Alphabet (United States)
In research, Alphabet (United States) stands highest in Computer Science (#3 of 4,417 worldwide), Artificial Intelligence (#2 of 1,862 worldwide) and Computer Vision and Pattern Recognition (#4 of 963 worldwide), 2022-now. Relative to its size it is most specialised in Hardware and Architecture, Computer Graphics and Computer-Aided Design and Signal Processing (Hardware and Architecture is 10.8× its share of world research).
- World rank, 2022-now
- #155 of 22,385 · #216 all time
- Rank in United States
- #48 of 3,332
- Research works
- 27k ▼ 4% vs 2018-21
- Citations
- 1.6M 57.0 per fractional work
- Top-10% rate
- 19.6% all institutions 10.0%
- vs its own fields
- 1.73× 1.00× is exactly expected
- Open access
- 36% world 40%
- International
- 27% world 18%
What is Alphabet (United States) known for in research?
The fields where it stands highest, 2022-now, ranked among every institution above the floor in each field.
| Field | World rank | Where that sits | Top-10% rate | Works | All time |
|---|---|---|---|---|---|
| Computer ScienceField | #3 of 4,417 | 22.5% | 2,031 | #9 | |
| Artificial IntelligenceSubfield | #2 of 1,862 | 20.3% | 856 | #5 | |
| Computer Vision and Pattern RecognitionSubfield | #4 of 963 | 29.7% | 439 | #17 | |
| Information SystemsSubfield | #9 of 1,321 | 21.2% | 203 | #16 | |
| Physical SciencesDomain | #94 of 10,670 | 22.4% | 2,712 | #143 | |
| Social SciencesDomain | #123 of 9,585 | 17.1% | 942 | #178 | |
| PsychologyField | #34 of 2,121 | 19.9% | 299 | #72 | |
| Computer Networks and CommunicationsSubfield | #16 of 728 | 18.5% | 153 | #30 | |
| EngineeringField | #169 of 5,901 | 20.5% | 427 | #280 | |
| Health SciencesDomain | #298 of 9,902 | 15.5% | 387 | #368 |
Each strip is that field's whole ranked pool, with the notch where Alphabet (United States) sits in it, in the colour of the band that rank falls in. The track under the rate is the rate itself: it carries no average mark, because the average differs by field (from about 6% in Dentistry to 21% in Economics).
Which of those standings moved
The same fields on both windows: where it stood over the whole record, and where it stands in 2022-now. The distance is the story, and it is the one thing the two rank columns above cannot show.
- Computer Science#9 #3
- Artificial Intelligence#5 #2
- Computer Vision and Pattern Recognition#17 #4
- Information Systems#16 #9
- Physical Sciences#143 #94
- Social Sciences#178 #123
- Psychology#72 #34
- Computer Networks and Communications#30 #16
- Engineering#280 #169
- Health Sciences#368 #298
A dot further right is a better standing. Fields it was not ranked in over the whole record are left out.
Which fields of science does Alphabet (United States) work in?
In 2022-now, 63% of research at Alphabet (United States) is in Physical Sciences, 21% in Social Sciences. Its largest fields are Computer Science (47%), Engineering (10%), Social Sciences (8%). It does 1.6× the world's share of Physical Sciences.
Each ring splits the one inside it: the four domains of science, their fields, their subfields. Size is fractional works in 2022-now. Every slice is a page; the interactive view zooms down to single topics and colours each by how much more of it this institution does than the world.
Its 4 domains and 26 fields, with their numbers
- Physical Sciences2,99163.2%
- Computer Science2,22046.9%
- Engineering4619.7%
- Environmental Science942.0%
- Physics and Astronomy901.9%
- Mathematics541.1%
- Earth and Planetary Sciences330.7%
- Materials Science280.6%
- Chemistry60.1%
- Energy30.1%
- Chemical Engineering10.0%
- Social Sciences1,01521.4%
- Social Sciences3637.7%
- Psychology3106.6%
- Decision Sciences1382.9%
- Business, Management and Accounting1032.2%
- Economics, Econometrics and Finance521.1%
- Arts and Humanities491.0%
- Health Sciences4098.6%
- Medicine2866.0%
- Health Professions1132.4%
- Dentistry70.1%
- Nursing20.0%
- Veterinary10.0%
- Life Sciences3216.8%
What does Alphabet (United States) specialise in?
Where its research is concentrated relative to its size: Hardware and Architecture takes 10.8× the share of its output that it takes of world research.
- Hardware and ArchitectureSubfield · 57.8 works11×
- Computer Graphics and Computer-Aided DesignSubfield · 35.6 works9.1×
- Signal ProcessingSubfield · 130.3 works9.1×
- Artificial IntelligenceSubfield · 945.1 works8.3×
- SoftwareSubfield · 25.1 works8.2×
- Computer Vision and Pattern RecognitionSubfield · 464.1 works7.8×
- Human-Computer InteractionSubfield · 60.3 works6.2×
- Computer ScienceField · 2,220.5 works6.0×
Location quotient, a volume reading rather than an impact one. It surfaces small, lopsided specialities.
