neural network – Page 4 – Andrew Fairless, Ph.D.

What I Read: LLM Pipelines, DSPy

By Andrew Fairless on August 13, 2024May 25, 2024

https://www.databricks.com/blog/optimizing-databricks-llm-pipelines-dspy Optimizing Databricks LLM Pipelines with DSPyby Arnav Singhvi and Daniel Pechi (JetBlue)May 23, 2024 “The key component of DSPy is self-improving pipelines…. Just as data pipelines and machine learningContinue readingWhat I Read: LLM Pipelines, DSPy

What I Read: LLM evaluation

By Andrew Fairless on August 7, 2024May 25, 2024

https://huggingface.co/blog/clefourrier/llm-evaluation Let’s talk about LLM evaluationMay 23, 2024Clémentine Fourrier “There are, to my knowledge, at the moment, 3 main ways to do evaluation: automated benchmarking, using humans as judges, andContinue readingWhat I Read: LLM evaluation

What I Read: LLM, DSPy Assertions and Suggestions

By Andrew Fairless on August 6, 2024May 25, 2024

https://learnbybuilding.ai/tutorials/guiding-llm-output-with-dspy-assertions-and-suggestions Guiding LLM Output with DSPy Assertions and SuggestionsBill Chambers “Assertions in DSPy allow you to define strict rules and constraints that the LLM’s output must (or maybe that youContinue readingWhat I Read: LLM, DSPy Assertions and Suggestions

What I Read: AI, Light-Based Chips

By Andrew Fairless on July 31, 2024May 24, 2024

https://www.quantamagazine.org/ai-needs-enormous-computing-power-could-light-based-chips-help-20240520 AI Needs Enormous Computing Power. Could Light-Based Chips Help?Amos Zeeberg5/20/24 10:40 AM “Optical neural networks, which use photons instead of electrons, have advantages over traditional systems. They also faceContinue readingWhat I Read: AI, Light-Based Chips

What I Read: Platonic Hypothesis

By Andrew Fairless on July 29, 2024May 24, 2024

https://phillipi.github.io/prh The Platonic Representation HypothesisMinyoung Huh, Brian Cheung, Tongzhou Wang, Phillip IsolaMITPosition Paper in ICML 2024 “Neural networks, trained with different objectives on different data and modalities, are converging toContinue readingWhat I Read: Platonic Hypothesis

What I Read: Game Theory, AI

By Andrew Fairless on July 25, 2024May 24, 2024

https://www.quantamagazine.org/game-theory-can-make-ai-more-correct-and-efficient-20240509 Game Theory Can Make AI More Correct and EfficientSteve NadisMay 9, 2024 “Researchers are drawing on ideas from game theory to improve large language models and make them moreContinue readingWhat I Read: Game Theory, AI

What I Read: Hallucinations, AI

By Andrew Fairless on July 9, 2024May 6, 2024

https://www.johndcook.com/blog/2024/03/26/hallucinations-of-ai-science-models/ Hallucinations of AI Science ModelsWayne Joubert3/26/24 11:35 AM “…standard DNN methods applied even to a simple 1-dimensional problem can result in “glitches”: the DNN as a whole matches theContinue readingWhat I Read: Hallucinations, AI

What I Read: Ring Attention

By Andrew Fairless on July 8, 2024April 23, 2024

https://coconut-mode.com/posts/ring-attention Ring Attention ExplainedKilian Haefeli, Simon Zirui Guo, Bonnie Li10 Apr 2024 “Context length in Large Language Models has expanded rapidly…. What if we we could use multiple devices toContinue readingWhat I Read: Ring Attention

What I Read: Flow Matching

By Andrew Fairless on June 26, 2024April 23, 2024

https://mlg.eng.cam.ac.uk/blog/2024/01/20/flow-matching.html An introduction to Flow MatchingTor Fjelde, Emile Mathieu, Vincent Dutordoir “Flow matching (FM) is a recent generative modelling paradigm which has rapidly been gaining popularity in the deep probabilisticContinue readingWhat I Read: Flow Matching

Tag: neural network