View source for Company:DeepMind

{{short description|Artificial intelligence company owned by Google}}
{{Infobox company
| name = DeepMind Technologies Limited
| logo = DeepMind logo.png
| image = DeepMind headquarters S2 Handyside.jpg
| image_caption = Headquarters in Kings Cross, London
| founded = {{start date and age|df=y|2010|9|23}}<ref>{{cite web|url=https://beta.companieshouse.gov.uk/company/07386350|title=DeepMind Technologies Limited – Overview (free company information from Companies House)|work=Companies House|access-date=13 March 2016}}</ref>
| location = [[Place:London|London]], UK<ref>{{Cite web |title=King's Cross - S2 Building - SES Engineering Services |url=https://www.ses-ltd.co.uk/case-study/kings-cross-s2-building/ |access-date=2022-07-14 |website=www.ses-ltd.co.uk |language=en}}</ref>
| founders = {{plain list|
*[[Biography:Demis Hassabis|Demis Hassabis]]
*[[Biography:Shane Legg|Shane Legg]]
*Mustafa Suleyman}}
| key_people = {{Unbulleted list
  |Demis Hassabis ([[Social:Chief executive officer|CEO]])
  |[[Biography:Lila Ibrahim|Lila Ibrahim]] ([[Social:Chief operating officer|COO]])}}
| industry = [[Artificial intelligence]]
| parent = {{Unbulleted list|class=nowrap|Google Inc. (2014–2015)|Alphabet Inc. (2015–present)}}
| type = [[Social:Subsidiary|Subsidiary]]
| num_employees = 1,000+ (2022)<ref>{{cite web |last1=Kahn |first1=Jeremy |title=Former DeepMind employees say A.I. company mishandled serious sexual assault complaints |url=https://fortune.com/2022/03/30/deepmind-sexual-harassment-allegations-mishandled-ft/ |website=Fortune |date=30 March 2022 |archive-url=https://web.archive.org/web/20220624000021/https://fortune.com/2022/03/30/deepmind-sexual-harassment-allegations-mishandled-ft/ |archive-date=24 June 2022 |url-status=live}}</ref>
| products = [[Software:AlphaGo|AlphaGo]], [[Software:AlphaStar|AlphaStar]], [[AlphaFold]], [[AlphaZero]]
| website = {{URL|deepmind.com}}
}}
{{Artificial intelligence}}
'''DeepMind Technologies''' is a British [[Artificial intelligence|artificial intelligence]] research laboratory founded in<!-- September--> 2010, and now a [[Social:Subsidiary|subsidiary]] of Alphabet Inc. DeepMind was acquired by [[Company:Google|Google]]<ref>{{Cite web|url=https://dealbook.nytimes.com/2014/01/27/google-acquires-british-artificial-intelligence-developer/|title=Google Acquires British Artificial Intelligence Developer|last=Bray|first=Chad|date=27 January 2014|website=DealBook|language=en|access-date=2019-11-04}}</ref> in 2014 and became a wholly owned subsidiary of Alphabet Inc., after Google's restructuring in 2015. The company is based in London, with research centres in Canada,<ref>{{cite web |title=About Us {{!}} DeepMind |url=https://deepmind.com/about/ |website=DeepMind}}</ref> France,<ref>{{cite web |title=A return to Paris {{!}} DeepMind |url=https://deepmind.com/blog/a-return-to-paris/ |website=DeepMind}}</ref> and the United States.

DeepMind has created a [[Neural network|neural network]] that learns how to play video games in a fashion similar to that of humans,<ref name="arxiv medium">{{cite web |title=The Last AI Breakthrough DeepMind Made Before Google Bought It |publisher= The Physics [[ArXiv|arXiv]] Blog |url= https://medium.com/the-physics-arxiv-blog/the-last-ai-breakthrough-deepmind-made-before-google-bought-it-for-400m-7952031ee5e1 |access-date= 12 October 2014|date= 2014-01-29 }}</ref> as well as a [[Neural Turing machine]],<ref name="arxiv">{{cite arXiv |eprint= 1410.5401 |title= Neural Turing Machines |last1= Graves  |first1= Alex |last2= Wayne |first2= Greg |last3= Danihelka |first3= Ivo |class= cs.NE |year= 2014 |author1-link= Alex Graves (computer scientist) }}</ref> 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 [[Biology:Short-term memory|short-term memory]] of the human brain.<ref>[http://www.technologyreview.com/view/533741/best-of-2014-googles-secretive-deepmind-startup-unveils-a-neural-turing-machine/ Best of 2014: Google's Secretive DeepMind Startup Unveils a "Neural Turing Machine"] {{Webarchive|url=https://web.archive.org/web/20151204081728/http://www.technologyreview.com/view/533741/best-of-2014-googles-secretive-deepmind-startup-unveils-a-neural-turing-machine/ |date=4 December 2015 }}, ''MIT Technology Review''</ref><ref name="DNCnature2016">{{Cite journal |last1=Graves |first1=Alex |last2=Wayne |first2= Greg |last3=Reynolds |first3=Malcolm |last4= Harley |first4=Tim |last5=Danihelka |first5= Ivo |last6=Grabska-Barwińska |first6= Agnieszka |last7=Colmenarejo |first7=Sergio Gómez |last8= Grefenstette |first8=Edward |last9=Ramalho |first9=Tiago |date=12 October 2016 |title= Hybrid computing using a neural network with dynamic external memory |journal=Nature |language=en |volume=538 |issue=7626 |doi= 10.1038/nature20101 |issn= 1476-4687 |pages=471–476 |pmid= 27732574 |bibcode= 2016Natur.538..471G|s2cid=205251479 |url=https://ora.ox.ac.uk/objects/uuid:dd8473bd-2d70-424d-881b-86d9c9c66b51 }}</ref>

DeepMind made headlines in 2016 after its [[Software:AlphaGo|AlphaGo]] program beat a human professional [[Software:Go (game)|Go]] player Lee Sedol, a world champion, in a five-game match, which was the subject of a documentary film.<ref>{{Citation|last=Kohs|first=Greg|title=AlphaGo|date=29 September 2017|url=https://www.imdb.com/title/tt6700846/|others=Ioannis Antonoglou, Lucas Baker, Nick Bostrom|access-date=9 January 2018}}</ref> A more general program, [[AlphaZero]], beat the most powerful programs playing [[Software:Go (game)|go]], chess and shogi (Japanese chess) after a few days of play against itself using [[Reinforcement learning|reinforcement learning]].<ref>{{Cite arXiv|first1=David|last1= Silver|first2=Thomas|last2= Hubert|first3= Julian|last3=Schrittwieser|first4= Ioannis|last4=Antonoglou |first5= Matthew|last5= Lai|first6= Arthur|last6= Guez|first7= Marc|last7= Lanctot|first8= Laurent|last8= Sifre|first9= Dharshan|last9= Kumaran|first10= Thore|last10= Graepel|first11= Timothy|last11= Lillicrap|first12= Karen|last12= Simonyan|first13=Demis |last13=Hassabis|author-link13=Demis Hassabis |eprint=1712.01815|title=Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm|class=cs.AI|date=5 December 2017}}</ref> In 2020, DeepMind made significant advances in the problem of [[Biology:Protein structure prediction|protein folding]] with [[AlphaFold]].<ref>{{cite news |last=Callaway |first=Ewen |date=2020-11-30 |title='It will change everything': DeepMind's AI makes gigantic leap in solving protein structures |url=https://www.nature.com/articles/d41586-020-03348-4 |work=Nature |access-date=2021-08-31}}</ref> In July 2022, it was announced that over 200 million predicted protein structures, representing virtually all known proteins, would be released on the AlphaFold database.<ref name=geddes>{{cite news |url=https://www.theguardian.com/technology/2022/jul/28/deepmind-uncovers-structure-of-200m-proteins-in-scientific-leap-forward |title=DeepMind uncovers structure of 200m proteins in scientific leap forward |first=Linda |last=Geddes|work=The Guardian|date=28 July 2022}}</ref><ref name="alphafold DB">{{cite web |url=https://www.deepmind.com/blog/alphafold-reveals-the-structure-of-the-protein-universe |title=AlphaFold reveals the structure of the protein universe |date=28 July 2022 |work=DeepMind }}</ref>

