Are you over 18 and want to see adult content?
More Annotations
A complete backup of www.haberturk.com/mucize-doktor-19-bolum-ali-nazli-ya-aciliyor-mucize-doktor-20-bolum-fragmani-yayinlandi-m
Are you over 18 and want to see adult content?
A complete backup of sportstar.thehindu.com/cricket/nz-vs-ind-live-score-updates-first-t20-kohli-rohit-sharma-kane-williamson-ne
Are you over 18 and want to see adult content?
A complete backup of www.nieuwsblad.be/cnt/dmf20200126_04821624
Are you over 18 and want to see adult content?
A complete backup of apnews.com/d503019511da4f2580c12f3199ddab79
Are you over 18 and want to see adult content?
Favourite Annotations
A complete backup of designhuette.com
Are you over 18 and want to see adult content?
A complete backup of bhaktisanskar.com
Are you over 18 and want to see adult content?
A complete backup of explorelearning.com
Are you over 18 and want to see adult content?
A complete backup of international-friends.net
Are you over 18 and want to see adult content?
A complete backup of djremixmasti.com
Are you over 18 and want to see adult content?
Text
ENGINEERING JOBS
Montreal, Canada. Led by Doina Precup, our Montreal office is home to a DeepMind research team that focuses on hierarchical and core reinforcement learning. Based in Google’s central Montreal office, the team brings together research scientists and engineers who focus on the problem of temporal abstraction and improvements in learningspeed
RESEARCH | DEEPMIND
A rapid and efficient learning rule for biological neural circuits. Eren Sezener, Agnieszka Grabska-Barwinska, et al. bioRxiv 2021. Download. Publication. Deep learning. Unsupervised learning & generative models. Variable-rate discrete representation learning. Sander Dieleman, Charlie Nash, etSCHOLARSHIPS
Diversity and equity in AI is paramount, not only for the innovative work that diverse teams produce, but because it’s crucial to mitigate the risks of bias in the development of algorithms and applications. To truly do this we must broaden participation and ensure that underrepresented voices are given the opportunity to shape the future of the field. ALPHAFOLD | DEEPMIND AlphaFold could help contribute to better and more efficient drug discovery by identifying the structure of many human proteins involved in disease. It could also help unlock new possibilities such as finding proteins and enzymes that break down industrial and plasticwaste or
ENGINEERING
Our multidisciplinary engineering team, with expertise ranging from software, hardware, and research engineers to designers, artists, and program managers, work across all DeepMind teams to deliver high-impact, state-of-the-art research. Many of our tools, libraries,environments, and
ALPHAGO | DEEPMIND
We created AlphaGo, a computer program that combines advanced search tree with deep neural networks. These neural networks take a description of the Go board as an input and process it through a number of different network layers containing millions of neuron-like connections. One neural network, the “policy network”, selects thenext move
ALPHAFOLD: USING AI FOR SCIENTIFIC DISCOVERY In our study published in Nature, we demonstrate how artificial intelligence research can drive and accelerate new scientific discoveries. We’ve built a dedicated, interdisciplinary team in hopes of using AI to push basic research forward: bringing together experts from the fields of structural biology, physics, and machine learning to apply cutting-edge techniques to predict the 3D DEEPMIND’S HEALTH TEAM JOINS GOOGLE HEALTH DeepMind’s health team joins Google Health. Over the last three years, DeepMind has built a team to tackle some of healthcare’s most complex problems—developing AI research and mobile tools that are already having a positive impact on patients and care teams. Today, with our healthcare partners, the team is excited to officially jointhe
RECURRENT EXPERIENCE REPLAY IN DISTRIBUTED REINFORCEMENT Building on the recent successes of distributed training of RL agents, in this paper we investigate the training of RNN-based RL agents from distributed prioritized experience replay. We study the effects of parameter lag resulting in representational drift and recurrent state staleness and empirically derive an improved training strategy. HOMEPAGE | DEEPMINDABOUTRESEARCHIMPACTBLOGSAFETY & ETHICSCAREERS This computational work represents a stunning advance on the protein-folding problem, a 50-year-old grand challenge in biology. It has occurred decades before many people in the field would have predicted." Use our education resources to help you explore the fascinating world of AI. CAREERS | DEEPMINDBLOGABOUTGAMES SYSTEMS ENGINEERINTERNSHIPSEXPLOREENGINEERING JOBS
