Legalist, a San Francisco-based litigation finance company started by two Harvard University dropouts and advised by retired 7th U.S. Circuit Court of Appeals Judge Richard Posner, has just raised $100 million, which it will use to fund plaintiffs in 100-200 commercial cases over the next two years. Legalist scrapes federal and state court records and then uses algorithms to predict case outcomes and determine the best cases in which to invest. It invests exclusively in mid-market cases that require less than $1 million in funding. "Legalist leads the new wave of technologists using artificial intelligence and machine learning to streamline and underwrite litigation investments," says the company's website. "Our proprietary technology has been recognized by leading publications as revolutionizing the way plaintiffs interact with the justice system."

LawGeex, a company whose product uses artificial intelligence to help in-house legal teams automate the review and approval of everyday contracts, is today announcing the closing of a $12 million Series B funding round led by venture capital fund Aleph. This investment brings the total funding for LawGeex to $21.5 million. In March 2017, LawGeex raised $7 million. Previous investors, including Lool Ventures, also participated in this round. This news follows several recent investments in legal AI companies, including $10 million in Luminance in November, $8.7 million in ROSS in October, and $12 million in Casetext in March 2017.

Artificial intelligence (AI) is rapidly moving to change the healthcare system. Driven by the juxtaposition of big data and powerful machine learning techniques, innovators have begun to develop tools to improve the process of clinical care, to advance medical research, and to improve efficiency. These tools rely on algorithms, programs created from health-care data that can make predictions or recommendations. However, the algorithms themselves are often too complex for their reasoning to be understood or even stated explicitly. Such algorithms may be best described as "black-box."

The eCommerceTimes column described combining "AI with blockchain allows for the secure, transparent review of data that is changed or moved over time, giving both the buyer and seller confidence in the validity, title and transfer of that bridge in Brooklyn." The May 18, 2017 column written by my Gardere colleagues Eric Levy, Eddie Block, and me and is entitled "Intertwining Artificial Intelligence With Blockchain" which describes Blockchain and includes a 1955 definition of AI from James McCarthy of Dartmouth College and a team of researchers as follows: An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.

Had artificial intelligence been named something less spooky, we'd probably worry about it less. Artificial intelligence, it seems, has a PR problem. While it's true that today's machines can credibly perform many tasks (playing chess, driving cars) that were once reserved for humans, that doesn't mean that the machines are growing more intelligent and ambitious. It just means they're doing what we built them to do. The robots may be coming, but they are not coming for us--because there is no "they."

By Nicole Black, Above the Law, March 4, 2017 This post has been viewed 21 times. This year, one of the topics that popped up over and over throughout the conference was artificial intelligence and its potential impact on the practice of law. In part the AI focus was attributable to the Keynote speaker on the opening day of the conference, Andrew McAfee, author of The Second Machine Age (affiliate link). His talk focused on ways that AI would disrupt business as usual in the years to come. His predictions were in part premised on his assertion that key technologies had improved greatly in recent years and as a result we're in the midst of a convergence of these technologies such that AI is finally coming of age.

The time, capital and personnel required to get basic AI technologies running in-house underscores why such implementation is limited to legal teams. Because of the heavy lifting and dedicated resources an AI implementation can take up, most early adopters are likely to be large corporations for whom AI can provide the most benefit for its cost. In addition to Cisco, McCarron noted that there are several other "larger behemoth" companies road mapping and implementing AI projects, noting Google's work to bring AI contract solutions from Seal Software into their legal operations as an example. But medium-sized companies like PayPal and eBay "are definitely not doing it" yet, she added, an indication that the resources needed for AI may still be too cost-prohibitive for some. So while the technology is still young and the market still evolving, for now, excitement over AI's ability to greatly modernize the legal industry is likely to be tempered by the reality of getting it up and running.

To build the cutting-edge technologies that enable conversational understanding and image recognition, we often apply combinations of machine learning technologies such as deep neural networks and graph-based machine learning. However, the machine learning systems that power most of these applications run in the cloud and are computationally intensive and have significant memory requirements. What if you want machine intelligence to run on your personal phone or smartwatch, or on IoT devices, regardless of whether they are connected to the cloud? Yesterday, we announced the launch of Android Wear 2.0, along with brand new wearable devices, that will run Google's first entirely "on-device" ML technology for powering smart messaging. This on-device ML system, developed by the Expander research team, enables technologies like Smart Reply to be used for any application, including third-party messaging apps, without ever having to connect with the cloud…so now you can respond to incoming chat messages directly from your watch, with a tap.

In much of machine learning, data used for training and inference undergoes a preprocessing step, where multiple inputs (such as images) are scaled to the same dimensions and stacked into batches. This lets high-performance deep learning libraries like TensorFlow run the same computation graph across all the inputs in the batch in parallel. Batching exploits the SIMD capabilities of modern GPUs and multi-core CPUs to speed up execution. However, there are many problem domains where the size and structure of the input data varies, such as parse trees in natural language understanding, abstract syntax trees in source code, DOM trees for web pages and more. In these cases, the different inputs have different computation graphs that don't naturally batch together, resulting in poor processor, memory, and cache utilization. Today we are releasing TensorFlow Fold to address these challenges.

In October 2015 we launched the Google Brain Residency, a 12-month program focused on jumpstarting a career for those interested in machine learning and deep learning research. This program is an opportunity to get hands on experience using the state-of-the-art infrastructure available at Google, and offers the chance to work alongside top researchers within the Google Brain team. Our first group of residents arrived in June 2016, working with researchers on problems at the forefront of machine learning. The wide array of topics studied by residents reflects the diversity of the residents themselves -- some come to the program as new graduates with degrees ranging from BAs to Ph.Ds in computer science to physics and mathematics to biology and neuroscience, while other residents come with years of industry experience under their belts. They all have come with a passion for learning how to conduct machine learning research.