DeepMind posted a blog post on 28 April 2022 on a single visual language model (VLM) named Flamingo that can accurately describe a picture of something with just a few training images.<ref>{{Cite web |title=Tackling multiple tasks with a single visual language model |url=https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model |access-date=2022-04-29 |website=www.deepmind.com |language=en}}</ref><ref>{{Cite web |last=Alayrac |first=Jean-Baptiste |title=Flamingo: a Visual Language Model for Few-Shot Learning |url=https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/tackling-multiple-tasks-with-a-single-visual-language-model/flamingo.pdf |journal=|year=2022 |arxiv=2204.14198 }}</ref> In July 2022, DeepMind announced the development of DeepNash, a model-free [[Multi-agent reinforcement learning|multi-agent reinforcement learning]] system capable of playing the board game Stratego at the level of a human expert.<ref>{{cite web|url=https://www.marktechpost.com/2022/07/09/deepmind-ai-researchers-introduce-deepnash-an-autonomous-agent-trained-with-model-free-multiagent-reinforcement-learning-that-learns-to-play-the-game-of-stratego-at-expert-level/|title=Deepmind AI Researchers Introduce 'DeepNash', An Autonomous Agent Trained With Model-Free Multiagent Reinforcement Learning That Learns To Play The Game Of Stratego At Expert Level|date=9 July 2022|website=MarkTechPost}}</ref>{{toclimit|3}}

== History ==

The [[Finance:Startup company|start-up]] was founded by [[Biography:Demis Hassabis|Demis Hassabis]], [[Biography:Shane Legg|Shane Legg]] and Mustafa Suleyman in September 2010.<ref>{{cite news | url=https://www.bloomberg.com/news/2014-01-27/google-buys-u-k-artificial-intelligence-company-deepmind.html | work=Bloomberg | title=Google Buys U.K. Artificial Intelligence Company DeepMind | date=27 January 2014 | access-date=13 November 2014 | url-access=limited | archive-url=https://web.archive.org/web/20141113165033/http://www.bloomberg.com/news/2014-01-27/google-buys-u-k-artificial-intelligence-company-deepmind.html | archive-date=13 November 2014 | url-status=bot: unknown }}</ref><ref>{{cite news|url=http://www.ft.com/cms/s/0/dfedc62e-874e-11e3-9c5c-00144feab7de.html | title=Google makes £400m move in quest for artificial intelligence | newspaper=Financial Times | date=27 January 2014 | access-date=13 November 2014}}</ref> Hassabis and Legg first met at the Gatsby Computational Neuroscience Unit at [[Organization:University College London|University College London]] (UCL).<ref>{{cite news|title=Demis Hassabis: 15 facts about the DeepMind Technologies founder|url=https://www.theguardian.com/technology/shortcuts/2014/jan/28/demis-hassabis-15-facts-deepmind-technologies-founder-google|newspaper=The Guardian|access-date=12 October 2014}}</ref>

Demis Hassabis has 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 of a human who had never seen the game would use to understand and attempt to master it.”<ref>{{Cite news|url=https://www.forbes.com/sites/bernardmarr/2017/02/02/how-googles-amazing-ai-start-up-deepmind-is-making-our-world-a-smarter-place/#3f5f079ddfff|title=How Google's Amazing AI Start-Up 'DeepMind' Is Making Our World A Smarter Place|last=Marr|first=Bernard|work=Forbes|access-date=30 June 2018|language=en}}</ref> 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,<ref>{{cite news|title=DeepMind buy heralds rise of the machines|url=http://www.ft.com/cms/s/0/b09dbd40-876a-11e3-9c5c-00144feab7de.html#axzz3G6ykG7uq|newspaper=Financial Times|date=27 January 2014|access-date=14 October 2014|last1=Cookson|first1=Robert}}</ref> as well as entrepreneurs Scott Banister,<ref>{{cite web|title=DeepMind Technologies Investors|url=https://angel.co/deepmind-technologies-limited|access-date=12 October 2014}}</ref> Peter Thiel,<ref>{{cite web |last1=Shead |first1=Sam |title=How DeepMind convinced billionaire Peter Thiel to invest without moving the company to Silicon Valley |url=https://www.businessinsider.com/how-deepmind-convinced-peter-thiel-to-invest-outside-silicon-valley-2017-7 |publisher=Business Insider}}</ref> and [[Biography:Elon Musk|Elon Musk]].<ref>{{cite web|url=http://www.ibtimes.co.uk/elon-musk-artificial-intelligence-potentially-more-dangerous-nukes-1459710|title=Elon Musk: Artificial Intelligence 'Potentially More Dangerous Than Nukes'|first=Anthony |last=Cuthbertson|date=2014-08-04|work=International Business Times UK}}</ref> Jaan Tallinn was an early investor and an adviser to the company.<ref>{{cite web|title=Recode.net – DeepMind Technologies Acquisition|url=http://recode.net/2014/01/26/exclusive-google-to-buy-artificial-intelligence-startup-deepmind-for-400m/|access-date=27 January 2014|date=2014-01-26}}</ref> On 26 January 2014, Google announced the company had acquired DeepMind for $500 million,<ref>{{cite news|url=https://www.reuters.com/article/google-deepmind-idUSL2N0L102A20140127|title=Google to buy artificial intelligence company DeepMind|date=26 January 2014|newspaper=Reuters|access-date=12 October 2014}}</ref><ref>{{cite news|url=https://www.theguardian.com/technology/2014/jan/27/google-acquires-uk-artificial-intelligence-startup-deepmind|title=Google Acquires UK AI startup Deepmind|newspaper=The Guardian|access-date=27 January 2014}}</ref><ref>{{cite news|url=https://techcrunch.com/2014/01/26/google-deepmind/|title=Report of Acquisition, TechCrunch|work=TechCrunch|access-date=27 January 2014}}</ref><ref>{{cite news|url=https://www.reuters.com/article/us-google-deepmind-idUSBREA0Q03220140127|title=Reuters Report|last=Oreskovic|first=Alexei|newspaper=Reuters|access-date=27 January 2014|date=2014-01-27}}</ref><ref>{{cite news|url=https://www.theverge.com/2014/1/26/5348640/google-deepmind-acquisition-robotics-ai|title=Google Acquires Artificial Intelligence Start-Up DeepMind|work=The Verge|access-date=27 January 2014}}</ref><ref>{{cite web|url=https://arstechnica.com/business/2014/01/google-acquires-ai-pioneer-deepmind-technologies/|title=Google acquires AI pioneer DeepMind Technologies|work=Ars Technica|access-date=27 January 2014|date=2014-01-27}}</ref> and that it had agreed to take over DeepMind Technologies. The sale to Google took place after [[Company:Facebook|Facebook]] reportedly ended negotiations with DeepMind Technologies in 2013.<ref>{{cite web|url=https://www.theinformation.com/Google-beat-Facebook-For-DeepMind-Creates-Ethics-Board|title=Google beats Facebook for Acquisition of DeepMind Technologies|access-date=27 January 2014}}</ref> The company was afterwards renamed Google DeepMind and kept that name for about two years.<ref name="nature2015" />

