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1. Can LLMs make trade-offs involving stipulated pain and pleasure states?

Author

Keeling, Geoff, Street, Winnie, Stachaczyk, Martyna, Zakharova, Daria, Comsa, Iulia M., Sakovych, Anastasiya, Logothetis, Isabella, Zhang, Zejia, Arcas, Blaise Agüera y, and Birch, Jonathan

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computers and Society
Abstract

Pleasure and pain play an important role in human decision making by providing a common currency for resolving motivational conflicts. While Large Language Models (LLMs) can generate detailed descriptions of pleasure and pain experiences, it is an open question whether LLMs can recreate the motivational force of pleasure and pain in choice scenarios - a question which may bear on debates about LLM sentience, understood as the capacity for valenced experiential states. We probed this question using a simple game in which the stated goal is to maximise points, but where either the points-maximising option is said to incur a pain penalty or a non-points-maximising option is said to incur a pleasure reward, providing incentives to deviate from points-maximising behaviour. Varying the intensity of the pain penalties and pleasure rewards, we found that Claude 3.5 Sonnet, Command R+, GPT-4o, and GPT-4o mini each demonstrated at least one trade-off in which the majority of responses switched from points-maximisation to pain-minimisation or pleasure-maximisation after a critical threshold of stipulated pain or pleasure intensity is reached. LLaMa 3.1-405b demonstrated some graded sensitivity to stipulated pleasure rewards and pain penalties. Gemini 1.5 Pro and PaLM 2 prioritised pain-avoidance over points-maximisation regardless of intensity, while tending to prioritise points over pleasure regardless of intensity. We discuss the implications of these findings for debates about the possibility of LLM sentience.

Published
2024

2. Non-Markovian effects in long-range polariton-mediated energy transfer

Author

Arnardottir, Kristin B., Fowler-Wright, Piper, Tserkezis, Christos, Lovett, Brendon W., and Keeling, Jonathan

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics, Physics - Optics, Quantum Physics
Abstract

Intramolecular energy transfer driven by near-field effects plays an important role in applications ranging from biophysics and chemistry to nano-optics and quantum communications. Advances in strong light-matter coupling in molecular systems have opened new possibilities to control energy transfer. In particular, long-distance energy transfer between molecules has been reported as the result of their mutual coupling to cavity photon modes, and the formation of hybrid polariton states. In addition to strong coupling to light, molecular systems also show strong interactions between electronic and vibrational modes. The latter can act as a reservoir for energy to facilitate off-resonant transitions, and thus energy relaxation between polaritonic states at different energies. However, the non-Markovian nature of those modes makes it challenging to accurately simulate these effects. Here we capture them via process tensor matrix product operator (PT-MPO) methods, to describe exactly the vibrational environment of the molecules combined with a mean-field treatment of the light-matter interaction. In particular, we study the emission dynamics of a system consisting of two spatially separated layers of different species of molecules coupled to a common photon mode, and show that the strength of coupling to the vibrational bath plays a crucial role in governing the dynamics of the energy of the emitted light; at strong vibrational coupling this dynamics shows strongly non-Markovian effects, eventually leading to polaron formation. Our results shed light on polaritonic long-range energy transfer, and provide further understanding of the role of vibrational modes of relevance to the growing field of molecular polaritonics., Comment: 9 pages, 5 figures

Published
2024

3. Percutaneous Mechanical Aspiration for Infective Endocarditis: Proceedings From an Inaugural Multidisciplinary Summit and Comprehensive Review.

Author

Marinacci, Lucas, Sethi, Sanjum, Paras, Molly, El Sabbagh, Abdallah, Secemsky, Eric, Sohail, M, Starck, Christoph, Bearnot, Benjamin, Yucel, Evin, Schaerf, Raymond, Akhtar, Yasir, Younes, Ahmad, Patton, Marquand, Villablanca, Pedro, Reddy, Seenu, Enter, Daniel, Moriarty, John, Keeling, William, El Hajj Younes, Stephanie, Kiell, Charles, and Rosenfield, Kenneth

Subjects
cardiac catheterization, endocarditis, substance-related disorders, suction, thrombectomy
Abstract

The clinical presentation and epidemiology of infective endocarditis (IE) have evolved over time. While the cornerstones of IE treatment remain antimicrobial therapy and surgery, percutaneous mechanical aspiration (PMA) has emerged as an option for carefully selected patients as a complementary modality, based on retrospective data, case series, and expert experience. In this comprehensive review, we summarize the proceedings from an inaugural summit dedicated to the discussion of PMA in the global management of IE, consisting of experts across multiple disciplines from diverse geographic regions and care environments. After conceptualizing the 3 major roles of PMA as a bridge to decision, destination therapy, and adjunctive therapy, we then review the clinical scenarios in which PMA might be considered by IE subtype. We discuss patient selection, the rationale for intervention, and the most recent evidence for each. Next, we consider PMA for IE in the larger context of our health care system across 3 domains: clinical collaboration, financial considerations, and academic innovation, emphasizing the importance of interdisciplinary teams and cross-organizational partnerships, reimbursement models, and the need for high-quality research. Finally, we outline what we determined to be the most pressing outstanding questions in this space. In doing so, we propose a national consortium to help organize efforts to move this field forward and share our progress in these endeavors to date. PMA for IE has great promise, but significant work remains if we are to fully realize its potential to safely and effectively improve outcomes for modern endocarditis patients.