Montreal, Canada. Led by Doina Precup, our Montreal office is home to a DeepMind research team that focuses on hierarchical and core reinforcement learning. Based in Google’s central Montreal office, the team brings together research scientists and engineers who focus on the problem of temporal abstraction and improvements in learningspeed
RESEARCH | DEEPMIND
A rapid and efficient learning rule for biological neural circuits. Eren Sezener, Agnieszka Grabska-Barwinska, et al. bioRxiv 2021. Download. Publication. Deep learning. Unsupervised learning & generative models. Variable-rate discrete representation learning. Sander Dieleman, Charlie Nash, etSCHOLARSHIPS
Diversity and equity in AI is paramount, not only for the innovative work that diverse teams produce, but because it’s crucial to mitigate the risks of bias in the development of algorithms and applications. To truly do this we must broaden participation and ensure that underrepresented voices are given the opportunity to shape the future of the field. ALPHAFOLD | DEEPMIND AlphaFold could help contribute to better and more efficient drug discovery by identifying the structure of many human proteins involved in disease. It could also help unlock new possibilities such as finding proteins and enzymes that break down industrial and plasticwaste or
ENGINEERING
Our multidisciplinary engineering team, with expertise ranging from software, hardware, and research engineers to designers, artists, and program managers, work across all DeepMind teams to deliver high-impact, state-of-the-art research. Many of our tools, libraries,environments, and
ALPHAGO | DEEPMIND
We created AlphaGo, a computer program that combines advanced search tree with deep neural networks. These neural networks take a description of the Go board as an input and process it through a number of different network layers containing millions of neuron-like connections. One neural network, the “policy network”, selects thenext move
ALPHAFOLD: USING AI FOR SCIENTIFIC DISCOVERY In our study published in Nature, we demonstrate how artificial intelligence research can drive and accelerate new scientific discoveries. We’ve built a dedicated, interdisciplinary team in hopes of using AI to push basic research forward: bringing together experts from the fields of structural biology, physics, and machine learning to apply cutting-edge techniques to predict the 3D DEEPMIND’S HEALTH TEAM JOINS GOOGLE HEALTH DeepMind’s health team joins Google Health. Over the last three years, DeepMind has built a team to tackle some of healthcare’s most complex problems—developing AI research and mobile tools that are already having a positive impact on patients and care teams. Today, with our healthcare partners, the team is excited to officially jointhe
RECURRENT EXPERIENCE REPLAY IN DISTRIBUTED REINFORCEMENT Building on the recent successes of distributed training of RL agents, in this paper we investigate the training of RNN-based RL agents from distributed prioritized experience replay. We study the effects of parameter lag resulting in representational drift and recurrent state staleness and empirically derive an improved training strategy.ABOUT | DEEPMIND
When we started DeepMind in 2010, there was far less interest in the field of AI than there is today. To accelerate the field, we took an interdisciplinary approach, bringing together new ideas and advances in machine learning, neuroscience, engineering, mathematics, simulation and computing infrastructure, along with new ways of organising scientific endeavour.BLOG | DEEPMIND
Planning winning strategies in unknown environments is a step forward in the pursuit of general-purpose algorithms. An introduction to our JAX ecosystem and why we find it useful for our AI research. In a major scientific advance, AlphaFold is recognised DEEPMIND: THE PODCAST So, to help us bridge that gap, we created DeepMind: The Podcast, a series that we hope will answer these questions and more, while also giving listeners an inside look at how AI research is done at an organisation like DeepMind. Listen and subscribe to the whole series on your favourite podcast app by searching for “DeepMind: ThePodcast”.
