There was very little real AI or blockchain news over the holidays, especially legal-related. But there was a plethora of posts reviewing 2018 and forecasting 2019 and beyond, so that’s the focus of this post. I suggest you skim these titles and then skim through the lists included in most of the posts; you’re likely

  • Will an A.I. Ever Become Sentient? “The quest for artificial intelligence could yield something that not only out-thinks humanity but can also feel like us.” Interesting (long) post here.
  • Also from Medium: Artificial Intelligence, Consciousness and the Self. This one too is interesting but rather long.
  • Capital One AI

  • Ron Friedmann has pulled together pieces from three WSJ articles to offer a bit of a plan for improving value to clients, and hence to the law firm. His thesis combines considerations of Customer Lifetime Value (CLV), Productivity, and Big Data.
  • This, from Eran Kahana of Stanford Law School: Artificial Intelligence and Computational

  • From Above the LawHead-To-Head Showdown Between AI-Driven Legal Research Tools. It’s Casetext versus LexisNexis and there’s a clear winner, but I expect today’s winner will lose tomorrow as all of these applications are improving so quickly.
  • “Former FBI lawyer Lisa Osofksy today allayed lingering doubts about the future of the Serious

  • A cheery way to start the day (NOT!): Future elections may be swayed by intelligent, weaponized chatbots. This post does not have a happy ending. “Bots versed in human language remain outliers for now. It still requires substantial expertise, computing power, and training data to equip bots with state-of-the-art language-processing algorithms. But it’s not

  • AI news from Manzama: Manzama Signals is ready for prime time. “Manzama Signals is our newest innovation designed to help firms leapfrog the competition by using proprietary algorithms to identify activities or indicators that may signal opportunities for law firms thereby helping legal professionals to better act upon opportunities. Signals employs data driven models to