Published
2024

4. Many-Body Open Quantum Systems

Author

Fazio, Rosario, Keeling, Jonathan, Mazza, Leonardo, and Schirò, Marco

Subjects
Quantum Physics, Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Quantum Gases, Condensed Matter - Statistical Mechanics, Condensed Matter - Strongly Correlated Electrons
Abstract

These Lecture Notes discuss the recent theoretical advances in the understanding of open quantum many-body physics in platforms where both dissipative and coherent processes can be tuned and controlled to a high degree. We start by reviewing the theoretical frameworks and methods used to describe and tackle open quantum many-body systems. We then discuss the use of dissipative processes to engineer many-body stationary states with desired properties and the emergence of dissipative phase transitions arising out of the competition between coherent evolution and dissipation. We review the dynamics of open quantum many body systems in the presence of correlated many-body dissipative processes, such as heating and many-body losses. Finally we provide a different perspective on open quantum many-body systems by looking at stochastic quantum trajectories, relevant for the case in which the environment represents a monitoring device, and the associated measurement-induced phase transitions., Comment: Lecture Notes, 185 pages, 40 figure

Published
2024

5. On the attribution of confidence to large language models

Author

Keeling, Geoff and Street, Winnie

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

Credences are mental states corresponding to degrees of confidence in propositions. Attribution of credences to Large Language Models (LLMs) is commonplace in the empirical literature on LLM evaluation. Yet the theoretical basis for LLM credence attribution is unclear. We defend three claims. First, our semantic claim is that LLM credence attributions are (at least in general) correctly interpreted literally, as expressing truth-apt beliefs on the part of scientists that purport to describe facts about LLM credences. Second, our metaphysical claim is that the existence of LLM credences is at least plausible, although current evidence is inconclusive. Third, our epistemic claim is that LLM credence attributions made in the empirical literature on LLM evaluation are subject to non-trivial sceptical concerns. It is a distinct possibility that even if LLMs have credences, LLM credence attributions are generally false because the experimental techniques used to assess LLM credences are not truth-tracking., Comment: 22 pages, 0 figures

Published
2024

6. OQuPy: A Python package to efficiently simulate non-Markovian open quantum systems with process tensors

Author

Fux, Gerald E., Fowler-Wright, Piper, Beckles, Joel, Butler, Eoin P., Eastham, Paul R., Gribben, Dominic, Keeling, Jonathan, Kilda, Dainius, Kirton, Peter, Lawrence, Ewen D. C., Lovett, Brendon W., O'Neill, Eoin, Strathearn, Aidan, and de Wit, Roosmarijn

Subjects
Quantum Physics
Abstract

Non-Markovian dynamics arising from the strong coupling of a system to a structured environment is essential in many applications of quantum mechanics and emerging technologies. Deriving an accurate description of general quantum dynamics including memory effects is however a demanding task, prohibitive to standard analytical or direct numerical approaches. We present a major release of our open source software package, OQuPy (Open Quantum System in Python), which provides several recently developed numerical methods that address this challenging task. It utilizes the process tensor approach to open quantum systems in which a single map, the process tensor, captures all possible effects of an environment on the system. The representation of the process tensor in a tensor network form allows an exact yet highly efficient description of non-Markovian open quantum systems (NM-OQS). The OQuPy package provides methods to (1) compute the dynamics and multi-time correlations of quantum systems coupled to single and multiple environments, (2) optimize control protocols for NM-OQS, (3) simulate interacting chains of NM-OQS, and (4) compute the mean-field dynamics of an ensemble of NM-OQS coupled to a common central system. Our aim is to provide an easily accessible and extensible tool for researchers of open quantum systems in fields such as quantum chemistry, quantum sensing, and quantum information., Comment: 20 pages, 15 figures

Published
2024
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7. LLMs achieve adult human performance on higher-order theory of mind tasks

Author

Street, Winnie, Siy, John Oliver, Keeling, Geoff, Baranes, Adrien, Barnett, Benjamin, McKibben, Michael, Kanyere, Tatenda, Lentz, Alison, Arcas, Blaise Aguera y, and Dunbar, Robin I. M.

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Human-Computer Interaction, I.2.7, H.1.2
Abstract

This paper examines the extent to which large language models (LLMs) have developed higher-order theory of mind (ToM); the human ability to reason about multiple mental and emotional states in a recursive manner (e.g. I think that you believe that she knows). This paper builds on prior work by introducing a handwritten test suite -- Multi-Order Theory of Mind Q&A -- and using it to compare the performance of five LLMs to a newly gathered adult human benchmark. We find that GPT-4 and Flan-PaLM reach adult-level and near adult-level performance on ToM tasks overall, and that GPT-4 exceeds adult performance on 6th order inferences. Our results suggest that there is an interplay between model size and finetuning for the realisation of ToM abilities, and that the best-performing LLMs have developed a generalised capacity for ToM. Given the role that higher-order ToM plays in a wide range of cooperative and competitive human behaviours, these findings have significant implications for user-facing LLM applications.

