 | |
| Type of business | Subsidiary |
|---|---|
| Founded | 23 September 2010 |
| Headquarters | |
| Founder(s) | |
| CEO | Demis Hassabis |
| General manager | Lila Ibrahim |
| Industry | Artificial Intelligence |
| Employees | 1,000+ as of June 2020 |
| Parent | Independent (2010–2014) Google Inc. (2014–2015) Alphabet Inc. (2015–present) |
| URL | www.deepmind.com |
Entrance of building where Google and DeepMind are located at 6 Pancras Square, London, UK.
DeepMind Technologies is a UK artificial intelligence company founded in September 2010, and acquired by Google in 2014. The company is based in London, with research centres in Canada, France, and the United States. In 2015, it became a wholly owned subsidiary of Alphabet Inc.
The company has created a neural network that learns how to play video games in a fashion similar to that of humans, as well as a Neural Turing machine, or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.
The company made headlines in 2016 after its AlphaGo program beat a human professional Go player Lee Sedol, the world champion, in a five-game match, which was the subject of a documentary film. A more general program, AlphaZero, beat the most powerful programs playing go, chess and shogi (Japanese chess) after a few days of play against itself using reinforcement learning.
History
The start-up was founded by Demis Hassabis, Shane Legg and Mustafa Suleyman in 2010. Hassabis and Legg first met at University College London's Gatsby Computational Neuroscience Unit.
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, Pong 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 2014, DeepMind received the "Company of the Year" award from Cambridge Computer Laboratory.
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 board. 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 as advisor. 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.
Machine learning
DeepMind Technologies' goal is to "solve intelligence", which they are trying to achieve by combining "the best techniques from machine learning and systems neuroscience to build powerful general-purpose learning algorithms".
They are trying to formalize intelligence in order to not only implement it into machines, but also understand the human brain, as Demis Hassabis explains:
[...] attempting to distil intelligence into an algorithmic construct may prove to be the best path to understanding some of the enduring mysteries of our minds.
Google Research has 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.
To date, the company has published research on computer systems
that are able to play games, and developing these systems, ranging from
strategy games such as Go to arcade games.
According to Shane Legg, human-level machine intelligence can be
achieved "when a machine can learn to play a really wide range of games
from perceptual stream input and output, and transfer understanding
across games[...]."
Research describing an AI playing seven different Atari 2600 video games (the Pong game in Video Olympics, Breakout, Space Invaders, Seaquest, Beamrider, Enduro, and Q*bert) reportedly led to the company's acquisition by Google. Hassabis has mentioned the popular e-sport game StarCraft as a possible future challenge, since it requires a high level of strategic thinking and handling imperfect information. The first demonstration of the DeepMind progress in StarCraft II occurred on 24 January 2019, on StarCrafts Twitch channel and DeepMind's YouTube channel.
In July 2018, researchers from DeepMind trained one of its systems to play the famous computer game Quake III Arena.
As of 2020, DeepMind has published over a thousand papers, including thirteen papers that were accepted by Nature or Science. DeepMind has received substantial media attention, especially 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 or Watson,
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 or Breakout.
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 demonstrated an AI system could surpass human abilities in games such as Pong, Breakout and Enduro, while surpassing state of the art performance on Seaquest, Beamrider, and Q*bert. 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 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 is 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 the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie, who at the time continuously held the world No. 1 ranking for two years. It used a supervised learning protocol, studying large numbers of games played by humans against each other.
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.
Technology
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
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.
WaveNet and WaveRNN
Also 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 users.
AlphaStar
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.
DeepMind Health
In July 2016, a collaboration between DeepMind and Moorfields Eye Hospital was announced to develop AI applications for healthcare. DeepMind would be applied to the analysis of anonymised eye scans, searching for early signs of diseases leading to blindness.
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.
There are also projects with the Royal Free London NHS Foundation Trust and Imperial College Healthcare NHS Trust to develop new clinical mobile apps linked to electronic patient records. Staff at the Royal Free Hospital
were reported as saying in December 2017 that access to patient data
through the app had saved a ‘huge amount of time’ and made a
‘phenomenal’ difference to the management of patients with acute kidney
injury. Test result data is sent to staff's mobile phones and alerts
them to change in the patient's condition. It also enables staff to see
if someone else has responded, and to show patients their results in
visual form.
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.
A complaint was filed to the Information Commissioner's Office (ICO), arguing that the data should be pseudonymised and encrypted. In May 2016, New Scientist
published a further article claiming that the project had failed to
secure approval from the Confidentiality Advisory Group of the Medicines and Healthcare products Regulatory Agency.
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.