In 2014, DeepMind received the "Company of the Year" award from Cambridge Computer Laboratory.<ref>{{cite web|title=Hall of Fame Awards: To celebrate the success of companies founded by Computer Laboratory graduates.|url=https://www.cl.cam.ac.uk/ring/awards.html|publisher=University of Cambridge|access-date=12 October 2014}}</ref>

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.<ref>{{Cite news|url=https://techcrunch.com/2017/08/31/documents-detail-deepminds-plan-to-apply-ai-to-nhs-data-in-2015/|title=Documents detail DeepMind's plan to apply AI to NHS data in 2015|last=Lomas|first=Natasha|work=TechCrunch|access-date=26 September 2017|language=en}}</ref>

After Google's acquisition the company established an [[Philosophy:Ethics of artificial intelligence|artificial intelligence ethics]] board.<ref>{{cite magazine |title=Inside Google's Mysterious Ethics Board |url=https://www.forbes.com/sites/privacynotice/2014/02/03/inside-googles-mysterious-ethics-board/|magazine=Forbes|access-date=12 October 2014|date=3 February 2014}}</ref> The ethics board for AI research remains a mystery, with both Google and DeepMind declining to reveal who sits on the board.<ref name="theguardian.com 2016-05-04">{{cite news|last1=Ramesh|first1=Randeep|title=Google's DeepMind shouldn't suck up our NHS records in secret |url=https://www.theguardian.com/commentisfree/2016/may/04/googles-deepmind-shouldnt-be-sucking-up-our-nhs-records-in-secret |newspaper=The Guardian |access-date=19 October 2016 |archive-url=https://web.archive.org/web/20161013145134/https://www.theguardian.com/commentisfree/2016/may/04/googles-deepmind-shouldnt-be-sucking-up-our-nhs-records-in-secret|archive-date=13 October 2016|date=4 May 2016}}</ref>  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.<ref>{{cite web |url=https://www.partnershiponai.org/#s-partners |title=Home/ Partnership on Artificial Intelligence to Benefit People and Society |year=2016 |author=<!--Staff writer(s); no by-line.--> |access-date=15 October 2016}}</ref> 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 [[Biography:Nick Bostrom|Nick Bostrom]] as advisor.<ref>{{cite news |url=https://www.theguardian.com/technology/2017/oct/04/google-deepmind-ai-artificial-intelligence-ethics-group-problems|title=DeepMind announces ethics group to focus on problems of AI|first=Alex|last=Hern|date=4 October 2017 |via=www.theguardian.com|newspaper=The Guardian}}</ref> In October 2017, DeepMind launched a new research team to investigate AI ethics.<ref>{{Cite news |url=http://www.businessinsider.com/deepmind-has-launched-a-new-ethics-and-society-research-team-2017-10|title=DeepMind has launched a new 'ethics and society' research team|work=Business Insider|access-date=25 October 2017 |language=en}}</ref><ref>{{Cite news |url=https://www.theverge.com/2017/10/4/16417978/deepmind-ai-ethics-society-research-group|title=DeepMind launches new research team to investigate AI ethics|work=The Verge|access-date=25 October 2017}}</ref>

In December 2019, co-founder Suleyman announced he would be leaving DeepMind to join Google, working in a policy role.<ref name= "SuleymanDeparture">Madhumita Murgia, [https://www.ft.com/content/02757f12-1780-11ea-9ee4-11f260415385 "DeepMind co-founder leaves for policy role at Google"], [[Social:Financial Times|Financial Times]], 5 December 2019</ref>

== Products and technologies ==
According to the company's website, DeepMind Technologies' goal is to combine "the best techniques from [[Machine learning|machine learning]] and [[Biology:Systems neuroscience|systems neuroscience]] to build powerful general-purpose learning algorithms".<ref name="DeepMind Website">{{cite web|title=DeepMind Technologies Website|url=https://www.deepmind.com/|publisher=DeepMind Technologies|access-date=11 October 2014}}</ref>

Google Research released a paper in 2016 regarding [[AI safety]] and avoiding undesirable behaviour during the AI learning process.<ref>{{Cite arXiv |last1=Amodei|first1=Dario |last2=Olah|first2=Chris |last3=Steinhardt|first3=Jacob |last4=Christiano|first4=Paul |last5=Schulman|first5=John |last6=Mané|first6=Dan |date=21 June 2016|title=Concrete Problems in AI Safety |eprint=1606.06565 |class=cs.AI}}</ref> Deepmind has also released several publications via its website.<ref>{{Cite web |url=https://deepmind.com/research/publications/ |title=Publications |website=DeepMind |access-date=11 September 2016}}</ref> In 2017 DeepMind released GridWorld, an open-source testbed for evaluating whether an algorithm learns to disable its [[Philosophy:AI control problem#Kill switch|kill switch]] or otherwise exhibits certain undesirable behaviours.<ref>{{cite news|title=DeepMind Has Simple Tests That Might Prevent Elon Musk's AI Apocalypse|url=https://www.bloomberg.com/news/articles/2017-12-11/deepmind-has-simple-tests-that-might-prevent-elon-musk-s-ai-apocalypse|access-date=8 January 2018|work=Bloomberg.com|date=11 December 2017}}</ref><ref>{{cite news|title=Alphabet's DeepMind Is Using Games to Discover If Artificial Intelligence Can Break Free and Kill Us All |url=http://fortune.com/2017/12/12/alphabet-deepmind-ai-safety-musk-games/ |access-date=8 January 2018|work=Fortune|language=en}}</ref>

In July 2018, researchers from DeepMind trained one of its systems to play the computer game ''[[Software:Quake III Arena|Quake III Arena]]''.<ref>[https://www.engadget.com/2018/07/03/deepmind-ai-quake-iii-arena-human/ "DeepMind AI’s new trick is playing ‘Quake III Arena’ like a human"]. ''Engadget''. 3 July 2018.</ref>

As of 2020, DeepMind has published over a thousand papers, including thirteen papers that were accepted by ''Nature'' or ''Science''.{{citation needed|date=September 2020|reason=Please cite a secondary source for this to avoid back-door promotion.}} 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.<ref>{{cite news |last1=Shead |first1=Sam |title=Why the buzz around DeepMind is dissipating as it transitions from games to science |url=https://www.cnbc.com/2020/06/05/google-deepmind-alphago-buzz-dissipates.html |access-date=12 June 2020 |work=CNBC |date=5 June 2020 |language=en}}</ref>

=== Deep reinforcement learning ===
As opposed to other AIs, such as [[Company:IBM|IBM]]'s Deep Blue or [[Engineering:Watson (computer)|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|deep learning]] on a [[Convolutional neural network|convolutional neural network]], with a novel form of [[Q-learning]], a form of model-free [[Reinforcement learning|reinforcement learning]].<ref name="nature2015" /><ref name="Atari Paper">{{cite arXiv|title=Playing Atari with Deep Reinforcement Learning |date=12 December 2013 |first1=Volodymyr |last1=Mnih |first2=Koray |last2=Kavukcuoglu |first3=David |last3=Silver |first4=Alex |last4=Graves |first5=Ioannis |last5=Antonoglou |first6=Daan |last6=Wierstra |first7=Martin |last7=Riedmiller |eprint=1312.5602|class=cs.LG }}</ref>  They test the system on video games, notably early arcade games, such as ''[[Software:Space Invaders|Space Invaders]]'' or ''[[Software:Breakout (video game)|Breakout]]''.<ref name="Atari Paper" /><ref name="hassabis talk" /> 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.<ref name="hassabis talk">{{cite AV media|title=Deepmind artificial intelligence @ FDOT14|url=https://www.youtube.com/watch?v=EfGD2qveGdQ|date=19 April 2014}}</ref>

