Haha. Lol! Translation: all your good results may be due to data leakage. Very gentle indeed.
RESEARCH
-
AI Eye Checks Predict Heart Disease Risk in Under One Minute
By
–
#AI eye checks can predict heart disease risk in less than minute, finds study
by @andrewgregory @guardian Read more: https://
buff.ly/3C8Mp1q #ArtificialIntelligence #DataScience #Tech #HealthTech #ML cc: @ronald_vanloon @pbalakrishnarao @chr1sa -
AI versus Machine Learning: Key Differences and Comparisons
By
–
#ArtificialIntelligence (#AI) vs. #MachineLearning (#ML): Key comparisons
by Shannon Flynn @VentureBeat Go to: https://
buff.ly/3zJIS8G #BigData #MI #Digital #DataScience #DeepLearning #DL cc: @bigdata @patrickgunz_ch @amuellerml -
Language shapes how we think about data
By
–
Changing how you speak about data can change how you think about data https://
youtu.be/2EazCb0m_WQ -
AGI Timeline and AI Research Involvement
By
–
When will AI match or surpass humans in essentially all cognitive tasks (aka AGI)?
Do you do research or work on AI? -
LLaMA Performance Analysis: Data Contamination and Benchmark Concerns
By
–
LLaMA has strong performance and is a great contribution. Some gentle feedback: it would be great to see a data contamination analysis, since most benchmarks evaluated are >2 years old, and BIG-Bench is omitted. So I'm curious if pre-training data contamination played a role.
-
Model Performance Limited to Benchmarks, Real Version Unavailable
By
–
The disclaimer is that its on benchmarks only and nobody has access to the real model yet…
-
Training to Convergence for Optimal Inference Performance
By
–
A real search engine shows no sign of the "inference optimal" phrase; I agree with your assessment that you posted in your own thread FWIW. Train to convergence if you want inference optimal.
-
Optimal in Relative Terms: Chinchilla and Model Training
By
–
They use "optimal" in a relative sense. I read it in a reply to my post, and then subsequently looked it up in LLM search engine—which happily told me why "Chinchilla is inference optimal because […]". But that's only relatively to undertrained models.
-
Embracing Innovation in Engineering and Technology
By
–
How to Embrace Innovation https://
mckinsey.com/featured-insig
hts/mckinsey-guide-to-managing-yourself/how-to-embrace-innovation
… @ASMEdotorg @3DSNorthAmerica @cyngn @MargaretSiegien @3DSdelmia @3DStherese @Cindybolt61 @fogoros @DrFerdowsi @CRudinschi @PawlowskiMario @IIoT_World @MEngineeringMag #Science #Engineering #Technology #SET