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Machine Learning Street Talk (MLST) cover image

Open-Ended AI: The Key to Superhuman Intelligence? - Prof. Tim Rocktäschel

Machine Learning Street Talk (MLST)

NOTE

Cultivating Open-Endedness in AI

In the field of artificial intelligence, focusing on training foundational models to learn from diverse sources, such as videos, is essential for creating more capable systems. The research is directed towards enabling these large language models to autonomously enhance their performance and adaptability through self-improvement mechanisms. Key concepts include the exploration of generative interactive environments and the development of methods like Prompt Reader, which facilitate self-referential improvement. The pursuit of open-endedness in AI emphasizes the importance of fostering creativity and adaptability, allowing models to generate a wide array of solutions to complex problems, echoing principles from influential figures in the AI community.

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