ALPHAFOLD | DEEPMIND AlphaFold could help contribute to better and more efficient drug discovery by identifying the structure of many human proteins involved in disease. It could also help unlock new possibilities such as finding proteins and enzymes that break down industrial and plasticwaste or
ENGINEERING
Our multidisciplinary engineering team, with expertise ranging from software, hardware, and research engineers to designers, artists, and program managers, work across all DeepMind teams to deliver high-impact, state-of-the-art research. Many of our tools, libraries,environments, and
COUNTERFACTUAL CREDIT ASSIGNMENT Credit assignment in reinforcement learning is about measuring an action’s influence on future rewards. This is made difficult by the fact that rewards are also affected by other random choices occurring in the future. In this paper we attempt to separate skill from luck. More precisely, we seek to disentangle the effect which an action had on the return from the effects of external factors SPECTRAL NORMALISATION FOR DEEP REINFORCEMENT LEARNING: AN Most of the recent deep reinforcement learning advances take an RL-centring perspective and focus on refinements of the training objective. We diverge from this view and show we can recover the performance of these developments not by changing the objective, but by regularising the value-function estimator. Constraining the Lipschitz constant of a single layer using spectral normalisation is DIVERSE PRETRAINED CONTEXT ENCODINGS IMPROVE DOCUMENT We develop a new architecture for adapting a sentence-level sequence-to-sequence transformer by incorporating multiple pretrained document context signals and assess the impact on translation performance of (1) different pretraining approaches for generating these signals, (2) the quantity of parallel data for which document context is available, and (3) conditioning on source,NEURIPS WORKSHOP
Offline reinforcement learning (RL) brings a promise of learning control policies in real-life applications by eliminating the need for online data collection and exploration. However, offline RL still needs a reward signal, which is often not readily available in practice. One solution to this problem is to learn reward functions. In this paper we would like to understand a) what type and A NEURAL NETWORK AUCTION FOR GROUP DECISION MAKING OVER A We propose a system for conducting an auction over locations in a continuous space. It enables participants to express their preferences over possible choices of location in the space, selecting the location that maximizes the total utility of all agents. We prevent agents from tricking the system into selecting a location that improves their individual utility at the expense of others by HOMEPAGE | DEEPMINDABOUTRESEARCHIMPACTBLOGSAFETY & ETHICSCAREERS This computational work represents a stunning advance on the protein-folding problem, a 50-year-old grand challenge in biology. It has occurred decades before many people in the field would have predicted." Use our education resources to help you explore the fascinating world of AI.SCHOLARSHIPS
Diversity and equity in AI is paramount, not only for the innovative work that diverse teams produce, but because it’s crucial to mitigate the risks of bias in the development of algorithms and applications. To truly do this we must broaden participation and ensure that underrepresented voices are given the opportunity to shape the future of the field. THE DEEP LEARNING LECTURE SERIES 2020 The Deep Learning Lecture Series 2020 is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence. The 12 video lectures cover topics from neural network foundations and optimisation through to generative adversarial networks and responsible innovation. DEEPMIND: THE PODCAST So, to help us bridge that gap, we created DeepMind: The Podcast, a series that we hope will answer these questions and more, while also giving listeners an inside look at how AI research is done at an organisation like DeepMind. Listen and subscribe to the whole series on your favourite podcast app by searching for “DeepMind: ThePodcast”.
DEEPMIND’S HEALTH TEAM JOINS GOOGLE HEALTH DeepMind’s health team joins Google Health. Over the last three years, DeepMind has built a team to tackle some of healthcare’s most complex problems—developing AI research and mobile tools that are already having a positive impact on patients and care teams. Today, with our healthcare partners, the team is excited to officially jointhe
USING AI TO PREDICT RETINAL DISEASE PROGRESSION Using AI to predict retinal disease progression. Vision loss among the elderly is a major healthcare issue: about one in three people have some vision-reducing disease by the age of 65. Age-related macular degeneration (AMD) is the most common cause of blindness in the developed world. In Europe, approximately 25% of those 60 and olderhave AMD.
EIGENGAME: PCA AS A NASH EQUILIBRIUM We present a novel view on principal component analysis (PCA) as a competitive game in which each approximate eigenvector is controlled by a player whose goal is to maximize their own utility function. We analyze the properties of this PCA game and the behavior of its gradient based updates. The resulting algorithm which combines elements from Oja's rule with a generalized Gram-Schmidt CONDITIONAL NEURAL PROCESSES Conditional Neural Processes. A notebook implementation of Conditional Neural Processes (CNPs) that can be run in the browser, with an overview of the different building blocks of the model and the code torun it.
