Location
In-person Austin Central Library 710 W. César Chávez St. Austin, TX 78701 Online On Zoom (register for event to receive link) |
Agenda
5:15-6:15 p.m. Networking 6:15-7:30 p.m. Presentation 7:30-7:45 p.m. Q&A 8:00 p.m. Networking at Trifecta |
Event Summary
The early stages of almost every civilization-changing technology shift tend to be exotic. Mathematicians arrive a few years in to restate early ad-hoc solutions in terms of algebraic objects, joint probability distributions, etc. In other words, evil mad scientistis run optimizations and perform risk analysis to inform large capital investments. In the case of the currently popularized notions of "AI" -- i.e., large transformer models with astoundingly improved abilities for sequence-to-sequence learning, diffusion, etc. -- the underlying technology is literally based on algebraic objects and joint probability distributions, so we can expect the later-stage math to work well.
The world's top firms which dominate online marketing (Alphabet, Meta, Microsoft, Apple, Amazon, etc.) have flexed their marketing muscles to jockey for pole positions, asserting claims of dominance in AI. Notable celebrities in the AI game seized this context to grab sensationalized headlines. Such claims -- coming from either end of the "AI Doomers" vs. "Accelerationists" false dicotomy -- have been largely ignorant of even the most basic post-WWII lessons in Philosophy and Poli Sci. To wit, most AI experts and their headline stories could be eviscerated by a first-year Anthropology student.
In spite of those theatrics, what had been a matter circa 2022 of a "magic eight ball" controlled by cartel-ish deep pockets is rapidly giving way to open source AI models ascending the Hugging Face leaderboards. This process is quite similar to how energy grids in the US regularized during the early 20th century. With these transformations, we're beginning to see AI apps flourish in industry. Moreover investments in advanced computing now parallel the total technology funding in Project Apollo during 1960-1973, when the US invested $25.8B for the moonshot (~$300B inflation adjusted in 2024).
In this talk we'll look into the industry AI apps gaining traction, plus a gentle intro to the advanced math and hardware trends driving cloud economics -- all from jargon-free, intelligible, busy-friend perspectives. TL;DR: "Thar be windfalls."
The early stages of almost every civilization-changing technology shift tend to be exotic. Mathematicians arrive a few years in to restate early ad-hoc solutions in terms of algebraic objects, joint probability distributions, etc. In other words, evil mad scientistis run optimizations and perform risk analysis to inform large capital investments. In the case of the currently popularized notions of "AI" -- i.e., large transformer models with astoundingly improved abilities for sequence-to-sequence learning, diffusion, etc. -- the underlying technology is literally based on algebraic objects and joint probability distributions, so we can expect the later-stage math to work well.
The world's top firms which dominate online marketing (Alphabet, Meta, Microsoft, Apple, Amazon, etc.) have flexed their marketing muscles to jockey for pole positions, asserting claims of dominance in AI. Notable celebrities in the AI game seized this context to grab sensationalized headlines. Such claims -- coming from either end of the "AI Doomers" vs. "Accelerationists" false dicotomy -- have been largely ignorant of even the most basic post-WWII lessons in Philosophy and Poli Sci. To wit, most AI experts and their headline stories could be eviscerated by a first-year Anthropology student.
In spite of those theatrics, what had been a matter circa 2022 of a "magic eight ball" controlled by cartel-ish deep pockets is rapidly giving way to open source AI models ascending the Hugging Face leaderboards. This process is quite similar to how energy grids in the US regularized during the early 20th century. With these transformations, we're beginning to see AI apps flourish in industry. Moreover investments in advanced computing now parallel the total technology funding in Project Apollo during 1960-1973, when the US invested $25.8B for the moonshot (~$300B inflation adjusted in 2024).
In this talk we'll look into the industry AI apps gaining traction, plus a gentle intro to the advanced math and hardware trends driving cloud economics -- all from jargon-free, intelligible, busy-friend perspectives. TL;DR: "Thar be windfalls."
About the Speaker
Paco Nathan, Managing Partner, Derwen, Inc.
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Paco Nathan, Managing Partner at Derwen, Inc., and author of Latent Space, along with other books, plus popular videos and tutorials about machine learning, natural language, graph technologies, and related topics. Known as a "player/coach", with +40 years of tech industry experience, ranging from Bell Labs to early-stage start-ups. Werner Herzog is his spirit animal.
Board member for Argilla.io; Advisor for KUNGFU.AI, DataSpartan; Lead committer on PyTextRank, kglab. Formerly: Director, Community Evangelism for Apache Spark at Databricks. Long, long ago, when the world was much younger, Paco led a media collective/indie bookstore/performance art space / large online community called FringeWare. Beginning in 1992, this was one of the first online bookstores and likely the first commercial use of a chat bot on the Internet. |