Protein-language models can be used to find them faster: either by enhancing existing molecules with targeted edits or by generating entirely new ones that could provide new hope for diseases like pancreatic cancer and ALS, for which more effective drugs have been elusive.
MACHINE LEARNING
-
H2O.ai Session: Kaggle Competition Winning Strategies with Mark Landry
By
–
Missed last week's Make with http://
H2O.ai session? Watch the recording and learn from http://
H2O.ai Director of Data Science and Product, and Kaggle Grandmaster, Mark Landry as he shares winning #kaggle competition strategies! https://
youtube.com/watch?v=Ic_SdJ
oalsQ&list=PLNtMya54qvOHza7wS9y7z1OjF2cGDQgyQ&index=18&t=9s
… -
Addressing AI and ML Skills Gaps: New Research Insights
By
–
Address #AI #ML Skills Gaps! New #research on state of #ArtificialIntelligence @SASsoftwareUKI Pleasure to contribute! https://
bit.ly/AIGaps https://
bit.ly/AIUKResearch #tech #innovation @dinisguarda #SAS #HR @RobMcCargow #100DaysOfCode @Paula_Piccard @Shi4Tech @baski_LA -
ML Explainability Workshop: Fraud Detection in Insurance Claims
By
–
Join our upcoming #AIworkshop on 21st Dec. to understand the role of ML Explainability in fraud detection. We will discuss various #XAI methods that can be used for Fraud Monitoring & AryaXAI case studies on claims fraud monitoring in Health Insurance. http://
bit.ly/3BfF7JN -
text-davinci-002 lacks RLHF; ChatGPT belongs to 002 line
By
–
We now know that text-davinci-002 doesn’t use RLHF either, and based on the model names used in API calls ChatGPT appears to be part of the 002 line, rather than 003 which uses RLHF.
-
Fast and Slow Thinking for Autonomous Robot Navigation
By
–
Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments Ganapini et al.: https://
arxiv.org/abs/2201.07050 #ArtificialIntelligence #DeepLearning #MachineLearning -
ChatGPT correctly answers Super Bowl question about Justin Bieber’s birth year
By
–
Here's a related tweet, from the pre-ChatGPT era. Nowadays, ChatGPT can correctly answer the question, "What NFL team won the Super Bowl in the year Justin Bieber was born?" without any CoT prompting, unlike prior GPT-3 versions.
-
NeurIPS Summary with Incorrect Acceptance Rate
By
–
Summary of NeurIPS, complete with wrong acceptance rate.
-
New Protocol Evaluates Machine Learning Model Input Salience Methods
By
–
#MachineLearning models sometimes make correct predictions by using information that is irrelevant to the task. Learn more about a new protocol for evaluating input salience methods, which can help verify that a model isn’t relying on such information → https://
goo.gle/3gZSOWl -
MIT Cheetah robot learns soccer through machine learning
By
–
How MIT's mini Cheetah #robot learns to play soccerhttps://t.co/2QKoukxPlU @BBC#Robots #AI @FrRonconi @mvollmer1 @YvesMulkers @Khulood_Almani @JAdP @BetaMoroney @PawlowskiMario @Fabriziobustama @enilev @CurieuxExplorer @CurieuxExplorer @FmFrancoise @Nicochan33 @CatherineAdenle pic.twitter.com/ZQXctoxkdD
— Marcus Borba (@marcusborba) 6 décembre 2022How MIT's mini Cheetah #robot learns to play soccer https://
youtube.com/watch?v=_vR-38
NcHtM
… @BBC #Robots #AI @FrRonconi @mvollmer1 @YvesMulkers @Khulood_Almani @JAdP @BetaMoroney @PawlowskiMario @Fabriziobustama @enilev @CurieuxExplorer @CurieuxExplorer @FmFrancoise @Nicochan33 @CatherineAdenle