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The release of ChatGPT, Gemini, and other large language model has drawn huge interests on foundations models. There is a broad consensus that foundations models will be the fundamental building blocks for future AI systems. However, there…

计算与语言 · 计算机科学 2024-07-17 Qinghua Lu , Liming Zhu , Xiwei Xu , Zhenchang Xing , Jon Whittle

The rapid advancement of AI technology has led to widespread applications of agent systems across various domains. However, the need for detailed architecture design poses significant challenges in designing and operating these systems.…

软件工程 · 计算机科学 2024-08-07 Jingwen Zhou , Qinghua Lu , Jieshan Chen , Liming Zhu , Xiwei Xu , Zhenchang Xing , Stefan Harrer

Foundation models, first introduced in 2021, refer to large-scale pretrained models (e.g., large language models (LLMs) and vision-language models (VLMs)) that learn from extensive unlabeled datasets through unsupervised methods, enabling…

Foundation models, such as large language models (LLMs), have been widely recognised as transformative AI technologies due to their capabilities to understand and generate content, including plans with reasoning capabilities. Foundation…

人工智能 · 计算机科学 2024-04-04 Qinghua Lu , Liming Zhu , Xiwei Xu , Zhenchang Xing , Stefan Harrer , Jon Whittle

Generative pretraining (the "GPT" in ChatGPT) enables language models to learn from vast amounts of internet text without human supervision. This approach has driven breakthroughs across AI by allowing deep neural networks to learn from…

神经元与认知 · 定量生物学 2025-09-23 Thomas Serre , Ellie Pavlick

Foundation models can be disruptive for future AI development by scaling up deep learning in terms of model size and training data's breadth and size. These models achieve state-of-the-art performance (often through further adaptation) on a…

人工智能 · 计算机科学 2022-12-20 Johannes Schneider

With the advent of foundation models like ChatGPT, educators are excited about the transformative role that AI might play in propelling the next education revolution. The developing speed and the profound impact of foundation models in…

计算机与社会 · 计算机科学 2024-05-21 Tianlong Xu , Richard Tong , Jing Liang , Xing Fan , Haoyang Li , Qingsong Wen

The rapid integration of Artificial Intelligence (AI) into organizational technology frameworks has transformed how organizations engage with AI-driven models, influencing both operational performance and strategic innovation. With the…

软件工程 · 计算机科学 2025-10-22 Johannes Schneider , Rene Abraham

Foundation models (FMs) are catalyzing a transformative shift in materials science (MatSci) by enabling scalable, general-purpose, and multimodal AI systems for scientific discovery. Unlike traditional machine learning models, which are…

机器学习 · 计算机科学 2025-06-27 Minh-Hao Van , Prateek Verma , Chen Zhao , Xintao Wu

As foundation models grow in both popularity and capability, researchers have uncovered a variety of ways that the models can pose a risk to the model's owner, user, or others. Despite the efforts of measuring these risks via benchmarks and…

密码学与安全 · 计算机科学 2025-06-04 David Piorkowski , Michael Hind , John Richards , Jacquelyn Martino

Foundation models refer to artificial intelligence (AI) models that are trained on massive amounts of data and demonstrate broad generalizability across various tasks with high accuracy. These models offer versatile, one-for-many or…

图像与视频处理 · 电气工程与系统科学 2024-11-06 Rina Bao , Erfan Darzi , Sheng He , Chuan-Heng Hsiao , Mohammad Arafat Hussain , Jingpeng Li , Atle Bjornerud , Ellen Grant , Yangming Ou

Foundation models (FMs) such as large language models have revolutionized the field of AI by showing remarkable performance in various tasks. However, they exhibit numerous limitations that prevent their broader adoption in many real-world…

人工智能 · 计算机科学 2024-02-05 Debarun Bhattacharjya , Junkyu Lee , Don Joven Agravante , Balaji Ganesan , Radu Marinescu

The rapid emergence of foundation models, particularly Large Language Models (LLMs) and Vision-Language Models (VLMs), has introduced a transformative paradigm in robotics. These models offer powerful capabilities in semantic understanding,…

机器人学 · 计算机科学 2025-07-15 Muhammad Tayyab Khan , Ammar Waheed

Foundation models pretrained on diverse data at scale have demonstrated extraordinary capabilities in a wide range of vision and language tasks. When such models are deployed in real world environments, they inevitably interface with other…

