universal intelligence measure
E217170
formal measure of intelligence
mathematical framework
theoretical construct in artificial intelligence
The universal intelligence measure is a formal, mathematical framework proposed to quantify and compare the general intelligence of agents across all possible environments.
All labels observed (2)
| Label | Occurrences |
|---|---|
| universal intelligence (measure) | 1 |
| universal intelligence measure canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T1923002 — 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: universal intelligence measure Context triple: [Shane Legg, hasConcept, universal intelligence measure]
-
A.
Superintelligence: Paths, Dangers, Strategies
Superintelligence: Paths, Dangers, Strategies is a 2014 book by philosopher Nick Bostrom that analyzes the potential development of superhuman artificial intelligence and the existential risks and strategic challenges it could pose to humanity.
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B.
Kolmogorov complexity
Kolmogorov complexity is a measure of the amount of information in an object, defined as the length of the shortest computer program that can produce it.
-
C.
Turing test
The Turing test is a benchmark in artificial intelligence that evaluates a machine's ability to exhibit human-like intelligence by determining whether its responses are indistinguishable from those of a human in conversation.
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D.
Blum complexity measures
Blum complexity measures are a formal framework in computational complexity theory that rigorously define and compare the resource usage (such as time or space) of algorithms via axiomatic conditions.
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E.
Kurzweil Applied Intelligence
Kurzweil Applied Intelligence is a technology company known for pioneering speech recognition and artificial intelligence software applications.
- 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: universal intelligence measure Target entity description: The universal intelligence measure is a formal, mathematical framework proposed to quantify and compare the general intelligence of agents across all possible environments.
-
A.
Superintelligence: Paths, Dangers, Strategies
Superintelligence: Paths, Dangers, Strategies is a 2014 book by philosopher Nick Bostrom that analyzes the potential development of superhuman artificial intelligence and the existential risks and strategic challenges it could pose to humanity.
-
B.
Kolmogorov complexity
Kolmogorov complexity is a measure of the amount of information in an object, defined as the length of the shortest computer program that can produce it.
-
C.
Turing test
The Turing test is a benchmark in artificial intelligence that evaluates a machine's ability to exhibit human-like intelligence by determining whether its responses are indistinguishable from those of a human in conversation.
-
D.
Blum complexity measures
Blum complexity measures are a formal framework in computational complexity theory that rigorously define and compare the resource usage (such as time or space) of algorithms via axiomatic conditions.
-
E.
Kurzweil Applied Intelligence
Kurzweil Applied Intelligence is a technology company known for pioneering speech recognition and artificial intelligence software applications.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
formal measure of intelligence
ⓘ
mathematical framework ⓘ theoretical construct in artificial intelligence ⓘ |
| alternativeTo |
IQ-like psychometric tests for machines
ⓘ
task-specific performance metrics ⓘ |
| assumes |
agents interacting sequentially with environments
ⓘ
computable environments ⓘ |
| basedOn |
Kolmogorov complexity
ⓘ
Solomonoff induction ⓘ algorithmic probability ⓘ |
| characteristic |
model-agnostic
ⓘ
not directly computable in practice ⓘ task-agnostic ⓘ theoretically grounded ⓘ |
| coreIdea | intelligence as ability to achieve goals in a wide range of environments ⓘ |
| critiquedFor |
dependence on choice of universal Turing machine
ⓘ
incomputability ⓘ limited practical applicability ⓘ |
| describedIn | A Formal Measure of Machine Intelligence ⓘ |
| field |
algorithmic information theory
ⓘ
artificial intelligence ⓘ machine learning ⓘ theoretical computer science ⓘ |
| formalizedAs | infinite sum over all computable environments ⓘ |
| hasMathematicalProperty | upper-bounds achievable performance of any computable agent in the AIXI framework ⓘ |
| influenced | research on general AI benchmarks ⓘ |
| influencedBy |
Andrey Kolmogorov
ⓘ
linked to:
Andrei Kolmogorov
Ray Solomonoff ⓘ |
| languageOfFormulation | mathematics ⓘ |
| proposedBy |
Marcus Hutter
ⓘ
Shane Legg ⓘ |
| publicationYear | 2007 ⓘ |
| purpose |
compare intelligence of different agents
ⓘ
quantify general intelligence of agents ⓘ |
| relatedConcept |
AGI evaluation
ⓘ
general intelligence ⓘ universal prior over environments ⓘ |
| relatedTo |
AIXI
ⓘ
linked to:
AIXI model
universal artificial intelligence ⓘ |
| scope |
all computable environments
ⓘ
all possible environments weighted by simplicity ⓘ |
| typicalAgentModel | reinforcement learning agent ⓘ |
| usesConcept |
environmental distributions
ⓘ
expected discounted reward ⓘ universal prior ⓘ |
| weightsEnvironmentsBy | algorithmic complexity ⓘ |
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: universal intelligence measure Description of subject: The universal intelligence measure is a formal, mathematical framework proposed to quantify and compare the general intelligence of agents across all possible environments.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.
Universal Intelligence: A Definition of Machine Intelligence
→
hasAbbreviation
→
universal intelligence (measure)
ⓘ
linked to: universal intelligence measure