Published
2024

8. Tree-like process tensor contraction for automated compression of environments

Author

Cygorek, Moritz, Lovett, Brendon W., Keeling, Jonathan, and Gauger, Erik M.

Subjects
Quantum Physics, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

The algorithm "automated compression of environments" (ACE) [Nat. Phys. 18, 662 (2022)] provides a versatile way of simulating an extremely broad class of open quantum systems. This is achieved by encapsulating the influence of the environment, which is determined by the interaction Hamiltonian(s) and initial states, into compact process tensor matrix product operator (PT-MPO) representations. The generality of the ACE method comes at high numerical cost. Here, we demonstrate that orders-of-magnitude improvement of ACE is possible by changing the order of PT-MPO contraction from a sequential to a tree-like scheme. The problem of combining two partial PT-MPOs with large inner bonds is solved by a preselection approach. The drawbacks of the preselection approach are that the MPO compression is suboptimal and that it is more prone to error accumulation than sequential combination and compression. We therefore also identify strategies to mitigate these disadvantages by fine-tuning compression parameters. This results in a scheme that is similar in compression efficiency and accuracy to the original ACE algorithm, yet is significantly faster. Our numerical experiments reach similar conclusions for bosonic and fermionic test cases, suggesting that our findings are characteristic of the combination of PT-MPOs more generally.

Published
2024

9. Raman-phonon-polariton condensation in a transversely pumped cavity

Author

Bourzutschky, Alexander N., Lev, Benjamin L., and Keeling, Jonathan

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Strongly Correlated Electrons, Quantum Physics
Abstract

Phonon polaritons are hybrid states of light and matter that are typically realised when optically active phonons couple strongly to photons. We suggest a new approach to realising phonon polaritons, by employing a transverse-pumping Raman scheme, as used in experiments on cold atoms in optical cavities. This approach allows hybridisation between an optical cavity mode and any Raman-active phonon mode. Moreover, this approach enables one to tune the effective phonon-photon coupling by changing the strength of the transverse pumping light. We show that such a system may realise a phonon-polariton condensate. To do this, we find the stationary states and use Floquet theory to determine their stability. We thus identify distinct superradiant and lasing states in which the polariton modes are macroscopically populated. We map out the phase diagram of these states as a function of pump frequencies and strengths. Using parameters for transition metal dichalcogenides, we show that realisation of these phases may be practicably obtainable. The ability to manipulate phonon mode frequencies and attain steady-state populations of selected phonon modes provides a new tool for engineering correlated states of electrons., Comment: 11 pages, 6 figures, and supplement

Published
2024
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10. The Ethics of Advanced AI Assistants

Author

Gabriel, Iason, Manzini, Arianna, Keeling, Geoff, Hendricks, Lisa Anne, Rieser, Verena, Iqbal, Hasan, Tomašev, Nenad, Ktena, Ira, Kenton, Zachary, Rodriguez, Mikel, El-Sayed, Seliem, Brown, Sasha, Akbulut, Canfer, Trask, Andrew, Hughes, Edward, Bergman, A. Stevie, Shelby, Renee, Marchal, Nahema, Griffin, Conor, Mateos-Garcia, Juan, Weidinger, Laura, Street, Winnie, Lange, Benjamin, Ingerman, Alex, Lentz, Alison, Enger, Reed, Barakat, Andrew, Krakovna, Victoria, Siy, John Oliver, Kurth-Nelson, Zeb, McCroskery, Amanda, Bolina, Vijay, Law, Harry, Shanahan, Murray, Alberts, Lize, Balle, Borja, de Haas, Sarah, Ibitoye, Yetunde, Dafoe, Allan, Goldberg, Beth, Krier, Sébastien, Reese, Alexander, Witherspoon, Sims, Hawkins, Will, Rauh, Maribeth, Wallace, Don, Franklin, Matija, Goldstein, Josh A., Lehman, Joel, Klenk, Michael, Vallor, Shannon, Biles, Courtney, Morris, Meredith Ringel, King, Helen, Arcas, Blaise Agüera y, Isaac, William, and Manyika, James

Subjects
Computer Science - Computers and Society
Abstract

This paper focuses on the opportunities and the ethical and societal risks posed by advanced AI assistants. We define advanced AI assistants as artificial agents with natural language interfaces, whose function is to plan and execute sequences of actions on behalf of a user, across one or more domains, in line with the user's expectations. The paper starts by considering the technology itself, providing an overview of AI assistants, their technical foundations and potential range of applications. It then explores questions around AI value alignment, well-being, safety and malicious uses. Extending the circle of inquiry further, we next consider the relationship between advanced AI assistants and individual users in more detail, exploring topics such as manipulation and persuasion, anthropomorphism, appropriate relationships, trust and privacy. With this analysis in place, we consider the deployment of advanced assistants at a societal scale, focusing on cooperation, equity and access, misinformation, economic impact, the environment and how best to evaluate advanced AI assistants. Finally, we conclude by providing a range of recommendations for researchers, developers, policymakers and public stakeholders.

