Add to it utter lack of diversity. https://t.co/URuvS16zSd https://t.co/UFg8E974hC
— Prof. Anima Anandkumar (@AnimaAnandkumar) April 10, 2020
Add to it utter lack of diversity. https://t.co/URuvS16zSd https://t.co/UFg8E974hC
— Prof. Anima Anandkumar (@AnimaAnandkumar) April 10, 2020
Karl Friston is using Dynamic Causal Modeling to understand #COVIDー19 https://t.co/2ePRlCI92S pic.twitter.com/guwjmEk6C6
— danilobzdok (@danilobzdok) April 10, 2020
Weak, one-sided article in @nytimes allegedly about getting machines to learn common sense that says little about what common sense is or how it might be learned
— Gary Marcus @home (@GaryMarcus) April 9, 2020
For balance & a clearer statement of real challenges, see https://t.co/Pt7HZbLIv5 https://t.co/j2BJKNUCX9
To beat COVID-19, we need contact tracing apps. But does that mean sacrificing our right to privacy?
— Nicky Case (@ncasenmare) April 9, 2020
HECK NO ✊
Here's a comic (collab w/ @carmelatroncoso) explaining how we can beat COVID-19 *and* Big Brother!
🧵COMIC THREAD BELOW 🧵
(as 1 page: https://t.co/RiZwE6bqwy ) pic.twitter.com/lie4ckkHqY
The 2nd wave of #COVID19 modeled from the China's 1st-wave data @TheLancet Premature relaxing of mitigation will increase the transmissibility (Ri); this requires real-time monitoringhttps://t.co/p4vJ6CZA56
— Eric Topol (@EricTopol) April 8, 2020
by @gmleunghku @hkumed w/ Kathy Leung, Joseph Wu pic.twitter.com/UqCZvCNPHV
The NYT writes about my favorite topic: Self-Supervised Learning.
— Yann LeCun (@ylecun) April 8, 2020
It is the latest craze in AI/Deep Learning.https://t.co/AjMqA1YFMA
ML Code Completeness Checklist: consistent and structured information in the README makes your code more popular and usable.
— Soumith Chintala (@soumithchintala) April 8, 2020
Sensible advice, backed by data.
Proposed by @paperswithcode and now part of the NeurIPS Code Submission process.
Read more: https://t.co/ev4Hr8SCVU pic.twitter.com/NjVgYX7eKa
Should you choose performance or accuracy? Why not both? Check out Quantization Aware Training using the Model Optimization Toolkit!
— TensorFlow (@TensorFlow) April 8, 2020
🛠 Read the blog → https://t.co/eLV0BIY5kg pic.twitter.com/YwheoY7wB2
The frequency of random seeds between 0 and 1000 on github (data from https://t.co/xwutMzNI2N) pic.twitter.com/Zmp7mwMWil
— Jake VanderPlas (@jakevdp) April 8, 2020
There’s been a rash of deep learning & VC bozos saying things like “with enough data, correlation is causation”. This is incorrect, damaging to the discourse, & makes us (empirical ML researchers/practitioners & tech broadly) look like idiots to the science-literate world.
— Zachary Lipton (@zacharylipton) April 6, 2020
How @google searches about #COVID19 teach us about the symptom complex and may also help to localize emerging hotspots
— Eric Topol (@EricTopol) April 5, 2020
"I can't smell"--> anosmia occurs in more than half of infections
"My eyes hurt"--> ~30% have eye symptomshttps://t.co/MTD4coDZue by @SethS_D @nytopinion
Agree with Dad again. Increasingly uncomfortable with the certainty about relative country positions being implied by data that is being measured in different way and has different levels of accuracy and reliability. https://t.co/VIfZHLsnWe
— Sam Freedman (@Samfr) April 3, 2020