The world's first artificially intelligent attorney, Ross, has been hired by its first official law firm- Baker & Hostetler. Other firms will shortly be making their announcements on hiring Ross as well. The law firm Baker & Hostetler have announced they will be hiring the world's first artificially intelligent attorney, Ross, for its bankruptcy practice- where almost 50 lawyers are employed. According to Baker & Hostetler's CEO and co-founder Andrew Arruda, other law firms have also signed licenses with Ross and will soon be making their announcements as well. Baker & Hostetler chief information officer Bob Craig explains why they hired the AI: "At BakerHostetler, we believe that emerging technologies like cognitive computing and other forms of machine learning can help enhance the services we deliver to our clients."

AI has become such a huge topic that late last year the White House released a report on the Future of Artificial Intelligence, which focused on the opportunities, considerations, and challenges of AI. As more and more industries, including healthcare and financial services, adopt AI technology, the technology's value will increase its impact on society as a whole. AI, machine learning and other technologies are already making a significant impact in several industries, including e-commerce, hospitality, and retail. While there has always been a slight tension among workers about robots taking over and claiming jobs, there is a lot to be said about how these types of technologies will, in fact, add more value, contribute to economic growth and augment the workplace so employees can work more effectively. It starts with the huge volumes of contracts that define business relationships, capture the rules of engagement and are the foundation of business transactions every day.

Research Goal: The goal of AI research should be to create not undirected intelligence, but beneficial intelligence. Research Funding: Investments in AI should be accompanied by funding for research on ensuring its beneficial use, including thorny questions in computer science, economics, law, ethics, and social studies, such as: How can we make future AI systems highly robust, so that they do what we want without malfunctioning or getting hacked? How can we grow our prosperity through automation while maintaining people's resources and purpose? How can we update our legal systems to be more fair and efficient, to keep pace with AI, and to manage the risks associated with AI? What set of values should AI be aligned with, and what legal and ethical status should it have? Science-Policy Link: There should be constructive and healthy exchange between AI researchers and policy-makers.

Several deep learning models have been proposed for question answering. However, due to their single-pass nature, they have no way to recover from local maxima corresponding to incorrect answers. To address this problem, we introduce the Dynamic Coattention Network (DCN) for question answering. The DCN first fuses co-dependent representations of the question and the document in order to focus on relevant parts of both. Then a dynamic pointing decoder iterates over potential answer spans. This iterative procedure enables the model to recover from initial local maxima corresponding to incorrect answers. On the Stanford question answering dataset, a single DCN model improves the previous state of the art from 71.0% F1 to 75.9%, while a DCN ensemble obtains 80.4% F1.

Whether it's a new employment contract, a rental contract, or sale contract, it needs to be checked before signing. Everyone knows the struggle of working through the dreaded small print, searching for pitfalls hidden in the tiniest details, and trying to make sense out of the bizarre language of law. In fairness to the layman, contract review is also a hustle for lawyers themselves. In 2014, commercial lawyer Noori Bechor got sick of the fact that 80 percent of his work was spent reviewing contracts. He figured the service could be done much cheaper, faster, and more accurately by a computer.

When you think about it, not a lot has changed in the legal world from the days of To Kill A Mockingbird to the latest John Grisham thriller. Sure, literature snobs may insist that Atticus Finch's flawless moral heroism should never be compared to the conflicted protagonists of contemporary legal page-turners, but in terms of the substance of how lawyers do their lawyering, the fundamentals have barely changed in 80 years, from the career track of a young lawyer to the set-up of a law firm. The same cannot be said of virtually any other profession. Indeed, the legal industry seems more dusty than dynamic; the robes and wrinkles that mark those at the top of the field hardly scream modernity. But change is afoot, as a couple of powerful market forces are driving law firms to adopt modern corporate efficiency.

Artificial Intelligence has permeated almost every industry, either in word or deed, in the last couple of years. From financial institutions to ride-hailing services such as Uber, companies are clambering over one another to take advantage of this technology to stay ahead of the competition. However, one area which Artificial Intelligence has been unable to find a platform in, until very recently, has been the legal sector. There is a belief that the legal sector, particularly when it comes to the courtroom environment, is reserved exclusively for sharp-suited lawyers, who have trained for years to be able to build and present a case in order to persuade a jury of their peers of the validity of their argument. However, the tide might now be turning.

Machine ethics offers an alternative solution for artificial intelligence (AI) safety governance. In order to mitigate risks in human-robot interactions, robots will have to comply with humanity's ethical and legal norms, once they've merged into our daily life with highly autonomous capability. In terms of technical challenges, there are still many open questions in machine ethics. For example, what is deontic logic and how can it be used for improving AI safety? How do we fashion the knowledge representation for ethical robots? These are all significant questions for us to investigate. In this interview, we invite Prof. Ronald C. Arkin to share his insights on robot ethics, with a focus on its technical aspects.

The past decade or so has seen a strong focus on risk and compliance technologies that make use of analytics in financial services. These technologies, which might be called "defense" technologies--in contrast to "offense" technologies that involve marketing and revenue growth--include applications and infrastructure for risk management, fraud prevention, regulatory, and anti-money laundering (AML) compliance. They bring the power of analytical insights--initially used for identifying marketing opportunities in many companies--to risk mitigation in banking. While these distinctions are somewhat blurred by integrating risk-based insights into "offense" activities, they are a useful shorthand. The Great Recession of the late 2000s drove both a greater focus on risk management and substantial new regulation for financial firms.

To say that AI is the number one buzz word in legal at the moment is to say the least. But there is still a big uncertainty about what it actually is and what it can do. A general misconception seems to be that Artificial Intelligence is a single "thing". It can be clause identification, anomaly detection, general due diligence review tools, cognitive systems, machine learning etc. One of the AI-seminars at LegalTech focused on the use of machine-learning technology in due diligence reviews, where the tools help lawyers review large sets of material, looking for risks and pitfalls.