人工智能 · 计算机科学 2023-03-08 Sherry Yang , Ofir Nachum , Yilun Du , Jason Wei , Pieter Abbeel , Dale Schuurmans

AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their…

机器学习 · 计算机科学 2022-07-14 Rishi Bommasani , Drew A. Hudson , Ehsan Adeli , Russ Altman , Simran Arora , Sydney von Arx , Michael S. Bernstein , Jeannette Bohg , Antoine Bosselut , Emma Brunskill , Erik Brynjolfsson , Shyamal Buch , Dallas Card , Rodrigo Castellon , Niladri Chatterji , Annie Chen , Kathleen Creel , Jared Quincy Davis , Dora Demszky , Chris Donahue , Moussa Doumbouya , Esin Durmus , Stefano Ermon , John Etchemendy , Kawin Ethayarajh , Li Fei-Fei , Chelsea Finn , Trevor Gale , Lauren Gillespie , Karan Goel , Noah Goodman , Shelby Grossman , Neel Guha , Tatsunori Hashimoto , Peter Henderson , John Hewitt , Daniel E. Ho , Jenny Hong , Kyle Hsu , Jing Huang , Thomas Icard , Saahil Jain , Dan Jurafsky , Pratyusha Kalluri , Siddharth Karamcheti , Geoff Keeling , Fereshte Khani , Omar Khattab , Pang Wei Koh , Mark Krass , Ranjay Krishna , Rohith Kuditipudi , Ananya Kumar , Faisal Ladhak , Mina Lee , Tony Lee , Jure Leskovec , Isabelle Levent , Xiang Lisa Li , Xuechen Li , Tengyu Ma , Ali Malik , Christopher D. Manning , Suvir Mirchandani , Eric Mitchell , Zanele Munyikwa , Suraj Nair , Avanika Narayan , Deepak Narayanan , Ben Newman , Allen Nie , Juan Carlos Niebles , Hamed Nilforoshan , Julian Nyarko , Giray Ogut , Laurel Orr , Isabel Papadimitriou , Joon Sung Park , Chris Piech , Eva Portelance , Christopher Potts , Aditi Raghunathan , Rob Reich , Hongyu Ren , Frieda Rong , Yusuf Roohani , Camilo Ruiz , Jack Ryan , Christopher Ré , Dorsa Sadigh , Shiori Sagawa , Keshav Santhanam , Andy Shih , Krishnan Srinivasan , Alex Tamkin , Rohan Taori , Armin W. Thomas , Florian Tramèr , Rose E. Wang , William Wang , Bohan Wu , Jiajun Wu , Yuhuai Wu , Sang Michael Xie , Michihiro Yasunaga , Jiaxuan You , Matei Zaharia , Michael Zhang , Tianyi Zhang , Xikun Zhang , Yuhui Zhang , Lucia Zheng , Kaitlyn Zhou , Percy Liang

This paper surveys foundation models for AI-enabled biological design, focusing on recent developments in applying large-scale, self-supervised models to tasks such as protein engineering, small molecule design, and genomic sequence design.…

人工智能 · 计算机科学 2025-05-20 Asher Moldwin , Amarda Shehu

Foundation models including large language models (LLMs) are increasingly attracting interest worldwide for their distinguished capabilities and potential to perform a wide variety of tasks. Nevertheless, people are concerned about whether…

软件工程 · 计算机科学 2024-02-23 Yue Liu , Qinghua Lu , Liming Zhu , Hye-Young Paik

The impressive performance of ChatGPT and other foundation-model-based products in human language understanding has prompted both academia and industry to explore how these models can be tailored for specific industries and application…

人工智能 · 计算机科学 2025-09-22 Haolong Chen , Hanzhi Chen , Zijian Zhao , Kaifeng Han , Guangxu Zhu , Yichen Zhao , Ying Du , Wei Xu , Qingjiang Shi

We survey applications of pretrained foundation models in robotics. Traditional deep learning models in robotics are trained on small datasets tailored for specific tasks, which limits their adaptability across diverse applications. In…

The proliferation of Large Language Models (LLMs) has s fueled a shift in robot learning from automation towards general embodied Artificial Intelligence (AI). Adopting foundation models together with traditional learning methods to robot…

机器人学 · 计算机科学 2023-11-27 Xuan Xiao , Jiahang Liu , Zhipeng Wang , Yanmin Zhou , Yong Qi , Qian Cheng , Bin He , Shuo Jiang
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