RECURRENT EXPERIENCE REPLAY IN DISTRIBUTED REINFORCEMENT Building on the recent successes of distributed training of RL agents, in this paper we investigate the training of RNN-based RL agents from distributed prioritized experience replay. We study the effects of parameter lag resulting in representational drift and recurrent state staleness and empirically derive an improved training strategy. A SURVEY ON CONTEXTUAL EMBEDDINGS Contextual embeddings, such as ELMo and BERT, move beyond global word representations like Word2Vec and achieve ground-breaking performance on a wide range of natural language processing tasks. Contextual embeddings assign each word a representation based on its context, thereby capturing uses of words across varied contexts and encodingknowledge that
HOMEPAGE | DEEPMINDABOUTRESEARCHIMPACTBLOGSAFETY & ETHICSCAREERS This computational work represents a stunning advance on the protein-folding problem, a 50-year-old grand challenge in biology. It has occurred decades before many people in the field would have predicted." Use our education resources to help you explore the fascinating world of AI.SCHOLARSHIPS
Diversity and equity in AI is paramount, not only for the innovative work that diverse teams produce, but because it’s crucial to mitigate the risks of bias in the development of algorithms and applications. To truly do this we must broaden participation and ensure that underrepresented voices are given the opportunity to shape the future of the field. THE DEEP LEARNING LECTURE SERIES 2020 The Deep Learning Lecture Series 2020 is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence. The 12 video lectures cover topics from neural network foundations and optimisation through to generative adversarial networks and responsible innovation. DEEPMIND: THE PODCAST So, to help us bridge that gap, we created DeepMind: The Podcast, a series that we hope will answer these questions and more, while also giving listeners an inside look at how AI research is done at an organisation like DeepMind. Listen and subscribe to the whole series on your favourite podcast app by searching for “DeepMind: ThePodcast”.
DEEPMIND’S HEALTH TEAM JOINS GOOGLE HEALTH DeepMind’s health team joins Google Health. Over the last three years, DeepMind has built a team to tackle some of healthcare’s most complex problems—developing AI research and mobile tools that are already having a positive impact on patients and care teams. Today, with our healthcare partners, the team is excited to officially jointhe
USING AI TO PREDICT RETINAL DISEASE PROGRESSION Using AI to predict retinal disease progression. Vision loss among the elderly is a major healthcare issue: about one in three people have some vision-reducing disease by the age of 65. Age-related macular degeneration (AMD) is the most common cause of blindness in the developed world. In Europe, approximately 25% of those 60 and olderhave AMD.
EIGENGAME: PCA AS A NASH EQUILIBRIUM We present a novel view on principal component analysis (PCA) as a competitive game in which each approximate eigenvector is controlled by a player whose goal is to maximize their own utility function. We analyze the properties of this PCA game and the behavior of its gradient based updates. The resulting algorithm which combines elements from Oja's rule with a generalized Gram-Schmidt CONDITIONAL NEURAL PROCESSES Conditional Neural Processes. A notebook implementation of Conditional Neural Processes (CNPs) that can be run in the browser, with an overview of the different building blocks of the model and the code torun it.
RECURRENT EXPERIENCE REPLAY IN DISTRIBUTED REINFORCEMENT Building on the recent successes of distributed training of RL agents, in this paper we investigate the training of RNN-based RL agents from distributed prioritized experience replay. We study the effects of parameter lag resulting in representational drift and recurrent state staleness and empirically derive an improved training strategy. A SURVEY ON CONTEXTUAL EMBEDDINGS Contextual embeddings, such as ELMo and BERT, move beyond global word representations like Word2Vec and achieve ground-breaking performance on a wide range of natural language processing tasks. Contextual embeddings assign each word a representation based on its context, thereby capturing uses of words across varied contexts and encodingknowledge that
RESEARCH | DEEPMIND
Our pioneering research includes deep learning, reinforcement learning, theory & foundations, neuroscience, unsupervised learning & generative models, control & robotics, and safety. THE DEEP LEARNING LECTURE SERIES 2020 The Deep Learning Lecture Series 2020 is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence. The 12 video lectures cover topics from neural network foundations and optimisation through to generative adversarial networks and responsible innovation.CAREERS | DEEPMIND
Montreal, Canada. Led by Doina Precup, our Montreal office is home to a DeepMind research team that focuses on hierarchical and core reinforcement learning. Based in Google’s central Montreal office, the team brings together research scientists and engineers who focus on the problem of temporal abstraction and improvements in learningspeed
ALPHAFOLD | DEEPMIND AlphaFold could help contribute to better and more efficient drug discovery by identifying the structure of many human proteins involved in disease. It could also help unlock new possibilities such as finding proteins and enzymes that break down industrial and plasticwaste or
RESEARCH | DEEPMIND
A rapid and efficient learning rule for biological neural circuits. Eren Sezener, Agnieszka Grabska-Barwinska, et al. bioRxiv 2021. Download. Publication. Deep learning. Unsupervised learning & generative models. Variable-rate discrete representation learning. Sander Dieleman, Charlie Nash, et al. arXiv 2021. OPEN PROBLEMS IN COOPERATIVE AI Problems of cooperation - in which agents seek ways to jointly improve their welfare - are ubiquitous and important. They can be found at scales ranging from our daily routines - such as highway driving, scheduling meetings, and collaborative work - to our global challenges - such as arms control, climate change, global commerce, and pandemicpreparedness.