Published
2024

11. A Mechanism-Based Approach to Mitigating Harms from Persuasive Generative AI

Author

El-Sayed, Seliem, Akbulut, Canfer, McCroskery, Amanda, Keeling, Geoff, Kenton, Zachary, Jalan, Zaria, Marchal, Nahema, Manzini, Arianna, Shevlane, Toby, Vallor, Shannon, Susser, Daniel, Franklin, Matija, Bridgers, Sophie, Law, Harry, Rahtz, Matthew, Shanahan, Murray, Tessler, Michael Henry, Douillard, Arthur, Everitt, Tom, and Brown, Sasha

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence
Abstract

Recent generative AI systems have demonstrated more advanced persuasive capabilities and are increasingly permeating areas of life where they can influence decision-making. Generative AI presents a new risk profile of persuasion due the opportunity for reciprocal exchange and prolonged interactions. This has led to growing concerns about harms from AI persuasion and how they can be mitigated, highlighting the need for a systematic study of AI persuasion. The current definitions of AI persuasion are unclear and related harms are insufficiently studied. Existing harm mitigation approaches prioritise harms from the outcome of persuasion over harms from the process of persuasion. In this paper, we lay the groundwork for the systematic study of AI persuasion. We first put forward definitions of persuasive generative AI. We distinguish between rationally persuasive generative AI, which relies on providing relevant facts, sound reasoning, or other forms of trustworthy evidence, and manipulative generative AI, which relies on taking advantage of cognitive biases and heuristics or misrepresenting information. We also put forward a map of harms from AI persuasion, including definitions and examples of economic, physical, environmental, psychological, sociocultural, political, privacy, and autonomy harm. We then introduce a map of mechanisms that contribute to harmful persuasion. Lastly, we provide an overview of approaches that can be used to mitigate against process harms of persuasion, including prompt engineering for manipulation classification and red teaming. Future work will operationalise these mitigations and study the interaction between different types of mechanisms of persuasion.

Published
2024

12. Gemma: Open Models Based on Gemini Research and Technology

Author

Gemma Team, Mesnard, Thomas, Hardin, Cassidy, Dadashi, Robert, Bhupatiraju, Surya, Pathak, Shreya, Sifre, Laurent, Rivière, Morgane, Kale, Mihir Sanjay, Love, Juliette, Tafti, Pouya, Hussenot, Léonard, Sessa, Pier Giuseppe, Chowdhery, Aakanksha, Roberts, Adam, Barua, Aditya, Botev, Alex, Castro-Ros, Alex, Slone, Ambrose, Héliou, Amélie, Tacchetti, Andrea, Bulanova, Anna, Paterson, Antonia, Tsai, Beth, Shahriari, Bobak, Lan, Charline Le, Choquette-Choo, Christopher A., Crepy, Clément, Cer, Daniel, Ippolito, Daphne, Reid, David, Buchatskaya, Elena, Ni, Eric, Noland, Eric, Yan, Geng, Tucker, George, Muraru, George-Christian, Rozhdestvenskiy, Grigory, Michalewski, Henryk, Tenney, Ian, Grishchenko, Ivan, Austin, Jacob, Keeling, James, Labanowski, Jane, Lespiau, Jean-Baptiste, Stanway, Jeff, Brennan, Jenny, Chen, Jeremy, Ferret, Johan, Chiu, Justin, Mao-Jones, Justin, Lee, Katherine, Yu, Kathy, Millican, Katie, Sjoesund, Lars Lowe, Lee, Lisa, Dixon, Lucas, Reid, Machel, Mikuła, Maciej, Wirth, Mateo, Sharman, Michael, Chinaev, Nikolai, Thain, Nithum, Bachem, Olivier, Chang, Oscar, Wahltinez, Oscar, Bailey, Paige, Michel, Paul, Yotov, Petko, Chaabouni, Rahma, Comanescu, Ramona, Jana, Reena, Anil, Rohan, McIlroy, Ross, Liu, Ruibo, Mullins, Ryan, Smith, Samuel L, Borgeaud, Sebastian, Girgin, Sertan, Douglas, Sholto, Pandya, Shree, Shakeri, Siamak, De, Soham, Klimenko, Ted, Hennigan, Tom, Feinberg, Vlad, Stokowiec, Wojciech, Chen, Yu-hui, Ahmed, Zafarali, Gong, Zhitao, Warkentin, Tris, Peran, Ludovic, Giang, Minh, Farabet, Clément, Vinyals, Oriol, Dean, Jeff, Kavukcuoglu, Koray, Hassabis, Demis, Ghahramani, Zoubin, Eck, Douglas, Barral, Joelle, Pereira, Fernando, Collins, Eli, Joulin, Armand, Fiedel, Noah, Senter, Evan, Andreev, Alek, and Kenealy, Kathleen

