is a British artificial intelligence
subsidiary of Alphabet Inc.
and research laboratory founded in September 2010. DeepMind was acquired
in 2014. The company is based in London, with research centres in Canada,
and the United States. In 2015, it became a wholly owned subsidiary of Alphabet Inc
, Google's parent company.
DeepMind Technologies Limited
Entrance of building where Google and DeepMind are located at 6 Pancras Square, London, UK.
During one of the interviews, Demis Hassabis said that the start-up began working on artificial intelligence technology by teaching it how to play old games from the seventies and eighties, which are relatively primitive compared to the ones that are available today. Some of those games included Breakout
and Space Invaders
. AI was introduced to one game at a time, without any prior knowledge of its rules. After spending some time on learning the game, AI would eventually become an expert in it. “The cognitive processes which the AI goes through are said to be very like those a human who had never seen the game would use to understand and attempt to master it.”
The goal of the founders is to create a general-purpose AI that can be useful and effective for almost anything.
Major venture capital firms Horizons Ventures
and Founders Fund
invested in the company,
as well as entrepreneurs Scott Banister, Peter Thiel
and Elon Musk
. Jaan Tallinn
was an early investor and an adviser to the company.
On 26 January 2014, Google announced the company had acquired DeepMind for $500 million,
and that it had agreed to take over DeepMind Technologies. The sale to Google took place after Facebook
reportedly ended negotiations with DeepMind Technologies in 2013.
The company was afterwards renamed Google DeepMind and kept that name for about two years.
In September 2015, DeepMind and the Royal Free NHS Trust signed their initial Information Sharing Agreement (ISA) to co-develop a clinical task management app, Streams.
After Google's acquisition the company established an artificial intelligence ethics
The ethics board for AI research remains a mystery, with both Google and DeepMind declining to reveal who sits on the board.
DeepMind, together with Amazon, Google, Facebook, IBM and Microsoft, is a founding member of Partnership on AI
, an organization devoted to the society-AI interface.
DeepMind has opened a new unit called DeepMind Ethics and Society and focused on the ethical and societal questions raised by artificial intelligence featuring prominent philosopher Nick Bostrom
In October 2017, DeepMind launched a new research team to investigate AI ethics.
In December 2019, Co-founder Suleyman announced he would be leaving DeepMind to join Google, working in a policy role.
Products and technologies
Google Research released a paper in 2016 regarding AI Safety
and avoiding undesirable behaviour during the AI learning process.
Deepmind has also released several publications via its website.
In 2017 DeepMind released GridWorld, an open-source testbed for evaluating whether an algorithm learns to disable its kill switch
or otherwise exhibits certain undesirable behaviours.
In July 2018, researchers from DeepMind trained one of its systems to play the computer game Quake III Arena
As of 2020, DeepMind has published over a thousand papers, including thirteen papers that were accepted by Nature
DeepMind received media attention during the AlphaGo period; according to a LexisNexis
search, 1842 published news stories mentioned DeepMind in 2016, declining to 1363 in 2019.
Deep reinforcement learning
As opposed to other AIs, such as IBM
's Deep Blue
, which were developed for a pre-defined purpose and only function within its scope, DeepMind claims that its system is not pre-programmed: it learns from experience, using only raw pixels as data input. Technically it uses deep learning
on a convolutional neural network
, with a novel form of Q-learning
, a form of model-free reinforcement learning
They test the system on video games, notably early arcade games
, such as Space Invaders
Without altering the code, the AI begins to understand how to play the game, and after some time plays, for a few games (most notably Breakout
), a more efficient game than any human ever could.
In 2013, DeepMind published research on an AI system that could surpass human abilities in games such as Pong
, while surpassing state of the art performance on Seaquest
, and Q*bert
This work reportedly led to the company's acquisition by Google.
DeepMind's AI had been applied to video games made in the 1970s and 1980s
; work was ongoing for more complex 3D games such as Doom
, which first appeared in the early 1990s.
In 2020, DeepMind published Agent57,
an AI Agent which surpasses human level performance on all 57 games of the Atari2600 suite.
AlphaGo and successors
In 2014, the company published research on computer systems that are able to play Go
In October 2015, a computer Go
program called AlphaGo, developed by DeepMind, beat the European Go champion Fan Hui
, a 2 dan
(out of 9 dan possible) professional, five to zero.
