Background
The evolution of chip technology indeed represents a remarkable trajectory, originating from the advent of computers in the 1940s. The revolutionary concept of integrated circuits, which significantly transformed the field, can be traced back to the genesis of Boolean algebra in 1854. This innovative idea of symbolizing logical operations via binary digits established the cornerstone for contemporary digital circuits. Over subsequent decades, the progression of chip development has burgeoned into an extensive industry and ecosystem. The advancements in semiconductor manufacturing technology have facilitated the production of progressively miniaturized yet more potent chips, with transistor dimensions now quantified in nanometers. This incessant trend towards miniaturization has catalyzed an exponential augmentation in computing power, fostering breakthroughs in diverse domains such as artificial intelligence, mobile technology, and the Internet of Things.
Evaluation Standards
We single out the latest AI achievements from 2022 to 2023 which already have growing impacts or would have impacts in the near future. Our evaluation criteria are as follows:
- The original or pioneer works in artificial intelligence or its subfields.
- Works that would play great role in promoting the development of artificial intelligence or its subfields.
- Works widely used or cited by industry or academia.
AI100: Top 100 AI achievements (2022-2023)
Overview of Top AI achievements
(Please note that the tree diagram can be zoomed in, zoomed out, and moved. You can click on the circles at the branches to expand or collapse the content of the diagram.)
Top AI Achievements
When considering the main contributors, we will only include the following in the list:
- The first author, including authors with equal contribution.
- The corresponding author, or the last author if there is no corresponding author.
If you have any comments or suggestions regarding the list, please feel free to email us at benchcouncil.evaluation@gmail.com.
Area | Work | Publications | Citation | Main Contributors | Institution | Country |
---|---|---|---|---|---|---|
Vision | Swin Transformer V2 | Swin Transformer V2: Scaling Up Capacity and Resolution | 769 | Ze Liu, Han Hu | Microsoft Research Asia | China |
Simmim | Simmim: A simple framework for masked image modeling | 652 | Zhenda Xie, Zheng Zhang, Yue Cao, Han Hu | Tsinghua University, Microsoft Research Asia, Xi'an Jiaotong University | China | |
Scaling ViT | Scaling vision transformers | 630 | Xiaohua Zhai, Alexander Kolesnikov,Lucas Beyer | Switzerland | ||
RepLKNet | Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs | 392 | Xiaohan Ding,Xiangyu Zhang | BNRist, Tsinghua University, MEGVII, Aberystwyth University | China, UK | |
LocalViT | Localvit: Bringing locality to vision transformers | 363 | Yawei Li, Luc Van Gool | ETH Zurich, KU Leuven | Switzerland, Belgium | |
LaMa | Resolution-robust Large Mask Inpainting with Fourier Convolutions | 305 | Roman Suvorov, Victor Lempitsky | Samsung, EPFL, Skolkovo Institute of Science and Technology | Russia, Switzerland, Korea | |
Instructpix2pix | Instructpix2pix: Learning to follow image editing instructions | 246 | Tim Brooks, Aleksander Holynski, Alexei A. Efros | UC Berkeley | USA | |
ConvNeXts | A ConvNet for the 2020s | 2508 | Zhuang Liu, Saining Xie | Facebook, UC Berkeley | USA | |
ConvMixer | Patches Are All You Need? | 247 | Asher Trockman, J. Zico Kolter | CMU, Bosch Center for AI | USA | |
CMT | CMT: Convolutional Neural Networks Meet Vision Transformers | 361 | Jianyuan Guo, Yunhe Wang, Chang Xu | University of Sydney, Huawei | Australia, China | |
Block-NeRF | Block-NeRF: Scalable Large Scene Neural View Synthesis | 279 | Matthew Tancik, Henrik Kretzschmar | UC Berkeley, Waymo, Google | USA | |
BEVFormer | BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers | 383 | Zhiqi Li, Wenhai Wang, Hongyang Li, Jifeng Dai | Nanjing University, Shanghai AI Lab, The University of Hong Kong | China | |
Prompt-to-Prompt | Prompt-to-prompt image editing with cross attention control | 328 | Amir Hertz, Daniel Cohen-Or | Google, Tel Aviv University | USA, Israel | |
TensoRF | TensoRF: Tensorial Radiance Fields | 372 | Anpei Chen, Zexiang Xu, Hao Su | ShanghaiTech University, Adobe, University of Tubingen, UC San Diego | China, USA, Germany | |
AlterNet | How Do Vision Transformers Work? | 288 | Namuk Park, Songkuk Kim | Yonsei University, NAVER AI Lab | Korea | |