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models. Gemma models demonstrate strong performance across academic benchmarks for language understanding, reasoning, and safety. We release two sizes of models (2 billion and 7 billion parameters), and provide both pretrained and fine-tuned checkpoints. Gemma outperforms similarly sized open models on 11 out of 18 text-based tasks, and we present comprehensive evaluations of safety and responsibility aspects of the models, alongside a detailed description of model development. We believe the responsible release of LLMs is critical for improving the safety of frontier models, and for enabling the next wave of LLM innovations.

Published
2024

13. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Author

Gemini Team, Georgiev, Petko, Lei, Ving Ian, Burnell, Ryan, Bai, Libin, Gulati, Anmol, Tanzer, Garrett, Vincent, Damien, Pan, Zhufeng, Wang, Shibo, Mariooryad, Soroosh, Ding, Yifan, Geng, Xinyang, Alcober, Fred, Frostig, Roy, Omernick, Mark, Walker, Lexi, Paduraru, Cosmin, Sorokin, Christina, Tacchetti, Andrea, Gaffney, Colin, Daruki, Samira, Sercinoglu, Olcan, Gleicher, Zach, Love, Juliette, Voigtlaender, Paul, Jain, Rohan, Surita, Gabriela, Mohamed, Kareem, Blevins, Rory, Ahn, Junwhan, Zhu, Tao, Kawintiranon, Kornraphop, Firat, Orhan, Gu, Yiming, Zhang, Yujing, Rahtz, Matthew, Faruqui, Manaal, Clay, Natalie, Gilmer, Justin, Co-Reyes, JD, Penchev, Ivo, Zhu, Rui, Morioka, Nobuyuki, Hui, Kevin, Haridasan, Krishna, Campos, Victor, Mahdieh, Mahdis, Guo, Mandy, Hassan, Samer, Kilgour, Kevin, Vezer, Arpi, Cheng, Heng-Tze, de Liedekerke, Raoul, Goyal, Siddharth, Barham, Paul, Strouse, DJ, Noury, Seb, Adler, Jonas, Sundararajan, Mukund, Vikram, Sharad, Lepikhin, Dmitry, Paganini, Michela, Garcia, Xavier, Yang, Fan, Valter, Dasha, Trebacz, Maja, Vodrahalli, Kiran, Asawaroengchai, Chulayuth, Ring, Roman, Kalb, Norbert, Soares, Livio Baldini, Brahma, Siddhartha, Steiner, David, Yu, Tianhe, Mentzer, Fabian, He, Antoine, Gonzalez, Lucas, Xu, Bibo, Kaufman, Raphael Lopez, Shafey, Laurent El, Oh, Junhyuk, Hennigan, Tom, Driessche, George van den, Odoom, Seth, Lucic, Mario, Roelofs, Becca, Lall, Sid, Marathe, Amit, Chan, Betty, Ontanon, Santiago, He, Luheng, Teplyashin, Denis, Lai, Jonathan, Crone, Phil, Damoc, Bogdan, Ho, Lewis, Riedel, Sebastian, Lenc, Karel, Yeh, Chih-Kuan, Chowdhery, Aakanksha, Xu, Yang, Kazemi, Mehran, Amid, Ehsan, Petrushkina, Anastasia, Swersky, Kevin, Khodaei, Ali, Chen, Gowoon, Larkin, Chris, Pinto, Mario, Yan, Geng, Badia, Adria Puigdomenech, Patil, Piyush, Hansen, Steven, Orr, Dave, Arnold, Sebastien M. R., Grimstad, Jordan, Dai, Andrew, Douglas, Sholto, Sinha, Rishika, Yadav, Vikas, Chen, Xi, Gribovskaya, Elena, Austin, Jacob, Zhao, Jeffrey, Patel, Kaushal, Komarek, Paul, Austin, Sophia, Borgeaud, Sebastian, Friso, Linda, Goyal, Abhimanyu, Caine, Ben, Cao, Kris, Chung, Da-Woon, Lamm, Matthew, Barth-Maron, Gabe, Kagohara, Thais, Olszewska, Kate, Chen, Mia, Shivakumar, Kaushik, Agarwal, Rishabh, Godhia, Harshal, Rajwar, Ravi, Snaider, Javier, Dotiwalla, Xerxes, Liu, Yuan, Barua, Aditya, Ungureanu, Victor, Zhang, Yuan, Batsaikhan, Bat-Orgil