This was the first time an artificial intelligence (AI) defeated a professional Go player.
Previously, computers were only known to have played Go at "amateur" level.
Go is considered much more difficult for computers to win compared to other games like chess
, due to the much larger number of possibilities, making it prohibitively difficult for traditional AI methods such as brute-force
In March 2016 it beat Lee Sedol
—a 9th dan Go player and one of the highest ranked players in the world—with a score of 4–1 in a five-game match
In 2017, an improved version, AlphaGo Zero, defeated AlphaGo 100 games to 0. AlphaGo Zero's strategies were self-taught. AlphaGo Zero was able to beat its predecessor after just three days with less processing power than AlphaGo; in comparison, the original AlphaGo needed months to learn how to play.
Later that year, AlphaZero, a modified version of AlphaGo Zero but for handling any two-player game of perfect information, gained superhuman abilities at chess and shogi. Like AlphaGo Zero, AlphaZero learned solely through self-play.
AlphaGo technology was developed based on the deep reinforcement learning
approach. This makes AlphaGo different from the rest of AI technologies on the market. With that said, AlphaGo's ‘brain’ was introduced to various moves based on the historical tournament data. The number of moves was increased gradually until it eventually processed over 30 million of them. The aim was to have the system mimic the human player and eventually become better. It played against itself and learned not only from its own defeats but wins as well; thus, it learned to improve itself over the time and increased its winning rate as a result.
AlphaGo used two deep neural networks: a policy network to evaluate move probabilities and a value network to assess positions. The policy network trained via supervised learning, and was subsequently refined by policy-gradient reinforcement learning
. The value network learned to predict winners of games played by the policy network against itself. After training these networks employed a lookahead Monte Carlo tree search
(MCTS), using the policy network to identify candidate high-probability moves, while the value network (in conjunction with Monte Carlo rollouts using a fast rollout policy) evaluated tree positions.
Zero trained using reinforcement learning in which the system played millions of games against itself. Its only guide was to increase its win rate. It did so without learning from games played by humans. Its only input features are the black and white stones from the board. It uses a single neural network, rather than separate policy and value networks. Its simplified tree search relies upon this neural network to evaluate positions and sample moves, without Monte Carlo rollouts. A new reinforcement learning algorithm incorporates lookahead search inside the training loop.
AlphaGo Zero employed around 15 people and millions in computing resources.
Ultimately, it needed much less computing power than AlphaGo, running on four specialized AI processors (Google TPUs
), instead of AlphaGo's 48.
AlphaFold 2 block design. The two attention-based transformation modules can be seen in the middle of the design. (Source:
In 2016, DeepMind turned its artificial intelligence to protein folding
, one of the toughest problems in science. In December 2018, DeepMind's AlphaFold won the 13th Critical Assessment of Techniques for Protein Structure Prediction
(CASP) by successfully predicting the most accurate structure for 25 out of 43 proteins. “This is a lighthouse project, our first major investment in terms of people and resources into a fundamental, very important, real-world scientific problem,” Hassabis said to The Guardian
In 2020, in the 14th CASP, AlphaFold's predictions achieved an accuracy score regarded as comparable with lab techniques. Dr Andriy Kryshtafovych, one of the panel of scientific adjudicators, described the achievement as "truly remarkable", and said the problem of predicting how proteins fold had been "largely solved".
WaveNet and WaveRNN
In 2016, DeepMind introduced WaveNet, a text-to-speech
system. It was originally too computationally intensive for use in consumer products, but in late 2017 it became ready for use in consumer applications such as Google Assistant
In 2018 Google launched a commercial text-to-speech product, Cloud Text-to-Speech, based on WaveNet.
In 2018, DeepMind introduced a more efficient model called WaveRNN co-developed with Google AI
In 2019, Google started to roll it out to Google Duo
In 2016, Hassabis discussed the game StarCraft
as a future challenge, since it requires strategic thinking and handling imperfect information.
In January 2019, DeepMind introduced AlphaStar, a program playing the real-time strategy game StarCraft II
. AlphaStar used reinforcement learning based on replays from human players, and then played against itself to enhance its skills. At the time of the presentation, AlphaStar had knowledge equivalent to 200 years of playing time. It won 10 consecutive matches against two professional players, although it had the unfair advantage of being able to see the entire field, unlike a human player who has to move the camera manually. A preliminary version in which that advantage was fixed lost a subsequent match.
