AutoScientist of Adaptation: the AI that self-trains
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For several years, the artificial intelligence (AI) research community has been awaiting the moment when AI systems will surpass human capabilities in self-improvement. Thanks to massive investments in new research laboratories, the resources to achieve this goal are more abundant than ever. Adaption, one of these emerging laboratories, has recently made a significant step toward this vision.
Last Wednesday, Adaption unveiled AutoScientist, an innovative tool that allows AI models to train autonomously by optimizing the fine-tuning process. This technology is applicable across various fields, but the Adaption team is focusing on accelerating and simplifying the training of advanced AI models.
Adaption's co-founder and CEO, Sara Hooker, who previously served as vice president of AI research at Cohere, describes AutoScientist as a new approach to the AI training process. She explains that this tool co-optimizes data and models, thereby learning the best way to acquire new skills. "This could finally enable successful training outside of large laboratories," she told TechCrunch.
AutoScientist is built on Adaption's existing data infrastructure, Adaptive Data, which facilitates the continuous creation of high-quality datasets. The tool is designed to transform these datasets into AI models that are constantly improving. Hooker states that Adaption's goal is to make the entire pipeline adaptable and capable of optimizing itself in real-time for any task.
The results achieved by AutoScientist are promising, with success rates more than doubling for various models, although these figures are difficult to compare with traditional benchmarks like SWE-Bench or ARC-AGI. Despite this, Adaption is confident that users will notice a significant improvement when using AutoScientist. To encourage adoption, the tool will be free for the first 30 days following its release.
According to Hooker, "just as code generation has enabled many tasks, AutoScientist could unlock significant innovation across various fields."
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