Statements (53)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:physicist
|
| gptkbp:competesWith |
machine learning
|
| gptkbp:focusesOn |
rule-based systems
|
| https://www.w3.org/2000/01/rdf-schema#label |
traditional AI
|
| gptkbp:isAssociatedWith |
Turing test
inference engines knowledge bases |
| gptkbp:isAvenueFor |
natural language processing
automated reasoning game playing |
| gptkbp:isBasedOn |
logical deduction
|
| gptkbp:isChallengedBy |
big data
cloud computing complex real-world problems emergent technologies |
| gptkbp:isCharacterizedBy |
expert systems
|
| gptkbp:isCriticizedFor |
high maintenance costs
lack of adaptability difficulty in scaling lack of common sense reasoning rigidity in problem-solving |
| gptkbp:isEvaluatedBy |
performance metrics
efficiency accuracy |
| gptkbp:isExploredIn |
academic research
industry applications government projects |
| gptkbp:isInfluencedBy |
cognitive science
philosophy of mind |
| gptkbp:isPartOf |
AI research
|
| gptkbp:isRelatedTo |
knowledge representation
search algorithms heuristic methods |
| gptkbp:isSupportedBy |
formal languages
logical frameworks |
| gptkbp:isUsedFor |
data analysis
decision support systems automated control systems |
| gptkbp:isUsedIn |
robotics
planning systems diagnostic_systems |
| gptkbp:isVisitedBy |
programming languages
algorithms computer scientists data scarcity computational power human expertise lack of learning capabilities |
| gptkbp:translatedInto |
gptkb:contemporary_AI
deep learning neural networks |
| gptkbp:uses |
symbolic reasoning
|
| gptkbp:wasPartOf |
1980s
|