VPT | Visual Prompt Tuning | 455 | Menglin Jia, Luming Tang, Ser-Nam Lim | Cornell University, Meta, University of Copenhagen | USA, Denmark | |
VAN | Visual attention network | 273 | Meng-Hao Guo, Shi-Min Hu | Tsinghua University, Nankai University, Fitten Tech | China | |
MaskFeat | Masked feature prediction for self-supervised visual pre-training | 380 | Chen Wei, Christoph Feichtenhofer | Facebook, Johns Hopkins University | USA | |
GFP-GAN | Towards real-world blind face restoration with generative facial prior | 261 | Xintao Wang, Ying Shan | Tencent | China | |
Multiresolution hash encoding | Instant Neural Graphics Primitives with a Multiresolution Hash Encoding | 1107 | Thomas Muller, Alexander Keller | NVIDIA | Switzerland, UK, USA, Germany | |
Video | VideoMAE | VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training | 362 | Zhan Tong, Limin Wang | Nanjing University, Tencent, Shanghai AI Lab | China |
Extension of MAE | Masked Autoencoders As Spatiotemporal Learners | 212 | Christoph Feichtenhofer, Haoqi Fan, Kaiming He | Meta | USA | |
Make-A-Video | Make-A-Video: Text-to-Video Generation without Text-Video Data | 306 | Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Devi Parikh, Sonal Gupta, Yaniv Taigman | Meta | USA | |
Speech | Whisper | Robust Speech Recognition via Large-Scale Weak Supervision | 730 | Alec Radford, Jong Wook Kim, Ilya Sutskever | OpenAI | USA |
Multimodal | Textual Inversions | An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion | 374 | Rinon Gal, Daniel Cohen-Or | Tel Aviv University, NVIDIA | Israel, USA |
Make-A-Scene | Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors | 217 | Oran Gafni, Yaniv Taigman | Meta | USA | |
Magic3d | Magic3d: High-resolution text-to-3d content creation | 214 | Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Tsung-Yi Lin | NVIDIA | USA | |
Blip-2 | Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models | 561 | Junnan Li, Steven Hoi | Salesforce Research | USA | |
BLIP | BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation | 987 | Junnan Li, Steven Hoi | Salesforce Research | USA | |
Parti | Scaling autoregressive models for content-rich text-to-image generation | 397 | Jiahui Yu, Yonghui Wu | USA | ||
OFA | OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework | 522 | Peng Wang, Chang Zhou | Alibaba | China | |
Gato | A Generalist Agent | 438 | Scott Reed, Konrad Zolna,Emilio Parisotto, Nando de Freitas | DeepMind | USA | |
Data2vec | Data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language | 477 | Alexei Baevski, Michael Auli | Meta, SambaNova | USA | |
LAION-5B | LAION-5B: An Open Large-Scale Dataset for Training Next Generation Image-Text Models | 642 | Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Ludwig Schmidt, Robert Kaczmarczyk, Jenia Jitsev | LAION, UC Berkeley, Gentec Data, TU Darmstadt, Hessian.AI, University of Washington Seattle, Technical University of Munich, Stability AI, Eleuther AI, Juelich Supercomputing Center (JSC) Research Center Juelich (FZJ) | USA, Germany | |
LLM | LLaMa | Llama: Open and efficient foundation language models | 1891 | Hugo Touvron, Thibaut Lavril, Gautier Izacard, Edouard Grave, Guillaume Lample | Meta | USA |
Self-consistency | Self-consistency improves chain of thought reasoning in language models | 325 | Xuezhi Wang, Denny Zhou | USA | ||
PaLM-E | Palm-e: An embodied multimodal language model | 304 | Danny Driess, Pete Florence | Google, TU Berlin | Germany | |
Palm 2 | Palm 2 technical report | 233 | USA | |||
PaLM | PaLM: Scaling Language Modeling with Pathways | 1869 | Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, Noah Fiedel | USA | ||
OPT | OPT: Open Pre-trained Transformer Language Models | 875 | Susan Zhang, Stephen Roller, Naman Goyal | Meta | USA | |
MT-NLG | Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model | 340 | Shaden Smith, Mostofa Patwary, Bryan Catanzaro | Microsoft, NVIDIA | USA | |
LLaMa 2 | Llama 2: Open foundation and fine-tuned chat models | 554 | Hugo Touvron, Thomas Scialom | Meta | USA | |
Zero-shot-CoT | Large Language Models are Zero-Shot Reasoners | 806 | Takeshi Kojima, Yusuke Iwasawa | The University of Tokyo | Japan | |
Scratchpad | Show your work: Scratchpads for intermediate computation with language models | 248 | Maxwell Nye, Augustus Odena | MIT, Google | USA | |
Minerva | Solving Quantitative Reasoning Problems with Language Models | 223 | Aitor Lewkowycz, Behnam Neyshabur, Guy Gur-Ari, Vedant Misra | USA | ||
Least-to-most prompting | Least-to-Most Prompting Enables Complex Reasoning in Large Language Models | 313 | Denny Zhou, Ed Chi | USA | ||
