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Jul 14 • 4 tweets • 3 min read
Working together with @YouTube’s product and engineering teams, we've helped optimise the decision-making processes that increase safety, decrease latency, and enhance the viewer, creator, and advertiser experience for all.

How? A 🧵...

dpmd.ai/dm-youtube 1/
Our team developed a label quality model that helps label videos with greater precision according to @YouTube’s ad friendly guidelines, improving how videos are identified and classified. 2/
We applied #MuZero to improve the VP9 codec, a coding format that helps compress & transmit video over the internet. It was then applied to some of @YouTube’s live traffic - resulting in an avg. 4% bitrate reduction, helping reduce internet traffic, data usage & loading times. 3/
Along with the @YouTube search team, we developed AutoChapters, an AI system that can rapidly summarise video transcripts and suggest chapter and video titles for YouTube creators. AutoChapters has helped save time for viewers and content creators alike. 4/4

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More from @DeepMind

DeepMind Profile picture
Apr 21
Recently the DeepMind Jax Ecosystem was joined by four new libraries: Mctx, KFAC-JAX, DM_AUX, and TF2JAX. See the thread 🧵 for more detail.

We hope that this fosters new research and applications in the ML community! 1/
Mctx provides AlphaZero and MuZero Monte Carlo tree search: dpmd.ai/mctx

Work by @fabiointheuk, Ivo Danihelka, @matteohessel, and @laurentsifre, with support from many others. 2/
KFAC-JAX is a library for second-order optimisation of neural networks, and for computing scalable curvature approximations (such as the one used in K-FAC): dpmd.ai/kfac-jax

Work by Aleksandar Botev and James Martens. 3/
Read 5 tweets
DeepMind Profile picture
Mar 9
Congratulations to @demishassabis, John Jumper, and David Baker who will be awarded the Wiley Foundation 20th annual Wiley Prize in Biomedical Sciences on April 1: dpmd.ai/Wiley-Prize 🧵 1/7
Demis and John accept the award on behalf of the @DeepMind team who worked on #AlphaFold, which was recognised as a solution to the “protein folding problem” at CASP14 in Nov 2020: dpmd.ai/casp14_blog 2/7
From the start, we committed to giving broad access to our work and, in July 2021, we published our methods in @Nature along with the open source code.

Paper: dpmd.ai/alphafold-meth…
Open Source: dpmd.ai/alphafold-gith… 3/7 Image
Read 7 tweets
DeepMind Profile picture
Sep 29, 2021
From packing an umbrella to preparing for extreme conditions, predicting short term weather patterns is crucial for daily life.

New research with the @metoffice and SOTA model advances the science of Precipitation Nowcasting - the prediction of rain: dpmd.ai/nowcasting 1/4
Today’s weather systems provide planet-scale predictions several days ahead, but often struggle to generate high-resolution predictions for short lead times. Nowcasting fills this performance gap, with predictions on rainfall within the next 1-2 hours. 2/4
Compared to widely used-nowcasting methods, meteorologists from @metoffice rated this method as their 1st choice 89% of the time.

There's more to do but our researchers hope this will act as a base for future work & promote greater integration of ML & environmental science. 3/4
Read 4 tweets
DeepMind Profile picture
Sep 9, 2021
Introducing the '21 DeepMind x @ai_ucl Reinforcement Learning Lecture Series, a comprehensive introduction to modern RL.

Follow along with our researchers are they explore Markov Decision Processes, sample-based learning algorithms & much more: dpmd.ai/2021RLseries 1/2 Image
Also find the full series via the DeepMind @YouTube channel: dpmd.ai/DeepMindxUCL21
In the first lecture of the series, Research Scientist Hado introduces the course and explores the fascinating connection between reinforcement learning and artificial intelligence: dpmd.ai/RLseries1

#DeepMindxUCL @ai_ucl Image
Read 10 tweets
DeepMind Profile picture
Aug 3, 2021
To tackle all the challenges we meet while solving intelligence, we need tools that are as adaptable as possible. Announcing the paper & code for Perceiver IO, an architecture that handles a wide range of data and tasks, all while scaling gracefully: dpmd.ai/perceiver-IO 1/4 Image
Perceiver IO has the benefits of the Perceiver - domain assumptions✖️, large data✓ - but can produce a huge variety of outputs. W/ one general architecture:

*SOTA on Sintel optical flow
*Beats BERT *without using tokens*
*Multimodal or simultaneous multi-task training

2/4
Perceiver IO can simplify how engineers build systems. For example, Transformers often group language inputs into smaller chunks (“tokenization”) before processing. But Perceiver IO does well without tokenization by learning how to process the raw inputs for itself. 3/4 Image
Read 4 tweets
DeepMind Profile picture
Jul 27, 2021
Reinforcement learning typically trains & tests agents on the same game. New work shows how our team trains generally capable agents on huge game spaces, resulting in agents that generalise to held-out test games, & learn behaviours like experimentation dpmd.ai/open-ended-blog 1/
Rather than training on a limited number of tasks, our team defines a whole universe of tasks that can be procedurally generated, from simple object finding games to complex strategic games like Capture the Flag. 2/
By constructing a hierarchical learning process with an open-ended and iteratively refined objective, it was possible to train agents that never stop learning, and develop increasingly general behaviour across games. 3/
Read 5 tweets

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