Field profile
Location quotient across every field it publishes in: outside the ring is more than an institution of this size would be expected to publish, inside it is less.
Rings at 0.5×, 1× and 2×. Widest outward: Computer Science, 6.0×. Every wedge is a field page.
All 26 fields, with their numbers
- Computer Science6.03×2,220 works
- Decision Sciences3.02×138 works
- Neuroscience2.47×190 works
- Psychology2.15×310 works
- Mathematics0.97×54 works
- Health Professions0.91×113 works
- Physics and Astronomy0.86×90 works
- Business, Management and Accounting0.67×103 works
- Engineering0.65×461 works
- Social Sciences0.61×363 works
- Economics, Econometrics and Finance0.51×52 works
- Earth and Planetary Sciences0.45×33 works
- Environmental Science0.40×94 works
- Biochemistry, Genetics and Molecular Biology0.37×94 works
- Arts and Humanities0.36×49 works
- Medicine0.31×286 works
- Dentistry0.29×7 works
- Materials Science0.17×28 works
- Agricultural and Biological Sciences0.14×31 works
- Veterinary0.13×1 works
- Immunology and Microbiology0.10×5 works
- Nursing0.10×2 works
- Pharmacology, Toxicology and Pharmaceutics0.09×2 works
- Chemistry0.08×6 works
- Energy0.07×3 works
- Chemical Engineering0.04×1 works
- Computer Science 6.0×
- Decision Sciences 3.0×
- Neuroscience 2.5×
- Psychology 2.1×
- Mathematics 1.0×
- Health Professions 0.9×
- Physics and Astronomy 0.9×
- Business, Management and Accounting 0.7×
- Engineering 0.7×
- Social Sciences 0.6×
- Economics, Econometrics and Finance 0.5×
- Earth and Planetary Sciences 0.5×
- Environmental Science 0.4×
- Biochemistry, Genetics and Molecular Biology 0.4×
- Arts and Humanities 0.4×
- Medicine 0.3×
- Dentistry 0.3×
- Materials Science 0.2×
- Agricultural and Biological Sciences 0.1×
- Veterinary 0.1×
- Immunology and Microbiology 0.1×
- Nursing 0.1×
- Pharmacology, Toxicology and Pharmaceutics 0.1×
- Chemistry 0.1×
- Energy 0.1×
- Chemical Engineering 0.0×
Who are the top researchers at Alphabet (United States)?
Ranked on the quality score, Hermann Gunkel, Jack D. Hidary and Mohan Raparthi lead among researchers whose main affiliation is Alphabet (United States).
- 1
Hermann Gunkel
United States · #1,914,631 worldwide 514 citations · 52 works
- 2
Jack D. Hidary
United States · #2,051,734 worldwide 992 citations · 54 works
- 3
Mohan Raparthi
United States · #2,080,731 worldwide 226 citations · 36 works
Which keywords describe research at Alphabet (United States)?
By fractional works in 2022-now, weighted toward what it does more of than the world: Deep Learning, Neural Networks, Machine Learning, Convolutional Neural Networks, Information Retrieval, Text Classification, Topic Modeling and Machine Translation.
- Speaker Verification
- Acoustic Modeling
- Meta-Learning
- Hidden Markov Models
- Image Captioning
- Semantic Reasoning
- Semi-Supervised Learning
- Neural Plasticity
- Convolutional Networks
- Statistical Machine Translation
- Corpus Linguistics
- Deep Neural Networks
- Bilingualism
- Unsupervised Learning
- Semantic Similarity
- Topic Modeling
- Artificial Intelligence
- Information Retrieval
- Convolutional Neural Networks
- Neural Networks
- Deep Learning
- Machine Learning
- Big Data
- Quality of Life
- Text Classification
- Machine Translation
- Word Representation
- Language Development
- Security
- Data Mining
- Neural Machine Translation
- Representation Learning
- Social Interaction
- Distributed Systems
- Surveillance
- Visual Question Answering
- Multimodal Fusion
- Speech Perception
- Hearing Loss
- Reinforcement Learning
Size is fractional works in 2022-now in the topics tagged with each word; colour is the word's share of this institution's work against its share of the world's. The 40 words are chosen for being large and distinctive. Each links to the topic it comes from most.