In July 2019, AlphaStar began playing against random humans on the public 1v1 European multiplayer ladder. Unlike the first iteration of AlphaStar, which played only Protoss
v. Protoss, this one played as all of the game's races, and had earlier unfair advantages fixed.
By October 2019, AlphaStar reached Grandmaster level on the StarCraft II
ladder on all three StarCraft
races, becoming the first AI to reach the top league of a widely popular esport
without any game restrictions.
Miscellaneous contributions to Google
Google has stated that DeepMind algorithms have greatly increased the efficiency of cooling its data centers.
In addition, DeepMind (alongside other Alphabet AI researchers) assists Google Play's
personalized app recommendations.
DeepMind has also collaborated with the Android
team at Google
for the creation of two new features which were made available to people with devices running Android
Pie, the ninth installment of Google's mobile operating system. These features, Adaptive Battery and Adaptive Brightness, use machine learning to conserve energy and make devices running the operating system easier to use. It is the first time DeepMind has used these techniques on such a small scale, with typical machine learning applications requiring orders of magnitude more computing power.
In August 2016, a research programme with University College London Hospital
was announced with the aim of developing an algorithm that can automatically differentiate between healthy and cancerous tissues in head and neck areas.
In November 2017, DeepMind announced a research partnership with the Cancer Research UK
Centre at Imperial College London with the goal of improving breast cancer detection by applying machine learning to mammography.
Additionally, in February 2018, DeepMind announced it was working with the U.S. Department of Veterans Affairs
in an attempt to use machine learning to predict the onset of acute kidney injury in patients, and also more broadly the general deterioration of patients during a hospital stay so that doctors and nurses can more quickly treat patients in need.
DeepMind developed an app called Streams, which sends alerts to doctors about patients at risk of acute risk injury.
On 13 November 2018, DeepMind announced that its health division and the Streams app would be absorbed into Google Health
Privacy advocates said the announcement betrayed patient trust and appeared to contradict previous statements by DeepMind that patient data would not be connected to Google accounts or services.
A spokesman for DeepMind said that patient data would still be kept separate from Google services or projects.
NHS data-sharing controversy
In April 2016, New Scientist
obtained a copy of a data sharing
agreement between DeepMind and the Royal Free London NHS Foundation Trust
. The latter operates three London hospitals where an estimated 1.6 million patients are treated annually. The agreement shows DeepMind Health had access to admissions, discharge and transfer data, accident and emergency, pathology and radiology, and critical care at these hospitals. This included personal details such as whether patients had been diagnosed with HIV
, suffered from depression
or had ever undergone an abortion
in order to conduct research to seek better outcomes in various health conditions.
In May 2017, Sky News
published a leaked letter from the National Data Guardian, Dame Fiona Caldicott
, revealing that in her "considered opinion" the data-sharing agreement between DeepMind and the Royal Free took place on an "inappropriate legal basis".
The Information Commissioner's Office ruled in July 2017 that the Royal Free hospital failed to comply with the Data Protection Act when it handed over personal data of 1.6 million patients to DeepMind.
DeepMind Ethics and Society
In October 2017, DeepMind announced a new research unit, DeepMind Ethics & Society.
Their goal is to fund external research of the following themes: privacy, transparency, and fairness; economic impacts; governance and accountability; managing AI risk; AI morality and values; and how AI can address the world's challenges. As a result, the team hopes to further understand the ethical implications of AI and aid society to seeing AI can be beneficial.
This new subdivision of DeepMind is a completely separate unit from the partnership of leading companies using AI, academia, civil society organizations and nonprofits of the name Partnership on Artificial Intelligence to Benefit People and Society
of which DeepMind is also a part.
The DeepMind Ethics and Society board is also distinct from the mooted AI Ethics Board that Google
originally agreed to form when acquiring DeepMind.
DeepMind Professors of machine learning
DeepMind sponsors three chairs
of machine learning:
- one at the University of Cambridge, held by Neil Lawrence, in the Department of Computer Science and Technology
- another at the University of Oxford. in the Department of Computer Science
- another at the University College London held by Marc Deisenroth. in the department of Computer Science at the Faculty of Engineering Sciences
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Last edited on 29 March 2021, at 15:25
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