Adding the blog from the Tensorflow team for more in-depth reference. https://
simplemlforsheets.com/tutorial.html
RESEARCH
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TensorFlow Blog Tutorial Reference for Machine Learning
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Breakthrough Prize Awarded for AlphaFold Achievement
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A huge honour to have received the Breakthrough Prize with John Jumper for #AlphaFold. The ceremony was a wonderful and fun event, and great to see science popularised in the mainstream, hopefully it will help to inspire the next generation to get into research and science!
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Deep Learning Institute Support Skills Development Metacognition
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Thanks Baskaran! And re #4 – here's the info on the #deeplearning Institute – superb support here in regards to actualizing on that 🙂 https://
lnkd.in/eVbDcbbi Also I think areas like #skills style / metacognition and skills confidence are key too, many thanks for sharing, Sally -
LLMs can outperform humans via recall and fungible intelligence
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It’s for sure googlable, and many others could be reversed with rainbow tables. It is recall. Widespread fungibility of intelligence and recall means LLMs can at times zoom past human performance even on tasks the designers never considered. That’s what calculators don’t do.
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On whether a model used external browsing or memorized hashes
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That’s a reasonable guess but in this instance it isn’t browsing the web or using any external resources. Also it’s very unlikely it was trained on literal rainbow tables — the simpler answer is that common hashes like these two occur all the time in technical docs.
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LLMs can outperform humans and keep improving
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I’m sure a machine made the hash but, yes, a human did copy and paste it into a webpage. The point stands the LLM is better at this task than that human, or any other human. More generally, LLMs do not always converge to human-level performance and then stop getting better.
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LLM Knowledge Creation and Human Verification Requirements
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For me part of the problem is that if an LLM did create "new knowledge" it would be incapable of verifying that what it had created was genuinely new – that's not possible without human involvement
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LLM Capabilities are Bounded by Human Skill in Training
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It’s important to remember LLM capability is bounded by the skill of the humans who train them. The only reason ChatGPT can identify common, short strings given their MD5 or SHA1 hashes is because that’s a completely ordinary talent that many humans have.
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LLMs as Tools for Human Knowledge Creation
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I don't see LLMs as systems that can to create new knowledge, I see them as a tool I can use to help ME create new knowledge
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AI Requires Domain Expertise: The Clinician Example
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Working with Generative AI for knowledge work, over the past few years, one thing is very evident: you have to pair powerful A.I. with knowledgeable individuals, to make the most out of it. Trying to make a non-clinically trained person a clinician by aiding them with powerful