.@mlfoundry's distributed compute service simplifies AI model training with veterans from Google DeepMind and Meta. Maximizing the utility of the computing power we already have, and will produce, arrives at the perfect moment in our AI trajectory - and why we led their Series A. Learn more: lsvp.com/stories/scaling-ai-… Cc: @ravirajjain and @ravi_lsvp

Are you GPU poor and want to be GPU rich? 🤑 What if we said the real bottleneck right now isn't GPU supply but rather pathetic GPU utilization? ⚡️ @mlfoundry got to 8 figures+ of revenue while operating in stealth This cracked team led by @jaredq_ lives at the edge of AI

In this article, I and co-authors @BorisHanin, @ChenLingjiao, @pbailis, Ion Stoica, and @matei_zaharia explore one of the most powerful ideas we have yet discovered to inform compound AI systems design: verifiability. In common situations where practitioners are willing to expend a higher budget to go beyond the capabilities frontier accessible to today's state-of-the-art (SOTA) monolithic models, they may be willing to invoke many model inference calls, composing them into “networks of networks” (NoNs) of sorts. The question then becomes: what principles should guide the composition of these NoNs? Inspired by TCS and PCP notions that often verification is easier than generation (as holds for classical problems like graph coloring), we construct “best-of-K” or “judge-based” Compound AI Systems, which explicitly separate “generator” modules from “verifier” modules. We posit that these systems are particularly helpful for “reasoning-based” or “procedural-knowledge” oriented tasks, which are often more verifiable, less so for factual or declarative-knowledge settings (and we can use these systems partly to help characterize tasks, including subjects in the MMLU, along these lines). Very neatly, it turns out we can analytically characterize when these systems can confer a gain and predict the gain’s extent. We hope people will extend these ideas to tackle some of the reasoning-oriented application frontiers that are a bit beyond the range of today’s SOTA models. Arxiv: arxiv.org/pdf/2407.16831

Chips and Pears - beat that combo. Come hang! 🐿 🍐👇 lu.ma/aihappyhour

If you like 🪄AI and Creativity🪄 come to our demo night meetup hosted by @mlfoundry at @getcaptionsapp in NYC Feat. demos from the best companies in creation: @getcaptionsapp, @udiomusic, @elevenlabs, @wand_app, and more Register here & see you there✌️lu.ma/pxasysmn

We’re honored to announce our seed and Series A funding, and thankful to our partners and investors! With a mission of orchestrating the world’s compute capacity, making it universally accessible and useful, exciting things are on the horizon. Learn more about what's ahead: mlfoundry.com/announcements/…