In 2013, DeepMind published research on an AI system that could surpass human abilities in games such as [[Software:Pong|Pong]], [[Software:Breakout (video game)|Breakout]] and [[Software:Enduro (video game)|Enduro]], while surpassing state of the art performance on [[Software:Seaquest (video game)|Seaquest]], [[Software:Beamrider|Beamrider]], and [[Software:Q*bert|Q*bert]].<ref>{{Cite web|url=https://venturebeat.com/2018/12/29/a-look-back-at-some-of-ais-biggest-video-game-wins-in-2018/|title=A look back at some of AI's biggest video game wins in 2018|date=2018-12-29|website=VentureBeat|language=en-US|access-date=2019-04-19}}</ref><ref>{{Cite arXiv|title=Playing Atari with Deep Reinforcement Learning|date=2013-12-19|eprint=1312.5602|language=en-US|last1=Mnih|first1=Volodymyr|last2=Kavukcuoglu|first2=Koray|last3=Silver|first3=David|last4=Graves|first4=Alex|last5=Antonoglou|first5=Ioannis|last6=Wierstra|first6=Daan|last7=Riedmiller|first7=Martin|class=cs.LG}}</ref> This work reportedly led to the company's acquisition by Google.<ref name="arxiv medium" /> DeepMind's AI had been applied to video games made in the 1970s and [[Software:History of video games#1980s|1980s]]; work was ongoing for more complex 3D games such as ''[[Software:Quake (video game)|Quake]]'', which first appeared in the 1990s.<ref name="hassabis talk" />

In 2020, DeepMind published Agent57,<ref>{{Cite arXiv|title=Agent57: Outperforming the Atari Human Benchmark|date=2020-03-30|eprint=2003.13350|language=en-US|author1=Adrià Puigdomènech Badia|last2=Piot|first2=Bilal|last3=Kapturowski|first3=Steven|last4=Sprechmann|first4=Pablo|last5=Vitvitskyi|first5=Alex|last6=Guo|first6=Daniel|last7=Blundell|first7=Charles|class=cs.LG}}</ref><ref>{{Cite web|url=https://deepmind.com/blog/article/Agent57-Outperforming-the-human-Atari-benchmark|title=Agent57: Outperforming the Atari Human Benchmark|date=2020-03-31|website=DeepMind|language=en-US|access-date=2020-05-25}}</ref> an AI Agent which surpasses human level performance on all 57 games of the Atari2600 suite.<ref>{{cite news |last1=Linder |first1=Courtney |title=This AI Can Beat Humans At All 57 Atari Games |url=https://www.popularmechanics.com/culture/gaming/a32006038/deepmind-ai-atari-agent57/ |access-date=9 June 2020 |work=Popular Mechanics |date=2 April 2020}}</ref>

=== AlphaGo and successors ===
{{Main|Software:AlphaGo|Software:AlphaGo Zero|AlphaZero|MuZero}}
In 2014, the company published research on computer systems that are able to play [[Software:Go (game)|Go]].<ref>{{cite book|title=Investigating the Limits of Monte-Carlo Tree Search Methods in Computer Go|date=12 July 2014|first1=Shih-Chieh |last1=Huang|first2=Martin |last2=Müller|publisher=Springer|doi=10.1007/978-3-319-09165-5_4|volume=8427|pages=39–48|series=Lecture Notes in Computer Science|isbn=978-3-319-09164-8|citeseerx=10.1.1.500.1701}}</ref>

In October 2015, a [[Software:Computer Go|computer Go]] program called AlphaGo, developed by DeepMind, beat the European Go champion Fan Hui, a [[Software:Go ranks and ratings|2 dan]] (out of 9 dan possible) professional, five to zero.<ref name="bbcgo">{{cite news|url=https://www.bbc.com/news/technology-35420579|title=Google achieves AI 'breakthrough' by beating Go champion|date=27 January 2016|newspaper=BBC News}}</ref> This was the first time an artificial intelligence (AI) defeated a professional Go player.<ref name="lemondego">{{cite news|url=http://www.lemonde.fr/pixels/article/2016/01/27/premiere-defaite-d-un-professionnel-du-go-contre-une-intelligence-artificielle_4854886_4408996.html|title=Première défaite d'un professionnel du go contre une intelligence artificielle|language=fr|date=27 January 2016|work=Le Monde}}</ref> Previously, computers were only known to have played Go at "amateur" level.<ref name="bbcgo" /><ref name="googlego">{{cite web|url=http://googleresearch.blogspot.com/2016/01/alphago-mastering-ancient-game-of-go.html|title=Research Blog: AlphaGo: Mastering the ancient game of Go with Machine Learning|date=27 January 2016|work=Google Research Blog}}</ref> 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 search|brute-force]].<ref name="bbcgo" /><ref name="googlego" />

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 [[Software:Future of Go Summit|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.<ref>{{Cite web|url=http://www.goratings.org/|title=World's Go Player Ratings|date=May 2017}}</ref><ref>{{Cite web|title=柯洁迎19岁生日 雄踞人类世界排名第一已两年|url=http://sports.sina.com.cn/go/2016-08-02/doc-ifxunyya3020238.shtml|language=zh|date=May 2017}}</ref> It used a [[Supervised learning|supervised learning]] protocol, studying large numbers of games played by humans against each other.<ref>{{Cite news|url=https://www.economist.com/news/science-and-technology/21730391-learning-play-go-only-start-latest-ai-can-work-things-out-without|title=The latest AI can work things out without being taught|newspaper=The Economist|access-date=19 October 2017|language=en}}</ref>

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.<ref>{{Cite news|url=https://www.bbc.com/news/technology-41668701|title=Google DeepMind: AI becomes more alien|last=Cellan-Jones|first=Rory|date=18 October 2017|work=BBC News|access-date=3 December 2017|language=en-GB}}</ref>

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 (reinforcement learning technique)|self-play]].

DeepMind researchers published a new model named MuZero that mastered the domains of [[Software:Go (game)|Go]], chess, shogi, and [[Engineering:Atari 2600|Atari 2600 games]] without human data, domain knowledge, or known rules.<ref>{{Cite web |title=MuZero: Mastering Go, chess, shogi and Atari without rules |url=https://www.deepmind.com/blog/muzero-mastering-go-chess-shogi-and-atari-without-rules |access-date=2022-04-29 |website=www.deepmind.com |language=en}}</ref><ref>{{Cite journal |last1=Schrittwieser |first1=Julian |last2=Antonoglou |first2=Ioannis |last3=Hubert |first3=Thomas |last4=Simonyan |first4=Karen |last5=Sifre |first5=Laurent |last6=Schmitt |first6=Simon |last7=Guez |first7=Arthur |last8=Lockhart |first8=Edward |last9=Hassabis |first9=Demis |last10=Graepel |first10=Thore |last11=Lillicrap |first11=Timothy |date=2020-12-23 |title=Mastering Atari, Go, chess and shogi by planning with a learned model |url=https://www.nature.com/articles/s41586-020-03051-4.epdf?sharing_token=kTk-xTZpQOF8Ym8nTQK6EdRgN0jAjWel9jnR3ZoTv0PMSWGj38iNIyNOw_ooNp2BvzZ4nIcedo7GEXD7UmLqb0M_V_fop31mMY9VBBLNmGbm0K9jETKkZnJ9SgJ8Rwhp3ySvLuTcUr888puIYbngQ0fiMf45ZGDAQ7fUI66-u7Y= |journal=Nature |language=en |volume=588 |issue=7839 |pages=604–609 |doi=10.1038/s41586-020-03051-4 |pmid=33361790 |arxiv=1911.08265 |bibcode=2020Natur.588..604S |s2cid=208158225 |issn=0028-0836}}</ref>