All 40 words, with their numbers
- Deep Learning790▲ 4.8×79 topics
- Neural Networks523▲ 7.2×36 topics
- Machine Learning467▲ 3.0×86 topics
- Convolutional Neural Networks186▲ 5.4×14 topics
- Big Data179▲ 4.0×30 topics
- Information Retrieval151▲ 15×6 topics
- Quality of Life144▲ 2.1×44 topics
- Artificial Intelligence142▲ 2.1×42 topics
- Text Classification140▲ 18×3 topics
- Topic Modeling136▲ 19×3 topics
- Machine Translation132▲ 17×2 topics
- Semantic Similarity131▲ 22×1 topic
- Word Representation131▲ 22×1 topic
- Unsupervised Learning128▲ 18×3 topics
- Language Development127▲ 21×6 topics
- Bilingualism118▲ 18×7 topics
- Security118▲ 3.7×19 topics
- Deep Neural Networks109▲ 19×3 topics
- Data Mining104▲ 3.0×17 topics
- Corpus Linguistics102▲ 10×5 topics
- Neural Machine Translation98▲ 23×1 topic
- Statistical Machine Translation98▲ 23×1 topic
- Representation Learning96▲ 13×3 topics
- Convolutional Networks95▲ 15×3 topics
- Social Interaction87▲ 4.7×11 topics
- Neural Plasticity84▲ 9.6×5 topics
- Distributed Systems79▲ 9.8×6 topics
- Semi-Supervised Learning79▲ 14×3 topics
- Surveillance76▲ 5.7×7 topics
- Semantic Reasoning75▲ 17×2 topics
- Visual Question Answering75▲ 17×2 topics
- Image Captioning74▲ 28×1 topic
- Multimodal Fusion74▲ 28×1 topic
- Hidden Markov Models68▲ 24×3 topics
- Speech Perception67▲ 17×3 topics
- Meta-Learning65▲ 16×2 topics
- Hearing Loss65▲ 12×4 topics
- Acoustic Modeling64▲ 36×1 topic
- Reinforcement Learning64▲ 12×4 topics
- Speaker Verification64▲ 36×1 topic
Which research topics does Alphabet (United States) publish most on?
By volume in 2022-now: Topic Modeling, Natural Language Processing Techniques, Multimodal Machine Learning Applications and Speech Recognition and Synthesis.
Area is fractional works; colour is the subfield each topic belongs to.
- 1 Topic Modeling Artificial Intelligence 131 works
- 2 Natural Language Processing Techniques Artificial Intelligence 98 works
- 3 Multimodal Machine Learning Applications Computer Vision and Pattern Recognition 74 works
- 4 Speech Recognition and Synthesis Artificial Intelligence 64 works
- 5 Language Development and Disorders Developmental and Educational Psychology 59 works
- 6 Domain Adaptation and Few-Shot Learning Artificial Intelligence 47 works
- 7 Advanced Vision and Imaging Computer Vision and Pattern Recognition 45 works
- 8 Privacy-Preserving Technologies in Data Artificial Intelligence 44 works
- 9 Hearing Loss and Rehabilitation Cognitive Neuroscience 40 works
- 10 Speech and Audio Processing Signal Processing 37 works
How open and international is its research?
Against the world's own shares: the tick on each track. Both are shares of its output, so they sit on one scale and can be read against each other as well as against the world.
World: 40% of research is openly available.
World: 18% is written across borders.
How has Alphabet (United States)'s research output changed?
Output in 2022-2025 was 4% lower than in 2018-2021.
The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.
Its share of all research
Growing output can still be a shrinking share: all of science grows. This is Alphabet (United States) as a percentage of every work published that year, 0.020% in 2024.
Fractional works of Alphabet (United States) over all fractional works that year. How the balance of science has shifted.
How much of it reaches the world top 10%
Year by year, against all institutions in the same year.
Of each year's output, the share that reached the world's 10% most cited for its field and year. Peak 24.8% in 2016.
Other research institutions in Mountain View
- Creative Commons
- Synopsys (United States)
- Search for Extraterrestrial Intelligence
- Khan Academy
- Intuit (United States)
- NortonLifeLock (United States)
- Sequoia (United States)
- El Camino Hospital
Which institutions have research most like Alphabet (United States)'s?
Among the world's leading institutions, Microsoft (United States) has the research profile closest to Alphabet (United States)'s, with both strong in Computer Science.
- 1
Microsoft (United States)
United States · Both strong in Computer Science, Decision Sciences 0.89 Compare
- 2
University of Luxembourg
Luxembourg · Both strong in Computer Science, Psychology, Decision Sciences 0.75 Compare
- 3
Macquarie University
Australia · Both strong in Neuroscience, Psychology, Decision Sciences 0.72 Compare
- 4
Vrije Universiteit Amsterdam
Netherlands · Both strong in Neuroscience, Psychology, Decision Sciences 0.70 Compare
- 5
Simon Fraser University
Canada · Both strong in Psychology, Computer Science, Neuroscience 0.67 Compare
Similarity is the correlation of each side's location quotients across every field it publishes in, on a log scale: 1 is the same shape of research, 0 is unrelated. It ignores size. How it is measured.
Compare Alphabet (United States) with
- vs
Uppsala University
- vs
Purdue University West Lafayette
- vs
University of Cologne
- vs
Georgia Institute of Technology
- vs
University of Utah
- vs
Tel Aviv University
Research measures only: rankings here say nothing about teaching, admissions or student experience. Comparable institutions and collaboration partners have tabs of their own, above.
Questions people ask
What does Google Research publish research on?
In research, Alphabet (United States) stands highest in Computer Science (#3 of 4,417 worldwide), Artificial Intelligence (#2 of 1,862 worldwide) and Computer Vision and Pattern Recognition (#4 of 963 worldwide), 2022-now. Relative to its size it is most specialised in Hardware and Architecture, Computer Graphics and Computer-Aided Design and Signal Processing (Hardware and Architecture is 10.8× its share of world research).
Also asked as
- What does Google DeepMind publish research on?
Discussion
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