I feel like, in the last couple of years, NeurIPS/ICML/ICLR have made a number of changes, often drastic. Change can be good. But I wish these changes were made with a bit more transparency, rationale, and potentially input from the community.
RESEARCH
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Sharing Advanced AI Techniques and Methods
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Happy to help spread some advanced techniques with AI, Alex.
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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) -
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/
… -
AI Dungeon’s Role in the Birth of LLM Prompt Engineering
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LLM prompt engineering was born in academia, but AI Dungeon was its cradle. Many people, including me, used GPT-3 for the first time via this game. Many useful prompting techniques were discovered by players who abused it for independent, informal research.
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Andrew Ng Thanks Contributors to The Batch Newsletter
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7/Exciting times ahead, and thank you Yoshua Bengio, @AlonHalevy
, @douwekiela
, @_beenkim
, and @Reza_Zadeh for writing for The Batch! -
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.