Researchers applied MuZero to solve the real world challenge of video compression with a set number of bits with respect to Internet traffic on sites such as [[Company:YouTube|YouTube]], [[Company:Twitch (service)|Twitch]], and [[Software:Google Meet|Google Meet]]. The goal of MuZero is to optimally compress the video so the quality of the video is maintained with a reduction in data. The final result using MuZero was a 6.28% average reduction in bitrate.<ref>{{Cite web |title=MuZero's first step from research into the real world |url=https://www.deepmind.com/blog/muzeros-first-step-from-research-into-the-real-world |access-date=2022-04-29 |website=www.deepmind.com |language=en}}</ref><ref>{{cite arXiv |last1=Mandhane |first1=Amol |last2=Zhernov |first2=Anton |last3=Rauh |first3=Maribeth |last4=Gu |first4=Chenjie |last5=Wang |first5=Miaosen |last6=Xue |first6=Flora |last7=Shang |first7=Wendy |last8=Pang |first8=Derek |last9=Claus |first9=Rene |last10=Chiang |first10=Ching-Han |last11=Chen |first11=Cheng |date=2022-02-14 |title=MuZero with Self-competition for Rate Control in VP9 Video Compression |class=eess.IV |eprint=2202.06626 }}</ref>

In October 2022, DeepMind unveiled a new version of AlphaZero, called AlphaTensor, in a paper published in ''Nature''.<ref name=AlphaTensor1>{{cite journal |url=https://www.nature.com/articles/d41586-022-03166-w |title=DeepMind AI invents faster algorithms to solve tough maths puzzles |date=5 October 2022 |journal=Nature |last=Hutson |first=Matthew|doi=10.1038/d41586-022-03166-w |pmid=36198824 |s2cid=252737506 }}</ref><ref name=AlphaTensor2>{{cite web |url=https://www.technologyreview.com/2022/10/05/1060717/deepmind-uses-its-game-playing-ai-to-best-a-50-year-old-record-in-computer-science/ |title=DeepMind's game-playing AI has beaten a 50-year-old record in computer science |date=5 October 2022 |website=MIT Technology Review |first=Will Douglas |last=Heaven}}</ref> The version discovered a faster way to perform [[Matrix multiplication|matrix multiplication]]{{snd}}one of the most fundamental tasks in computing{{snd}}using reinforcement learning.<ref name=AlphaTensor1 /><ref name=AlphaTensor2 /> For example, AlphaTensor figured out how to multiply two [[Modular arithmetic|mod-2]] 4x4 matrices in only 47 multiplications, unexpectedly beating the 1969 Strassen algorithm record of 49 multiplications.<ref>{{cite news |date=November 2022 |title=AI Reveals New Possibilities in Matrix Multiplication |work=Quanta Magazine |url=https://www.quantamagazine.org/ai-reveals-new-possibilities-in-matrix-multiplication-20221123/ |access-date=26 November 2022}}</ref>

==== Technology ====
AlphaGo technology was developed based on the deep [[Reinforcement learning|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 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.{{fact|date=November 2022}}

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|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.<ref name=":0">{{cite journal |first1=David |last1=Silver|first2= Julian|last2= Schrittwieser|first3= Karen|last3= Simonyan|first4= Ioannis|last4= Antonoglou|first5= Aja|last5= Huang|author-link5=Aja Huang|first6=Arthur|last6= Guez|first7= Thomas|last7= Hubert|first8= Lucas|last8= Baker|first9= Matthew|last9= Lai|first10= Adrian|last10= Bolton|first11= Yutian|last11= Chen|author-link11=Chen Yutian|first12= Timothy|last12= Lillicrap|first13=Hui|last13= Fan|author-link13=Fan Hui|first14= Laurent|last14= Sifre|first15= George van den|last15= Driessche|first16= Thore|last16= Graepel|first17= Demis|last17= Hassabis |author-link17=Demis Hassabis|title=Mastering the game of Go without human knowledge|journal=Nature|issn= 0028-0836|pages=354–359|volume =550|issue =7676|doi =10.1038/nature24270|pmid=29052630|date=19 October 2017|bibcode=2017Natur.550..354S|s2cid=205261034|url= http://discovery.ucl.ac.uk/10045895/1/agz_unformatted_nature.pdf}}{{closed access}}</ref>

AlphaGo Zero was 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. A new reinforcement learning algorithm incorporates lookahead search inside the training loop.<ref name=":0" /> AlphaGo Zero employed around 15 people and millions in computing resources.<ref>{{Cite news|url=https://www.technologyreview.com/s/609141/alphago-zero-shows-machines-can-become-superhuman-without-any-help/|title=The world's smartest game-playing AI—DeepMind's AlphaGo—just got way smarter|last=Knight|first=Will|work=MIT Technology Review|access-date=19 October 2017|language=en}}</ref> Ultimately, it needed much less computing power than AlphaGo, running on four specialized AI processors (Google [[Engineering:Tensor processing unit|TPUs]]), instead of AlphaGo's 48.<ref>{{Cite news|url=https://www.theverge.com/2017/10/18/16495548/deepmind-ai-go-alphago-zero-self-taught|title=DeepMind's Go-playing AI doesn't need human help to beat us anymore|last=Vincent|first=James|date=18 October 2017|work=The Verge|access-date=19 October 2017}}</ref>

=== AlphaFold ===
{{Main|AlphaFold}}

In 2016, DeepMind turned its artificial intelligence to [[Biology:Protein structure prediction|protein folding]], a long-standing problem in [[Biology:Molecular biology|molecular biology]]. 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 ''{{wipe|The Guardian}}''.<ref>{{Cite web|url=https://www.theguardian.com/science/2018/dec/02/google-deepminds-ai-program-alphafold-predicts-3d-shapes-of-proteins|title=Google's DeepMind predicts 3D shapes of proteins|last=Sample|first=Ian|date=2018-12-02|website={{wipe|The Guardian}}|language=en|access-date=2018-12-03}}</ref> 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".<ref>{{Cite web|url=https://www.bbc.co.uk/news/science-environment-55133972|title=One of biology's biggest mysteries 'largely solved' by AI|last=Briggs|first=Helen|date=2020-11-30|website=BBC News|language=en|access-date=2020-11-30}}</ref><ref>{{Cite web|url=https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology|title=AlphaFold: a solution to a 50-year-old grand challenge in biology|publisher=DeepMind|date=2020-11-30|website=DeepMind|language=en|access-date=2020-11-30}}</ref><ref>{{cite news |last1=Shead |first1=Sam |title=DeepMind solves 50-year-old 'grand challenge' with protein folding A.I. |url=https://www.cnbc.com/2020/11/30/deepmind-solves-protein-folding-grand-challenge-with-alphafold-ai.html |access-date=30 November 2020 |publisher=cnbc.com |date=30 November 2020}}</ref>

In July 2021, the open-source RoseTTAFold and AlphaFold2 were released to allow scientists to run their own versions of the tools. A week later DeepMind announced that AlphaFold had completed its prediction of nearly all human proteins as well as the entire [[Biology:Proteome|proteome]]s of 20 other widely studied organisms.<ref>{{cite journal |title= What's next for AlphaFold and the AI protein-folding revolution|first=Ewen |last=Callaway|journal=Nature|volume=604|date=2022|issue= 7905|pages= 234–238|doi= 10.1038/d41586-022-00997-5 |pmid= 35418629|bibcode= 2022Natur.604..234C|s2cid= 248156195|doi-access= free}}</ref> The structures were released on the AlphaFold Protein Structure Database. In July 2022, it was announced that the predictions of over 200 million proteins, representing virtually all known proteins, would be released on the AlphaFold database.<ref name=geddes /><ref name="alphafold DB"/>

