Deep Learning: A Revolutionary Approach to Artificial Intelligence
E260037
"Deep Learning: A Revolutionary Approach to Artificial Intelligence" is a book by neuroscientist and AI researcher Terrence Sejnowski that explains the principles, history, and impact of deep learning for a broad audience.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Deep Learning: A Revolutionary Approach to Artificial Intelligence canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T2373534 — 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.
Target entity: Deep Learning: A Revolutionary Approach to Artificial Intelligence Context triple: [Terrence Sejnowski, notableWork, Deep Learning: A Revolutionary Approach to Artificial Intelligence]
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A.
Deep Learning (book)
Deep Learning (book) is a foundational textbook that systematically introduces the theory and practice of modern deep neural networks, co-authored by leading researchers including Yoshua Bengio.
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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.
“Learning representations by back-propagating errors”
“Learning representations by back-propagating errors” is a landmark 1986 research paper that popularized the backpropagation algorithm for training multi-layer neural networks, helping to launch the modern field of deep learning.
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D.
Gradient-based learning applied to document recognition
"Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
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E.
Large-Scale Distributed Deep Networks
Large-Scale Distributed Deep Networks is a seminal research work that introduced methods for training deep neural networks efficiently across large-scale distributed computing infrastructure, enabling breakthroughs in modern large-scale AI systems.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Target entity: Deep Learning: A Revolutionary Approach to Artificial Intelligence Target entity description: "Deep Learning: A Revolutionary Approach to Artificial Intelligence" is a book by neuroscientist and AI researcher Terrence Sejnowski that explains the principles, history, and impact of deep learning for a broad audience.
-
A.
Deep Learning (book)
Deep Learning (book) is a foundational textbook that systematically introduces the theory and practice of modern deep neural networks, co-authored by leading researchers including Yoshua Bengio.
-
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.
“Learning representations by back-propagating errors”
“Learning representations by back-propagating errors” is a landmark 1986 research paper that popularized the backpropagation algorithm for training multi-layer neural networks, helping to launch the modern field of deep learning.
-
D.
Gradient-based learning applied to document recognition
"Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
-
E.
Large-Scale Distributed Deep Networks
Large-Scale Distributed Deep Networks is a seminal research work that introduced methods for training deep neural networks efficiently across large-scale distributed computing infrastructure, enabling breakthroughs in modern large-scale AI systems.
- F. None of above. chosen
Statements (41)
| Predicate | Object |
|---|---|
| instanceOf | book ⓘ |
| aimsTo |
discuss societal impact of deep learning
ⓘ
make deep learning understandable to lay readers ⓘ place deep learning in historical context ⓘ |
| author |
Terrence Sejnowski
ⓘ
surface form:
Terrence J. Sejnowski
Terrence Sejnowski ⓘ |
| countryOfPublication |
United States of America
ⓘ
surface form:
United States
|
| covers |
applications of deep learning
ⓘ
backpropagation ⓘ history of artificial intelligence ⓘ neural networks ⓘ relationship between neuroscience and AI ⓘ representation learning ⓘ |
| describes |
connections between brain function and deep networks
ⓘ
how deep learning systems learn from data ⓘ |
| discusses |
deep learning breakthroughs in the 2010s
ⓘ
ethical and social implications of AI ⓘ future of artificial intelligence ⓘ |
| explains |
history of deep learning
ⓘ
impact of deep learning ⓘ principles of deep learning ⓘ |
| field |
artificial intelligence
ⓘ
neuroscience ⓘ |
| genre |
non-fiction
ⓘ
popular science ⓘ |
| hasContributor | MIT Press ⓘ |
| hasForm |
ebook
ⓘ
print book ⓘ |
| hasTitle | Deep Learning: A Revolutionary Approach to Artificial Intelligence self-link ⓘ |
| intendedAudience |
general audience
ⓘ
non-specialists ⓘ |
| language | English ⓘ |
| mainSubject |
artificial intelligence
ⓘ
deep learning ⓘ machine learning ⓘ |
| perspectiveFrom |
artificial intelligence research
ⓘ
neuroscience ⓘ |
| publisher | MIT Press ⓘ |
| targetReader |
policy makers interested in AI impact
ⓘ
professionals in non-technical fields curious about AI ⓘ students interested in AI ⓘ |
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.
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.
Subject: Deep Learning: A Revolutionary Approach to Artificial Intelligence Description of subject: "Deep Learning: A Revolutionary Approach to Artificial Intelligence" is a book by neuroscientist and AI researcher Terrence Sejnowski that explains the principles, history, and impact of deep learning for a broad audience.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.