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| 2018 || May 9 || {{w|AI}} development || DeepMind develops a neural network loosely modeled on mammalian brains, that is better at navigating a maze than humans.<ref>{{cite web |title=DeepMind AI developed navigation neurons to solve a maze like us |url=https://www.newscientist.com/article/2168406-deepmind-ai-developed-navigation-neurons-to-solve-a-maze-like-us/ |website=newscientist.com |accessdate=1 June 2019}}</ref><ref>{{cite web |last1=Sample |first1=Ian |title=Google DeepMind's AI program learns human navigation skills |url=https://www.theguardian.com/technology/2018/may/09/googles-ai-program-deepmind-learns-human-navigation-skills |website=theguardian.com |accessdate=1 June 2019}}</ref><ref>{{cite web |last1=Gent |first1=Edd |title=This DeepMind AI Spontaneously Developed Digital Navigation ‘Neurons’ Like Ours |url=https://singularityhub.com/2018/05/14/this-deepmind-ai-spontaneously-developed-navigation-neurons-like-ours/ |website=singularityhub.com |accessdate=1 June 2019}}</ref><ref>{{cite web |last1=Quach |first1=Katyanna |title=DeepMind: Get a load of our rat-like AI. 'Ere, look. It solves mazes and stuff |url=https://www.theregister.co.uk/2018/05/10/deepmind_rat_like_ai_mazes/ |website=theregister.co.uk |accessdate=1 June 2019}}</ref>
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| 2018 || June 14 || {{w|AI}} development || DeepMind develops a neural network that teaches itself to ‘imagine’ a scene from different viewpoints, based on just a single image. The new type of computer vision algorithm can generate 3D models of a scene from 2D snapshots. It can tease out , unraveling details from the static images to guess at and solving spatial relationships, including the camera’s position. Dubbed a Generative Query Network (GQN), the system gets rid of labels and focuses on what's known as {{w|unsupervised learning}}.<ref>{{cite web |last1=Whyte |first1=Chelsea |title=DeepMind’s AI can ‘imagine’ a world based on a single picture |url=https://www.newscientist.com/article/2171675-deepminds-ai-can-imagine-a-world-based-on-a-single-picture/ |website=newscientist.com |accessdate=1 June 2019}}</ref><ref>{{cite web |last1=Bogle |first1=Ariel |title=Who needs humans? Google's DeepMind algorithm can teach itself to see |url=https://www.abc.net.au/news/science/2018-06-15/googles-deepmind-algorithm-can-teach-itself-to-see/9861590 |website=abc.net |accessdate=1 June 2019}}</ref><ref>{{cite web |last1=Wiggers |first1=Kyle |title=Google’s DeepMind develops AI that can render 3D objects from 2D pictures |url=https://venturebeat.com/2018/06/14/googles-deepmind-develops-ai-that-can-render-3d-objects-from-2d-pictures/ |website=venturebeat.com |accessdate=1 June 2019}}</ref><ref>{{cite web |title=Neural scene representation and rendering |url=https://science.sciencemag.org/content/360/6394/1204 |website=science.sciencemag.org |accessdate=1 June 2019}}</ref>
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| 2018 || June 15 || Controversy || The DeepMind Health Independent Reviewers’ 2018 report warns about the potential for DeepMind Health to be able to “exert excessive monopoly power” as a result of the data access and streaming infrastructure that’s bundled with provision of the Streams app, which would position DeepMind as the access-controlling intermediary between the structured health data and any other third parties.<ref>{{cite web |last1=Lomas |first1=Natasha |title=UK report warns DeepMind Health could gain ‘excessive monopoly power’ |url=https://techcrunch.com/2018/06/15/uk-report-warns-deepmind-health-could-gain-excessive-monopoly-power/ |website=techcrunch.com |accessdate=3 June 2019}}</ref><ref>{{cite web |last1=Lomas |first1=Natasha |title=Building health AIs should be UK ambition, says strategy review |url=https://techcrunch.com/2017/08/31/building-health-ais-should-be-uk-ambition-says-strategy-review/ |website=techcrunch.com |accessdate=3 June 2019}}</ref>
