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Scientific discovery in the age of artificial intelligence

NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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Randomized entity-wise factorization for multi-agent reinforcement learning

arXiv

引用

arXiv 2020年

作者： Iqbal, Shariq Schroeder de Witt, Christian A. Peng, Bei Böhmer, Wendelin Whiteson, Shimon Sha, Fei Department of Computer Science University of Southern California Department of Computer Science University of Oxford Department of Software Technology Delft University of Technology Google Research

Multi-agent settings in the real world often involve tasks with varying types and quantities of agents and non-agent entities;however, common patterns of behavior often emerge among these agents/entities. Our method aims to leverage these commonalities by asking the question: "What is the expected utility of each agent when only considering a randomly selected subgroup of its observed entities?" By posing this counterfactual question, we can recognize state-action trajectories within sub-groups of entities that we may have encountered in another task and use what we learned in that task to inform our prediction in the current one. We then reconstruct a prediction of the full returns as a combination of factors considering these disjoint groups of entities and train this "randomly factorized" value function as an auxiliary objective for value-based multi-agent reinforcement learning. By doing so, our model can recognize and leverage similarities across tasks to improve learning efficiency in a multi-task setting. Our approach, Randomized Entity-wise Factorization for Imagined Learning (R E F I L), outperforms all strong baselines by a significant margin in challenging multi-task StarCraft micromanagement settings. Copyright © 2020, The Authors. All rights reserved.

关键词： Factorization

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Optimal Scaling Quantum Linear-Systems Solver via Discrete Adiabatic Theorem

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PRX Quantum 2022年第4期3卷 040303-040303页

作者： Pedro C.S. Costa Dong An Yuval R. Sanders Yuan Su Ryan Babbush Dominic W. Berry Department of Physics and Astronomy Macquarie University Sydney New South Wales 2109 Australia Joint Center for Quantum Information and Computer Science University of Maryland College Park Maryland 20742 USA Google Quantum AI Venice California 90291 USA Centre for Quantum Software and Information University of Technology Sydney Sydney New South Wales 2007 Australia

Recently, several approaches to solving linear systems on a quantum computer have been formulated in terms of the quantum adiabatic theorem for a continuously varying Hamiltonian. Such approaches have enabled near-linear scaling in the condition number κ of the linear system, without requiring a complicated variable-time amplitude amplification procedure. However, the most efficient of those procedures is still asymptotically suboptimal by a factor of log (κ). Here, we prove a rigorous form of the adiabatic theorem that bounds the error in terms of the spectral gap for intrinsically discrete-time evolutions. In combination with the qubitized quantum walk, our discrete adiabatic theorem gives a speed-up for all adiabatic algorithms. Here, we use this combination to develop a quantum algorithm for solving linear systems that is asymptotically optimal, in the sense that the complexity is strictly linear in κ, matching a known lower bound on the complexity. Our O[κlog (1/ϵ)] complexity is also optimal in terms of the combined scaling in κ and the precision ϵ. Compared to existing suboptimal methods, our algorithm is simpler and easier to implement. Moreover, we determine the constant factors in the algorithm, which would be suitable for determining the complexity in terms of gate counts for specific applications.

关键词： Adiabatic quantum optimization Quantum algorithms Quantum simulation

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Pandemics in silico: Scaling Agent-based Simulations on Realistic Social Contact Networks

arXiv

引用

arXiv 2024年

作者： Kitson, Joy Costello, Ian Chen, Jiangzhuo Jiménez, Diego Hoops, Stefan Mortveit, Henning Meneses, Esteban Yeom, Jae-Seung Marathe, Madhav V. Bhatele, Abhinav Department of Computer Science University of Maryland College Park United States Google Inc. Mountain View United States Biocomplexity Institute and Initiative University of Virginia Charlottesville United States Max Planck Computing and Data Facility Garching Germany National Advanced Computing Collaboratory National High Technology Center San José Costa Rica Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore United States

Preventing the spread of infectious diseases requires implementing interventions at various levels of government and evaluating the potential impact and efficacy of those preemptive measures. Agent-based modeling can be used for detailed studies of the spread of such diseases in the presence of possible interventions. The computational cost of modeling epidemic diffusion through large social contact networks necessitates the use of parallel algorithms and resources in order to achieve quick turnaround times. In this work, we present Loimos, a scalable parallel framework for simulating epidemic diffusion. Loimos uses a hybrid of time-stepping and discrete event simulation to model disease spread, and is implemented on top of Charm++, an asynchronous, many-task runtime that enables over-decomposition and adaptive overlap of computation and communication. We demonstrate that Loimos is able to achieve significant speedups while scaling to large core counts. In particular, Loimos is able to simulate 200 days of a COVID-19 outbreak on a digital twin of California in about 42 seconds, for an average of 4.6 billion traversed edges per second (TEPS), using 4096 cores on Perlmutter at NERSC. © 2024, CC BY.

