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."

Artificial intelligence is coming for the service economy, according to Allstate Corp. Chief Executive Officer Tom Wilson. "It's going to rip through this economy like a tsunami," Wilson said Thursday in an interview on Bloomberg TV from Aspen, Colorado. Automation will affect a wide swath of workers, from traders to taxi drivers. McKinsey & Co. estimates that more than 400 million people worldwide could be looking for work by 2030 because technology took their jobs. That change has already come to the auto insurance business.

Applications of deep learning to medical disciplines including ophthalmology, dermatology, radiology, and pathology have recently shown great promise to increase both the accuracy and availability of high-quality healthcare to patients around the world. At Google, we have also published results showing that a convolutional neural network is able to detect breast cancer metastases in lymph nodes at a level of accuracy comparable to a trained pathologist. However, because direct tissue visualization using a compound light microscope remains the predominant means by which a pathologist diagnoses illness, a critical barrier to the widespread adoption of deep learning in pathology is the dependence on having a digital representation of the microscopic tissue. Today, in a talk delivered at the Annual Meeting of the American Association for Cancer Research (AACR), with an accompanying paper "An Augmented Reality Microscope for Real-time Automated Detection of Cancer" (under review), we describe a prototype Augmented Reality Microscope (ARM) platform that we believe can possibly help accelerate and democratize the adoption of deep learning tools for pathologists around the world. The platform consists of a modified light microscope that enables real-time image analysis and presentation of the results of machine learning algorithms directly into the field of view.

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.