I am not arguing that a paper cannot be politically valuable and instigate debates despite representing not very good philosophy and computer science. The question whether the community benefited or was harmed as a result is still subject of debate, as it is often with politics.
RESEARCH
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Generative AI’s Impact on Forecasting Methods
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How will generative AI change forecasting? SAS data scientist Spiros Potamitis looks ahead.
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Human Mind Efficiency Versus LLM Computational Requirements
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Our own minds appear to be much more compact than an LLM, we know far less yet are highly generalistic, getting to a working epistemology and the ability to draw correct conclusions with far less data than today's AI models. It must be possible to recreate this.
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Parrots vs GPT-4: Beyond Stochastic Parrots Debate
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Hm, has anyone done a comparison between the abilities of parrots and GPT-4? This kind of argument is pronounced in the stochastic parrots paper, too: more motivated by politics than philosophy or empirical analysis.
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Semantic LLM Editing for Compact Knowledge Representation
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I expect that once we can edit LLMs semantically, we can produce compact core LLMs that may not be good at style imitation, but that can quickly and reliably extract knowledge from text and store and exchange it in a kind of universally interpretable mental representation code.
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Neural Network Structure and Semantic Interpretability in LLMs
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The neural network that encodes the LLM has no canonical form, its structure depends on the architecture, the training data, and the order in which it is processed. It is still very difficult to extract a semantically interpretable representation from an LLM.
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SAS Explores LLMs and Forecasting Software Fusion
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New podcast . Hear SAS R&D's Michele Trovero and Spiros Potamitis explore the fusion and future of Language Model-based AI (LLMs) with forecasting software. http://
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Self-rewarding Language Model Learns and Improves Itself
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> be me self-rewarding LM
> write my own prompts
> write my own responses
> give myself feedback
> be me smarter self-rewarding LM -
Yuhuai AI Insights: Successful Thesis Defense Achievement
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Check out insights from @Yuhu_ai_
. To echo Yuhuai, I have been saying many times that this was a heroic effort from @thtrieu_
. There have been many ups and downs, and I am glad that Trieu succeeded (and defended his thesis last week)! -
Fine-tuning Chat Models with Cohere Python SDK
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Learn about fine-tuning a model for Chat to improve its performance at a specific task. This article includes practical code examples using Cohere's Python SDK. Learn more about it now.