LaMDA | LaMDA: Language Models for Dialog Applications | 676 | Romal Thoppilan, Quoc Le | USA | ||
InstructGPT | Training language models to follow instructions with human feedback | 2840 | Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Paul Christiano, Jan Leike, Ryan Lowe | OpenAI | USA | |
HuggingChat | Hugging Face | USA | ||||
GPT-NeoX-20B | GPT-NeoX-20B: An Open-Source Autoregressive Language Model | 292 | Sid Black, Stella Biderman, Eric Hallahan | Eleuther AI | USA | |
GPT-4 | OpenAI | USA | ||||
Flan finetuning | Scaling instruction-finetuned language models | 671 | Hyung Won Chung, Le Hou, Shayne Longpre, Jason Wei | USA | ||
FLAN | Finetuned language models are zero-shot learners | 1065 | Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Andrew M. Dai, Quoc Le | USA | ||
Flamingo | Flamingo: a visual language model for few-shot learning | 942 | Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Karen Simonyan | DeepMind | USA | |
ERNIE Bot | Baidu | China | ||||
Emergent abilities | Emergent Abilities of Large Language Models | 805 | Jason Wei, William Fedus | Google, Stanford University,UNC Chapel Hill | USA | |
Claude | Anthropic | USA | ||||
ChatGPT | ChatGPT: Optimizing Language Models for Dialogue | 551 | OpenAI | USA | ||
Chain-of-Thought Prompting | Chain of Thought Prompting Elicits Reasoning in Large Language Models | 1542 | Jason Wei, Denny Zhou | USA | ||
BLOOM | BLOOM: A 176B-Parameter Open-Access Multilingual Language Model | 609 | ||||
Bard | USA | |||||
Pali | Pali: A jointly-scaled multilingual language-image model | 210 | Xi Chen, Radu Soricut | USA | ||
LLaVA | Visual instruction tuning | 279 | Haotian Liu, Chunyuan Li, Yong Jae Lee | University of Wisconsin-Madison, Microsoft, Columbia University | USA | |
Inner monologue | Inner monologue: Embodied reasoning through planning with language models | 252 | Wenlong Huang, Fei Xia, Ted Xiao | USA | ||
Constitutional AI | Constitutional AI: Harmlessness from AI Feedback | 234 | Yuntao Bai, Jared Kaplan | Anthropic | USA | |
CoCoOP | Conditional Prompt Learning for Vision-Language Models | 421 | Kaiyang Zhou, Ziwei Liu | Nanyang Technological Universit | Singapor | |
Evaluation Analysis | PolyCoder | A Systematic Evaluation of Large Language Models of Code | 206 | Frank F. Xu, Vincent J. Hellendoorn | CMU | USA |
BIG-bench | Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models | 398 | ||||
Design space of diffusion models | Elucidating the design space of diffusion-based generative models | 350 | Tero Karras, Samuli Laine | NVIDIA | USA | |
Role of demonstrations | Rethinking the role of demonstrations: What makes in-context learning work? | 427 | Sewon Min, Luke Zettlemoyer | University of Washington, Meta, Allen Institute for AI | USA | |
Fine-Tuning | Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution | 275 | Ananya Kumar, Percy Liang | Stanford University | USA | |
Language Models as Zero-Shot Planners | Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents | 324 | Wenlong Huang, Deepak Pathak, Igor Mordatch | UC Berkeley, CMU, Google | USA | |
Experiments with gpt-4 | Sparks of artificial general intelligence: Early experiments with gpt-4 | 849 | Sebastien Bubeck, Yi Zhang | Microsoft | USA | |
Deep Learning for Tabular Data | Tabular Data: Deep Learning is Not All You Need | 593 | Ravid Shwartz-Ziv, Amitai Armon | Intel | USA | |
Analysis with gpt-3 | What Makes Good In-Context Examples for GPT-3? | 464 | Jiachang Liu, Weizhu Chen | Duke University, Microsoft | USA | |
Gpts are gpts | Gpts are gpts: An early look at the labor market impact potential of large language models | 216 | Tyna Eloundou, Sam Manning, Pamela Mishkin, Daniel Rock | OpenAI, OpenResearch, University of Pennsylvania | USA | |
Vision transformers are robust learners | Vision transformers are robust learners | 209 | Sayak Paul, Pin-Yu Chen | Carted, IBM | USA | |
Evaluation of chatgpt | A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity | 381 | Yejin Bang, Pascale Fung | The Hong Kong University of Science and Technology | China | |
Tree-based models v.s. deep learning | Why do tree-based models still outperform deep learning on tabular data? | 354 | Leo Grinsztajn, Gael Varoquaux | Inria Saclay, Sorbonne University | France | |
Quantifying Memorization | Quantifying Memorization Across Neural Language Models | 212 | Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, Chiyuan Zhang | Google, University of Pennsylvania, Cornell University | USA | |