MUZERO: MASTERING GO, CHESS, SHOGI AND ATARI WITHOUT RULES In 2016, we introduced AlphaGo, the first artificial intelligence (AI) program to defeat humans at the ancient game of Go. Two years later, its successor - AlphaZero - learned from scratch to master Go, chess and shogi. Now, in a paper in the journal Nature, we describe MuZero, a significant step forward in the pursuit of general-purpose algorithms. MuZero masters Go, chess, shogi and Atari DISCOVERING REINFORCEMENT LEARNING ALGORITHMS Reinforcement learning (RL) algorithms update an agent's parameters according to one of several possible rules, discovered manually through years of research. Automating the discovery of update rules from data could lead to more efficient algorithms, or algorithms that are better adapted to specific environments. Although there have been prior attempts at addressing this significant TRAFFIC PREDICTION WITH ADVANCED GRAPH NEURAL NETWORKS Traffic prediction with advanced Graph Neural Networks. By partnering with Google, DeepMind is able to bring the benefits of AI to billions of people all over the world. From reuniting a speech-impaired user with his original voice, to helping users discover personalised apps, we can apply breakthrough research to immediate real-world problems END-TO-END ADVERSARIAL TEXT-TO-SPEECH Modern text-to-speech synthesis pipelines typically involve multiple processing stages, each of which is designed or learnt independently from the rest. In this work, we take on the challenging task of learning to synthesise speech from normalised text or phonemes in an end-to-end manner, resulting in models which operate directly on character or phoneme input sequences and produce raw speech HOMEPAGE | DEEPMINDABOUTRESEARCHIMPACTBLOGSAFETY & ETHICSCAREERS This computational work represents a stunning advance on the protein-folding problem, a 50-year-old grand challenge in biology. It has occurred decades before many people in the field would have predicted." Use our education resources to help you explore the fascinating world of AI. CAREERS | DEEPMINDBLOGABOUTGAMES SYSTEMS ENGINEERINTERNSHIPSEXPLOREENGINEERING JOBS
Montreal, Canada. Led by Doina Precup, our Montreal office is home to a DeepMind research team that focuses on hierarchical and core reinforcement learning. Based in Google’s central Montreal office, the team brings together research scientists and engineers who focus on the problem of temporal abstraction and improvements in learningspeed
ABOUT | DEEPMIND
Global labs. We started as an interdisciplinary team based in London, and have opened labs across the UK, USA, Canada, and France. DeepMind is home to scientific researchers, ethicists, program managers, game designers, and more. Together, we’re solving some of the most fascinating challenges in AI today.SCHOLARSHIPS
Diversity and equity in AI is paramount, not only for the innovative work that diverse teams produce, but because it’s crucial to mitigate the risks of bias in the development of algorithms and applications. To truly do this we must broaden participation and ensure that underrepresented voices are given the opportunity to shape the future of the field. DEEPMIND: THE PODCAST So, to help us bridge that gap, we created DeepMind: The Podcast, a series that we hope will answer these questions and more, while also giving listeners an inside look at how AI research is done at an organisation like DeepMind. Listen and subscribe to the whole series on your favourite podcast app by searching for “DeepMind: ThePodcast”.
