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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.<\/p>\n
On Wednesday, Adaption<\/a> introduced a new product called AutoScientist<\/a> 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.<\/p>\n 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. \u201cWhat\u2019s 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,\u201d Hooker told TechCrunch. \u201cIt suggests we can finally allow for successful frontier AI trainings outside of these labs\u201d<\/p>\n AutoScientist builds on the company\u2019s existing data offering, Adaptive Data<\/a>, 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. \u201cOur view at Adaption is that the whole stack should be completely adaptable, and should basically optimize on the fly to whatever task you have,\u201d Hooker says.<\/p>\n 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 \u2014 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\u2019t applicable.<\/p>\n Still, Adaption is confident that users will see the difference once they try AutoScientist out \u2014 so confident that the lab is making the tool free to use for the first 30 days after its release.<\/p>\n \u201cThe same way that code generation unlocked a lot of tasks, this is going to unlock a lot of innovation at the frontier of different fields,\u201d Hooker says.<\/p>\n<\/div>\n When you purchase through links in our articles, we may earn a small commission<\/a>. This doesn\u2019t affect our editorial independence.<\/em><\/p>\n