Diffusion Model Application | Latent Diffusion Models | High-Resolution Image Synthesis with Latent Diffusion Models | 3361 | Robin Rombach, Andreas Blattmann, Bjorn Ommer | Ludwig Maximilian University of Munich IWR, Heidelberg University, Runway | Germany |
Imagic | Imagic: Text-Based Real Image Editing with Diffusion Models | 259 | Bahjat Kawar, Shiran Zada, Michal Irani | Google, Technion, Weizmann Institute of Science | USA, Israel | |
Imagen video | Imagen video: High definition video generation with diffusion models | 330 | Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Tim Salimans | USA | ||
Imagen | Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding | 1754 | Chitwan Saharia, William Chan, Mohammad Norouzi | Canada | ||
Hierarchical Text-Conditional Image Generation | Hierarchical Text-Conditional Image Generation with CLIP Latents | 2521 | Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen | OpenAI | USA | |
GeoDiff | GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation | 216 | Minkai Xu, Stefano Ermon, Jian Tang | Mila-Quebec AI Institute, University of Montreal, Stanford University, HEC Montreal, CIFAR | Canada, USA | |
Gen2 | Runway | Germany | ||||
eDiffi | eDiffi: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers | 213 | Yogesh Balaji, Ming-Yu Liu | NVIDIA | USA | |
DreamFusion | DreamFusion: Text-to-3D using 2D Diffusion | 419 | Ben Poole, Ben Mildenhall | Google, UC Berkeley | USA | |
DreamBooth | DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation | 495 | Nataniel Ruiz, Kfir Aberman | Google, Boston University | USA | |
Dpm-solver | Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps | 286 | Cheng Lu, Jianfei Chen, Jun Zhu | BNRist, Tsinghua University, Renmin University of China | China | |
Diffusion-LM | Diffusion-LM Improves Controllable Text Generation | 241 | Xiang Lisa Li, Tatsunori B. Hashimoto | Stanford University | USA | |
DDRM | Denoising Diffusion Restoration Models | 226 | Bahjat Kawar, Jiaming Song | Technion, Stanford University, NVIDIA | Israel, USA | |
ControlNet | Adding conditional control to text-to-image diffusion models | 410 | Lvmin Zhang, Maneesh Agrawala | Stanford University | USA | |
Classifier-free guidance | Classifier-Free Diffusion Guidance | 865 | Jonathan Ho, Tim Salimans | USA | ||
Card | Card: Classification and regression diffusion models | 448 | Xizewen Han, Huangjie Zheng, Mingyuan Zhou | The University of Texas at Austin | USA | |
Detection Segmentation | YOLOv7 | YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors | 2226 | Chien-Yao Wang, Hong-Yuan Mark Liao | Institute of Information Science Academia Sinica | China |
YOLOv6 | YOLOv6: A single-stage object detection framework for industrial applications | 553 | Chuyi Li, Lulu Li, Hongliang Jiang, Kaiheng Weng, Yifei Geng, Liang Li, Zaidan Ke, Qingyuan Li, Meng Cheng, Weiqiang Nie, Yiduo Li, Bo Zhang, Xiaoming Xu | Meituan | China | |
ViTDet | Exploring Plain Vision Transformer Backbones for Object Detection | 327 | Yanghao Li, Ross Girshick, Kaiming He | USA | ||
Swin UNETR | Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images | 319 | Ali Hatamizadeh, Daguang Xu | NVIDIA, Vanderbilt University | USA | |
Segment anything | Segment anything | 963 | Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Piotr Dollar, Ross Girshick | Meta | USA | |
PETR | PETR: Position Embedding Transformation for Multi-View 3D Object Detection | 201 | Yingfei Liu, Tiancai Wang, Jian Sun | MEGVII | China | |
Dino | Dino: Detr with improved denoising anchor boxes for end-to-end object detection | 397 | Hao Zhang, Feng Li, Shilong Liu, Lei Zhang | The Hong Kong University of Science and Technology, Tsinghua University, International Digital Economy Academy | China | |
Bytetrack | Bytetrack: Multi-object tracking by associating every detection box | 587 | Yifu Zhang, Xinggang Wang | Huazhong University of Science and Technology, The University of Hong Kong, ByteDance | China | |
DN-DETR | DN-DETR: Accelerate DETR Training by Introducing Query DeNoising | 245 | Feng Li, Hao Zhang, Lei Zhang | The Hong Kong University of Science and Technology, Tsinghua University, IDEA | China | |
Detic | Detecting Twenty-thousand Classes using Image-level Supervision | 239 | Xingyi Zhou, Ishan Misra | Meta, The University of Texas at Austin | USA | |
AI4Science | SignalP 6.0 | SignalP 6.0 predicts all five types of signal peptides using protein language models | 650 | Felix Teufel, Henrik Nielsen | Technical University of Denmark, ETH Zurich, University of Copenhagen, Stanford University, Copenhagen University Hospital, Wellcome Genome Campus, Stockholm University | Denmark, Switzerland, USA, UK, Sweden |
