Noam Shazeer
E457852
Noam Shazeer is an AI researcher and engineer best known as a co-creator of the Transformer architecture and a key contributor to large-scale neural language models.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Noam Shazeer canonical | 9 |
How this entity was disambiguated
This entity first appeared as the object of triple T4651097 — 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: Noam Shazeer Context triple: [Transformer, introducedBy, Noam Shazeer]
-
A.
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.
-
B.
Jonathon Shlens
Jonathon Shlens is a computer scientist and researcher known for his contributions to deep learning and computer vision, including influential work at Google.
-
C.
Nicolas Heess
Nicolas Heess is a machine learning researcher known for his work in deep reinforcement learning, including contributions to algorithms such as Deep Deterministic Policy Gradient (DDPG).
-
D.
Yasha Mazur
Yasha Mazur is the conflicted Jewish magician and acrobat at the center of Isaac Bashevis Singer’s novel, whose personal struggles with faith, love, and morality drive the story’s tragic arc.
-
E.
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.
- 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: Noam Shazeer Target entity description: Noam Shazeer is an AI researcher and engineer best known as a co-creator of the Transformer architecture and a key contributor to large-scale neural language models.
-
A.
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.
-
B.
Jonathon Shlens
Jonathon Shlens is a computer scientist and researcher known for his contributions to deep learning and computer vision, including influential work at Google.
-
C.
Nicolas Heess
Nicolas Heess is a machine learning researcher known for his work in deep reinforcement learning, including contributions to algorithms such as Deep Deterministic Policy Gradient (DDPG).
-
D.
Yasha Mazur
Yasha Mazur is the conflicted Jewish magician and acrobat at the center of Isaac Bashevis Singer’s novel, whose personal struggles with faith, love, and morality drive the story’s tragic arc.
-
E.
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.
- F. None of above. chosen
Statements (45)
| Predicate | Object |
|---|---|
| instanceOf |
artificial intelligence researcher
ⓘ
computer scientist ⓘ person ⓘ software engineer ⓘ |
| coAuthorOf |
Attention Is All You Need
ⓘ
Exploring the Limits of Language Modeling ⓘ On Layer Normalization in the Transformer Architecture ⓘ Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer ⓘ
linked to:
Sparsely-Gated Mixture-of-Experts layer
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity ⓘ |
| coAuthorWith |
Aidan N. Gomez
ⓘ
Ashish Vaswani ⓘ Illia Polosukhin ⓘ Jakob Uszkoreit ⓘ Llion Jones ⓘ Niki Parmar ⓘ Łukasz Kaiser ⓘ
linked to:
Lukasz Kaiser
|
| contributedTo |
Google’s large-scale language models
ⓘ
Transformer architecture ⓘ |
| developed |
Sparsely-Gated Mixture-of-Experts layer
ⓘ
Switch Transformer architecture ⓘ |
| educatedAt | Brown University ⓘ |
| employer |
Google
ⓘ
Google Brain ⓘ Google Research ⓘ |
| fieldOfWork |
artificial intelligence
ⓘ
large language models ⓘ machine learning ⓘ natural language processing ⓘ |
| gender | male ⓘ |
| knownFor |
Mixture-of-Experts models
ⓘ
Sparsely-Gated Mixture-of-Experts layer ⓘ Switch Transformer ⓘ
linked to:
Transformer
Tensor Processing Unit software contributions ⓘ co-creating the Transformer architecture ⓘ contributions to large-scale neural language models ⓘ work on attention mechanisms in neural networks ⓘ |
| nationality | American ⓘ |
| notableWork |
Sparsely-Gated Mixture-of-Experts layer
ⓘ
Switch Transformer model ⓘ Transformer architecture ⓘ
linked to:
Transformer
|
| positionHeld | research scientist at Google ⓘ |
| researchInterest |
efficient training of large models
ⓘ
neural network architectures ⓘ scaling laws for neural networks ⓘ sequence modeling ⓘ |
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: Noam Shazeer Description of subject: Noam Shazeer is an AI researcher and engineer best known as a co-creator of the Transformer architecture and a key contributor to large-scale neural language models.
Referenced by (9)
Full triples — surface form annotated when it differs from this entity's canonical label.