I swear DeepSeek open-sourcing everything is some Sun-Tzu shit. America is trying to build trillion-dollar AI monopolies, and China is trying to make that impossible. If the secret recipe to AGI is (eventually) public, the business model changes. Valuations compress.
RESEARCH
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Deep Learning ConvNets: Core AI Beyond Language Models
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The vast majority of them use deep learning, i.e. ConvNets trained with backprop. It's not LLMs but it is AI.
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Deep Learning Without LLMs: Neural Net Backpropagation Focus
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It's all based on neural net / deep learning trained with backprop.
But none of it uses LLMs, GPT, or other token-based generative architectures. -
LLMs vs Vision Systems: Neural Nets and Architecture Differences
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Absolutely nothing. They both use neural nets and backprop. But LLMs are generative architectures trained on sequences of discrete symbols. Vision systems used in AEBS and other applications use ConvNets or ViTs trained to detect and classify from labelled samples.
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Anthropic Accelerates Product Development Timelines with Latest Models
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My biggest takeaways from Claude Code's Head of Product @_catwu
: 1. Anthropic’s product development timelines have gone from six months to one month, sometimes one week, sometimes one day. Part of this acceleration is access to the latest models (i.e. Mythos). Another is -
LLMs Dominated by Industry Limits Academic Research Contribution
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I didn't say that. I said that LLM is in the hands of industry, is largely engineering, and requires too much computing resources for academics to contribute significantly. More importantly, LLMs are today's technology. Academic researchers, particularly PhD students, should be
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Joint Embedding Architectures: Vision Encoders Beyond LLMs
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Through 1. Vision encoders that are not LLMs. They are actually Joint Embedding Architectures that embed images and text description in the same space
2. Painfully exhaustive training on enormous amounts of declarative facts about the physical world. You can train them to answer -
Why LLMs fail at physical world understanding compared to JEPA
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We still don't have AI systems that understand the physical world as well as a cat. JEPA are getting there. LLMs and other generative architectures are not. Generative architectures, particularly those that produce discrete tokens like LLMs simply ***DO NOT WORK*** for
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Exponential Growth is Actually Sigmoid Curves in Disguise
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People criticize me because they fail to see that every real-world process that looks like an exponential is really the initial segment of a sigmoid. Even processes that remain exponential for a while are actually a succession of small sigmoids, each corresponding to a major