Deep Mind announced that its agent beated human performance in Quake III CTF (Capture The Flag)

https://deepmind.com/blog/capture-the-flag/

#rl #quake3 #deepmind

Neural scene representation and rendering

In June #DeepMind introduced Generative Query Network (#GQN) framework within which machines learn to perceive their surroundings by training only on data obtained by themselves as they move around scenes.

Link: https://deepmind.com/blog/neural-scene-representation-and-rendering/

#DeepMind new release: Neural Processes (#NPs) that generalise #GQN ’s training regime to other few-shot prediction tasks such as regression and classification

Arxiv 1: https://arxiv.org/abs/1807.01622
Arxiv 2: https://arxiv.org/abs/1807.01613

#ICML2018

Teams at #DeepMind and #Moorfields have developed AI technology that can detect eye disease and prioritise patients. 'Clinically applicable deep learning for diagnosis and referral in retinal OCT' has been published online in #NatureMedicine today:
https://www.nature.com/articles/s41591-018-0107-6

#cv #dl

Neural nets are terrible at arithmetic & counting. If you train one in 1 to 10, it will do okay on 3 + 5 but fail miserably for 1000 + 3000. Resolving this, «Neural Arithmetic Logic Units» can track time, do arithmetic on images of numbers, & extrapolate, providing better results than other architectures.

https://arxiv.org/pdf/1808.00508.pdf

#nn #architecture #concept #deepmind #arithmetic

Paper «A Probabilistic U-Net for Segmentation of Ambiguous Images» from #NIPS2018 spotlight presentation.

Github: https://github.com/SimonKohl/probabilistic_unet
Github: Arxiv: https://arxiv.org/abs/1806.05034

#DeepMind #segmentation #cv

#DeepMind ’s library for deep learning on graphs.


ArXiV: https://arxiv.org/abs/1806.01261
Github: https://github.com/deepmind/graph_nets

🎓 Free «Advanced Deep Learning and Reinforcement Learning» course.

#DeepMind researchers have released video recordings of lectures from «Advanced Deep Learning and Reinforcement Learning» a course on deep RL taught at #UCL earlier this year.

YouTube Playlist: https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs

#course #video #RL #DL

​​🔥 AlphaFold: Using AI for scientific discovery.

#DeepMind has significally improved protein folding prediction.

Protein folding is important because it allows to predict function along with the functioning mechanism.

Website: https://deepmind.com/blog/alphafold/
Guardian: https://www.theguardian.com/science/2018/dec/02/google-deepminds-ai-program-alphafold-predicts-3d-shapes-of-proteins

#bioinformatics #alphafold #genetics

​​Papers from #DeepMind panel at #NIPS2018

Work on radiotherapy planning: https://arxiv.org/abs/1809.04430
Triaging eye diseases: https://www.nature.com/articles/s41591-018-0107-6
Probabilistic U-net: https://arxiv.org/abs/1806.05034

#segmentation #CV #Unet

#DeepMind will show AI playing #Starcraft II.

Starts in 8 hours (6:00 PM GMT)

youtube.com/c/deepmind / https://www.twitch.tv/starcraft

#RL

​​Large Scale Adversarial Representation Learning

DeepMind shows that GANs can be harnessed for unsupervised representation learning, with state-of-the-art results on ImageNet. Reconstructions, as shown in paper, tend to emphasise high-level semantics over pixel-level details.

Link: https://arxiv.org/abs/1907.02544

#DeepMind #GAN #CV #DL #SOTA

DeepMind's Behaviour Suite for Reinforcement Learning

DeepMind released Behaviour Suite for Reinforcement Learning, or ‘bsuite’ – a collection of carefully-designed experiments that investigate core capabilities of RL agents.

bsuite was built to do two things:

1. Offer clear, informative, and scalable experiments that capture key issues in RL
2. Study agent behaviour through performance on shared benchmarks

GitHub: https://github.com/deepmind/bsuite
Paper: https://arxiv.org/abs/1908.03568v1
Google colab: https://colab.research.google.com/drive/1rU20zJ281sZuMD1DHbsODFr1DbASL0RH

#RL #DeepMind #Bsuite

Applying machine learning optimization methods to the production of a quantum gas

#DeepMind developed machine learning techniques to optimise the production of a Bose-Einstein condensate, a quantum-mechanical state of matter that can be used to test predictions of theories of many-body physics.

ArXiV: https://arxiv.org/abs/1908.08495

#Physics #DL #BEC

​​🔥DeepMind’s AlphaStar beats top human players at strategy game StarCraft II

AlphaStar by Google’s DeepMind can now play StarCraft 2 so well that it places in the 99.8 percentile on the European server. In other words, way better than even great human players, achieving performance similar to gods of StarCraft.

Solution basically combines reinforcement learning with a quality-diversity algorithm, which is similar to an evolutionary algorithm.

What’s difficult about StarCraft and how is it different to recent #Go and #Chess AI solutions: even finding winning strategy (StarCraft is famouse to closeness to rock-scissors-paper, not-so-transitive game design, as chess and go), is not enough to win, since the result depends on execution on different macro and micro levels at different timescales.

How that is applicable in real world: basically, it is running logistics, manufacture, research with complex operations and different units.

Why this matters: it brings AI one step closer to running real business.

Blog post: https://deepmind.com/blog/article/AlphaStar-Grandmaster-level-in-StarCraft-II-using-multi-agent-reinforcement-learning
Nature: https://www.nature.com/articles/d41586-019-03298-6
ArXiV: https://arxiv.org/abs/1902.01724
Nontechnical video: https://www.youtube.com/watch?v=6eiErYh_FeY

#Google #GoogleAI #AlphaStar #Starcraft #Deepmind #nature #AlphaZero

​​LOGAN: Latent Optimisation for Generative Adversarial Networks

Game-theory motivated algorithm from #DeepMind improves the state-of-the-art in #GAN image generation by over 30% measured in FID.

ArXiV: https://arxiv.org/abs/1912.00953