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San Francisco-based AI research laboratory OpenAI has added another member to its popular GPT (Generative Pre-trained Transformer) family. In a new paper, OpenAI researchers introduce GPT-f, an automated prover and proof assistant for the Metamath formalization language.

While artificial neural networks have made considerable advances in computer vision, natural language processing, robotics and so on, OpenAI believes they also have potential in the relatively underexplored area of reasoning tasks. The new research explores this potential by applying a transformer language model to automated theorem proving.

Automated theorem proving tends to require general and flexible reasoning to efficiently check the correctness of proofs. This makes it an appealing domain for checking the reasoning capabilities of language models and for the study of reasoning in general. The ability to verify proofs also helps researchers as it enables the automatic generation of new problems that can be used as training data.

Cutting calories significantly may not be an easy task for most, but it’s tied to a host of health benefits ranging from longer lifespan to a much lower chance of developing cancer, heart disease, diabetes and neurodegenerative conditions such as Alzheimer’s.

A new study from teams led by Scripps Research Professors Bruno Conti, Ph.D., and Gary Siuzdak, Ph.D., illuminates the critical role that temperature plays in realizing these diet-induced health benefits. Through their findings, the scientists pave the way toward creating a medicinal compound that imitates the valuable effects of reduced body temperature.

The research appears in Science Signaling.

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In the last video in this series we discussed the differences between deep learning and machine learning, how and when the field of deep learning was officially born, and it’s rise to mainstream popularity. The focus of this video then will be on artificial neural networks, more specifically – their structure.

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