AI Technology & Industry Review
The London-based AI research firm DeepMind has introduced AlphaFold-Multimer, a model that can predict the structure of multi-chain protein complexes with increased accuracy.
A research team from University of Washington, Microsoft, DeepMind and Allen Institute for AI develop a method to convert pretrained transformers into efficient RNNs. The Transformer-to-RNN (T2R) approach speeds up generation and reduces memory cost.
A research team from DeepMind and Alberta University proposes Policy-guided Heuristic Search (PHS), a novel search algorithm that uses both a heuristic function and a policy while offering guarantees on the search loss that relate to both the quality of the heuristic and the policy.
A research team from DeepMind proposes manipulation-independent representations (MIR) that can support successful imitation of behaviours demonstrated by previously unseen manipulator morphologies using only visual observations.
DeepMind has designed a family of Normalizer-Free ResNets (NFNets) that can be trained in larger batch sizes and stronger data augmentations and have set new SOTA validation accuracies on ImageNet.
A research team from DeepMind and University College London have released Alchemy, a novel open-source benchmark for meta-RL research.
In a bid to solve the temporal generalization problem of modern language models, a team of DeepMind researchers propose it’s time to develop adaptive language models that will remain up-to-date in our ever-changing world.
DeepMind, the UK-based AI arm of Google’s parent company Alphabet, has lost GBP 477 million in 2019 which is 1.5 percent increase from one year prior.
Google’s UK-based lab and research company DeepMind says its AlphaFold AI system has solved the protein folding problem, a grand challenge that has vexed the biology research community for half a century.
A new DeepMind scalable environment simulator takes a digital approach to the question, enabling the examination of environmental factors on AI agents.
Google Research and DeepMind debut Long-Range Arena (LRA) benchmark for Transformer research on tasks with long sequence lengths.
DeepMind and Universidade de Lisboa introduce graph-based toolkit for analyzing and comparing multiplayer games
In a new paper, researchers from Google, OpenAI, and DeepMind introduce “behaviour priors,” a framework designed to capture common movement and interaction patterns that are shared across a set of related tasks or contexts.
Probability trees may have been around for decades, but they have received little attention from the AI and ML community.
A team from Google, University of Cambridge, DeepMind, and Alan Turing Institute have proposed a new type of Transformer dubbed Performer, based on a Fast Attention Via positive Orthogonal Random features (FAVOR+) backbone mechanism.
From an augmented view of an image, the researchers trained the online network to predict the target network representation of the same image under a different augmented view.
DeepMind unveiled a partnership with Google Maps that has leveraged advanced GNNs to improve ETA accuracy.
The proposed algorithm is approximately 180 times faster than an automated random search of the parameter space.
DeepMind and McGill University researchers believe the problem could be solved through a strategic “divide-and-conquer” approach.
Researchers conducted a thorough empirical study of hyperparameter selection for offline RL, aiming to identify and develop more reliable and effective approaches.
Researchers proposed a new transformer architecture called “Performer” — based on what they call fast attention via orthogonal random features (FAVOR).
New research from UK based AI company and research lab DeepMind is enabling AI agents to perceive dynamic real-world environments more like humans do.
DeepMind introduced a new approach designed to improve the generalizability and efficiency of algorithms represented by neural networks.
DeepMind researchers call attention to another “fundamentally novel” development in AI research — deep reinforcement learning (deep RL) — which they believe also has vital implications for neuroscience and deserves more attention from neuroscientists.
DeepMind researchers released several new models and a tutorial for their dm_control software stack for physics-based simulation and reinforcement learning environments using MuJoCo physics.
Google Brain in Zürich and DeepMind London researchers believe one of the world’s most popular image databases may need a makeover.
DeepMind researchers have developed EATS, a generative model trained adversarially in an end-to-end manner that achieves performance comparable to SOTA models.
DeepMind researchers introduce a framework that aims to solve the problem by enabling simple RL agent implementations to be run at different scales of execution.
Researchers add syntactic biases to determine whether and where they can help BERT achieve better understanding.
Turing awardee and backpropagation pioneer Geoffrey Hinton recently co-authored a paper that takes a look back at backpropagation and explores its potential to contribute to understanding how the human cortex learns.
Researchers unify DNN normalization layers and activation functions into a single computation graph.
XTREME, a multi-task benchmark that evaluates cross-lingual generalization capabilities of multilingual representations across 40 languages and nine tasks.
DeepMind released the structure predictions for six proteins associated with SARS-CoV-2, the virus that causes COVID-19, using the most up-to-date version of their AlphaFold system.
Synced Global AI Weekly February 23rd
DeepMind announced yesterday the release of Haiku and RLax — new JAX libraries designed for neural networks and reinforcement learning respectively.
Now, DeepMind and University College London (UCL) have introduced a new deep network called MEMO which matches SOTA results on Facebook’s bAbI dataset for testing text understanding and reasoning, and is the first and only architecture capable of solving long sequence novel reasoning tasks.
A new study suggests DeepMind’s amazing game-playing algorithm AlphaZero could help unlock the power and potential of quantum computing.
Synced Global AI Weekly January 19th
DeepMind researchers have uncovered parallels between how brains react to dopamine and the trending AI theory of distributional reinforcement learning.
Precup discussed the latest developments in the field: “Reinforcement learning allows autonomous agents to learn how to act in a stochastic, unknown environment, with which they can interact.”