=== WaveNet and WaveRNN===
{{Main|WaveNet}}
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 [[Software:Google Assistant|Google Assistant]].<ref>{{cite news|title=Here's Why Google's Assistant Sounds More Realistic Than Ever Before|url=http://fortune.com/2017/10/05/google-assistant-deepmind-wavenet-speech-ai/|access-date=20 January 2018|work=Fortune|date=5 October 2017|language=en}}</ref><ref>{{cite news|last1=Gershgorn|first1=Dave|title=Google's voice-generating AI is now indistinguishable from humans|url=https://qz.com/1165775/googles-voice-generating-ai-is-now-indistinguishable-from-humans/|access-date=20 January 2018|work=Quartz}}</ref> In 2018 Google launched a commercial text-to-speech product, Cloud Text-to-Speech, based on WaveNet.<ref name="cnbc money">{{Cite news|url=https://www.cnbc.com/2018/03/31/how-google-makes-money-from-alphabets-deepmind-ai-research-group.html|title=Google is finding ways to make money from Alphabet's DeepMind A.I. technology|last=Novet|first=Jordan|date=31 March 2018|work=CNBC|access-date=3 April 2018}}</ref><ref>{{cite web|title=Introducing Cloud Text-to-Speech powered by DeepMind WaveNet technology|url=https://cloudplatform.googleblog.com/2018/03/introducing-Cloud-Text-to-Speech-powered-by-Deepmind-WaveNet-technology.html|website=Google Cloud Platform Blog|access-date=5 April 2018|language=en}}</ref>

In 2018, DeepMind introduced a more efficient model called WaveRNN co-developed with [[Company:Google AI|Google AI]].<ref>{{Cite web|url=https://deepmind.com/research/publications/efficient-neural-audio-synthesis|title=Efficient Neural Audio Synthesis|website=Deepmind|access-date=2020-04-01}}</ref><ref>{{Cite web|url=https://deepmind.com/blog/article/Using-WaveNet-technology-to-reunite-speech-impaired-users-with-their-original-voices|title=Using WaveNet technology to reunite speech-impaired users with their original voices|website=Deepmind|access-date=2020-04-01}}</ref> In 2020 WaveNetEQ, a packet loss concealment method based on a WaveRNN architecture, was presented.<ref>{{cite conference | last1=Stimberg | first1=Florian | last2=Narest | first2=Alex | last3=Bazzica | first3=Alessio | last4=Kolmodin | first4=Lennart | last5=Barrera Gonzalez | first5=Pablo | last6=Sharonova | first6=Olga | last7=Lundin | first7=Henrik | last8=Walters | first8=Thomas C. | title=2020 54th Asilomar Conference on Signals, Systems, and Computers | chapter=WaveNetEQ — Packet Loss Concealment with WaveRNN | publisher=IEEE | date=1 November 2020 | pages=672–676 | doi=10.1109/ieeeconf51394.2020.9443419 | isbn=978-0-7381-3126-9 }}</ref>  In 2019, Google started to roll WaveRNN with WavenetEQ out to [[Software:Google Duo|Google Duo]] users.<ref>{{Cite web|url=http://ai.googleblog.com/2020/04/improving-audio-quality-in-duo-with.html|title=Improving Audio Quality in Duo with WaveNetEQ|website=Google AI Blog|language=en|access-date=2020-04-01}}</ref>

=== AlphaStar ===
{{Main|Software:AlphaStar}}
In 2016, Hassabis discussed the game ''StarCraft'' as a future challenge, since it requires strategic thinking and handling imperfect information.<ref>{{cite web|url=https://www.theverge.com/2016/3/10/11192774/demis-hassabis-interview-alphago-google-deepmind-ai|title=DeepMind founder Demis Hassabis on how AI will shape the future|date=10 March 2016|website=The Verge}}</ref>

In January 2019, DeepMind introduced AlphaStar, a program playing the real-time strategy game ''[[Software:StarCraft II|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.<ref>{{Cite news|url=http://www.extremetech.com/gaming/284441-deepmind-ai-challenges-pro-starcraft-ii-players-wins-almost-every-match|title=DeepMind AI Challenges Pro StarCraft II Players, Wins Almost Every Match|date=24 January 2019|work=Extreme Tech|access-date=24 January 2019|language=en-GB}}</ref>

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.<ref>{{cite news|url=https://arstechnica.com/gadgets/2019/07/deepmind-ai-takes-on-the-public-in-starcraft-ii-multiplayer/|title=DeepMind AI is secretly lurking on the public StarCraft II 1v1 ladder|last1=Amadeo|first1=Ron|date=11 July 2019|work=Ars Technica|access-date=18 September 2019}}</ref><ref>{{Cite web|url=https://www.reddit.com/r/starcraft/comments/cgvu6r/i_played_against_alphastardeepmind/|title=I played against AlphaStar/Deepmind|access-date=2019-07-27|language=en|website=[[Company:Reddit|reddit]]|date=23 July 2019}}</ref> 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.<ref>{{Cite news|url=https://deepmind.com/blog/article/AlphaStar-Grandmaster-level-in-StarCraft-II-using-multi-agent-reinforcement-learning|title=AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning|date=31 October 2019|website=DeepMind Blog|access-date=31 October 2019|language=en-GB}}</ref>

=== AlphaCode ===
In 2022, DeepMind unveiled AlphaCode, an AI-powered coding engine that creates computer programs at a rate comparable to that of an average programmer, with the company testing the system against coding challenges created by Codeforces utilized in human competitive programming competitions.<ref>{{Cite web |last=Vincent |first=James |date=February 2, 2022 |title=DeepMind says its new AI coding engine is as good as an average human programmer |url=https://www.theverge.com/2022/2/2/22914085/alphacode-ai-coding-program-automatic-deepmind-codeforce |url-status=live |archive-url=https://web.archive.org/web/20220202163608/https://www.theverge.com/2022/2/2/22914085/alphacode-ai-coding-program-automatic-deepmind-codeforce |archive-date=February 2, 2022 |access-date=February 3, 2022 |website=[[The Verge]]}}</ref> AlphaCode earned a rank equivalent to 54% of the median score on Codeforces after being trained on [[GitHub]] data and Codeforce problems and solutions. The program was required to come up with a unique solution and stopped from duplicating answers.
=== Gato ===
{{main|Software:Gato (DeepMind)}}
Gato is a "generalist agent" that learns multiple tasks simultaneously.
=== Miscellaneous contributions to Google ===
Google has stated that DeepMind algorithms have greatly increased the efficiency of cooling its data centers.<ref>{{cite web|title=DeepMind AI Reduces Google Data Centre Cooling Bill by 40% |url=https://www.deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-by-40 |website=DeepMind Blog}} 20 July 2016.</ref> In addition, DeepMind (alongside other Alphabet AI researchers) assists [[Software:Google Play|Google Play's]] personalized app recommendations.<ref name="cnbc money"/> DeepMind has also collaborated with the [[Software:Android (operating system)|Android]] team at [[Company:Google|Google]] for the creation of two new features which were made available to people with devices running [[Software:Android (operating system)|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.<ref>{{cite web |title=DeepMind, meet Android {{!}} DeepMind |url=https://deepmind.com/blog/deepmind-meet-android/ |website=DeepMind Blog}} 8 May 2018.</ref>

