Hi! π Welcome to The Big Y!
Big week in AI (again) with OpenAIβs Sora release (very cool!!!) and Gemini 1.5 with their 1M token context length (also impressive!). These are getting covered extensively so I thought I would cover something related but quite different.
Amid Gemini releases, Google Deepmind also released a paper focused on the use of transformer technology applied to chess play. Previous iterations of chess engines like AlphaZero and Stockfish 16 are based on advanced search algorithms such as Monte Carlo tree search to plan and make their chess moves and in the case of Stockfish even augmented by chess knowledge and heuristics. Using a transformer model the Deepmind researchers were able to reach an Elo score of 2895 against humans, outperforming GPT 3.5 Turbo and AlphaZero without its Monte-Carlo tree search. In the paper, the researchers tried different variations of scale, and it was evident that the bigger model performed significantly better than their smallest model.
The key piece to pull out of this paper is that the transformer technology that has been powering our generative AI explosion can also be applied to domains involving predictions. For example, if you had an ecommerce business with a bunch of purchasing data, you could probably utilize it to create a transformer model that could produce highly effective recommendations. If transformers can be customized and used, rather than relying on engineering complex and specialized systems such as AlphaZero, outcomes could be improved and potentially easier to achieve. We are already fine-tuning transformer models for language outputs, the next step is to get better at structured data and predictive outcomes.
You know the AI bubble is going strong when SoftBank is in the mix. SoftBank is said to be looking for $100 billion for an AI chip project, just hot off the gossip that Sam Altman is looking to raise $5 to $7 trillion for his chip project π±
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Thanks for reading! Have a great week! π
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