The creator of a chatbot which overturned more than 160,000 parking fines and helped vulnerable people apply for emergency housing is now turning the bot to helping refugees claim asylum. The original DoNotPay, created by Stanford student Joshua Browder, describes itself as "the world's first robot lawyer", giving free legal aid to users through a simple-to-use chat interface. The chatbot, using Facebook Messenger, can now help refugees fill in an immigration application in the US and Canada. For those in the UK, it helps them apply for asylum support. The London-born developer worked with lawyers in each country, as well as speaking to asylum seekers whose applications have been successful.

When should a criminal defendant be required to await trial in jail rather than at home? Software could significantly improve judges' ability to make that call--reducing crime or the number of people stuck waiting in jail. In a new study from the National Bureau of Economic Research, economists and computer scientists trained an algorithm to predict whether defendants were a flight risk from their rap sheet and court records using data from hundreds of thousands of cases in New York City. When tested on over a hundred thousand more cases that it hadn't seen before, the algorithm proved better at predicting what defendants will do after release than judges. Jon Kleinberg, a computer science professor at Cornell involved in the research, says one goal of the project was to show policymakers the potential benefits to society of using machine learning in the criminal justice system.

Supervised machine learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world? We want models to be not only good, but interpretable. And yet the task of interpretation appears underspecified. Papers provide diverse and sometimes non-overlapping motivations for interpretability, and offer myriad notions of what attributes render models interpretable. Despite this ambiguity, many papers proclaim interpretability axiomatically, absent further explanation. In this paper, we seek to refine the discourse on interpretability. First, we examine the motivations underlying interest in interpretability, finding them to be diverse and occasionally discordant. Then, we address model properties and techniques thought to confer interpretability, identifying transparency to humans and post-hoc explanations as competing notions. Throughout, we discuss the feasibility and desirability of different notions, and question the oft-made assertions that linear models are interpretable and that deep neural networks are not.

In 2005, after spending some 25 years in and around the Law, I set about writing down what I had learned and valued most. What emerged as of central importance in my career and for the firm I created was relationships; with colleagues, clients and indeed anyone I dealt with in the course of practice and business. Professional, working, relationships are different to personal relationships, and perhaps a little simpler in some respects, and they deserve our deliberate attention. My book was published by the American Bar Association in 2007. In 2015 I completed a second edition which has just been published, again by the ABA, in North America.

This week's milestones in the history of technology include the invention of the telephone, automating telephone exchanges and textile weaving, and the idea of searching for information through associative links. The first-ever nationally televised awards ceremony devoted to the Internet is broadcast. U.S. patent 174,465 for "Improvement in telegraphy" is issued to 29-year-old Alexander Graham Bell. This was the patent for his invention of the telephone, covering "the method of, and apparatus for, transmitting vocal or other sounds telegraphically ... by causing electrical undulations, similar in form to the vibrations of the air accompanying the said vocal or other sound." E-book publisher Rosetta Books wins the lawsuit brought against it by Random House for acquiring titles directly from authors.

Alfred Bester's classic science fiction novel, The Demolished Man, presents a future society where the Mosaic Multiplex Prosecution Computer -- called "Mose" for short -- has to approve every criminal charge before it can go to trial. He keeps the cops honest. Powell, a telepathic detective, tells the police commissioner, "You know Old Man Mose. He's going to insist on hard fact evidence." Decades later, artificial intelligence really is getting into the legal field.

Key engineers and executives from pioneering companies such as Google and Tesla have jumped ship to form their own self-driving startups, such as Lucid Motors and Otto. Google's program, in turn, was shaped by a handful of engineers from two universities: Given the high stakes, it's not surprising that some of the companies where these engineers landed don't get along. Both Tesla and Waymo accused their former employees of stealing trade secrets (Aurora denied the allegations and Uber said it is reviewing the suit). Here's a partial family tree of the self-driving industry, showing how some of the key players have moved from one company or school to another.

Several weeks after winning U.S. patent protection for its data preparation platform, Tamr Inc. announced a deal with Hewlett Packard Enterprise to resell its data unification software. The reseller deal certifies that Tamr's technology has been validated for interoperability with HPE systems as part of its third-party software and hardware offerings. It also raises the visibility of the startup's platform given the breadth of HPE's sales channel. Hewlett Packard Pathfinder was an early investor in Tamr, which was spun out of the Massachusetts Institute of Technologies' Computer Science and Artificial Intelligence Laboratory in 2014. The startup, based in Cambridge, Mass., raised more than $25 million in a Series B funding round in June 2015.

The Washington fight over the future of Obamacare will have enormous repercussions for our health care system, which now accounts for nearly 18 percent of the U.S. economy. First there was "open washing," the marketing strategy for dressing up proprietary software as open source. Next came "cloud washing," whereby … Advances in artificial intelligence, machine learning, and deep learning are impacting businesses. But, the terms are often used interchangeably. As consumer banking becomes increasingly virtual, banks are setting the bar high for their non-human ambassadors.

If news, polls and investment figures are any indication, Artificial Intelligence and Machine Learning will soon become an inherent part of everything we do in our daily lives. Backing up the argument are a slew of innovations and breakthroughs that have brought the power and efficiency of AI into various fields including medicine, shopping, finance, news, fighting crime and more. TNW Conference won best European Event 2016 for our festival vibe. See what's in store for 2017. But the explosion of AI has also highlighted the fact that while machines will plug some of the holes human-led efforts leave behind, they will bring disruptive changes and give rise to new problems that can challenge the economical, legal and ethical fabric of our societies.