machine learning – Page 14 – Andrew Fairless, Ph.D.

What I Read: Perspectives on diffusion

By Andrew Fairless on August 30, 2023July 30, 2023

https://sander.ai/2023/07/20/perspectives.html Perspectives on diffusionSander DielemanJuly 20, 2023 “Diffusion models appear to come in many shapes and forms…. these various perspectives each reveal new connections and are a breeding ground forContinue readingWhat I Read: Perspectives on diffusion

What I Read: Topological Data Analysis

By Andrew Fairless on August 23, 2023July 9, 2023

https://datarefiner.com/feed/why-tda Why you should use Topological Data Analysis over t-SNE or UMAP?Edward KibardinApril 17, 2023 “Have you tried to run a complex supervised machine learning model, only to find unexpectedContinue readingWhat I Read: Topological Data Analysis

What I Read: Disagreement Modelling

By Andrew Fairless on August 21, 2023July 9, 2023

https://koaning.io/posts/large-disagreement-models/ Large Disagreement ModellingVincent D. Warmerdam2023-05-26 “So instead of fully relying on a large language model, how might we use it effectively in existing pipelines?”

What I Read: LLM Agents

By Andrew Fairless on August 17, 2023July 9, 2023

https://lilianweng.github.io/posts/2023-06-23-agent/ LLM Powered Autonomous AgentsLilian WengJune 23, 2023 “Building agents with LLM (large language model) as its core controller is a cool concept…. it can be framed as a powerfulContinue readingWhat I Read: LLM Agents

What I Read: artificial intelligence really hard

By Andrew Fairless on August 16, 2023July 9, 2023

https://thegradient.pub/why-transformative-artificial-intelligence-is-really-really-hard-to-achieve/ Why transformative artificial intelligence is really, really hard to achieveArjun Ramani and Zhengdong Wang26.Jun.2023“1. The transformational potential of AI is constrained by its hardest problems

What I Read: Ways Digital Minds Know

By Andrew Fairless on August 8, 2023July 9, 2023

https://moultano.wordpress.com/2023/06/28/the-many-ways-that-digital-minds-can-know/ The Many Ways that Digital Minds Can KnowRyan Moulton “…aspects of search engine quality are better analogies to describe the properties of LLMs than “generalization” and “memorization” are, andContinue readingWhat I Read: Ways Digital Minds Know

What I Read: Systems of Intelligence, defensible business model

By Andrew Fairless on August 7, 2023July 9, 2023

https://greylock.com/greymatter/the-new-new-moats/ The New New MoatsWhy Systems of Intelligence are still the next defensible business modelby: Jerry Chen06.21.2023 “Orienting oneself amid this Cambrian explosion of AI startups requires asking a basicContinue readingWhat I Read: Systems of Intelligence, defensible business model

What I Read: Attack Impacts AI Chatbots

By Andrew Fairless on August 4, 2023August 1, 2023

https://www.wired.com/story/ai-adversarial-attacks/ A New Attack Impacts Major AI Chatbots—and No One Knows How to Stop ItWill KnightAug 1, 2023 7:00 AM “Researchers found a simple way to make ChatGPT, Bard, andContinue readingWhat I Read: Attack Impacts AI Chatbots

What I Read: Neural Networks, Data, Fake

By Andrew Fairless on August 3, 2023July 9, 2023

https://www.quantamagazine.org/neural-networks-need-data-to-learn-even-if-its-fake-20230616/ Neural Networks Need Data to Learn. Even If It’s Fake.Amos ZeebergJune 16, 2023 “Real data can be hard to get, so researchers are turning to synthetic data to trainContinue readingWhat I Read: Neural Networks, Data, Fake

What I Read: Sparse Networks

By Andrew Fairless on July 31, 2023June 15, 2023

https://www.quantamagazine.org/sparse-neural-networks-point-physicists-to-useful-data-20230608/ Sparse Networks Come to the Aid of Big PhysicsSteve NadisJune 8, 2023 “A novel type of neural network is helping physicists with the daunting challenge of data analysis.”

Tag: machine learning