""What's super exciting about it is that it co-optimizes both the data and the model, and learns the best way to basically learn any capability," Hooker told TechCrunch. "It suggests we can finally allow for successful frontier AI trainings outside of these labs""
""Our view at Adaption is that the whole stack should be completely adaptable, and should basically optimize on the fly to whatever task you have," Hooker says."
"AutoScientist builds on the company's existing data offering, Adaptive Data, which aims to make it easier to build high-quality datasets over time. AutoScientist, meanwhile, is designed to turn those continuously improving datasets into continuously improving AI models."
"In its launch materials, Adaption boasts that AutoScientist has more than doubled win-rates across different models - impressive numbers, but difficult to put into context. Since the system is built to adapt models to specific tasks, conventional benchmarks like SWE-Bench or ARC-AGI aren't appli"
AutoScientist is a product that helps AI models learn specific capabilities quickly using an automated approach to conventional fine-tuning. It targets faster, easier training and fine-tuning of frontier-level AI models across many fields. The system co-optimizes both the data and the model, learning the best way to acquire capabilities. It builds on Adaptive Data, which focuses on creating high-quality datasets over time, and turns continuously improving datasets into continuously improving AI models. The goal is an adaptable training stack that optimizes on the fly for the task. Launch materials claim more than doubled win-rates across different models, while standard benchmarks may not directly apply because the system is task-adaptive.
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