https://www.quantamagazine.org/new-theory-suggests-chatbots-can-understand-text-20240122/ New Theory Suggests Chatbots Can Understand TextAnil Ananthaswamy1/22/24 “Far from being “stochastic parrots,” the biggest large language models seem to learn enough skills to understand the words they’re processing.”
What I Read: Natural Language, supply chains
https://datasciencecampus.ons.gov.uk/using-natural-language-processing-for-the-analysis-of-global-supply-chains/ Using Natural Language Processing for the analysis of global supply chainsIoannis Kaloskampis Bernard Peat Charles McGowan Paige Hunter Melissa Bui David BradnumMay 11, 2023 “…we explored whether cutting-edge data
What I Read: Geometric Deep Learning
https://thegradient.pub/towards-geometric-deep-learning/ Towards Geometric Deep LearningMichael Bronstein18.Feb.2023 “Geometric Deep Learning is an umbrella term for approaches considering a broad class of ML problems from the perspectives of symmetry and invariance.”
What I Read: Beyond Message Passing, Graph Neural Networks
https://thegradient.pub/graph-neural-networks-beyond-message-passing-and-weisfeiler-lehman/ Beyond Message Passing: a Physics-Inspired Paradigm for Graph Neural NetworksMichael Bronstein07.May.2022 “…the “node and edge-centric” mindset of current graph deep learning schemes imposes strong limitations… we propose physics-inspired “continuous”
What I Read: Graph ML, missing node features
https://blog.twitter.com/engineering/en_us/topics/insights/2022/graph-machine-learning-with-missing-node-features Graph machine learning with missing node featuresEmanuele RossiMaria GorinovaThursday, 17 March 2022 “…GNNs typically run under the assumption of a full set of features available for all nodes. In
What I Read: Researchers Build AI That Builds AI
https://www.quantamagazine.org/researchers-build-ai-that-builds-ai-20220125/ Researchers Build AI That Builds AIAnil AnanthaswamyJanuary 25, 2022“By using hypernetworks, researchers can now preemptively fine-tune artificial neural networks, saving some of the time and expense of training.”