, Wirth, Mateo, Qin, James, Danihelka, Ivo, Doshi, Tulsee, Chadwick, Martin, Chen, Jilin, Jain, Sanil, Le, Quoc, Kar, Arjun, Gurumurthy, Madhu, Li, Cheng, Sang, Ruoxin, Liu, Fangyu, Lamprou, Lampros, Munoz, Rich, Lintz, Nathan, Mehta, Harsh, Howard, Heidi, Reynolds, Malcolm, Aroyo, Lora, Wang, Quan, Blanco, Lorenzo, Cassirer, Albin, Griffith, Jordan, Das, Dipanjan, Lee, Stephan, Sygnowski, Jakub, Fisher, Zach, Besley, James, Powell, Richard, Ahmed, Zafarali, Paulus, Dominik, Reitter, David, Borsos, Zalan, Joshi, Rishabh, Pope, Aedan, Hand, Steven, Selo, Vittorio, Jain, Vihan, Sethi, Nikhil, Goel, Megha, Makino, Takaki, May, Rhys, Yang, Zhen, Schalkwyk, Johan, Butterfield, Christina, Hauth, Anja, Goldin, Alex, Hawkins, Will, Senter, Evan, Brin, Sergey, Woodman, Oliver, Ritter, Marvin, Noland, Eric, Giang, Minh, Bolina, Vijay, Lee, Lisa, Blyth, Tim, Mackinnon, Ian, Reid, Machel, Sarvana, Obaid, Silver, David, Chen, Alexander, Wang, Lily, Maggiore, Loren, Chang, Oscar, Attaluri, Nithya, Thornton, Gregory, Chiu, Chung-Cheng, Bunyan, Oskar, Levine, Nir, Chung, Timothy, Eltyshev, Evgenii, Si, Xiance, Lillicrap, Timothy, Brady, Demetra, Aggarwal, Vaibhav, Wu, Boxi, Xu, Yuanzhong, McIlroy, Ross, Badola, Kartikeya, Sandhu, Paramjit, Moreira, Erica, Stokowiec, Wojciech, Hemsley, Ross, Li, Dong, Tudor, Alex, Shyam, Pranav, Rahimtoroghi, Elahe, Haykal, Salem, Sprechmann, Pablo, Zhou, Xiang, Mincu, Diana, Li, Yujia, Addanki, Ravi, Krishna, Kalpesh, Wu, Xiao, Frechette, Alexandre, Eyal, Matan, Dafoe, Allan, Lacey, Dave, Whang, Jay, Avrahami, Thi, Zhang, Ye, Taropa, Emanuel, Lin, Hanzhao, Toyama, Daniel, Rutherford, Eliza, Sano, Motoki, Choe, HyunJeong, Tomala, Alex, Safranek-Shrader, Chalence, Kassner, Nora, Pajarskas, Mantas, Harvey, Matt, Sechrist, Sean, Fortunato, Meire, Lyu, Christina, Elsayed, Gamaleldin, Kuang, Chenkai, Lottes, James, Chu, Eric, Jia, Chao, Chen, Chih-Wei, Humphreys, Peter, Baumli, Kate, Tao, Connie, Samuel, Rajkumar, Santos, Cicero Nogueira dos, Andreassen, Anders, Rakićević, Nemanja, Grewe, Dominik, Kumar, Aviral, Winkler, Stephanie, Caton, Jonathan, Brock, Andrew, Dalmia, Sid, Sheahan, Hannah, Barr, Iain, Miao, Yingjie, Natsev, Paul, Devlin, Jacob, Behbahani, Feryal, Prost, Flavien, Sun, Yanhua, Myaskovsky, Artiom, Pillai, Thanumalayan Sankaranarayana, Hurt, Dan, Lazaridou, Angeliki, Xiong, Xi, Zheng, Ce, Pardo, Fabio, Li, Xiaowei, Horgan, Dan, Stanton, Joe, Ambar, Moran, Xia, Fei, Lince, Alejandro, Wang, Mingqiu, Mustafa, Basil, Webson, Albert, Lee, Hyo, Anil, Rohan, Wicke, Martin, Dozat, Timothy, Sinha, Abhishek, Piqueras, Enrique, Dabir, Elahe, Upadhyay, Shyam, Boral, Anudhyan, Hendricks, Lisa Anne, Fry, Corey, Djolonga, Josip, Su, Yi, Walker, Jake, Labanowski, Jane, Huang, Ronny, Misra, Vedant, Chen, Jeremy, Skerry-Ryan, RJ, Singh, Avi, Rijhwani, Shruti, Yu, Dian, Castro-Ros, Alex, Changpinyo, Beer, Datta, Romina, Bagri, Sumit, Hrafnkelsson, Arnar Mar, Maggioni, Marcello, Zheng, Daniel, Sulsky, Yury, Hou, Shaobo, Paine, Tom Le, Yang, Antoine, Riesa, Jason, Rogozinska, Dominika, Marcus, Dror, Badawy, Dalia El, Zhang, Qiao, Wang, Luyu, Miller, Helen, Greer, Jeremy, Sjos, Lars