The quest for edge AI seems central to Apple's future approach, and to support it the company will consider the acquisition of smaller AI firms who can deliver optimized, compressed AI models. The company also intends to work with third-party models to shrink and adapt them to work more fully on Apple's hardware. Doing so is important, as the more intelligence Apple can put at the edge, the more it can reduce demand on hosted cloud-based AI, which will reduce infrastructure costs.
On Thursday, the Laude Institute announced its first batch of Slingshots grants, aimed at "advancing the science and practice of artificial intelligence." Designed as an accelerator for researchers, the Slingshots program is meant to provide resources that would be unavailable in most academic settings, whether it's funding, compute power, or product and engineering support. In exchange, the recipients pledge to produce some final work product, whether it's a startup, an open-source codebase, or another type of artifact.
My name is Mark Kurtz. I was the CTO at a startup called Neural Magic. We were acquired by Red Hat end of last year, and now working under the CTO arm at Red Hat. I'm going to be talking about GenAI at scale. Essentially, what it enables, a quick overview on that, costs, and generally how to reduce the pain. Running through a little bit more of the structure, we'll go through the state of LLMs and real-world deployment trends.