关键词： Discrete event simulation

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Silly rules improve the capacity of agents to learn stable enforcement and compliance behaviors 19

Silly rules improve the capacity of agents to learn stable e...

引用

19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020

作者： Köster, Raphael Hadfield-Menell, Dylan Hadfield, Gillian K. Leibo, Joel Z. DeepMind United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Center for Human-Compatible AI United States Schwartz Reisman Institute for Technology and Society University of Toronto Vector Institute Center for Human-Compatible AI OpenAI Canada

ISBN: (纸本)9781450375184

How can societies learn to enforce and comply with social norms? Many if not most human norms are functional. Rules that punish non-cooperative behavior, for example, support cooperation. An intriguing feature of human normativity is that many social norms concern behaviors that have no direct impact on material well-being. Examples include rules about what color clothing one wears to a funeral [7] or whether one uses one's left or right hand in particular tasks [2]. Such apparently pointless rules are ubiquitous, often acquiring great social meaning despite the absence of functionality. Hadfield-Menell et al. (2019) call these norms "silly rules" and distinguish them from "important rules," such as rules that govern resource sharing or prohibit harmful conduct, that directly impact welfare [3]. © 2020 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). All rights reserved.

关键词： Reinforcement learning

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Quartic Quantum Speedups for Planted Inference

引用

Physical Review X 2025年第2期15卷 021077-021077页

作者： Alexander Schmidhuber Ryan O’Donnell Robin Kothari Ryan Babbush Google Quantum AI Venice California USA Center for Theoretical Physics Massachusetts Institute of Technology Cambridge Massachusetts USA Computer Science Department Carnegie Mellon University Pittsburgh Pennsylvania USA

We describe a quantum algorithm for the Planted Noisy kXOR Problem (also known as Sparse Learning Parity with Noise) that achieves a nearly quartic (fourth-power) speedup over the best known classical algorithm while using exponentially less space. Our work generalizes and simplifies prior work of Hastings [Quantum 4, 237 (2020)], by building on his quantum algorithm for the tensor principal component analysis (PCA) problem. We achieve our quantum speedup using a general framework based on the Kikuchi method (recovering the quartic speedup for Tensor PCA), and we anticipate it will yield similar speedups for further planted inference problems. These speedups rely on the fact that planted inference problems naturally instantiate the guided sparse Hamiltonian problem. Since the Planted Noisy kXOR Problem has been used as a component of certain cryptographic constructions, our work suggests that some of these are susceptible to superquadratic quantum attacks.

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Learning Noise via Dynamical Decoupling of Entangled Qubits

arXiv

引用

arXiv 2022年

作者： McCourt, Trevor Neill, Charles Lee, Kenny Quintana, Chris Chen, Yu Kelly, Julian Smelyanskiy, V.N. Dykman, M.I. Korotkov, Alexander Chuang, Isaac L. Petukhov, A.G. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Co-Design Center for Quantum Advantage Massachusetts Institute of Technology CambridgeMA02139 United States Google Quantum AI Santa BarbaraCA United States Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States

Noise in entangled quantum systems is difficult to characterize due to many-body effects involving multiple degrees of freedom. This noise poses a challenge to quantum computing, where two-qubit gate performance is critical. Here, we develop and apply multi-qubit dynamical decoupling sequences that characterize noise that occurs during two-qubit gates. In our superconducting system comprised of Transmon qubits with tunable couplers, we observe noise that is consistent with flux fluctuations in the coupler that simultaneously affects both qubits and induces noise in their entangling parameter. The effect of this noise on the qubits is very different from the well-studied single-qubit dephasing. Additionally, steps are observed in the decoupled signals, implying the presence of non-Gaussian noise. Copyright © 2022, The Authors. All rights reserved.