Galactica | Galactica: A Large Language Model for Science | 220 | Ross Taylor, Robert Stojnic | Meta | USA | |
RL-designed magnetic controller | Magnetic control of tokamak plasmas through deep reinforcement learning | 417 | Jonas Degrave, Federico Felici, Jonas Buchli, Michael Neunert, Brendan Tracey | DeepMind, EPFL | UK, Switzerland | |
ESMFold | Evolutionary-scale prediction of atomic-level protein structure with a language model | 451 | Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Alexander Rives | Meta,New York University, Stanford University, MIT | USA | |
EDM | Equivariant Diffusion for Molecule Generation in 3D | 235 | Emiel Hoogeboom, Victor Garcia Satorras, Clement Vignac, Max Welling | University of Amsterdam, EPFL | Netherlands, Switzerland | |
ColabFold | ColabFold: making protein folding accessible to all | 2624 | Milot Mirdita, Sergey Ovchinnikov, Martin Steinegger | Max Planck Institute for Multidisciplinary Sciences, Seoul National University, The University of Tokyo, Michigan State University, Harvard University | Germany,South Korea, Japan, USA | |
AlphaFold DB | AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models | 2989 | Mihaly Varadi, Demis Hassabis, Sameer Velankar | DeepMind | UK | |
AI4Others | AlphaTensor | Discovering faster matrix multiplication algorithms with reinforcement learning | 267 | Alhussein Fawzi, Matej Balog, Aja Huang, Thomas Hubert, Bernardino Romera-Paredes, Pushmeet Kohli | DeepMind | USA |
AlphaCode | Competition-Level Code Generation with AlphaCode | 382 | Yujia Li, David Choi, Junyoung Chung, Nate Kushman , Julian Schrittwieser, Remi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d'Autume, Oriol Vinyals | DeepMind | USA | |
Robots | Learning robust perceptive locomotion | Learning robust perceptive locomotion for quadrupedal robots in the wild | 331 | Takahiro Miki | ETH Zurich, KAIST, Intel | Switzerland, Korea, USA |
SayCan | Do As I Can, Not As I Say: Grounding Language in Robotic Affordances | 510 | USA | |||
Ensemble | Model soups | Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time | 333 | Mitchell Wortsman, Yair Carmon, Simon Kornblith, Ludwig Schmidt | University of Washington, Columbia University, Google, Meta, Tel Aviv University | USA, Israel |
AI4Others | Symbiotic Creativity | Pathway to Future Symbiotic Creativity | 1 | Yike Guo | HKUST | China |
Detection Segmentation | OSTrack | Joint feature learning and relation modeling for tracking: A one-stream framework | 118 | Botao Ye, Xilin Chen | ICT CAS | China |
Top AI Contributors
Contributor | Institution | Country |
---|---|---|
Denny Zhou | USA | |
Jason Wei | USA | |
Daniel Cohen-Or | Tel Aviv University | Israel |
Junnan Li | Salesforce Research | USA |
Steven Hoi | Salesforce Research | USA |
Takahiro Miki | ETH Zurich | Switzerland |
Yike Guo | HKUST | China |
Bahjat Kawar | USA | |
Christoph Feichtenhofer | Meta | USA |
Han Hu | Microsoft Research Asia | China |
Hugo Touvron | Meta | USA |
Jonathan Ho | USA | |
Tim Salimans | USA | |
Kaiming He | Meta | USA |
Quoc Le | USA | |
Wenlong Huang | USA | |
Yaniv Taigman | Meta | USA |
Feng Li | The Hong Kong University of Science and Technology | China |
Hao Zhang | The Hong Kong University of Science and Technology | China |
Lei Zhang | International Digital Economy Academy | China |
Chitwan Saharia | USA | |
William Chan | USA | |
Alexander Keller | NVIDIA | Germany |
Alexei Baevski | Meta | USA |
Ali Hatamizadeh | NVIDIA | USA |
Amir Hertz | Tel Aviv University | Israel |
Amitai Armon | Intel | USA |
Ananya Kumar | Stanford University | USA |
Asher Trockman | CMU | USA |
Augustus Odena | USA | |
Ben Mildenhall | USA | |
Ben Poole | USA | |
Botao Ye | ICT CAS | China |
Chang Zhou | Alibaba | China |
Chen Wei | USA | |
Chien-Yao Wang | Institute of Information Science Academia Sinica | China |
Daguang Xu | USA | USA |
Danny Driess | Germany | |
Ed Chi | USA | |
Felix Teufel | Technical University of Denmark | Denmark |
Frank F. Xu | CMU | USA |
Gael Varoquaux | Inria Saclay | France |
Henrik Kretzschmar | Waymo | USA |
Henrik Nielsen | Technical University of Denmark | Denmark |
Hong-Yuan Mark Liao | Institute of Information Science Academia Sinica | China |
Ishan Misra | Meta | USA |
J. Zico Kolter | CMU | USA |
Jared Kaplan | Anthropic | USA |
Jiachang Liu | Duke University | USA |
Jiahui Yu | USA | |
Jiaming Song | NVIDIA | USA |
Kaiyang Zhou | Nanyang Technological Universit | Singapor |
Kfir Aberman | USA | |
Leo Grinsztajn | Inria Saclay | France |
