Saying "the AI war has begun" by comparing Bing ChatGPT and Google Bard shows a complete misunderstanding of how search engines work today, which are already *spoil alert* AI models. @Numerama, please stop this bullshit and raise the level a bit.
MACHINE LEARNING
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Inductive Bias and Data Shape Emergence of AI Abilities
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Inductive bias, data and other changes does influence the point where emergent abilities emerge. We showed this in UL2R paper: https://
arxiv.org/abs/2210.11399 I also wrote a blogpost: -
Loss Function Finiteness in Langevin Dynamics Analysis
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(2)The Loss function defined in @UCIrvine paper also appears to become infinite while we ensure the loss is finite and results in valid Langevin dynamics.
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Diffusion Models in Function Spaces: Key Differences from Prior Work
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Yes, we are aware of paper on diffusion models in function spaces from @UCIrvine There are two main differences from our work: (1) We assume data in function space vs. other work: data discretized with IID Gaussian noise. Latter not well-defined as resolution increases. See Fig
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AI-generated painting sold for $432,500 at 2019 auction
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A reminder of how far we've come in just a few years: This AI-generated painting sold at auction in 2019 for $432,500
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Genome-Scale Language Models Learn SARS-CoV-2 Evolutionary Landscape
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Tomorrow @argonne
's Arvind Ramanathan shares with our community how his team built genome-scale language models that can learn the evolutionary landscape of SARS-CoV-2 genomes. This research won the 2022 ACM Gordon Bell Special Prize. Join us! https://
hubs.li/Q01CV9rF0 -
GPT Implementation in 60 Lines of NumPy and JAX
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Late to the party but "GPT in 60 Lines of NumPy" / picoGPT is nicely done: https://
jaykmody.com/blog/gpt-from-
scratch/
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– good supporting links/pointers
– flexes some of the benefits of JAX: 1) trivial to port numpy -> jax.numpy, 2) get gradients, 3) batch with jax.vmap
– inferences gpt-2 checkpoints -
Model Observability: Future-Proofing Your ML Strategy
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How can you stay on top of the growing number of production #models and issues interfering with their performance? It’s all about model #observability. Find out how it can help you future-proof your #ML strategy. https://
bit.ly/3x9pHUS