AI Essentials

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Discover how AI, Machine Learning and advanced algorithms impact our lives, our jobs and the economy thanks to expert articles that include discussion on the potential, limits and consequences of AI

AI Essentials

Top news of the week: 16.02.2021.

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University of California, Berkeley
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Research

@pabbeel shared
On Feb 15, 2021
RT @full_stack_dl: Live on our site: materials from week 3 of CS194-080: FSDL. Continuing our deep learning review, this week we talk about recurrent neural networks. https://t.co/gjMA5bVNGo
Open
Full Stack Deep Learning - Spring 2021

Full Stack Deep Learning - Spring 2021

Hands-on program for software developers familiar with the basics of deep learning seeking to expand their skills.

@jure shared
On Feb 15, 2021
AI for public health workshop at ICLR 2021. 2 weeks left to submit! Both applied and methodological work is welcome, including in-progress, submitted, or recently published papers. https://t.co/TkRnziSeMl https://t.co/wQCzxpMfVG
Open
AI for Public Health

AI for Public Health

# Overview The COVID-19 pandemic has cast a spotlight on the importance of public health. Even beyond this current emergency, public health is an essential component of population-level ...

@xamat shared
On Feb 11, 2021
Want to understand the @US_FDA's plan for AI/ML? I read a bunch of documents for you and summarized in a blog post: https://t.co/YCTDFLqCc9
Open
The AI/ML FDA Plan

The AI/ML FDA Plan

Want to understand what the FDA’s plan for AI and ML devices is? I read a bunch of documents for you and summarized in this post.

@rodneyabrooks shared
On Feb 14, 2021
New blog post today; an essay on a somewhat esoteric topic. The history of programming languages for robot arms in factories. With stories from AI Labs at Stanford and MIT. https://t.co/cnuXNgtkJV
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The Origin of Robot Arm Programming Languages

The Origin of Robot Arm Programming Languages

In the late sixties, mechanical engineer Victor Scheinman at the Stanford AI Lab designed what became known as the Stanford Arm. By late 1974 Raphael Finkel, Russell Taylor, …

@mxlearn shared
On Feb 12, 2021
[D] What can OpenAI or DeepMind accomplish in the future? How can they benefit people's daily lives in the future? https://t.co/amMgymGdYi
Open
[D] What can OpenAI or DeepMind accomplish in the future? How can they benefit people's daily lives in the future?

[D] What can OpenAI or DeepMind accomplish in the future? How can they benefit people's daily lives in the future?

0 votes and 5 comments so far on Reddit

@shakir_za shared
On Feb 13, 2021
RT @hen_str: It’s out 🎉 Our blog post on @NeurIPSConf 2020 poster sessions and the Memementor experience for spontaneous mentor sessions -- we also released open source code to build your own poster sessions in Gather Town. By @yboureau and me. https://t.co/GS9QT4NLcJ
Open
NeurIPS 2020 Online Experiments: Gather Town Poster Sessions and Mementor

NeurIPS 2020 Online Experiments: Gather Town Poster Sessions and Mementor

Y-Lan Boureau, Facebook AI Research Hendrik Strobelt, MIT-IBM Watson AI Lab at IBM Research

@hardmaru shared
On Feb 12, 2021
RT @zzznah: New Neural CA article "Self-Organising Textures" is live on @distillpub !!! From @eyvindn, @drmichaellevin, @RandazzoEttore and me. Remember Hexells and all those living textures I've been posting lately? That's how we do it, and you can now too! https://t.co/ynQgMLshz7
Open
Self-Organising Textures

Self-Organising Textures

Neural Cellular Automata learn to generate textures, exhibiting surprising properties.

@MSFTResearch shared
On Feb 11, 2021
Researchers introduce denoised smoothing. With the addition of a pretrained denoiser, randomized smoothing can be applied to make existing pretrained classifiers provably robust against adversarial examples without custom training: https://t.co/gfbQAohBS5 #NeurIPS2020
Open
Denoised smoothing: Provably defending pretrained classifiers against adversarial examples

Denoised smoothing: Provably defending pretrained classifiers against adversarial examples

Microsoft researchers introduce denoised smoothing. With the addition of a pretrained denoiser, randomized smoothing can be applied to make existing pretrained classifiers provably robust ...

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