The original AI doomers are leaving the sinking ship of AI doomerism.
AI
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Evaluating prompt building tools for serious AI projects
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Writing custom GPT instructions works for playing around but won't be sufficient for a serious project. I was exploring if there are any existing prompt builders around and tested @promptmetheus_ It can give you an idea of what prompting may look like in the future.
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Updated submission requirements for AI GPTs
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Now it has a higher entry bar, requiring you to record a video describing your GPT and provide a category name and Twitter handle. Looks like I will record a video soon Made by @johnrushx
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Tech Leaders’ Philanthropic Transitions: Gates, Zuckerberg and Social Impact
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The transition is particularly striking in some people in prominent positions. A good example is Bill Gates who transitioned from ruthless CEO of a company with monopolistic tendencies to one of the most effective philanthropists in the world. Mark Zuckerberg and Priscilla Chan
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Tokenization makes exact repetition of tokens harder than counting
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A few things:
– “aaaa…” is tokenized differently from “ a a a a…” in a way that makes it much easier to say (many a’s = 1 token)
– Counting “out loud” is easy, including FizzBuzz variants. What’s hard is making it say exactly 100 repetitions of a token sequence unnumbered -
PPO training discourages repeating ‘a’ 100 times due to OOD risk
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The latter, but it’s not “policy” as in something a human decided. Attempting to say “a” x100 risks going OOD and derailing into nonsense, and even if it doesn’t it can’t reliably count to exactly 100 in its head. So, it learns from PPO that trying is a bad idea.
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LLM Hallucinations: GPT-3, GPT4All, and Google get Super Bowl winner wrong
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Oct 2022: I ask GPT-3, who won the Super Bowl the year Justin Bieber was born? GPT-3 says Packers. It’s the Cowboys. Example is added to LangChain. Other LLMs answer. Answers are added to GitHub. Now, citing GPT4All, Google says it’s the Buffalo Bills — who have never won:
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GQA Architecture in 70B Language Models Explained
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Oh wait this article is correct for a single layer of a 13b model — in fact it's only the 70b model that uses GQA AFAICT! 😀
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Yi-6B Language Model Now Available on Hugging Face
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Yi-6B available here https://
huggingface.co/01-ai -
Automatic Differentiation Implementation Guide in Python
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Victor MARTIN ~ Understanding Automatic Differentiation in 30 lines of Python https://
bit.ly/3t0ejMg #AI #MachineLearning #DeepLearning #LLMs #DataScience