AI has entered the chat - by Ben Lee and Paulius Mui, MD

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In 2017, the Google Brain team’s landmark paper Attention Is All You Need introduced the Transformer architecture, which drastically improved performance and quality in building large language models (LLMs). What followed was a convergence in approach across ML / AI research, resulting in composability of ideas and compounding of improvements. 2020 saw the OpenAI team publish Scaling Laws for Neural Language Models which led to the emergence of the Scaling Hypothesis - TL;DR: pump model size, data, and compute and you’ll see increasingly sophisticated behavior in AI models. Then of course, a small “research preview” called ChatGPT surfaced the capabilities and potential of LLMs through a consumer-facing interface, and became the fastest growing consumer app of all time (100M MAUs in just two months!). Here’s a fantastic primer on most of the concepts behind the tech. The resulting Cambrian explosion of AI innovation has occurred at a breakneck pace - so much so that, a few days ago, ma

Yeah, it’s another AI post in a sea of them. First of all, a self-deprecative disclaimer: Below, we cover: Context on what’s led up to the current AI zeitgeist Opportunity spaces for AI applications in clinical workflow where players are converging, and why Segmenting the current space and the competition dynamics between players Anticipating how new players can make space for themselves in the space A clinician’s perspective on these happenings, by Paulius Mui, MD A look at the recent financial and commercial activity in the space, by Blake Madden This post is hefty - thanks for…

AI has entered the chat - by Ben Lee and Paulius Mui, MD

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