Ward van Breda

Research scientist. Entrepreneur. Energy. Health care. Robotics.

[David van Dijk @david_van_dijk]

We are looking for a new #postdoc! Interested in #MachineLearning #AI and biomedical data? Background in CS, Math, or related field preferred. Please retweet and help us find an amazing candidate! #postdocjobs #academicjobs #compbio #singlecell @YaleCompsci @YaleMed

[Google AI @GoogleAI]

Announcing the Real-World RL task suite, an #OpenSource set of benchmarks that highlight and encourage research on the core problems that limit the effectiveness of #ReinforcementLearning in applied systems. Learn more at: https://t.co/KsoYHHSvob

[Ward van Breda @wardvanbreda]

OpenAI debuts gigantic GPT-3 language model with 175 billion parameters | VentureBeat https://t.co/BkJr9tFbwF

[Ward van Breda @wardvanbreda]

Facebook’s “radioactive data” tracks the images used to train an AI - via @techreview https://t.co/sFGBRXktuW

[AI at Meta @AIatMeta]

We have open-sourced wav2letter@anywhere, an inference framework for online speech recognition that delivers state-of-the-art performance. https://t.co/1W7PsVu8tO

[Hugging Face @huggingface]

Ever wanted to combine the NLU superpowers of BERT with the generation superpowers of GPT-2? It's now possible in transformers thanks to @remilouf! https://t.co/acWk8DSA2n

[Barret Zoph @barret_zoph]

*New paper* RandAugment: a new data augmentation. Better & simpler than AutoAugment. Main idea is to select transformations at random, and tune their magnitude. It achieves 85.0% top-1 on ImageNet.

Paper: https://t.co/d4LYDsYhUB

Code: https://t.co/of1SNGfbPe

[François Chollet @fchollet]

New Keras feature: the TextVectorization layer. It takes as input strings and takes care of text standardization, tokenization, and vocabulary indexing.

This enables you to create models that process raw strings.

End-to-end text classification example: https://t.co/xPAw4FIY2b

[TensorFlow @TensorFlow]

BodyPix 2.0 has been released, including multi-person segmentation support and a new live demo!

To learn more, read the post by @tylerzhu3, @oveddan, @greenbeandou, @dsmilkov, @karlssonper, @ire_alva, @nsthorat.

Details here → https://t.co/Zq8dwiNO5A

[Ward van Breda @wardvanbreda]

The computing power needed to train AI is now rising seven times faster than ever before - MIT Technology Review https://t.co/bymxcohjJB

[Towards Data Science @TDataScience]

Slam Dunk Video Classification (Tensorflow 2.0 by @CShorten30 https://t.co/sKu6QIBHk7

[Ward van Breda @wardvanbreda]

Netflix open-sources Polynote to simplify data science and machine learning workflows | VentureBeat https://t.co/tRfTdrpEig

[Ward van Breda @wardvanbreda]

Google achieves state-of-the-art NLP performance with an enormous language model and data set | VentureBeat https://t.co/TD8jOPpS5P

[TensorFlow @TensorFlow]

Stylize images with arbitrary styles in #TensorFlow. We released a model on #TFHub, try it out yourself in our new Colab!

Details here → https://t.co/SAUsxlTXsi

[François Chollet @fchollet]

I just wrote an extensive TensorFlow 2.0 + Keras overview, targeted at deep learning researchers: https://t.co/k694J95PI8

Hope you will find it useful! Let me know if you have any feedback.

[François Chollet @fchollet]

An end-to-end tutorial showing how to deploy Keras models on Android via TFLite: https://t.co/QRGYs2EJMP

[NVIDIA Robotics @NVIDIARobotics]

Take a class on us! Enroll in our “Getting Started with AI on Jetson Nano” #NVDLI course today. We want to put the power of #AI in your hands. https://t.co/AFieu5VNhl

[Aakash Kumar Nain @A_K_Nain]

As promised, here is the first super clean notebook showcasing @TensorFlow 2.0. An example of end-to-end DL with interpretability.
Cc: @fchollet @random_forests @DynamicWebPaige

https://t.co/uXstcbMKhO

PS: Wait for more!

[OpenAI @OpenAI]

We've fine-tuned GPT-2 using human feedback for tasks such as summarizing articles, matching the preferences of human labelers (if not always our own). We're hoping this brings safety methods closer to machines learning values by talking with humans. https://t.co/ok9jeMP5zj

[Nature Portfolio @NaturePortfolio]

A Nature paper describes an artificial intelligence system that can predict acute kidney injury up to 48 hours before it occurs. The approach could help identify patients who are at risk and enable earlier treatment. https://t.co/uEKdVcW0cY