ALPHAFOLD | DEEPMIND AlphaFold could help contribute to better and more efficient drug discovery by identifying the structure of many human proteins involved in disease. It could also help unlock new possibilities such as finding proteins and enzymes that break down industrial and plasticwaste or
DEEPMIND’S HEALTH TEAM JOINS GOOGLE HEALTH DeepMind’s health team joins Google Health. Over the last three years, DeepMind has built a team to tackle some of healthcare’s most complex problems—developing AI research and mobile tools that are already having a positive impact on patients and care teams. Today, with our healthcare partners, the team is excited to officially jointhe
RECURRENT EXPERIENCE REPLAY IN DISTRIBUTED REINFORCEMENT Building on the recent successes of distributed training of RL agents, in this paper we investigate the training of RNN-based RL agents from distributed prioritized experience replay. We study the effects of parameter lag resulting in representational drift and recurrent state staleness and empirically derive an improved training strategy. GRID LONG SHORT-TERM MEMORY This paper introduces Grid Long Short-Term Memory, a network of LSTM cells arranged in a multidimensional grid that can be applied to vectors, sequences or higher dimensional data such as images. The network differs from existing deep LSTM architectures in that the cells are connected between network layers as well as along the spatiotemporal dimensions of the data. AQUA-RAT (ALGEBRA QUESTION ANSWERING WITH RATIONALES AQuA-RAT (Algebra Question Answering with Rationales) A large-scale dataset consisting of approximately 100,000 algebraic word problems. The solution to each question is explained step-by-step using natural language. This data is used to train a program generation model that learns to generate the explanation, while generating the program that HOMEPAGE | DEEPMINDABOUTRESEARCHIMPACTBLOGSAFETY & ETHICSCAREERS This computational work represents a stunning advance on the protein-folding problem, a 50-year-old grand challenge in biology. It has occurred decades before many people in the field would have predicted." Use our education resources to help you explore the fascinating world of AI. CAREERS | DEEPMINDBLOGABOUTGAMES SYSTEMS ENGINEERINTERNSHIPSEXPLOREENGINEERING JOBS
Montreal, Canada. Led by Doina Precup, our Montreal office is home to a DeepMind research team that focuses on hierarchical and core reinforcement learning. Based in Google’s central Montreal office, the team brings together research scientists and engineers who focus on the problem of temporal abstraction and improvements in learningspeed
ABOUT | DEEPMIND
Global labs. We started as an interdisciplinary team based in London, and have opened labs across the UK, USA, Canada, and France. DeepMind is home to scientific researchers, ethicists, program managers, game designers, and more. Together, we’re solving some of the most fascinating challenges in AI today.SCHOLARSHIPS
Diversity and equity in AI is paramount, not only for the innovative work that diverse teams produce, but because it’s crucial to mitigate the risks of bias in the development of algorithms and applications. To truly do this we must broaden participation and ensure that underrepresented voices are given the opportunity to shape the future of the field. DEEPMIND: THE PODCAST So, to help us bridge that gap, we created DeepMind: The Podcast, a series that we hope will answer these questions and more, while also giving listeners an inside look at how AI research is done at an organisation like DeepMind. Listen and subscribe to the whole series on your favourite podcast app by searching for “DeepMind: ThePodcast”.