=== Sports ===
DeepMind researchers have applied machine learning models to the sport of football, often referred to as soccer in North America, to modelling the behaviour of football players, including the goalkeeper, defenders, and strikers during different scenarios such as penalty kicks. The researchers used heat maps and cluster analysis to organize players based on their tendency to behave a certain way during the game when confronted with a decision on how to score or prevent the other team from scoring. The researchers mention that machine learning models could be used to democratize the football industry by automatically selecting interesting video clips of the game that serve as highlights. This can be done by searching videos for certain events, which is possible because video analysis is an established field of machine learning. This is also possible because of extensive sports analytics based on data including annotated passes or shots, sensors that capture data about the players movements many times over the course of a game, and game theory models.<ref>{{Cite web |title=Advancing sports analytics through AI research |url=https://www.deepmind.com/blog/advancing-sports-analytics-through-ai-research |access-date=2022-04-29 |website=www.deepmind.com |language=en}}</ref><ref>{{Cite journal |last1=Tuyls |first1=Karl |last2=Omidshafiei |first2=Shayegan |last3=Muller |first3=Paul |last4=Wang |first4=Zhe |last5=Connor |first5=Jerome |last6=Hennes |first6=Daniel |last7=Graham |first7=Ian |last8=Spearman |first8=William |last9=Waskett |first9=Tim |last10=Steel |first10=Dafydd |last11=Luc |first11=Pauline |date=2021-05-06 |title=Game Plan: What AI can do for Football, and What Football can do for AI |url=https://www.jair.org/index.php/jair/article/view/12505 |journal=Journal of Artificial Intelligence Research |language=en |volume=71 |pages=41–88 |doi=10.1613/jair.1.12505 |s2cid=227013043 |issn=1076-9757|doi-access=free }}</ref>

=== Archaeology ===
Google has unveiled a new archaeology document program named Ithaca after the home island of mythical hero Odysseys. The deep neural network helps researchers restore the empty text of damaged documents, identify the place they originated from, and give them a definite accurate date. The work builds on another text analysis network named Pythia.<ref>{{Cite web |title=Restoring ancient text using deep learning: a case study on Greek epigraphy |url=https://www.deepmind.com/publications/restoring-ancient-text-using-deep-learning-a-case-study-on-greek-epigraphy |access-date=2022-04-29 |website=www.deepmind.com |language=en}}</ref> The model Ithaca achieves 62% accuracy in restoring damaged texts, 71% location accuracy, and has a dating precision of 30 years. The tool has already been used by historians and ancient Greek archaeologists to make new discoveries in ancient Greek history. The team is working on extending the model to other ancient languages, including [[Social:Demotic (Egyptian)|Demotic]], [[Social:Akkadian language|Akkadian]], [[Social:Hebrew language|Hebrew]], and [[Social:Mayan languages|Mayan]].<ref>{{Cite web |title=Predicting the past with Ithaca |url=https://www.deepmind.com/blog/predicting-the-past-with-ithaca |access-date=2022-04-29 |website=www.deepmind.com |language=en}}</ref>

===Sparrow===
[[Sparrow (bot)|Sparrow]] is an artificial intelligence-powered [[Chatbot|chatbot]] developed by DeepMind to build safer machine learning systems by using a mix of human feedback and Google search suggestions.<ref>[https://www.marktechpost.com/2022/09/28/deepmind-introduces-sparrow-an-artificial-intelligence-powered-chatbot-developed-to-build-safer-machine-learning-systems/ Gupta, K.,  "Deepmind Introduces ‘Sparrow,’ An Artificial Intelligence-Powered Chatbot Developed To Build Safer Machine Learning Systems.", Marktechpost, 2022, September 28]</ref>

===Chinchilla AI===
[[Chinchilla AI]] is a language model developed by DeepMind.<ref>[https://dataconomy.com/2023/01/what-is-chinchilla-ai-chatbot-deepmind/ Eray Eliaçık, "Chinchilla AI is coming for the GPT-3’s throne", Dataconomy, January 12, 2023]</ref>

== DeepMind Health ==
In July 2016, a collaboration between DeepMind and Moorfields Eye Hospital was announced to develop [[Medicine:Artificial intelligence in healthcare|AI applications for healthcare]].<ref>{{cite news|url=https://www.bbc.com/news/technology-36713308|title=Google's DeepMind to peek at NHS eye scans for disease analysis|date=6 July 2016|publisher=BBC|last1=Baraniuk|first1=Chris|access-date=6 July 2016}}</ref> DeepMind would be applied to the analysis of [[Data anonymization|anonymised]] eye scans, searching for early signs of diseases leading to [[Medicine:Blindness|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.<ref>{{cite news|url=https://www.bbc.co.uk/news/technology-37230806|title=Google DeepMind targets NHS head and neck cancer treatment|date=31 August 2016|publisher=BBC|last1=Baraniuk|first1=Chris|access-date=5 September 2016}}</ref>

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.<ref>{{cite news|title=DeepMind announces second NHS partnership|url=http://www.itpro.co.uk/public-sector/27833/deepmind-announces-second-nhs-partnership|access-date=23 December 2016|publisher=IT Pro|date=23 December 2016}}</ref>  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 changes 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.<ref>{{cite news|title=Google DeepMind's Streams technology branded 'phenomenal'|url=https://www.digitalhealth.net/2017/12/google-deepmind-streams-royal-free/|access-date=23 December 2017|publisher=Digital Health|date=4 December 2017}}</ref>{{unreliable source?|reason=Personally never heard of digitalhealth.net, quick search didn't produce evidence that it passes WP:RS or WP:DUE. Inclusion of a customer testimonial from the hospital may regardless be problematic.|date=December 2017}}

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.<ref>{{cite web|url=https://siliconangle.com/blog/2017/11/24/google-deepmind-announces-new-research-partnership-fight-breast-cancer-ai/|title=Google DeepMind announces new research partnership to fight breast cancer with AI|date=24 November 2017|website=Silicon Angle}}</ref> 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.<ref>{{cite web|url=https://venturebeat.com/2018/02/22/googles-deepmind-wants-ai-to-spot-kidney-injuries/|title=Google's DeepMind wants AI to spot kidney injuries|date=22 February 2018|website=Venture Beat}}</ref>

DeepMind developed an app called Streams, which sends alerts to doctors about patients at risk of acute kidney injury.<ref>{{cite web |last=Evenstad |first=Lis |url=https://www.computerweekly.com/news/252443164/DeepMind-Health-must-be-transparent-to-gain-public-trust-review-finds |title=DeepMind Health must be transparent to gain public trust, review finds |website=ComputerWeekly.com |date=15 June 2018 |access-date=14 November 2018}}</ref> On 13 November 2018, DeepMind announced that its health division and the Streams app would be absorbed into [[Medicine:Google Health|Google Health]].<ref>{{cite news |last=Vincent |first=James |url=https://www.theverge.com/2018/11/13/18091774/google-deepmind-health-absorbing-streams-team-ai-assistant-nurse-doctor|title=Google is absorbing DeepMind's health care unit to create an 'AI assistant for nurses and doctors' |work=The Verge |date=13 November 2018 |access-date=14 November 2018}}</ref> 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.<ref>{{cite news |last=Hern |first=Alex |url=https://www.theguardian.com/technology/2018/nov/14/google-betrays-patient-trust-deepmind-healthcare-move |title=Google 'betrays patient trust' with DeepMind Health move |date=14 November 2018 |newspaper=The Guardian |access-date=14 November 2018}}</ref><ref>{{cite magazine |last=Stokel-Walker |first=Chris |url=https://www.wired.co.uk/article/google-deepmind-nhs-health-data |title=Why Google consuming DeepMind Health is scaring privacy experts |magazine=Wired |date=14 November 2018 |access-date=15 November 2018}}</ref> A spokesman for DeepMind said that patient data would still be kept separate from Google services or projects.<ref>{{cite news |last= Murphy |first=Margi |url= https://www.telegraph.co.uk/technology/2018/11/14/deepmind-boss-defends-controversial-google-health-deal/ |archive-url=https://ghostarchive.org/archive/20220112/https://www.telegraph.co.uk/technology/2018/11/14/deepmind-boss-defends-controversial-google-health-deal/ |archive-date=12 January 2022 |url-access=subscription |url-status=live |title=DeepMind boss defends controversial Google health deal |date=14 November 2018 |work=The Telegraph |access-date=14 November 2018}}{{cbignore}}</ref>

