On Thursday, the Laude Institute introduced its first batch of Slingshots grants, aimed toward “advancing the science and observe of synthetic intelligence.”
Designed as an accelerator for researchers, the Slingshots program is supposed to offer sources that might be unavailable in most educational settings, whether or not it’s funding, compute energy, or product and engineering help. In alternate, the recipients pledge to provide some remaining work product, whether or not it’s a startup, an open-source codebase, or one other sort of artifact.
The preliminary cohort is fifteen tasks, with a selected concentrate on the tough drawback of AI analysis. A few of these tasks will likely be acquainted to TechCrunch readers, together with the command-line coding benchmark Terminal Bench and the most recent model of the long-running ARC-AGI mission.
Others take a contemporary strategy to a long-established analysis drawback. Method Code, constructed by researchers at CalTech and UT Austin, goals to provide an analysis of AI brokers’ capability to optimize present code, whereas the Columbia-based BizBench proposes a complete benchmark for “white-collar AI brokers.” Different grants discover new constructions for reinforcement studying or mannequin compression.
SWE-Bench co-founder John Boda Yang can be a part of the cohort, as chief of the brand new CodeClash mission. Impressed by the success of SWE-Bench, CodeClash will assess code by means of a dynamic competition-based framework, which Yang hopes will make the outcomes extra complete.
“I do suppose folks persevering with to guage on core third-party benchmarks drives progress,” Yang advised TechCrunch. “I’m a bit of bit fearful a couple of future the place benchmarks simply turn into particular to firms.”
