The DOI matches its Crossref record.
Computers vs. Brains
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In contrast, biology does a lot with a little: organic brains are millions of times more efficient in energy consumption than computers.”
From Human- versus Artificial Intelligence (2021) · cites this paper as Fischetti, 2011
Citations
Cites
Still being checked.
Cited by
Human- versus Artificial Intelligence
Korteling, J. E. (., van de Boer-Visschedijk, G. C., Blankendaal, R. A. M., Boonekamp, R. C., & Eikelboom, A. R. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.622364 · Cited by 619
“The human brain consumes less energy than a lightbulb, whereas a supercomputer with comparable computational performance uses enough electricity to power quite a village (Fischetti, 2011).”
BackgroundScaling for edge inference of deep neural networks
Xu, X., Ding, Y., Hu, S. X., Niemier, M., Cong, J., Hu, Y., & Shi, Y. (2018) · Nature Electronics · doi:10.1038/s41928-018-0059-3 · Cited by 462
Spintronic Nanodevices for Bioinspired Computing
Grollier, J., Querlioz, D., & Stiles, M. D. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2016.2597152 · Cited by 433
Memristor devices for neural networks
Jeong, H., & Shi, L. (2018) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/aae223 · Cited by 151
Analog Neuromorphic Module Based on Carbon Nanotube Synapses
Shen, A. M., Chen, C.-L., Kim, K.-H., Cho, B., Tudor, A., & Chen, Y. (2013) · ACS Nano · doi:10.1021/nn401946s · Cited by 100
Metadata
- Authors
- Fischetti, M.
- Corresponding author
- Fischetti, Mark
- Venue
- Scientific American
- Published
- 2011-10-18
- Volume
- 305
- Issue
- 5
- Pages
- 104-104
- ISSN
- 0036-8733 (ISSN-L)
- OpenAlex
- W40032168
- Cited by
- 20
- References
- 0
- Citations by year
- 2025: 1 · 2023: 1 · 2021: 1 · 2020: 1 · 2019: 1 · 2018: 5 · 2016: 2 · 2015: 3 · 2014: 3 · 2013: 2
- FWCI
- 1.47
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Scientific American (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Cognitive Computing and Networks
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Cybernetics and Technology in Society
- Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
- Keywords
- artificial intelligence, human brain, machine intelligence
- MeSH terms
- Brain, Computers, Humans
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Computers vs. Brains. https://app.cochat.ai/papers/10.1038/scientificamerican1111-104
- The paper
- Fischetti, M. (2011). Computers vs. Brains. Scientific American. https://doi.org/10.1038/scientificamerican1111-104
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