Lowe, Nova, Azade, Zen, Heiga, Chaabouni, Rahma, Rosca, Mihaela, Jiang, Jiepu, Chen, Charlie, Liu, Ruibo, Sainath, Tara, Krikun, Maxim, Polozov, Alex, Lespiau, Jean-Baptiste, Newlan, Josh, Cankara, Zeyncep, Kwak, Soo, Xu, Yunhan, Chen, Phil, Coenen, Andy, Meyer, Clemens, Tsihlas, Katerina, Ma, Ada, Gottweis, Juraj, Xing, Jinwei, Gu, Chenjie, Miao, Jin, Frank, Christian, Cankara, Zeynep, Ganapathy, Sanjay, Dasgupta, Ishita, Hughes-Fitt, Steph, Chen, Heng, Reid, David, Rong, Keran, Fan, Hongmin, van Amersfoort, Joost, Zhuang, Vincent, Cohen, Aaron, Gu, Shixiang Shane, Mohananey, Anhad, Ilic, Anastasija, Tobin, Taylor, Wieting, John, Bortsova, Anna, Thacker, Phoebe, Wang, Emma, Caveness, Emily, Chiu, Justin, Sezener, Eren, Kaskasoli, Alex, Baker, Steven, Millican, Katie, Elhawaty, Mohamed, Aisopos, Kostas, Lebsack, Carl, Byrd, Nathan, Dai, Hanjun, Jia, Wenhao, Wiethoff, Matthew, Davoodi, Elnaz, Weston, Albert, Yagati, Lakshman, Ahuja, Arun, Gao, Isabel, Pundak, Golan, Zhang, Susan, Azzam, Michael, Sim, Khe Chai, Caelles, Sergi, Keeling, James, Sharma, Abhanshu, Swing, Andy, Li, YaGuang, Liu, Chenxi, Bostock, Carrie Grimes, Bansal, Yamini, Nado, Zachary, Anand, Ankesh, Lipschultz, Josh, Karmarkar, Abhijit, Proleev, Lev, Ittycheriah, Abe, Yeganeh, Soheil Hassas, Polovets, George, Faust, Aleksandra, Sun, Jiao, Rrustemi, Alban, Li, Pen, Shivanna, Rakesh, Liu, Jeremiah, Welty, Chris, Lebron, Federico, Baddepudi, Anirudh, Krause, Sebastian, Parisotto, Emilio, Soricut, Radu, Xu, Zheng, Bloxwich, Dawn, Johnson, Melvin, Neyshabur, Behnam, Mao-Jones, Justin, Wang, Renshen, Ramasesh, Vinay, Abbas, Zaheer, Guez, Arthur, Segal, Constant, Nguyen, Duc Dung, Svensson, James, Hou, Le, York, Sarah, Milan, Kieran, Bridgers, Sophie, Gworek, Wiktor, Tagliasacchi, Marco, Lee-Thorp, James, Chang, Michael, Guseynov, Alexey, Hartman, Ale Jakse, Kwong, Michael, Zhao, Ruizhe, Kashem, Sheleem, Cole, Elizabeth, Miech, Antoine, Tanburn, Richard, Phuong, Mary, Pavetic, Filip, Cevey, Sebastien, Comanescu, Ramona, Ives, Richard, Yang, Sherry, Du, Cosmo, Li, Bo, Zhang, Zizhao, Iinuma, Mariko, Hu, Clara Huiyi, Roy, Aurko, Bijwadia, Shaan, Zhu, Zhenkai, Martins, Danilo, Saputro, Rachel, Gergely, Anita, Zheng, Steven, Jia, Dawei, Antonoglou, Ioannis, Sadovsky, Adam, Gu, Shane, Bi, Yingying, Andreev, Alek, Samangooei, Sina, Khan, Mina, Kocisky, Tomas, Filos, Angelos, Kumar, Chintu, Bishop, Colton, Yu, Adams, Hodkinson, Sarah, Mittal, Sid, Shah, Premal, Moufarek, Alexandre, Cheng, Yong, Bloniarz, Adam, Lee, Jaehoon, Pejman, Pedram, Michel, Paul, Spencer, Stephen, Feinberg, Vladimir, Xiong, Xuehan, Savinov, Nikolay, Smith, Charlotte, Shakeri, Siamak, Tran, Dustin, Chesus, Mary, Bohnet, Bernd, Tucker, George, von Glehn, Tamara, Muir, Carrie, Mao, Yiran, Kazawa, Hideto, Slone, Ambrose, Soparkar, Kedar, Shrivastava, Disha, Cobon-Kerr, James, Sharman, Michael, Pavagadhi, Jay, Araya, Carlos, Misiunas, Karolis, Ghelani, Nimesh, Laskin, Michael, Barker, David, Li, Qiujia, Briukhov, Anton, Houlsby, Neil, Glaese, Mia, Lakshminarayanan, Balaji, Schucher, Nathan, Tang, Yunhao, Collins, Eli, Lim, Hyeontaek, Feng, Fangxiaoyu, Recasens, Adria, Lai, Guangda, Magni, Alberto, De