Limin Wang | Nanjing University | China |
Luc Van Gool | KU Leuven | Belgium |
Luke Zettlemoyer | University of Washington | USA |
Luming Tang | Cornell University | USA |
Lvmin Zhang | Stanford University | USA |
Maneesh Agrawala | Stanford University | USA |
Matthew Tancik | UC Berkeley | USA |
Maxwell Nye | USA | |
Meng-Hao Guo | Tsinghua University | China |
Michael Auli | Meta | USA |
Ming-Yu Liu | NVIDIA | USA |
Namuk Park | Yonsei University | Korea |
Nataniel Ruiz | USA | |
Oran Gafni | Meta | USA |
Pascale Fung | The Hong Kong University of Science and Technology | China |
Peng Wang | Alibaba | China |
Percy Liang | Stanford University | USA |
Pete Florence | Germany | |
Pin-Yu Chen | IBM | USA |
Radu Soricut | USA | |
Ravid Shwartz-Ziv | Intel | USA |
Rinon Gal | Tel Aviv University | Israel |
Robert Stojnic | Meta | USA |
Romal Thoppilan | USA | |
Roman Suvorov | Samsung AI Center Moscow | Russia |
Ross Girshick | USA | |
Ross Taylor | Meta | USA |
Saining Xie | USA | |
Samuli Laine | NVIDIA | USA |
Sayak Paul | Carted | USA |
Sebastien Bubeck | Microsoft | USA |
Sewon Min | University of Washington | USA |
Shi-Min Hu | Tsinghua University | China |
Songkuk Kim | Yonsei University | Korea |
Takeshi Kojima | The University of Tokyo | Japan |
Tatsunori B. Hashimoto | Stanford University | USA |
Tero Karras | NVIDIA | USA |
Thomas Muller | NVIDIA | Switzerland |
Thomas Scialom | Meta | USA |
Victor Lempitsky | Samsung AI Center Moscow | Russia |
Vincent J. Hellendoorn | CMU | USA |
Weizhu Chen | Microsoft | USA |
William Fedus | USA | |
Xi Chen | USA | |
Xiang Lisa Li | Stanford University | USA |
Xiangyu Zhang | MEGVII | China |
Xiaohan Ding | BNRist | China |
Xilin Chen | ICT CAS | China |
Xinggang Wang | Huazhong University of Science and Technology | China |
Xingyi Zhou | Meta | USA |
Xintao Wang | Tencent | China |
Xuezhi Wang | USA | |
Yawei Li | ETH Zurich | Switzerland |
Yejin Bang | The Hong Kong University of Science and Technology | China |
Yi Zhang | Microsoft | USA |
Yifu Zhang | Huazhong University of Science and Technology | China |
Ying Shan | Tencent | China |
Yogesh Balaji | NVIDIA | USA |
Yonghui Wu | USA | |
Yuntao Bai | Anthropic | USA |
Yusuke Iwasawa | The University of Tokyo | Japan |
Ze Liu | Microsoft Research Asia | China |
Zhan Tong | Nanjing University | China |
Zhuang Liu | USA | |
Ziwei Liu | Nanyang Technological Universit | Singapor |
Ludwig Schmidt | University of Washington Seattle | USA |
Pamela Mishkin | OpenAI | USA |
Alec Radford | OpenAI | USA |
Aleksander Holynski | UC Berkeley | USA |
Alexander Kolesnikov | Switzerland | |
Alexei A. Efros | UC Berkeley | USA |
Andreas Blattmann | Heidelberg University | Germany |
Anpei Chen | ShanghaiTech University | China |
Bjorn Ommer | Heidelberg University | Germany |
Bryan Catanzaro | NVIDIA | USA |
Chang Xu | University of Sydney | Australia |
Cheng Lu | Tsinghua University | China |
Chunyuan Li | Microsoft | USA |
Deepak Pathak | CMU | USA |
Demis Hassabis | DeepMind | UK |
Eric Hallahan | Eleuther AI | USA |
Fei Xia | USA | |
Hao Su | UC San Diego | USA |
Haoqi Fan | Meta | USA |
Haotian Liu | University of Wisconsin-Madison | USA |
Huangjie Zheng | The University of Texas at Austin | USA |
Igor Mordatch | USA | |
Ilya Sutskever | OpenAI | USA |
Jian Sun | MEGVII | China |
Jian Tang | Mila-Quebec AI Institute | Canada |
Jianfei Chen | Tsinghua University | China |
Jianyuan Guo | University of Sydney | Australia |
Jong Wook Kim | OpenAI | USA |
Jun Zhu | Tsinghua University | China |
Lucas Beyer | Switzerland | |
Martin Steinegger | Seoul National University | Korea |
Menglin Jia | Cornell University | USA |
Michal Irani | USA | |
Mihaly Varadi | DeepMind | UK |
Milot Mirdita | Max Planck Institute for Multidisciplinary Sciences | Germany |
Mingyuan Zhou | The University of Texas at Austin | USA |
Minkai Xu | Mila-Quebec AI Institute | Canada |
Mohammad Norouzi | USA | |
Mostofa Patwary | NVIDIA | USA |
Naman Goyal | Meta | USA |
Robin Rombach | Heidelberg University | Germany |
Sameer Velankar | DeepMind | UK |
Ser-Nam Lim | University of Copenhagen | USA |
Sergey Ovchinnikov | Harvard University | USA |
Shaden Smith | Microsoft | USA |
Shiran Zada | USA | |
Sid Black | Eleuther AI | USA |
Stefano Ermon | Stanford University | USA |
Stella Biderman | Eleuther AI | USA |
Stephen Roller | Meta | USA |
Susan Zhang | Meta | USA |
Ted Xiao | USA | |
Tiancai Wang | MEGVII | China |
Tim Brooks | UC Berkeley | USA |
Xiaohua Zhai | Switzerland | |
Xizewen Han | The University of Texas at Austin | USA |
