“The reason doing better is hard is because demonstrations of thinking are ‘entangled’ with knowledge, in the training data. Therefore, the models have to first get larger before they can get smaller, because we need their (automated) help to refactor and mold the training data
RESEARCH
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Yi-Large Demonstrates Excellence in Logic and Reasoning Challenges
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Check out Yi-Large in action as it excels in some of the toughest logic and reasoning challenges Thank you to @MatthewBerman for this great validation! What other challenges would you like to see Yi-Large take on? Let us know Watch the full video:
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ChatGPT’s decimal comparison errors like 9.11 vs 9.9
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9.11 looking like 9/11 doesn’t explain 12345.11 > 12345.5, or 2525.11 – 2525.9 = 0.21 You can use Python, and often ChatGPT will by default. What makes this more surprising than other calculation errors is it affects numbers the model wrongly assumes it can handle without code.
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Time-varying seasonality in advanced forecast models
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Incorporating time-varying seasonality in forecast models | GAMbler https://
bit.ly/3S4gRCB
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Sandia Labs Director Testifies on AI Economic Growth Potential
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Jennifer Gaudioso, Director of the Center for Computing Research at Sandia National Laboratories, testified in front of the United States Congress Joint Economic Committee's hearing on Artificial Intelligence and its potential to fuel economic growth and improve governance.
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Should we build viscerally felt artificial emotions?
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Yes, but do we really want viscerally felt artificial emotions? What would be the rationale for building it, other than curiosity?
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Offline Tracker: Synthetic Data Generation for Tesla Self-Driving Networks
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This is not very different from Tesla with self-driving networks. What is the "offline tracker" (presented in AI day)? It is a synthetic data generating process, taking the previous, weaker (or e.g. singleframe, or bounding box only) models, running them over clips in an offline
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LLM Model Size Competition Moving Towards Smaller, Smarter Models
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LLM model size competition is intensifying… backwards! My bet is that we'll see models that "think" very well and reliably that are very very small. There is most likely a setting even of GPT-2 parameters for which most people will consider GPT-2 "smart". The reason current
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Farewell GPT 3.5: The Era of Advanced Language Models Begins
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Adiós GPT 3.5, gracias por el pescado
