“From Machine Learning to Machine Reasoning”
E367292
“From Machine Learning to Machine Reasoning” is a scholarly work by Léon Bottou that explores how to extend traditional machine learning methods toward systems capable of more general, structured reasoning.
All labels observed (1)
| Label | Occurrences |
|---|---|
| “From Machine Learning to Machine Reasoning” canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T3542939 — 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: “From Machine Learning to Machine Reasoning” Context triple: [Léon Bottou, hasPublication, “From Machine Learning to Machine Reasoning”]
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A.
Universal Intelligence: A Definition of Machine Intelligence
"Universal Intelligence: A Definition of Machine Intelligence" is a foundational paper by Shane Legg (with Marcus Hutter) that formally defines and mathematically characterizes general machine intelligence using concepts from algorithmic information theory and reinforcement learning.
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B.
Cambrian intelligence: The early history of the new AI
Cambrian Intelligence: The Early History of the New AI is a book by roboticist Rodney Brooks that outlines his influential behavior-based approach to artificial intelligence and robotics in contrast to traditional symbolic AI.
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C.
Logical Methods in Computer Science
Logical Methods in Computer Science is a peer-reviewed open-access journal focusing on theoretical computer science, particularly logic and its applications to computer science.
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D.
Technical Committee on Robot Learning
The Technical Committee on Robot Learning is a specialized IEEE Robotics and Automation Society group that advances research and collaboration at the intersection of machine learning and robotics.
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E.
Soar cognitive architecture
The Soar cognitive architecture is a general-purpose framework for modeling and understanding human cognition through unified theories of problem solving, learning, and decision-making.
- 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: “From Machine Learning to Machine Reasoning” Target entity description: “From Machine Learning to Machine Reasoning” is a scholarly work by Léon Bottou that explores how to extend traditional machine learning methods toward systems capable of more general, structured reasoning.
-
A.
Universal Intelligence: A Definition of Machine Intelligence
"Universal Intelligence: A Definition of Machine Intelligence" is a foundational paper by Shane Legg (with Marcus Hutter) that formally defines and mathematically characterizes general machine intelligence using concepts from algorithmic information theory and reinforcement learning.
-
B.
Cambrian intelligence: The early history of the new AI
Cambrian Intelligence: The Early History of the New AI is a book by roboticist Rodney Brooks that outlines his influential behavior-based approach to artificial intelligence and robotics in contrast to traditional symbolic AI.
-
C.
Logical Methods in Computer Science
Logical Methods in Computer Science is a peer-reviewed open-access journal focusing on theoretical computer science, particularly logic and its applications to computer science.
-
D.
Technical Committee on Robot Learning
The Technical Committee on Robot Learning is a specialized IEEE Robotics and Automation Society group that advances research and collaboration at the intersection of machine learning and robotics.
-
E.
Soar cognitive architecture
The Soar cognitive architecture is a general-purpose framework for modeling and understanding human cognition through unified theories of problem solving, learning, and decision-making.
- F. None of above. chosen
Statements (43)
| Predicate | Object |
|---|---|
| instanceOf |
computer science paper
ⓘ
scholarly article ⓘ |
| aimsTo |
bridge gap between learning and reasoning
ⓘ
guide development of more general AI systems ⓘ |
| argues |
machine learning and reasoning should be integrated
ⓘ
reasoning can emerge from composition of learned components ⓘ symbolic and statistical approaches can be reconciled ⓘ |
| author | Léon Bottou ⓘ |
| contributesTo |
design of modular AI systems
ⓘ
theory of machine reasoning ⓘ understanding of deep learning architectures ⓘ |
| creator | Léon Bottou ⓘ |
| discusses |
data representations for reasoning
ⓘ
interfaces between modules in AI systems ⓘ learning algorithms for compositional structures ⓘ |
| field |
artificial intelligence
ⓘ
machine learning ⓘ machine reasoning ⓘ |
| focusesOn |
compositionality in learning systems
ⓘ
generalization beyond training distribution ⓘ limitations of traditional machine learning ⓘ scalable reasoning architectures ⓘ |
| hasPerspective |
emphasis on practical system design
ⓘ
engineering-oriented view of reasoning systems ⓘ |
| language | English ⓘ |
| mainTopic |
composition of learned modules
ⓘ
extension of machine learning to reasoning ⓘ inference mechanisms ⓘ learning procedures ⓘ modular learning systems ⓘ reasoning systems ⓘ representation learning ⓘ structured reasoning ⓘ |
| proposes |
framework for composing learned functions
ⓘ
incremental path from learning to reasoning ⓘ view of reasoning as algebraic manipulation of modules ⓘ |
| relatedTo |
AI architectures
ⓘ
deep learning ⓘ graphical models ⓘ neural networks ⓘ probabilistic reasoning ⓘ structured prediction ⓘ symbolic reasoning ⓘ |
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: “From Machine Learning to Machine Reasoning” Description of subject: “From Machine Learning to Machine Reasoning” is a scholarly work by Léon Bottou that explores how to extend traditional machine learning methods toward systems capable of more general, structured reasoning.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.