=== NHS data-sharing controversy ===
In April 2016, ''New Scientist'' obtained a copy of a [[Data sharing|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 [[Medicine:Abortion|abortion]] in order to conduct research to seek better outcomes in various health conditions.<ref>{{cite news |url=https://www.newscientist.com/article/2086454-revealed-google-ai-has-access-to-huge-haul-of-nhs-patient-data |title=Revealed: Google AI has access to huge haul of NHS patient data |first=Hal |last=Hodson |work=New Scientist |date=29 April 2016 }}</ref><ref>{{cite news |url=https://www.newscientist.com/article/mg23030722-900-big-data-if-theres-nothing-to-hide-why-be-secretive/ |title=Leader: If Google has nothing to hide about NHS data, why so secretive? |work=New Scientist |date=4 May 2016 }}</ref>

A complaint was filed to the Information Commissioner's Office (ICO), arguing that the data should be pseudonymised and encrypted.<ref>{{cite news |url=http://www.computerweekly.com/news/450296175/ICO-probes-Google-DeepMind-patient-data-sharing-deal-with-NHS-Hospital-Trust |title=ICO probes Google DeepMind patient data-sharing deal with NHS Hospital Trust |first=Caroline |last=Donnelly |work=Computer Weekly |date=12 May 2016 }}</ref> 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 [[Organization:Medicines and Healthcare products Regulatory Agency|Medicines and Healthcare products Regulatory Agency]].<ref>{{cite news |url=https://www.newscientist.com/article/2088056-exclusive-googles-nhs-deal/ |title=Did Google's NHS patient data deal need ethical approval? |first=Hal |last=Hodson |work=New Scientist |date=25 May 2016 |access-date=28 May 2016 }}</ref>

In 2017, the ICO concluded a year-long investigation that focused on how the Royal Free NHS Foundation Trust tested the app, Streams, in late 2015 and 2016.<ref>{{Cite web|archive-url=https://web.archive.org/web/20180616142219/https://ico.org.uk/about-the-ico/news-and-events/news-and-blogs/2017/07/royal-free-google-deepmind-trial-failed-to-comply-with-data-protection-law/|url=https://ico.org.uk/about-the-ico/news-and-events/news-and-blogs/2017/07/royal-free-google-deepmind-trial-failed-to-comply-with-data-protection-law/|title=Royal Free - Google DeepMind trial failed to comply with data protection law|date=2017-08-17|website=ico.org.uk|language=en|access-date=2018-02-15|archive-date=16 June 2018 }}</ref> The ICO found that the Royal Free failed to comply with the Data Protection Act when it provided patient details to DeepMind, and found several shortcomings in how the data was handled, including that patients were not adequately informed that their data would be used as part of the test. DeepMind published its thoughts<ref>{{Cite web|url=https://deepmind.com/blog/ico-royal-free/|title=The Information Commissioner, the Royal Free, and what we've learned {{!}} DeepMind|website=DeepMind|access-date=2018-02-15}}</ref> on the investigation in July 2017, saying “we need to do better” and highlighting several activities and initiatives they had initiated for transparency, oversight and engagement. This included developing a patient and public involvement strategy<ref>{{Cite web|url=https://deepmind.com/applied/deepmind-health/patients/|title=For Patients {{!}} DeepMind|website=DeepMind|access-date=2018-02-15}}</ref> and being transparent in its partnerships.

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".<ref>{{cite news |url=http://news.sky.com/story/google-received-16-million-nhs-patients-data-on-an-inappropriate-legal-basis-10879142/ |title=Google received 1.6 million NHS patients' data on an 'inappropriate legal basis' |first=Alexander J |last=Martin |work=Sky News |date=15 May 2017 |access-date=16 May 2017 }}</ref> 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.<ref>{{cite news |first=Alex |last=Hern |url=https://www.theguardian.com/technology/2017/jul/03/google-deepmind-16m-patient-royal-free-deal-data-protection-act |title=Royal Free breached UK data law in 1.6m patient deal with Google's DeepMind |newspaper=The Guardian |date=3 July 2017}}</ref>

== DeepMind Ethics and Society ==
In October 2017, DeepMind  announced a new research unit, DeepMind Ethics & Society.<ref>{{Cite news|url=https://deepmind.com/blog/why-we-launched-deepmind-ethics-society/|title=Why we launched DeepMind Ethics & Society|work=DeepMind Blog|access-date=25 March 2018}}</ref> 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.<ref>{{Cite news|url=https://www.wired.co.uk/article/deepmind-ethics-and-society-artificial-intelligence|title=DeepMind's new AI ethics unit is the company's next big move|last=Temperton|first=James|work=Wired (UK)|access-date=3 December 2017}}</ref>

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.<ref>{{cite news|last1=Hern|first1=Alex|title=DeepMind announces ethics group to focus on problems of AI|url=https://www.theguardian.com/technology/2017/oct/04/google-deepmind-ai-artificial-intelligence-ethics-group-problems|access-date=8 December 2017|work={{wipe|The Guardian}}|date=4 October 2017}}</ref> The DeepMind Ethics and Society board is also distinct from the mooted AI Ethics Board that [[Company:Google|Google]] originally agreed to form when acquiring DeepMind.<ref>{{cite news |last1=Hern |first1=Alex |title=DeepMind announces ethics group to focus on problems of AI |url=https://www.theguardian.com/technology/2017/oct/04/google-deepmind-ai-artificial-intelligence-ethics-group-problems |access-date=12 June 2020 |work=The Guardian |date=4 October 2017}}</ref>

==DeepMind Professors of machine learning==
DeepMind sponsors three [[Social:Academic ranks in the United Kingdom|chairs]] of machine learning:

# At the [[Organization:University of Cambridge|University of Cambridge]], held by Neil Lawrence,<ref>{{Cite web|url=https://www.cam.ac.uk/research/news/cambridge-appoints-first-deepmind-professor-of-machine-learning|title=Cambridge appoints first DeepMind Professor of Machine Learning|date=18 September 2019|website=University of Cambridge}}</ref> in the Department of Computer Science and Technology,
# At the [[Organization:University of Oxford|University of Oxford]], held by [[Biography:Michael Bronstein|Michael Bronstein]],<ref>{{Cite web|url=http://www.cs.ox.ac.uk/news/1862-full.html|title=DeepMind funds new post at Oxford University – the DeepMind Professorship of Artificial Intelligence|website=Department of Computer Science}}</ref> in the Department of Computer Science, and
# At the [[Organization:University College London|University College London]], held by Marc Deisenroth,<ref>{{Cite web|url=https://www.ucl.ac.uk/news/2019/nov/deepmind-renews-its-commitment-ucl|title=DeepMind renews its commitment to UCL |date=29 March 2021|website=University College London}}</ref> in the Department of Computer Science.

==See also==
* [[Glossary of artificial intelligence]]
* [[Company:OpenAI|OpenAI]]
* [[Company:Google Brain|Google Brain]]

== References ==
{{reflist|refs=
<ref name="nature2015">{{cite journal |last1= Mnih|first1= Volodymyr |last2= Kavukcuoglu|first2= Koray |last3= Silver|first3= David |date= 26 February 2015 |title= Human-level control through deep reinforcement learning|journal=Nature|volume= 518|issue= 7540|pages= 529–33|doi= 10.1038/nature14236 |pmid=25719670|bibcode= 2015Natur.518..529M |s2cid= 205242740 }}</ref>
}}

== External links ==
* [https://github.com/deepmind GitHub Repositories]

{{Differentiable computing}}
{{Existential risk from artificial intelligence}}


[[Category:Deep learning]]
[[Category:Game artificial intelligence]]
[[Category:Applied machine learning]]
[[Category:Companies (Software)]]
[[Category:Companies]]

{{Sourceattribution|DeepMind|1}}