Farshid Shekari

[Andrew Ng @AndrewYNg]

Deep Learning is getting really good on Big Data/millions of images. But Small Data is important too. Am seeing many exciting applications at Landing AI where you can get good results w/100 images. Hope more researchers work on Small Data--ML needs more innovations there.

[François Chollet @fchollet]

Today more people are working on deep learning than ever before -- around two orders of magnitude more than in 2014. And the rate of progress as I see it is the slowest in 5 years.

Time for something new

[Reza Zadeh @Reza_Zadeh]

Overview of technical papers at SIGGRAPH 2018 on 08/12 in Vancouver. My favorites so far:
- Learn to kick a ball despite being bombarded with distractions
- Learn Quadruped motion control
- Learn to predict air flow around an object as it is modified
https://t.co/vZSi5NdLps

[Google AI @GoogleAI]

Check out a @naturemethods paper where we and our collaborators present a new type of recurrent neural network that can improve the accuracy of automated interpretation of connectomics data by an order-of-magnitude over previous deep learning techniques. https://t.co/CnYbJ6VLAj

[Reza Zadeh @Reza_Zadeh]

Dense human pose estimation: mapping all human pixels of an RGB image to 3D surface of the human body. With ground-truth dataset of image-to-surface correspondences manually annotated on 50K COCO images.

Paper: https://t.co/eashiOKMNV
Project & data: https://t.co/kJukloqaZi

[Yann LeCun @ylecun]

"Clever machines", as The Economist calls them, will not replace radiologists, any more than auto ated teller machines replaced bank tellers. But it will certainly transform their job for the... https://t.co/KAA2nONjWM

[NVIDIA AI Developer @NVIDIAAIDev]

Watch how @NIH researchers are using #deeplearning and @NVIDIA #GPUs to automatically detect lung abnormalities. https://t.co/gOPNXjxETP

[François Chollet @fchollet]

We haven't yet solved even 10% of the problems we could solve with existing AI/ML techniques. Even if new research were to deliver nothing from now on, there still wouldn't be another AI winter. AI/ML will keep on delivering for years to come.

[Andrew Ng @AndrewYNg]

AI+ethics is important, but has been partly hijacked by the AGI (artificial general intelligence) hype. Let's cut out the AGI nonsense and spend more time on the urgent problems: Job loss/stagnant wages, undermining democracy, discrimination/bias, wealth inequality.

[François Chollet @fchollet]

No matter where you are, there's always room to grow. You came this far, now it would be a shame to just stay there

[François Chollet @fchollet]

Google has published its ethics principles for AI development. https://t.co/k9mpuHMLVR Thanks @sundarpichai! Very happy to see this.

[Google AI @GoogleAI]

Some new research from an intern in our Zürich office shows an approach to tSNE that allows real-time interactive visualization of large, high-dimensional datasets by leveraging GPU capabilities through WebGL. Oh, and it's open source too! Check it out ↓ https://t.co/78BmtziMbu

[NVIDIA AI Developer @NVIDIAAIDev]

Just in time for the World Cup! Watch how researchers from Google, Facebook, and @UW are using @NVIDIA #GPUs to transform a standard YouTube video of a soccer game into a moving 3D hologram. #CVPR2018 https://t.co/NYlnKpQKHn

[François Chollet @fchollet]

New Keras release: Keras 2.2.0. Available on PyPI. https://t.co/MIMlyO6709

[François Chollet @fchollet]

New release of Keras coming this week. 2.2.0. Lots of important updates. Excellent work from contributors -- open-source keeps amazing me.

[NVIDIA AI Developer @NVIDIAAIDev]

An average MRI in the United States costs $2,611 - and many rural areas do not have the imaging machines. To help patients, researchers in South Korea used #deeplearning and @NVIDIA #GPUs to convert CT scans to MRIs.
https://t.co/NSJqLMJH0w

[François Chollet @fchollet]

Introducing "Machine Learning Practica", from Google AI: interactive courses to master the foundations of machine learning, with exercises in Colab. Already covers convnets, data augmentation, and fine tuning -- all with Keras: https://t.co/ESDRlw7YV1

[NVIDIA AI Developer @NVIDIAAIDev]

70M people in the U.S. suffer from gastrointestinal diseases. To help with the labor intensive process of examining ultrasounds, researchers from @VanderbiltU & @SiemensHealth are using #deeplearning and @NVIDIA #GPUs to automatically interpret the images. https://t.co/DBOJf2pkgk

[François Chollet @fchollet]

The folks at AWS have developed a MXNet backend for Keras, with extensive feature coverage and great performance. Hopefully we can merge it into core Keras in the future (some technical blockers there). For now it exists as a fork: https://t.co/43Rhrk0o8j

[OpenAI @OpenAI]

Releasing Gym Retro — 1000+ games for reinforcement learning research, plus an integrator tool to add your own classic games. https://t.co/V5unhRknY4