关键词： Degrees of freedom (mechanics)

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Meta-control of social learning strategies

arXiv

引用

arXiv 2021年

作者： Yaman, Anil Bredeche, Nicolas Çaylak, Onur Leibo, Joel Z. Lee, Sang Wan Computer Science Department Vrije Universiteit Amsterdam Amsterdam Netherlands Institut des Systèmes Intelligents et de Robotique Sorbonne Université CNRS Paris France Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands DeepMind London United Kingdom Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Center for Neuroscience-Inspired AI Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Program of Brain and Cognitive Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of KI for Health Science and Technology Korea Advanced Institute of Science and Technology Daejeon Korea Republic of KI for Artificial Intelligence Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Computer Science Department Vrije Universiteit Amsterdam Amsterdam Netherlands

Social learning, copying other's behavior without actual experience, offers a cost-effective means of knowledge acquisition. However, it raises the fundamental question of which individuals have reliable information: successful individuals versus the majority. The former and the latter are known respectively as success-based and conformist social learning strategies. We show here that while the success-based strategy fully exploits the benign environment of low uncertainly, it fails in uncertain environments. On the other hand, the conformist strategy can effectively mitigate this adverse effect. Based on these findings, we hypothesized that meta-control of individual and social learning strategies provides effective and sample-efficient learning in volatile and uncertain environments. Simulations on a set of environments with various levels of volatility and uncertainty confirmed our hypothesis. The results imply that meta-control of social learning affords agents the leverage to resolve environmental uncertainty with minimal exploration cost, by exploiting others' learning as an external knowledge base. Copyright © 2021, The Authors. All rights reserved.

关键词： Learning systems

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NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis

arXiv

引用

arXiv 2022年

作者： Muhammad, Shamsuddeen Hassan Adelani, David Ifeoluwa Ruder, Sebastian Ahmad, Ibrahim Said Abdulmumin, Idris Bello, Bello Shehu Choudhury, Monojit Emezue, Chris Chinenye Abdullahi, Saheed Salahudeen Aremu, Anuoluwapo Jorge, Alípio Brazdil, Pavel LIAAD - INESC TEC Portugal Faculty of Sciences University of Porto Portugal Saarland University Germany Google Research Bayero University Kano Nigeria Faculty of Computer Science and Information Technology Bayero University Kano Nigeria Department of Computer Science Ahmadu Bello University Zaria Nigeria Microsoft Research India Germany Technical University of Munich Germany Kaduna state University Nigeria Masakhane NLP HausaNLP

Sentiment analysis is one of the most widely studied applications in NLP, but most work focuses on languages with large amounts of data. We introduce the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria-Hausa, Igbo, Nigerian-Pidgin, and Yorùbá-consisting of around 30,000 annotated tweets per language, including a significant fraction of code-mixed tweets. We propose text collection, filtering, processing, and labeling methods that enable us to create datasets for these low-resource languages. We evaluate a range of pre-trained models and transfer strategies on the dataset. We find that language-specific models and language-adaptive fine-tuning generally perform best. We release the datasets, trained models, sentiment lexicons, and code to incentivize research on sentiment analysis in under-represented languages. © 2022, CC BY.

关键词： Sentiment analysis

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Large Language Models for Traffic and Transportation Research: Methodologies, State of the Art, and Future Opportunities

arXiv

引用

arXiv 2025年

作者： Yan, Yimo Liao, Yejia Xu, Guanhao Yao, Ruili Fan, Huiying Sun, Jingran Wang, Xia Sprinkle, Jonathan An, Ziyan Ma, Meiyi Cheng, Xi Liu, Tong Ke, Zemian Zou, Bo Barth, Matthew Kuo, Yong-Hong Department of Data and Systems Engineering The University of Hong Kong Hong Kong Department of Civil Materials and Environmental Engineering University of Illinois ChicagoIL United States Department of Electrical Engineering University of California RiversideCA United States Buildings and Transportation Science Division Oak Ridge National Laboratory TN United States University of California RiversideCA United States School of Civil and Environmental Engineering Georgia Institute of Technology GA United States University of Texas AustinTX United States Department of Computer Science Vanderbilt University TN United States Cornell University NY United States Department of Civil and Environmental Engineering University of Illinois Urbana-Champaign IL United States Google Inc. CA United States

The rapid rise of Large Language Models (LLMs) is transforming traffic and transportation research, with significant advancements emerging between the years 2023 and 2025 – a period marked by the inception and swift growth of adopting and adapting LLMs for various traffic and transportation applications. However, despite these significant advancements, a systematic review and synthesis of the existing studies remain lacking. To address this gap, this paper provides a comprehensive review of the methodologies and applications of LLMs in traffic and transportation, highlighting their ability to process unstructured textual data to advance transportation research. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. Our analysis identifies critical research gaps. From the methodological perspective, many research gaps can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, we identify numerous opportunities for LLMs to tackle a variety of traffic and transportation challenges, building upon existing research. By synthesizing these findings, this review not only clarifies the current state of LLM adoption and adaptation in traffic and transportation but also proposes future research directions, paving the way for smarter and more sustainable transportation systems. © 2025, CC BY-NC-SA.

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