind of risk, that kind of mistake, was made twice as often for white defendants as for African-American defendants.

We present a novel subset scan method to detect if a probabilistic binary classifier has statistically significant bias -- over or under predicting the risk -- for some subgroup, and identify the characteristics of this subgroup. This form of model checking and goodness-of-fit test provides a way to interpretably detect the presence of classifier bias or regions of poor classifier fit. This allows consideration of not just subgroups of a priori interest or small dimensions, but the space of all possible subgroups of features. To address the difficulty of considering these exponentially many possible subgroups, we use subset scan and parametric bootstrap-based methods. Extending this method, we can penalize the complexity of the detected subgroup and also identify subgroups with high classification errors. We demonstrate these methods and find interesting results on the COMPAS crime recidivism and credit delinquency data.

A UK hospital did not do enough to protect the privacy of patients when it shared data with Google, the UK's Information Commission (ICO) has ruled. The ICO censured the Royal Free NHS Foundation Trust about data handed over during trials of a novel way to detect kidney injuries. Among other failings, the ICO said the hospital did not tell patients enough about the way their data was used. The trust said it would tackle "shortcomings" in its data-handling. Details on about 1.6 million patients was provided to Google's DeepMind division during the early stages of the medical trial last year.

When a group of museums and researchers in the Netherlands unveiled a portrait entitled The Next Rembrandt, it was something of a tease to the art world. It wasn't a long lost painting but a new artwork generated by a computer that had analysed thousands of works by the 17th-century Dutch artist Rembrandt Harmenszoon van Rijn. The computer used something called machine learning to analyse and reproduce technical and aesthetic elements in Rembrandt's works, including lighting, colour, brush-strokes and geometric patterns. The result is a portrait produced based on the styles and motifs found in Rembrandt's art but produced by algorithms. This is just one example in a growing body of works generated by computers.

And will regulation be harmonised throughout Europe?