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Problem Space a ranking of the record

TheProblems surrounding AI

22 problems match this view. 55% rest on at least one peer-reviewed study, and 5% clear the high-evidence bar. The latest was filed Oct 10, 2026.

Every problem in this view, one square each, shaded by the strength of its evidence. High 1 Moderate 21 Emerging 0
22 results
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Sector: science

Matching records · page 1 of 1

2
Evidence-backed problemPeer-reviewedScience

Learning the output distributions of brickwork random quantum circuits is average-case hard in the statistical query model, requiring super polynomially many queries at super logarithmic depth to achieve constant success probability.

Source article: On the average-case complexity of learning output distributions of quantum circuits

Quantum
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3
Evidence-backed problemPeer-reviewedScience

The most frequently reported challenges involved bias, errors, and limitations. Background Generative artificial intelligence (GenAI) chatbots have shown utility in assisting with various research tasks.

Source article: Attitudes and Perceptions of Generative Artificial Intelligence Chatbots in the Scientific Process of Traditional, Complementary, and Integrative Medicine Research: A Large-Scale, International Cross-Sectional Survey

Journal of Evidence-Based Medicine
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4
Evidence-backed problemPeer-reviewedScience

AI was primarily applied for health outcome prediction and risk stratification, whereas comparatively limited attention was directed toward preventive applications such as exposure monitoring and noise control.

Source article: Applications of artificial intelligence in occupational noise assessment and noise‑related health outcomes: a scoping review

International Journal of Environmental Health Research
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5
Evidence-backed problemPeer-reviewedClimate

Gene-environment interactions shape cytokine-mediated inflammation and cardiovascular risk: An exposome-wide association study identified environmental determinants of inflammation, and gene-environment interactions were assessed using multi-ancestry polygenic risk scores.

Source article: Gene-environment interactions shape cytokine-mediated inflammation and cardiovascular risk

European Journal of Preventive Cardiology
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6
Evidence-backed problemPeer-reviewedScience

AI models for craniofacial soft tissue prediction lack independent external validation, leaving generalizability beyond the study dataset unestablished.

Source article: Artificial intelligence -based modeling and comparative evaluation of craniofacial soft tissue and subcutaneous fat thickness using ct imaging for forensic identification in a northwestern indian population

International Journal of Legal Medicine
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7
Evidence-backed problemPeer-reviewedScience

Explainable deep learning improves human mental models of self-driving cars: The opacity of these black-box planners makes it challenging to accurately anticipate when they will fail 4-6 , with potentially catastrophic consequences 7-9 .

Source article: Explainable deep learning improves human mental models of self-driving cars

Nature
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8
Evidence-backed problemPeer-reviewedScience

The optimized ANN and Logistic Regression frameworks exhibited the highest overall discriminative power (AUC > 0.99), while the Random Forest algorithm achieved the peak classification accuracy (97.10%).

Source article: Evaluating machine learning and neural network architectures for forensic sex estimation using mandibular ramus and notch features on panoramic radiographs

International Journal of Legal Medicine
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9
Evidence-backed problemPeer-reviewedClimate

Rapid growth of deep learning workloads increases energy consumption and carbon emissions, while current carbon tracking and static scheduling fail to deliver carbon awareness in real ML operations.

Source article: A carbon aware job scheduling framework for data center sustainability using deep learning training

Discover Artificial Intelligence
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12
Evidence-backed problemPeer-reviewedClimate

AI workloads' environmental impact is growing rapidly but remains hard to quantify because data center operators do not separate AI and non-AI reporting, with AI alone projected to reach 32.6-79.7 million tons CO2 and 312.5-764.6 billion liters of water in 2025.

Source article: The carbon and water footprints of data centers and what this could mean for artificial intelligence

Patterns
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20
Evidence-backed problemJournalismScience

Commercial pressure to release AI tools before capabilities and flaws are fully understood has produced chatbots with easily bypassed guardrails that fail unpredictably while giving confident human-like answers

Source article: Race for AI is making Hindenburg-style disaster ‘a real risk’, says leading expert

The Guardian
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