ALPHAFOLD | DEEPMIND AlphaFold could help contribute to better and more efficient drug discovery by identifying the structure of many human proteins involved in disease. It could also help unlock new possibilities such as finding proteins and enzymes that break down industrial and plasticwaste or
DEEPMIND’S HEALTH TEAM JOINS GOOGLE HEALTH DeepMind’s health team joins Google Health. Over the last three years, DeepMind has built a team to tackle some of healthcare’s most complex problems—developing AI research and mobile tools that are already having a positive impact on patients and care teams. Today, with our healthcare partners, the team is excited to officially jointhe
RECURRENT EXPERIENCE REPLAY IN DISTRIBUTED REINFORCEMENT Building on the recent successes of distributed training of RL agents, in this paper we investigate the training of RNN-based RL agents from distributed prioritized experience replay. We study the effects of parameter lag resulting in representational drift and recurrent state staleness and empirically derive an improved training strategy. GRID LONG SHORT-TERM MEMORY This paper introduces Grid Long Short-Term Memory, a network of LSTM cells arranged in a multidimensional grid that can be applied to vectors, sequences or higher dimensional data such as images. The network differs from existing deep LSTM architectures in that the cells are connected between network layers as well as along the spatiotemporal dimensions of the data. AQUA-RAT (ALGEBRA QUESTION ANSWERING WITH RATIONALES AQuA-RAT (Algebra Question Answering with Rationales) A large-scale dataset consisting of approximately 100,000 algebraic word problems. The solution to each question is explained step-by-step using natural language. This data is used to train a program generation model that learns to generate the explanation, while generating the program thatSCIENCE | DEEPMIND
Like the astronomers who built and used powerful telescopes to expand our understanding of the universe, our Science team builds innovative AI and machine learning systems. This work is pushing the boundaries of possibility in diverse fields such as climate science, protein folding, and quantum chemistry. Joining together from a broad range of DEEPMIND: THE PODCAST So, to help us bridge that gap, we created DeepMind: The Podcast, a series that we hope will answer these questions and more, while also giving listeners an inside look at how AI research is done at an organisation like DeepMind. Listen and subscribe to the whole series on your favourite podcast app by searching for “DeepMind: ThePodcast”.
IMPACT | DEEPMIND
Proteins are complex molecules that are essential to life. Each has its own unique 3D shape that determines how it works and what it does. Knowing how proteins fold could help scientists understand the biological processes of every living thing. To accelerate progress, we created AlphaFold, a system which accurately predicts the shape ofproteins.
OPERATIONS | DEEPMIND Overview. DeepMind has a remarkable track record of scientific breakthroughs. Such extraordinary work is a direct result of the brilliant and diverse people we bring together. Our Operations team works hard to make DeepMind the best environment in the world foradvancing AI
WELCOME TO THE DEEPMIND PODCAST Subscribe on Apple podcasts, Google podcasts, Spotify, Deezer or your favourite podcast app by searching for “DeepMind: The Podcast”.. We’re really proud of the programmes and hope they'll spark the curiosity of listeners to explore the world of AI further. To help, we have compiled a list of further reading for each episode in the show notes, drawing on the work of other labs andDEEPMIND LAB
DeepMind Lab is a 3D customisable game-like platform tailored for agent-based AI research. It is observed from a first-person viewpoint, through the eyes of the simulated agent. ALPHAFOLD | DEEPMIND AlphaFold could help contribute to better and more efficient drug discovery by identifying the structure of many human proteins involved in disease. It could also help unlock new possibilities such as finding proteins and enzymes that break down industrial and plasticwaste or
USING AI TO PREDICT RETINAL DISEASE PROGRESSION Using AI to predict retinal disease progression. Vision loss among the elderly is a major healthcare issue: about one in three people have some vision-reducing disease by the age of 65. Age-related macular degeneration (AMD) is the most common cause of blindness in the developed world. In Europe, approximately 25% of those 60 and olderhave AMD.
