Hear talks from experts on cutting-edge topics in AI, ML, and computer vision on July 16

This session focuses on practical techniques for using small open-source language models (SLMs) in enterprise settings. We'll explore modern workflows for adapting SLMs with domain-specific pre-training, instruction fine-tuning, and alignment. Along the way, we will introduce and demonstrate open-source tools such as DistillKit, Spectrum, and MergeKit, which implement advanced techniques crucial for achieving task-specific accuracy while optimizing computational costs. We'll also discuss some of the models and solutions built by Arcee AI. Join us to learn how small, efficient, and adaptable models can transform your AI applications.

This talk explores how to make AI faster and more sustainable. We’ll look at the high costs and carbon impact of fine-tuning and self deploying models, and show how optimization techniques available in the Pruna library can reduce size and latency with little to no quality loss.

Most AI teams go through the same story: fast early progress, and then suddenly things slow down. The AI isn’t broken, but new changes don’t seem to help, and it’s not even clear how to tell if things are getting better. I’ve faced this plateau myself—both in my own work and while helping other teams. In this talk, I’ll share what I’ve learned about getting unstuck: how to build genuine confidence in your AI, what “trust” really means in practice, and practical steps to move from “it kind of works” to “this is actually improving.” My goal is to give you real-world ideas you can use when you hit the same wall.

We’ll examine conceptual frameworks, potential applications, and future directions of technologies that can “see” and “act” with increasing independence. The discussion will touch on both current limitations and promising horizons in this evolving field.