A theorem prover is a dynamical system. Its initial state is the axioms, and its final one is their deductive closure.
MACHINE LEARNING
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ACL Conference Decline in Influential NLP Papers 2022
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Can't help but notice that approximately 0 of this list of 20+ influential papers was submitted to *ACL/EMNLP in 2022. What happened? Back in the late 2010's, *ACL had some of the best work (BERT, etc)
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Testing GraphViz Reformatting Outside AI Training Data
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(*SPOILERS for Glass Onion – I needed a movie that was outside the training window*) @elzr pointed out Riley's GraphViz experiments (
https://
x.com/goodside/statu
s/1561549768987496449?s=20
…) as a reformatting, and I wanted to doublecheck it doesn't only regurgitate from things it knows (for useful generality) -
AI Output Eloquence vs. Reasoning: The ‘Nonsense Conclusion’ Effect
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Agreed — but it's also somewhat independent. E.g., think of ChatGPT outputs that eloquently reach a nonsense conclusion by confidently asserting things that don't follow. There's a "Hey, wait a minute…" effect when style exceeds reasoning.
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Three Tech Predictions: Transformers, ROS2, and Indoor Farming
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“I never make predictions. I never have and I never will.” Tony Blair(?). Me neither, except: Transformers, ROS2, and Indoor Farming.
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Stanford Buffalo Universities Advance Language Modeling State Space Models
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Stanford & Buffalo U Advance Language Modelling with State Space Models https://
syncedreview.com/2023/01/03/sta
nford-buffalo-u-advance-language-modelling-with-state-space-models/
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Generative AI and Active Learning: AI Systems Selecting Data for Labeling
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6/Reza Zadeh sees generative AI bringing progress to active learning, where a system picks its own examples to be labeled to improve the data. With generative AI, he sees a potential revolution in algorithms generating new data to request to be labeled.
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AI Explainability: From Engineering to Fundamental Scientific Principles
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5/Been Kim discusses AI explainability. AI has taken an engineering-centric approach, where researchers devise techniques via trial and error, and she urges developing fundamental scientific principles that make explanations more trustworthy and accurate.
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Key Directions for AI: Multimodality, Safety, Data-Centric Approaches, and Evaluation
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4/Douwe Kiela points out key directions: Multimodality, grounding, and interaction so AI understands us better; alignment, attribution, and uncertainty to make models safer; data-centric AI to improve scaling; and better ways to evaluate AI models.
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Yoshua Bengio Advocates New Architectures for Conceptual Discovery
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Yoshua Bengio wants to develop new architectures that can discover and reason with high-level concepts, rather than just brute force the learning process by scaling up existing models' data and compute.