Yanghao Li | USA | |
Yingfei Liu | MEGVII | China |
Yong Jae Lee | University of Wisconsin-Madison | USA |
Yunhe Wang | Huawei Noahs Ark Lab | China |
Zexiang Xu | Adobe | China |
Aitor Lewkowycz | USA | |
Behnam Neyshabur | USA | |
Clement Vignac | EPFL | Switzerland |
Daniel Rock | University of Pennsylvania | USA |
Emiel Hoogeboom | University of Amsterdam | Netherlands |
Emilio Parisotto | DeepMind | USA |
Guy Gur-Ari | USA | |
Hongyang Li | Shanghai AI Laboratory | China |
Hyung Won Chung | USA | |
Jifeng Dai | Shanghai AI Laboratory | China |
Konrad Zolna | DeepMind | USA |
Le Hou | USA | |
Max Welling | University of Amsterdam | Netherlands |
Mitchell Wortsman | University of Washington | USA |
Nando de Freitas | DeepMind | USA |
Sam Manning | OpenAI | USA |
Scott Reed | DeepMind | USA |
Shayne Longpre | USA | |
Shilong Liu | Tsinghua University | China |
Simon Kornblith | USA | |
Tyna Eloundou | OpenAI | USA |
Vedant Misra | USA | |
Victor Garcia Satorras | University of Amsterdam | Netherlands |
Wenhai Wang | Shanghai AI Laboratory | China |
Yair Carmon | Tel Aviv University | Israel |
Yue Cao | Microsoft Research Asia | China |
Zhenda Xie | Tsinghua University | China |
Zheng Zhang | Microsoft Research Asia | China |
Zhiqi Li | Shanghai AI Laboratory | China |
Thomas Hubert | DeepMind | USA |
Aditya Ramesh | OpenAI | USA |
Alex Nichol | OpenAI | USA |
Alexander Rives | Meta | USA |
Antoine Miech | DeepMind | USA |
Brendan Tracey | DeepMind | UK |
Brian Hie | Meta | USA |
Casey Chu | OpenAI | USA |
Edouard Grave | Meta | USA |
Federico Felici | EPFL | Switzerland |
Gautier Izacard | Meta | USA |
Guillaume Lample | Meta | USA |
Halil Akin | Meta | USA |
Jay Whang | USA | |
Jean-Baptiste Alayrac | DeepMind | USA |
Jeff Donahue | DeepMind | USA |
Jonas Buchli | DeepMind | UK |
Jonas Degrave | DeepMind | UK |
Karen Simonyan | DeepMind | USA |
Mark Chen | OpenAI | USA |
Michael Neunert | DeepMind | UK |
Pauline Luc | DeepMind | USA |
Prafulla Dhariwal | OpenAI | USA |
Roshan Rao | Meta | USA |
Thibaut Lavril | Meta | USA |
Zeming Lin | Meta | USA |
Aja Huang | DeepMind | USA |
Alexander Kirillov | Meta | USA |
Alhussein Fawzi | DeepMind | USA |
Andrew M. Dai | USA | |
Bernardino Romera-Paredes | DeepMind | USA |
Chen-Hsuan Lin | NVIDIA | USA |
Chiyuan Zhang | USA | |
Daphne Ippolito | USA | |
Eric Mintun | Meta | USA |
Florian Tramer | USA | |
Hanzi Mao | Meta | USA |
Jun Gao | NVIDIA | USA |
Katherine Lee | USA | |
Kelvin Guu | USA | |
Maarten Bosma | USA | |
Matej Balog | DeepMind | USA |
Matthew Jagielski | USA | |
Nicholas Carlini | USA | |
Nikhila Ravi | Meta | USA |
Piotr Dollar | Meta | USA |
Pushmeet Kohli | DeepMind | USA |
Towaki Takikawa | NVIDIA | USA |
Tsung-Yi Lin | NVIDIA | USA |
Vincent Y. Zhao | USA | |
Xiaohui Zeng | NVIDIA | USA |
Adam Polyak | Meta | USA |
Devi Parikh | Meta | USA |
Sonal Gupta | Meta | USA |
Thomas Hayes | Meta | USA |
Uriel Singer | Meta | USA |
Xi Yin | Meta | USA |
Aakanksha Chowdhery | USA | |
Douglas Eck | USA | |
Jacob Devlin | USA | |
Jeff Dean | USA | |
Kathy Meier-Hellstern | USA | |
Noah Fiedel | USA | |
Sharan Narang | USA | |
Slav Petrov | USA | |
Cade Gordon | UC Berkeley | USA |
Carroll L. Wainwright | OpenAI | USA |
Christoph Schuhmann | LAION | USA |
Diogo Almeida | OpenAI | USA |
Jan Leike | OpenAI | USA |
Jeff Wu | OpenAI | USA |
Jenia Jitsev | LAION | USA |
Long Ouyang | OpenAI | USA |
Mehdi Cherti | LAION | USA |
Paul Christiano | OpenAI | USA |
Richard Vencu | LAION | USA |
Robert Kaczmarczyk | LAION | USA |
Romain Beaumont | LAION | USA |
Ross Wightman | LAION | USA |
Ryan Lowe | OpenAI | USA |
Xu Jiang | OpenAI | USA |
Bo Zhang | Meituan | China |
Chuyi Li | Meituan | China |
Hongliang Jiang | Meituan | China |
Kaiheng Weng | Meituan | China |
Liang Li | Meituan | China |
Lulu Li | Meituan | China |
Meng Cheng | Meituan | China |
Qingyuan Li | Meituan | China |
Weiqiang Nie | Meituan | China |
Xiaoming Xu | Meituan | China |
Yiduo Li | Meituan | China |
Yifei Geng | Meituan | China |
Zaidan Ke | Meituan | China |
Agustin Dal Lago | DeepMind | USA |
Cyprien de Masson d'Autume | DeepMind | USA |
David Choi | DeepMind | USA |
Felix Gimeno | DeepMind | USA |
James Keeling | DeepMind | USA |
Julian Schrittwieser | DeepMind | USA |
Junyoung Chung | DeepMind | USA |
Nate Kushman | DeepMind | USA |
Oriol Vinyals | DeepMind | USA |
Peter Choy | DeepMind | USA |
Remi Leblond | DeepMind | USA |
Tom Eccles | DeepMind | USA |
Yujia Li | DeepMind | USA |
Top AI Institutions
Ranking | Institute | Grade | Country |
---|---|---|---|