A SHORT NOTE ON THE KINETICS-700 HUMAN ACTION DATASET We describe an extension of the DeepMind Kinetics human action dataset from 600 classes to 700 classes, where for each class there are at least 600 video clips from different YouTube videos. This paper details the changes introduced for this new release of the dataset, and includes a comprehensive set of statistics as well as baseline results using the I3D neural network architecture. A CAUSAL BAYESIAN NETWORKS VIEWPOINT ON FAIRNESS We offer a graphical interpretation of unfairness in a dataset as the presence of an unfair causal path in the causal Bayesian network representing the data-generation mechanism. We use this viewpoint to revisit the recent debate surrounding the COMPAS pretrial risk assessment tool and, more generally, to point out that fairness evaluation on a model requires careful considerations on the* About
* Research
* Impact
* Blog
* Safety & Ethics
* Careers
*
*
*
WHAT IF SOLVING ONE PROBLEM COULD UNLOCK SOLUTIONS TO THOUSANDS MORE?Find out more
ARTIFICIAL INTELLIGENCE COULD BE ONE OF HUMANITY’S MOST USEFUL INVENTIONS. WE RESEARCH AND BUILD SAFE AI SYSTEMS THAT LEARN HOW TO SOLVE PROBLEMS AND ADVANCE SCIENTIFIC DISCOVERY FOR ALL. ------------------------- LATEST FROM DEEPMINDBlog post
Research
AGENT57: OUTPERFORMING THE HUMAN ATARI BENCHMARK Agent57: Outperforming the Human Atari Benchmark31 Mar 2020
Open Source
COMPUTATIONAL PREDICTIONS OF PROTEIN STRUCTURES ASSOCIATED WITHCOVID-19
The scientific community has galvanised in response to the recent... Publication + Authors' NotesSafety
ARTIFICIAL INTELLIGENCE, VALUES AND ALIGNMENT Iason Gabriel, arXiv 2020Download
Blog post
Research
A NEW MODEL AND DATASET FOR LONG-RANGE MEMORY This blog introduces a new long-range memory model, the Compressive Transformer, alongside a new benchmark for...10 Feb 2020
BREAKTHROUGHS
ALPHAGO: FINDING NEW MOVES IN AN ANCIENT GAMEFind out more
BREAKTHROUGHS
ALPHAZERO: SHEDDING NEW LIGHT ON CHESS, SHOGI, AND GOFind out more
BREAKTHROUGHS
ALPHAFOLD: USING AI FOR SCIENTIFIC DISCOVERYFind out more
BREAKTHROUGHS
AI REDUCES GOOGLE DATA CENTRE COOLING BILLFind out more
BREAKTHROUGHS
PREDICTING EYE DISEASE WITH MOORFIELDS EYE HOSPITALFind out more
02
/05
Latest Opportunities FROM PROGRAM MANAGERS TO RESEARCH SCIENTISTS, EXPLORE OUR OPEN ROLESSafety & Ethics
GUIDING THE SAFE DEVELOPMENT AND USE OF AI > BY DEEPENING OUR CAPACITY TO ASK HOW AND WHY, AI WILL ADVANCE THE > FRONTIERS OF KNOWLEDGE AND UNLOCK WHOLE NEW AVENUES OF SCIENTIFIC > DISCOVERY, IMPROVING THE LIVES OF BILLIONS OF PEOPLE.">
DeepMind CEO Demis Hassabis Writing in The EconomistRead more
DEEPMIND SCHOLARS: SHAQUILLE'S STORY3 mins
Publication + Authors' NotesDeep learning
INTERNATIONAL EVALUATION OF AN AI SYSTEM FOR BREAST CANCER SCREENING Scott Mayer McKinney, Marcin T. Sieniek, et al. Nature 2020Download
News
ENTERING OUR TENTH YEAR AT DEEPMIND We’ve come a long way in building the organisation we need to achieve our long-term mission.05 Dec 2019
Podcast
DEEPMIND: THE PODCAST Curious about AI and want to learn more? Download the first season of our podcast with Hannah Fry.Listen now
Featured publication Reinforcement learning MASTERING ATARI, GO, CHESS AND SHOGI BY PLANNING WITH A LEARNED MODEL Julian Schrittwieser, Ioannis Antonoglou, et al. arXiv 2019Download
Blog post
Research
DOPAMINE AND TEMPORAL DIFFERENCE LEARNING: A FRUITFUL RELATIONSHIP BETWEEN NEUROSCIENCE AND AI A recent development in computer science may provide a deep, parsimonious explanation for several previously unexplained...15 Jan 2020
Blog post
Research
ALPHAFOLD: USING AI FOR SCIENTIFIC DISCOVERY Our Nature paper describes AlphaFold, a system that generates 3D models of proteins that are far more accurate than any...15 Jan 2020
About
* Our story
* Recent progress
* Our global community* Leadership
* Teams
* Access to science
Research
* Blog
* Open source
* Publications
Impact
* Real-world impact
* Significant breakthroughsBlog
* Research
* News
Safety & Ethics
* Overview
* Technical safety
* Ethics & Society
Careers
* A unique mission
* Teams
* Diversity matters
* Locations
* Internships
PressTerms & Conditions Privacy Policy Modern Slavery StatementAlphabet
Inc
*
*
DeepMind may serve cookies to analyse traffic to this site. Information about your use of this site is shared with DeepMind for that purposeSee details OK, got itDetails
Copyright © 2024 ArchiveBay.com. All rights reserved. Terms of Use | Privacy Policy | DMCA | 2021 | Feedback | Advertising | RSS 2.0