Category Archives: Machine Learning

In AI, No Evolution Without Evaluation

At the recent Legalweek New York AI Bootcamp Workshop, I was reminded of a very small, cheap pocket dictionary that I once bought at a book fair when I was in third grade. One day, while looking up definitions, I came across the entry for “bull.” Bull was defined as “the opposite of cow.” Curious, I looked up “cow.” It was defined as “the opposite of bull.” Nothing about both terms referred to bovines, gender or any other defining description. Just that bull and cow are each other’s opposites.

At the boot camp—designed to cover the foundation, use cases and legal considerations to separate the value of AI technology from “the noise”—I learned that “machine learning” is “not expert systems,” and “expert systems” is “not machine learning.” How is this any more helpful than my third grade dictionary? Continue reading

Deep Learning in E-Discovery: Moving Past the Hype

blog_lightbulb_with_flareDeep learning. The term seems to be ubiquitous these days. Everywhere from self-driving cars and speech transcription to victories in the game “Go” and cancer diagnosis. If we measure things by press coverage, deep learning seems poised to make every other form of machine learning obsolete.

Recently, Catalyst’s founder and CEO John Tredennick interviewed Catalyst’s chief scientist, Dr. Jeremy Pickens (who we at Catalyst call Dr. J), about how deep learning works and how it might be applied in the legal arena.

JT: Good afternoon Dr. J. I have been reading about deep learning and would like to know more about how it works and what it might offer the legal profession. Continue reading