Good post. A lot of interest atm in wiring up LLMs to a wider compute infrastructure via text I/O (e.g. calculator, python interpreter, google search, scratchpads, databases, …). The LLM becomes the "cognitive engine" orchestrating resources, its thought stack trace in raw text
MACHINE LEARNING
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Nonlinear Vector Autoregression Emerging Over Random Reservoir Methods
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me too! I saw a couple of successful applications of them in real-world settings such as diagnoses of signals. I think though the latest developments suggest nonlinear vector autoregression instead of random reservoir which is an interesting direction:
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AI Predicts Patient Response to Cancer Treatment
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#ArtificialIntelligence can potentially predict a patient’s response to cancer treatment https://
interestingengineering.com/health/artific
ial-intelligence-predict-response-cancer-treatment
… @IntEngineering #Healthcare #AI #MachineLearning #DataScience #BigData #Analytics #100DaysofCode #IoT #serverless #womenwhocode #DeepLearning #DigitalTransformation -
DeepMind’s Epistemic Networks Reduce LLM Fine-Tuning Data Requirements
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DeepMind’s Epistemic Neural Networks Enable Large Language Model Fine-Tuning With 50% Less Data https://
syncedreview.com/2022/11/16/dee
pminds-epistemic-neural-networks-enable-large-language-model-fine-tuning-with-50-less-data/
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Domino Named Top 200 North American Tech Company Again
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We are honored to be included for the 2nd consecutive year among the top 200 premier North American technology companies, as rated by Deloitte's #Fast500 list. We're ever grateful to our amazing customers, without whom this wouldn’t be possible: https://
domino.buzz/3Ap2Ma3 #MLOps -
NFL Knowledge Gap, ML Speed, and Accuracy-Explainability Trade-offs
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Also:
1. The speaker in your paper doesn't know the NFL.
2. We can do even weak-knowledge ML a lot faster than evolution.
3. There's very often a tradeoff between accuracy and transparency/explainability, and often accuracy is more important. -
Tabula-Rasa Model Oxymoron: NFL Theorem and Fundamental Assumptions
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"Tabula-rasa model" is an oxymoron, by the NFL theorem. The question is what are the fundamental assumptions that need to be built in. Causality is a candidate. The laws of physics are another.
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Climate Models Replaced by Superior Alternative Method
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Who needs climate models now? This does the job much better.
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Learning Actions from Data Rather Than Building Them In
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We don't need to build it in, because it's very easy to learn from data (cf. infants). And we arguably shouldn't, because what your actions are can change (e.g., moving your hands vs. driving your car).
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Expert Choice: Novel Mixture-of-Experts Routing Algorithm
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Introducing a novel mixture-of-experts routing algorithm, called Expert Choice, that can achieve optimal load balancing between experts while allowing heterogeneous token-to-expert mapping. Learn how it’s done at https://
goo.gle/3OdpO9t