While Large Language Models (LLMs) have shown remarkable abilities, they are hindered by significant resource consumption and considerable latency due to autoregressive processing. In this study, we introduce Adaptive N-gram Parallel Decoding (ANPD), an innovative and lossless approach that accelerates inference by …
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Creating a Large Language Model (LLM) requires a lot of data – as implied by the name, LLMs need voluminous input data to be able to function well. Much of that content comes from the Internet, and early models have been seeded by crawling the whole Web.
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That is the new Daron Acemoglu paper, and he is skeptical about its overall economic effects.  Here is part of the abstract: Using existing estimates on exposure to AI and productivity improvements at the task level, these macroeconomic effects appear nontrivial but modest—no more than a 0.71% increase in total factor …
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Unstructured effortlessly extracts and transforms complex data for use with every major vector database and LLM framework.
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Machine learning methods such as neural networks are quickly finding uses in everything from text generation to construction cranes. Excitingly, those same t...
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AI's push into the services market represents a $4.6 trillion opportunity. Continue reading
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We’re asking for feedback on a proposed Acceptable Use Policy update to address the use of synthetic and manipulated media tools for non-consensual intimate imagery and disinformation while protecting valuable research.
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For those of us who have not spent the past few years building ChatGPT from the ground up, how does it work? From Evan Morikawa, who leads the Applied engineering team at OpenAI
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Article URL: https://venturebeat.com/ai/meta-challenges-transformer-architecture-with-megalodon-llm/

Comments URL: https://news.ycombinator.com/item?id=40104084

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