This week brings us one chatbot, one'robot judge' closer in the AI revolution of legal. While financial, services and other industries charge head-on into the technological future, legal's embrace of technology has been a reluctant move at best. As Clutch Group president Brandon Daniels told me in a conversation earlier this week, when it comes to legal, "the application of artificial intelligence tends to create some trepidation." Originally published on LegalTech News. This material may not be published, broadcast, rewritten, or redistributed.

"The law is an ass," said Charles Dickens. That may be so but it is a predictable one at that. Researchers at University College London, the University of Sheffield and the University of Pennsylvania applied an AI algorithm to the judicial decisions of 584 cases that went through the European Court of Human Rights and found patterns in the text. Having learned from these cases, the algorithm was able to predict the outcome of other cases with 79 percent accuracy. Interestingly, it was found that rather than legal argument being predictive of case outcomes, the most reliable factors were non-legal elements: language used, topics covered and circumstances mentioned in the case text.

The Snasci Logo comprises of three smaller rings, intersected by a large ring. Symbolically, this represents an adaptation of the Three Laws of Robotics by the science fiction author Isaac Asimov. The rules first appeared in his short story "Runaround" (1942). Quoting from the "Handbook of Robotics, 56th Edition, 2058 A.D.", the laws are: Whilst these laws are broadly acceptable for a robot, they are too narrow for an Artificial General Intelligence. An artificial General Intelligence must deal with scenarios that go beyond physical interaction with humans.

Artificial Lawyer caught up recently with Dan Rubins, the Founder and CEO – and also the CTO – of Legal Robot, one of the new breed of AI-driven document review start-ups. We discussed how he moved from working in a medical services company to joining the fray as a legal AI pioneer, how working on smart contracts may be where the company eventually ends up and why there's a big world of opportunity out there. The San Francisco-based founder of Legal Robot, Dan Rubins, is not a lawyer by background. But, his experiences have taught him a lot about the inefficiencies of document review, while his long term interest in technology and programming has also helped. Perhaps, most fundamentally, Rubins is a self-proclaimed engineer.

At this time, all participants are in a listen-only mode. A brief question-and-answer session will follow the formal presentation. It is now my pleasure to introduce your host Ken Stillwell, CFO and Senior VP of Pegasystems. Before we begin, I'd like to read our Safe Harbor Statement. Certain statements contained in this presentation, including but not limited to, statements related to future earnings, bookings, revenue and mix of license revenue may be construed as forward-looking statements as defined by the Private Securities Litigation Reform Act of 1995. The words expects, anticipates, intends, plans, believes, could, estimates, may, targets, strategies, intends to, projects, forecasts and guidance, and other similar expressions, identify forward-looking statements, which speak only as of the date the statement was made and are based on current expectations and assumptions. Because such statements deal with future events, they are subject to various risks and uncertainties. Actual results for the fiscal year 2016 and beyond could differ materially from the Company's current expectations. Factors that could cause the Company's results to differ materially from those expressed in the forward-looking statements are contained in the Company's press release announcing its Q3 2016 earnings, and in the Company's filings with the Securities and Exchange Commission, including its quarterly report on Form 10-Q for the quarter ended September 30, 2016, its Annual Report on Form 10-K for the year ended December 31, 2015 and other recent filings with the SEC. Although subsequent events may cause the Company's view to change, the company undertakes no obligation to revise or update forward-looking statements, whether as a result of new information, future events or otherwise, since these statements may no longer be accurate or timely. And with that, I'll turn the call over to Alan Trefler, Founder and CEO of Pegasystems. I'm pleased it was a strong Q3, overall. Q3 is generally provide limited visibility given vacations and schedules especially in Europe. And I had spoken about Brexit on the last call and I'm pleased to say that concerns have not materialized with the exception of currency of course. And I'm pleased to see the continued progress we're making towards having less lumpy quarters despite the inherent lumpiness of this business, even in the face of those currency headwinds. Those currency headwinds caught a couple of points off of our results.

Safeguarding privacy in machine learning is highly desirable, especially in collaborative studies across many organizations. Privacy-preserving distributed machine learning (based on cryptography) is popular to solve the problem. However, existing cryptographic protocols still incur excess computational overhead. Here, we make a novel observation that this is partially due to naive adoption of mainstream numerical optimization (e.g., Newton method) and failing to tailor for secure computing. This work presents a contrasting perspective: customizing numerical optimization specifically for secure settings. We propose a seemingly less-favorable optimization method that can in fact significantly accelerate privacy-preserving logistic regression. Leveraging this new method, we propose two new secure protocols for conducting logistic regression in a privacy-preserving and distributed manner. Extensive theoretical and empirical evaluations prove the competitive performance of our two secure proposals while without compromising accuracy or privacy: with speedup up to 2.3x and 8.1x, respectively, over state-of-the-art; and even faster as data scales up. Such drastic speedup is on top of and in addition to performance improvements from existing (and future) state-of-the-art cryptography. Our work provides a new way towards efficient and practical privacy-preserving logistic regression for large-scale studies which are common for modern science.

In order to receive a patent, the subject of the protection must be a human invention. Some want to change that and allow computers programmed to "artificial intelligence" sophistication to receive patents. New research published by the University of Surrey in Boston College Law Review is calling for inventions by computers to be legally granted patents. The research states that the rapid increase in computer power is posing new challenges when it comes to patenting an invention. Artificial intelligence is playing an ever larger role in innovation -- with major players such as IBM, Pfizer and Google investing heavily in creative computing -- but current patent law does not recognise computers as inventors.

A law professor at the University of Surrey is arguing that it should be possible for computer-based artificial intelligence (AI) systems to be formally considered as inventors for any invention they contribute to, much in the same way a person would. The argument forms part of a paper, which has been published in the Boston College Law Review, entitled I Think, Therefore I Invent: Creative Computers and the Future of Patent Law. In its introduction the report makes the point that while inventions by computers have been granted patents previously, the concept of computer inventorship has never actually been considered by the courts. The concept of giving creative computers the credit for their own inventions may sound surreal but, in reality, they have been generating potentially patentable ideas for decades without acknowledgment. As Professor Ryan Abbott points out in his paper, 'machines have been autonomously generating patentable results for at least twenty years and the pace of such invention is likely increasing.'

The Guardian recently reported on a new AI software capable of predicting the outcome of trials developed by a group of British scientists at University College London.[1] After examining English language data sets for 584 cases relating to torture and degrading treatment, fair trials, and privacy, the AI verdict was the same as the one delivered by the court in 79% of the cases. What's the point, you may ask? Not to replace judges and juries by artificial intelligence, if that's what you fear. As the lead researcher on this project, Dr. Nikolaos Aletras, explains: "We don't see AI replacing judges or lawyers, but we think they'd find it useful for rapidly identifying patterns in cases that lead to certain outcomes.

The history of robots taking over human jobs is long and littered with doomsday predictions. Just last May, mega-manufacturer Foxconn reportedly replaced 60,000 workers with automated technology. Amazon's warehouses thrive on machines that can move packages. And as Martin Ford, author of the New York Times bestseller Rise of the Robots tells it, this tech will only replace more jobs in the coming decade -- even in traditional white-collar office roles. Ford keynoted Orange Silicon Valley's "A.I. and the Future of Corporate Finance" event, which welcomed a panel of experts to OSV's Spear Street space to discuss the role that artificial intelligence has to place in bookkeeping and auditing operations.