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I have not only read the Let's Verify step by Step paper released less than 24 hours ago, I have combed the release notes and appendix, read most of the linked papers and done my own tests. It's true, performance is massively boosted, and not just for mathematics but science and other domains too. I'll show you comparisons with GPT 3 and PaLM 2, and demonstrate that new records are coming soon. I will also cover the 'synthetic data event horizon' and what might have gone into GPT 4's training. I'll show you how PRM works vs ORM, and why...

A new A.I. paper is released: “Tree of Thoughts: Deliberate Problem Solving with Large Language Models” It’s by researchers at Princeton University and Google DeepMind. It's shows how increase the ability for GPT-4 to autonomously solve complex problems... but it comes with a warning.         Paper: https://arxiv.org/abs/2305.10601 PDF: https://arxiv.org/pdf/2305.10601.pdf    

The 2 Exaflops Aurora supercomputer is a beast of a machine and the system will be used to power the Aurora genAI AI model. Announced at today's ISC23 keynote, the Aurora genAI model is going to be trained on General text, Scientific texts, Scientific data, and codes related to the domain. This is going to be a purely science-focused generative AI model with potential applications being Systems Biology Cancer Research Climate Science Cosmology Polymer Chemistry and Materials Science The foundations of the Intel Aurora genAI model are Megatron and DeepSpeed. Most importantly, the target size for the new model is...