David Silver
E41249
David Silver is a leading artificial intelligence researcher best known for his work at DeepMind on reinforcement learning and the development of the AlphaGo system.
All labels observed (1)
| Label | Occurrences |
|---|---|
| David Silver canonical | 22 |
How this entity was disambiguated
This entity first appeared as the object of triple T307355 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David Silver Context triple: [DeepMind, hasKeyPerson, David Silver]
-
A.
Demis Hassabis
Demis Hassabis is a British artificial intelligence researcher, neuroscientist, and entrepreneur best known as the co-founder and CEO of DeepMind, a leading AI company acquired by Google.
-
B.
Shane Legg
Shane Legg is a computer scientist and AI researcher best known as a co-founder of DeepMind and for his influential work on artificial general intelligence.
-
C.
Ian Goodfellow
Ian Goodfellow is a machine learning researcher best known for inventing Generative Adversarial Networks (GANs) and co-authoring the influential textbook "Deep Learning."
-
D.
Ilya Sutskever
Ilya Sutskever is a leading artificial intelligence researcher and co-founder of OpenAI, known for his pioneering work in deep learning and neural networks.
-
E.
John Schulman
John Schulman is an AI researcher and entrepreneur best known as a co-founder of OpenAI and a key contributor to advances in deep reinforcement learning and large language models.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: David Silver Target entity description: David Silver is a leading artificial intelligence researcher best known for his work at DeepMind on reinforcement learning and the development of the AlphaGo system.
-
A.
Demis Hassabis
Demis Hassabis is a British artificial intelligence researcher, neuroscientist, and entrepreneur best known as the co-founder and CEO of DeepMind, a leading AI company acquired by Google.
-
B.
Shane Legg
Shane Legg is a computer scientist and AI researcher best known as a co-founder of DeepMind and for his influential work on artificial general intelligence.
-
C.
Ian Goodfellow
Ian Goodfellow is a machine learning researcher best known for inventing Generative Adversarial Networks (GANs) and co-authoring the influential textbook "Deep Learning."
-
D.
Ilya Sutskever
Ilya Sutskever is a leading artificial intelligence researcher and co-founder of OpenAI, known for his pioneering work in deep learning and neural networks.
-
E.
John Schulman
John Schulman is an AI researcher and entrepreneur best known as a co-founder of OpenAI and a key contributor to advances in deep reinforcement learning and large language models.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
academic
ⓘ
artificial intelligence researcher ⓘ computer scientist ⓘ machine learning researcher ⓘ |
| academicDegree | PhD in computer science ⓘ |
| citizenship | United Kingdom ⓘ |
| coFounded |
Elixir Studios
ⓘ
Playfish ⓘ |
| doctoralAdvisor | Satinder Singh ⓘ |
| educatedAt |
University College London
ⓘ
University of Cambridge ⓘ
linked to:
Cambridge University
|
| employer |
Google DeepMind
ⓘ
linked to:
DeepMind
|
| fieldOfWork |
artificial intelligence
ⓘ
deep learning ⓘ machine learning ⓘ reinforcement learning ⓘ |
| hasTaughtAt | University College London ⓘ |
| honor |
Fellow of the Royal Society
ⓘ
linked to:
Fellow of the Royal Society (FRS)
|
| knownFor |
AlphaZero
ⓘ
MuZero ⓘ deep reinforcement learning ⓘ leading development of AlphaGo ⓘ reinforcement learning research ⓘ work at DeepMind ⓘ |
| memberOf | Royal Society ⓘ |
| notablePaper |
Mastering Atari, Go, chess and shogi by planning with a learned model
ⓘ
linked to:
MuZero
Mastering chess and shogi by self-play with a general reinforcement learning algorithm ⓘ
linked to:
AlphaZero
Mastering the game of Go with deep neural networks and tree search ⓘ
linked to:
AlphaGo
Mastering the game of Go without human knowledge ⓘ
linked to:
MuZero
|
| notableWork |
AlphaGo
ⓘ
AlphaGo Zero ⓘ AlphaZero ⓘ DQN-style deep reinforcement learning methods ⓘ MuZero ⓘ |
| position | principal research scientist at DeepMind ⓘ |
| publishedIn |
Nature
ⓘ
Science ⓘ |
| receivedAward |
ACM Prize in Computing
ⓘ
Royal Academy of Engineering Silver Medal ⓘ Royal Society Mullard Award ⓘ |
| researchInterest |
Monte Carlo tree search
ⓘ
game-playing AI ⓘ model-based reinforcement learning ⓘ planning in reinforcement learning ⓘ |
| taughtCourse | UCL course on reinforcement learning ⓘ |
| thesisTopic | reinforcement learning ⓘ |
| worksAt |
Google DeepMind
ⓘ
linked to:
DeepMind
|
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: David Silver Description of subject: David Silver is a leading artificial intelligence researcher best known for his work at DeepMind on reinforcement learning and the development of the AlphaGo system.
Referenced by (22)
Full triples — surface form annotated when it differs from this entity's canonical label.