I follow 70,000 in AI looking for stuff like this.
RESEARCH
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AI Detects Early Lung Cancer Signs Doctors Miss
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Promising new #AI can detect early signs of lung #cancer that doctors can't see https://
nbcnews.to/3LDlwYe #HealthTech -
AMD Single Node Training: Scaling Challenges for Large Models
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single node training. and that makes a difference. i’m not fully sold about amd and multinode large scale training, but one step at a time
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Which Technologies Have the Greatest Impact on Humanity?
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Which of these do you think are having the bigger impact on humanity?
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TMLR Announces First Selection of Expert Reviewers
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We have just finalized our first selection of TMLR Expert Reviewers. These are reviewers who have done particularly exemplary work in evaluating TMLR submissions. See the following page for details and the list of reviewers:
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TMLR Expert Certification Program for Published Papers
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Additionally, to highlight their contribution and expertise, any paper they publish at TMLR is also given an Expert Certification. Check out the following page to find the list of these papers so far:
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Machine Unlearning: Beyond Simple Data Removal from AI
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I’ve been hearing RE MUnL (surely there’s a better acronym!) increasingly over the past year as generative AI proliferated. It’s not nearly as simple as removing data (eg an artists’ paintings or an author’s books) from a dataset. I bet we’ll see it progress more in the years TK.
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Skill vs Intelligence: Understanding AI Misconceptions
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Most misconceptions about AI would be cleared if folks had a decent understanding of the distinction between skill and intelligence, and between memorization and generalization.
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Google Tackles Machine Unlearning: The AI Forgetting Challenge
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we hear a lot about machine learning, but get ready for machine *un*learning/forgetting — getting an AI model to disregard what it picked up from training data, which is very tricky! looks like google is starting to take it seriously with this challenge.
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Synthetic Data: The Next Frontier for Large Language Models
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Aidan’s perspective on synthetic data as the next frontier for LLMs is intriguing.