What do you mean? LLMs are being used as a tool of scientific discovery too: Like to understand the complexity of the human genome. (In human language!) The 'generative' part of AI models will become more important too, as we come to the limits of human knowledge and RL is
@ninadschick
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AI Revolution in Hard Sciences: Discovery to Climate
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Skepticism is healthy, but if you're not factoring AI's revolution in the hard sciences (Biology/Physics/Chemistry!) you're either badly informed or arguing in bad faith. This is already changing: 1. Scientific Discovery
2. Healthcare
3. Material Sciences
4. Climate Science -
AlphaFold Nobel Prize: AI Revolutionizes Protein Structure Prediction
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Yes. AlphaFold (Nobel Prize winner FYI) perhaps the best example. It solved one of the most complex problems in biology: mapping 3d structures of proteins (IE, the building blocks of life.) It does it w/ anatomical-level accurary catapulting the entire discipline of biology
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AI Demonstrably Improves Human Knowledge Systems
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"AI won't improve on human knowledge" is a horrible take that is already demonstrably untrue.
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AI’s Trajectory: From Knowledge Work to Superintelligence
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And even crazier: If we extrapolate from AI’s current trajectory, its impact on knowledge work is obvious. But that’s just the beginning. This leads to another revolutionary phase: once we’ve built an 'AI brain' that’s smarter than us—one that can reason, 'think', and
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AI Masters Logic First: Why Creativity Remains Elusive
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The paradox of AI: The jobs we assumed required the most human intelligence—math, coding, complex reasoning—are the ones AI is mastering first. Why? Partly, because they are AI-adjacent. Meanwhile, AI still struggles to write truly groundbreaking prose (think a new Shakespeare)
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AI Model Terms of Service: Synthetic Training Data Restrictions
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And be that as it may, isn’t it a totally ridiculous stipulation in the TOC anyway? Use the model for anything except as synthetic training data?
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Competitive AI Breakthrough Strategy Exposed to Market
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I assume because they wanted to keep breakthrough close to their chest for competitive advantage? If that was the case the competition has clearly been blown wide open.
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Scaling Reasoning Models: From Thousands to Millions of GPUs
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This is the interesting bit. As we move to the next phase of scaling reasoning models w RL, data and compute converge. Next breakthroughs require moving from 100’000’s of GPUs to millions of GPUs.
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NVIDIA GPU Infrastructure Cost Analysis for AI
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If they have 50,000 NVIDIA GPUs at $30-40k each, that’s $1.5 – 2bn. Just saying.