
On AI Evals, TAR vs. LLMs and What Will be Different Next Year - Aron Ahmadia (VP Applied Science, Relativity)
- Interview
- Technology
In advance of this year's RelFest in Chicago, Aron Ahmadia, Vice President of Applied Science at Relativity, joins the podcast to discuss AI evaluation, AI benchmarking and the transition from traditional Technology Assisted Review (TAR) to generative AI in legal document review and eDiscovery. The conversation covers the daily operations of a global AI team, the concept of "evaluation as a product," and the methodology behind testing large language models (LLMs) for legal tasks. Aron also discusses a recent study that compared the accuracy and efficiency of TAR and human document review against Relativity's generative AI tool, aiR for...
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electronic discovery and legal document review generally.
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Is this episode right for you?
B2 · Upper-intermediate
Comfortable if you already follow TV news
- 80% of the words are B1 or easier.
- 192 words a minute: natural, fairly fast speech.
- 152 harder words (B2 and up) in the whole episode.
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This is Tech-to- electronic discovery and legal document review generally. So let me take you back to 1994. An imagine 12-year-old boy sitting in a small two-partner PI law firm, he's manually networking independent copies of DOS computers to install windows for
work groups. What this kid doesn't know yet back in 1994 is that this is his first step into a three-decade journey into legal technology. Fast forward to today and that same person is helping architect the future of AI for one of the biggest legal tech companies in the world. My guest today is Dr. Aaron Amadea. He's
the Vice President of Applied Science and Relativity. Like many people on the show, he had a background that wasn't necessarily legal before he ended up in the legal industry. Before leading AI Relativity, Dr. Amadea under PhD in Applied Math
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