Seattle-based machine learning startup Dato, which originally launched as GraphLab, announced today that it has changed its name again. Now, the company is known as Turi, an ode to computer science legend Alan Turing. This past September, Turi -- then Dato -- found itself in a trademark infringement argument with fellow tech startup Datto after Dato changed its name from GraphLab in January 2015. Datto, which has been offering data backup and recovery services from its Connecticut headquarters since 2007, first complained about Dato's new name just weeks after the company finished its rebranding from GraphLab. Datto argued that Dato's new name would cause confusion.

A new legal technology company called Deftr is today launching a tool powered by artificial intelligence that helps professionals diagram intricate corporate structures. The tool reads text in real time, as it is being typed, then turns that text into a shareable, interactive graphic illustrating a corporate structure or transaction. In a press release being issued today, Deftr's co-founder and CEO Matthew Osman explained: Building charts to represent corporate structures or transactions essentially by hand is a laborious process all too familiar to anyone in corporate life. It's the reason a lot of associates don't have weekends. Our tool automates this outdated, manual process, allowing a user to build a chart in a fraction of the time and focus their efforts on high-level, analytical work. The tool should be of value to law firms, in-house legal departments, accounting firms, financial services and management consultants, Deftr says.

As a futurist, an entrepreneur, and a lawyer, I always get asked, 'What do you think is the next big thing in the legal world?' I always begin my response with a catch-all reply: 'The next big thing is anything that helps you attract and keep a client. No client equals no business. A bit of a cliché, I know. But you must constantly rethink how to do things better and be more efficient by using the latest research, thinking, and innovations.'

We investigate a long-debated question, which is how to create predictive models of recidivism that are sufficiently accurate, transparent, and interpretable to use for decision-making. This question is complicated as these models are used to support different decisions, from sentencing, to determining release on probation, to allocating preventative social services. Each use case might have an objective other than classification accuracy, such as a desired true positive rate (TPR) or false positive rate (FPR). Each (TPR, FPR) pair is a point on the receiver operator characteristic (ROC) curve. We use popular machine learning methods to create models along the full ROC curve on a wide range of recidivism prediction problems. We show that many methods (SVM, Ridge Regression) produce equally accurate models along the full ROC curve. However, methods that designed for interpretability (CART, C5.0) cannot be tuned to produce models that are accurate and/or interpretable. To handle this shortcoming, we use a new method known as SLIM (Supersparse Linear Integer Models) to produce accurate, transparent, and interpretable models along the full ROC curve. These models can be used for decision-making for many different use cases, since they are just as accurate as the most powerful black-box machine learning models, but completely transparent, and highly interpretable.

On June 27, the US Office of Science and Technology Policy (OSTP) issued a Request For Information on Artificial Intelligence (RFI). The RFI seeks public input on the tools, technologies, and training needed to further research and implementation of artificial intelligence (AI). Public feedback on these important questions will enable the OSTP to develop guidance on how the law should balance AI's potential benefits with its numerous threats. For example, self-driving cars could improve driving safety and provide mobility for people with disabilities. Likewise, the use of AI in healthcare has the potential to dramatically improve the quality and accuracy of medical care.

A man who complained that Google was "watching" him is expected to appear in federal court this week on charges that he destroyed one of the company's self-driving cars and terrorized its Silicon Valley campus with gunfire and arson attacks. Raul Murillo Diaz, 30, of Oakland was taken into custody on June 1 in connection with a firebombing at Google headquarters in Mountain View, Calif., according to the U.S. District court affidavit. Diaz is set to appear in court Friday for a detention hearing. Diaz admitted to setting two fires and a discharging a firearm at the Google campus, according to the affidavit. He told investigators he planned to commit a second shooting on June 30, but was pulled over by Google security.

A judge in Connecticut Wednesday said he planned to rule within a week in a father and son's case against the Federal Aviation Administration over YouTube videos of gun-toting, flame-throwing drones. Austin Haughwout, 19, of Clinton, and his father, Bret Haughwout, produced the videos. They've refused to comply with subpoenas issued by the U.S. attorney's office on behalf of the FAA, saying the subpoenas violate their constitutional right to be free from unreasonable searches and seizures and questioning the agency's authority to regulate recreational drones. U.S. District Judge Jeffrey Meyer gave both sides a deadline of Monday, July 11 to file any additional documents. One of the Haughwouts' videos, viewed more than 3.7 million times, shows a flying drone equipped with a handgun firing rounds.

Apparently, a crash related to Tesla's autopilot feature was material, before it wasn't. On Tuesday, Fortune reported that Elon Musk and Tesla Motors may have withheld a material fact from shareholders when it failed to disclose that a driver had died using the semi-self driving "autopilot" feature in one of the company's vehicles. The fatal accident, the first known case related to the autopilot feature, occurred 11 days before Musk and Tesla sold 2 billion shares in an offering on May 18. Yet the company made no mention of the crash in its offering documents. The news of the accident didn't come out until last week, when it was reported by federal highway authorities--six weeks after the offering.

An appeals court has ruled that a former employee of a company, whose computer access credentials were revoked, had acted "without authorization" in violation of the Computer Fraud and Abuse Act, when he and other former employees used the login credentials of a current employee to gain access to data on the employer's computers. The opinion of the court is likely to be controversial as it is expected to have implications on commonplace sharing of passwords by husbands, co-workers and friends even for innocuous purposes. One of the three judges, Stephen Reinhardt, for example, dissented from the majority opinion, stating that "people frequently share their passwords, notwithstanding the fact that websites and employers have policies prohibiting it." The CFAA in his view "does not make the millions of people who engage in this ubiquitous, useful, and generally harmless conduct into unwitting federal criminals." Whatever other liability, criminal or civil, the former employee may have incurred in his improper attempt to compete with his former employer, he has not violated the CFAA, Judge Reinhardt wrote.

An artificial intelligence platform, called Rechtwijzer, could soon give lawyers in Australia a run for their money by being called on in legal battlegrounds like divorce, custody, employment and debt disputes. National Legal Aid and RMIT University are showcasing Dutch technology that could make time-consuming and expensive court conflict a thing of the past. The technology would be similar to eBay's dispute resolution service that helps people log on, rather than lawyer up. The dispute resolution robot was born in the Netherlands and can mediate everything from divorces, tenancy disputes, and employment, debt and consumer matters. For custody matters, for example, it will ask the ages of the children to be sensitive to their development needs.