For years, AI researchers have anticipated the moment when AI systems will be able to improve themselves better than humans could. With investors pouring money into a new generation of research-driven AI labs, there are more resources than ever available to pursue the goal. Now, one of those neolabs has taken a major step towards making it real.
On Wednesday, Adaption introduced a new product called AutoScientist that helps models learn specific capabilities quickly by using an automated approach to conventional fine-tuning. The techniques are applicable to a wide range of fields, but the Adaptation team is particularly focused on the potential for speeding up and easing the process of training and fine-tuning a frontier-level AI model.
According to co-founder and CEO Sara Hooker, who previously worked as VP of AI research at Cohere, AutoScientist represents a new way to approach the AI training process. “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”
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. “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.
Of course, that approach will only be as good as the results. In its launch materials, Adaption boasts that AutoScientist has more than doubled win-rates across different models — impressive numbers, but difficult t …