ArticleProceedings of the National Academy of Sciences of the United States of America2020
The unreasonable effectiveness of deep learning in artificial intelligence.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
58 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of machine learning to undifferentiated chest pain in the emergency department: A systematic review.PloS one · 2021Pooled it
- Diversity-enabled sweet spots in layered architectures and speed-accuracy trade-offs in sensorimotor control.Proceedings of the National Academy of Sciences of the United States of America · 2021Trial
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- Can AI help with the hardest thing: pro health behavior change.NPJ cardiovascular health · 2026Review
- Invariant visual object and face learning in the ventral cortical visual pathway: A biologically plausible model.PLoS computational biology · 2026Article
- Unreasonable effectiveness of training AI models locally.BMJ quality & safety · 2025Article
- Performance of Predefined Search Patterns for Identifying Documented Goals-of-Care Discussions in Inpatient Electronic Health Records.Journal of palliative medicine · 2025Article
- Machine learning in point-of-care testing: innovations, challenges, and opportunities.Nature communications · 2025Review
- Navigating artificial general intelligence development: societal, technological, ethical, and brain-inspired pathways.Scientific reports · 2025Article
- Optimization design of interior space based on the two-stage deep learning network and Single sample-driven method.PloS one · 2025Article
- The Iconic α-Helix: From Pauling to the Present.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Data Flow-Based Strategies to Improve the Interpretation and Understanding of Machine Learning Models.Bioengineering (Basel, Switzerland) · 2024Article
- ViTDroid: Vision Transformers for Efficient, Explainable Attention to Malicious Behavior in Android Binaries.Sensors (Basel, Switzerland) · 2024Article
- Representations and generalization in artificial and brain neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2024Review
- Review on brain-computer interface technologies in healthcare.Biophysical reviews · 2023Review
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- Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data.Briefings in bioinformatics · 2023Article
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- Large Language Models and the Reverse Turing Test.Neural computation · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning networks have been trained to recognize speech, caption photographs, and translate text between languages at high levels of performance. Although applications of deep learning networks to real-world problems have become ubiquitous, our understanding of why they are so effective is lacking. These empirical results should not be possible according to sample complexity in statistics and nonconvex optimization theory. However, paradoxes in the training and effectiveness of deep learning networks are being investigated and insights are being found in the geometry of high-dimensional spaces. A mathematical theory of deep learning would illuminate how they function, allow us to assess the strengths and weaknesses of different network architectures, and lead to major improvements. Deep learning has provided natural ways for humans to communicate with digital devices and is foundational for building artificial general intelligence. Deep learning was inspired by the architecture of the cerebral cortex and insights into autonomy and general intelligence may be found in other brain regions that are essential for planning and survival, but major breakthroughs will be needed to achieve these goals.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.