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...
You can tell a lot about a person from how they react to something.
That’s why Facebook’s various “Like” buttons are so powerful. Clicking a reaction icon isn’t just a way to register an emotional response, it’s also a way for Facebook to refine its sense of who you are. So when you “Love” a photo of a friend’s baby, and click “Angry” on an article about the New England Patriots winning the Super Bowl, you’re training Facebook to see you a certain way: You are a person...
A new tool being released today for patent lawyers and paralegals uses artificial intelligence and natural language processing to help prepare responses to office actions and then uses analytics to help predict how the case is likely to develop.
The new tool, called SmartShell, has been developed by TurboPatent, a Seattle, Wash., company that specializes in developing technologies to automate and streamline patent drafting, prosecution and quality evaluation.
SmartShell automates the creation...
Kirk Jenkins likes to know the odds. As an undergrad at Harvard, the Chicago-based Sedgwick partner built regression models to predict decisions by the Federal Reserve Bank. So it’s no surprise that now, as a lawyer, he is among the early believers in the value of legal data analytics. And he has a message for the rest of the profession: there’s no going back.
“Fundamentally, this is about moving the mindset of lawyers towards thinking about litigation the way that clients do,” Jenkins...
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...
A recently launched company helps consumers find lawyers based on their win rates for particular types of cases.
Called Justice Toolbox, the startup uses data mined from official state court records to compute and display how many cases a lawyer has won and lost and the lawyer’s approximate win rate.
Users can search the site by lawyer name, case type or location or they can browse listings by the same criteria.Currently operating only in Maryland and the District of Columbia, the free service...
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...
Data: Your firm needs to have numbers that will help in management decisions in a format that software can handle. Ideally, the data has been collected in one or more spreadsheets, but database repositories can also contribute. Despite the hype about "big data," law firms don't possess such large-scale pools of data. Still, you can actually do useful analyses and, more fruitfully, make predictions with modest amounts of data. For example, with a spreadsheet having details on 50 or more closed cases...
Software that works on Wall Street is changing how business is done and who profits from it.
At its height back in 2000, the U.S. cash equities trading desk at Goldman Sachs’s New York headquarters employed 600 traders, buying and selling stock on the orders of the investment bank’s large clients. Today there are just two equity traders left.
Automated trading programs have taken over the rest of the work, supported by 200 computer engineers. Marty Chavez, the company’s deputy chief financial...