1 | 27.87 | USA | |
2 | Meta | 12.12 | USA |
3 | NVIDIA | 5.83 | USA |
4 | OpenAI | 5.33 | USA |
5 | Stanford University | 4.26 | USA |
6 | UC Berkeley | 2.77 | USA |
7 | Microsoft | 2.33 | USA |
8 | Anthropic | 2.00 | USA |
8 | Salesforce Research | 2.00 | USA |
9 | Tsinghua University | 1.92 | China |
10 | CMU | 1.83 | USA |
11 | The Hong Kong University of Science and Technology | 1.67 | China |
12 | The University of Texas at Austin | 1.50 | USA |
13 | EPFL | 1.33 | Switzerland |
13 | Intel | 1.33 | USA |
13 | Microsoft Research Asia | 1.33 | China |
13 | Runway | 1.33 | Germany |
13 | Tencent | 1.33 | China |
14 | MEGVII | 1.25 | China |
15 | Tel Aviv University | 1.20 | Israel |
15 | The University of Tokyo | 1.20 | Japan |
16 | Eleuther AI | 1.10 | USA |
17 | Alibaba | 1.00 | China |
17 | Baidu | 1.00 | China |
17 | HKUST | 1.00 | China |
17 | Hugging Face | 1.00 | USA |
17 | ICT CAS | 1.00 | China |
17 | Institute of Information Science Academia Sinica | 1.00 | China |
17 | Meituan | 1.00 | China |
17 | Nanyang Technological Universit | 1.00 | Singapor |
18 | ETH Zurich | 0.98 | Switzerland |
19 | MIT | 0.75 | USA |
20 | Cornell University | 0.67 | USA |
20 | Nanjing University | 0.67 | China |
20 | Shanghai AI Lab | 0.67 | China |
20 | Technion | 0.67 | USA |
20 | The University of Hong Kong | 0.67 | China |
20 | University of Pennsylvania | 0.67 | USA |
21 | BNRist | 0.58 | China |
22 | Columbia University | 0.53 | USA |
22 | University of Washington | 0.53 | USA |
23 | Bosch Center for AI | 0.50 | USA |
23 | Boston University | 0.50 | USA |
23 | Carted | 0.50 | USA |
23 | Duke University | 0.50 | USA |
23 | Huawei | 0.50 | China |
23 | IBM | 0.50 | USA |
23 | Inria Saclay | 0.50 | France |
23 | Johns Hopkins University | 0.50 | USA |
23 | KU Leuven | 0.50 | Belgium |
23 | NAVER AI Lab | 0.50 | Korea |
23 | SambaNova | 0.50 | USA |
23 | Sorbonne University | 0.50 | France |
23 | TU Berlin | 0.50 | Germany |
23 | University of Amsterdam | 0.50 | Netherlands |
23 | University of Sydney | 0.50 | Australia |
23 | Vanderbilt University | 0.50 | USA |
23 | Yonsei University | 0.50 | Korea |
24 | University of Copenhagen | 0.48 | USA |
25 | Allen Institute for AI | 0.33 | USA |
25 | ByteDance | 0.33 | China |
25 | Fitten Tech | 0.33 | China |
25 | Heidelberg University | 0.33 | Germany |
25 | Huazhong University of Science and Technology | 0.33 | China |
25 | International Digital Economy Academy | 0.33 | China |
25 | KAIST | 0.33 | Korea |
25 | Ludwig Maximilian University of Munich & IWR | 0.33 | Germany |
25 | Nankai University | 0.33 | China |
25 | OpenResearch | 0.33 | USA |
25 | Renmin University of China | 0.33 | China |
25 | Samsung | 0.33 | Korea |
25 | Skolkovo Institute of Science and Technology | 0.33 | Russia |
25 | UNC Chapel Hill | 0.33 | USA |
25 | University of Wisconsin-Madison | 0.33 | USA |
25 | Waymo | 0.33 | USA |
25 | Weizmann Institute of Science | 0.33 | Israel |
25 | Xian Jiaotong University | 0.33 | China |
26 | Aberystwyth University | 0.25 | USA |
26 | Adobe | 0.25 | China |
26 | New York University | 0.25 | USA |
26 | ShanghaiTech University | 0.25 | China |
26 | UC San Diego | 0.25 | USA |
26 | University of Tubingen | 0.25 | Germany |
27 | CIFAR | 0.20 | Canada |
27 | HEC Montreal | 0.20 | Canada |
27 | Harvard University | 0.20 | USA |
27 | Max Planck Institute for Multidisciplinary Sciences | 0.20 | Germany |
27 | Michigan State University | 0.20 | USA |
27 | Mila-Quebec AI Institute | 0.20 | Canada |
27 | Seoul National University | 0.20 | Korea |
27 | University of Montreal | 0.20 | Canada |
28 | Copenhagen University Hospital | 0.14 | Denmark |
28 | Stockholm University | 0.14 | Sweden |
28 | Technical University of Denmark | 0.14 | Denmark |
28 | Wellcome Genome Campus | 0.14 | USA |
29 | Gentec Data | 0.10 | USA |
29 | Hessian.AI | 0.10 | USA |
29 | Juelich Supercomputing Center (JSC) Research Center Juelich (FZJ) | 0.10 | USA |
29 | LAION | 0.10 | USA |
29 | Stability AI | 0.10 | USA |
29 | TU Darmstadt | 0.10 | Germany |
29 | Technical University of Munich | 0.10 | Germany |
29 | University of Washington Seattle | 0.10 | USA |
Top AI Countries
Ranking | Country | Grade |
---|---|---|
1 | USA | 76.87 |
2 | China | 19.33 |
3 | Germany | 3.83 |
4 | Switzerland | 3.62 |
5 | Israel | 2.50 |
6 | UK | 2.45 |
7 | Korea | 1.67 |
8 | Canada | 1.50 |
9 | Japan | 1.25 |
10 | France | 1.00 |
10 | Singapor | 1.00 |
11 | Denmark | 0.70 |
12 | Australia | 0.50 |
12 | Belgium | 0.50 |
12 | Netherlands | 0.50 |
13 | Russia | 0.33 |
14 | South Korea | 0.25 |
15 | Sweden | 0.20 |