The cognitive dissonance here is that these models don't learn during interactions. No online learning: it's literally the most fruitless debate you can imagine for its participants. The debate only has real impact in the minds of humans reading it, if they exist. https://
x.com/LiorOnAI/statu
/LiorOnAI/status/1903017981632020682
…
AGI
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AI Models Cannot Learn During Interactions: Debate Impact Analysis
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AI Agent Solves Complex Wedding Seating Chart Problem
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At #GTC25, see the power of AI reasoning in action 💡 https://t.co/rqIaPBrQPS
— NVIDIA AI (@NVIDIAAI) 21 mars 2025
Watch how an #AIagent tackles a complex wedding seating chart, navigating family dynamics & guest preferences to ensure everyone has a seat. pic.twitter.com/zl7sAe88IyAt #GTC25, see the power of AI reasoning in action https://
nvda.ws/4ikWXyx Watch how an #AIagent tackles a complex wedding seating chart, navigating family dynamics & guest preferences to ensure everyone has a seat. -
Compositionality Over End-to-End Learning: The Future of AI Models
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Much of the field obsesses over end-to-end learning. But strong generalization requires compositionality: building modular, reusable abstractions, and reassembling them on the fly when faced with novelty. The models of the future won't be just pipes, they will be Lego castles.
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o1 Pro AI Model Now Available on Poe Platform
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You can try o1 pro at https://
poe.com/o1-pro, and across all platforms. (2/2) -
Provide Relevant Context to Improve AI Model Accuracy
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8). Provide relevant context For complex reasoning tasks, consider providing relevant context upfront rather than asking the model to make assumptions. This improves accuracy and reduces hallucinations.
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Confidence Thresholds and Computational Resource Trade-offs in AI
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– Yes, higher confidence thresholds meant more questions went unanswered – something we either need to accept, depending on the risk level, or be willing to throw more compute in when confidence is critical
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Compute Power Drives AI Model Accuracy and Reliability
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– Yes, across the board more compute = more accuracy (although without any confidence thresholds, you'll still get wild guesses or wrong answers)
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Risk Penalty Weights Impact on Model Performance
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They also experimented with different risk levels. On one end of the spectrum: no penalty for wrong answers. On the other: the penalty of wrong answers weighted 20x more than the reward for correct ones. What they found:
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Sick Canvas and Agent Experience Innovation
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sick canvas + agent experience!!! https://t.co/HMmaj7amed
— Harrison Chase (@hwchase17) 19 mars 2025sick canvas + agent experience!!!