Cao, Nicola, Siddhant, Aditya, Ashwood, Zoe, Orbay, Jordi, Dehghani, Mostafa, Brennan, Jenny, He, Yifan, Xu, Kelvin, Gao, Yang, Saroufim, Carl, Molloy, James, Wu, Xinyi, Arnold, Seb, Chang, Solomon, Schrittwieser, Julian, Buchatskaya, Elena, Radpour, Soroush, Polacek, Martin, Giordano, Skye, Bapna, Ankur, Tokumine, Simon, Hellendoorn, Vincent, Sottiaux, Thibault, Cogan, Sarah, Severyn, Aliaksei, Saleh, Mohammad, Thakoor, Shantanu, Shefey, Laurent, Qiao, Siyuan, Gaba, Meenu, Chang, Shuo-yiin, Swanson, Craig, Zhang, Biao, Lee, Benjamin, Rubenstein, Paul Kishan, Song, Gan, Kwiatkowski, Tom, Koop, Anna, Kannan, Ajay, Kao, David, Schuh, Parker, Stjerngren, Axel, Ghiasi, Golnaz, Gibson, Gena, Vilnis, Luke, Yuan, Ye, Ferreira, Felipe Tiengo, Kamath, Aishwarya, Klimenko, Ted, Franko, Ken, Xiao, Kefan, Bhattacharya, Indro, Patel, Miteyan, Wang, Rui, Morris, Alex, Strudel, Robin, Sharma, Vivek, Choy, Peter, Hashemi, Sayed Hadi, Landon, Jessica, Finkelstein, Mara, Jhakra, Priya, Frye, Justin, Barnes, Megan, Mauger, Matthew, Daun, Dennis, Baatarsukh, Khuslen, Tung, Matthew, Farhan, Wael, Michalewski, Henryk, Viola, Fabio, Quitry, Felix de Chaumont, Lan, Charline Le, Hudson, Tom, Wang, Qingze, Fischer, Felix, Zheng, Ivy, White, Elspeth, Dragan, Anca, Alayrac, Jean-baptiste, Ni, Eric, Pritzel, Alexander, Iwanicki, Adam, Isard, Michael, Bulanova, Anna, Zilka, Lukas, Dyer, Ethan, Sachan, Devendra, Srinivasan, Srivatsan, Muckenhirn, Hannah, Cai, Honglong, Mandhane, Amol, Tariq, Mukarram, Rae, Jack W., Wang, Gary, Ayoub, Kareem, FitzGerald, Nicholas, Zhao, Yao, Han, Woohyun, Alberti, Chris, Garrette, Dan, Krishnakumar, Kashyap, Gimenez, Mai, Levskaya, Anselm, Sohn, Daniel, Matak, Josip, Iturrate, Inaki, Chang, Michael B., Xiang, Jackie, Cao, Yuan, Ranka, Nishant, Brown, Geoff, Hutter, Adrian, Mirrokni, Vahab, Chen, Nanxin, Yao, Kaisheng, Egyed, Zoltan, Galilee, Francois, Liechty, Tyler, Kallakuri, Praveen, Palmer, Evan, Ghemawat, Sanjay, Liu, Jasmine, Tao, David, Thornton, Chloe, Green, Tim, Jasarevic, Mimi, Lin, Sharon, Cotruta, Victor, Tan, Yi-Xuan, Fiedel, Noah, Yu, Hongkun, Chi, Ed, Neitz, Alexander, Heitkaemper, Jens, Sinha, Anu, Zhou, Denny, Sun, Yi, Kaed, Charbel, Hulse, Brice, Mishra, Swaroop, Georgaki, Maria, Kudugunta, Sneha, Farabet, Clement, Shafran, Izhak, Vlasic, Daniel, Tsitsulin, Anton, Ananthanarayanan, Rajagopal, Carin, Alen, Su, Guolong, Sun, Pei, V, Shashank, Carvajal, Gabriel, Broder, Josef, Comsa, Iulia, Repina, Alena, Wong, William, Chen, Warren Weilun, Hawkins, Peter, Filonov, Egor, Loher, Lucia, Hirnschall, Christoph, Wang, Weiyi, Ye, Jingchen, Burns, Andrea, Cate, Hardie, Wright, Diana Gage, Piccinini, Federico, Zhang, Lei, Lin, Chu-Cheng, Gog, Ionel, Kulizhskaya, Yana, Sreevatsa, Ashwin, Song, Shuang, Cobo, Luis C., Iyer, Anand, Tekur, Chetan, Garrido, Guillermo, Xiao, Zhuyun, Kemp, Rupert, Zheng, Huaixiu Steven, Li, Hui, Agarwal, Ananth, Ngani, Christel, Goshvadi, Kati, Santamaria-Fernandez, Rebeca, Fica, Wojciech, Chen, Xinyun, Gorgolewski, Chris, Sun, Sean, Garg, Roopal, Ye, Xinyu, Eslami, S. M. Ali, Hua, Nan, Simon, Jon, Joshi, Pratik, Kim, Yelin, Tenney, Ian, Potluri, Sahitya, Thiet, Lam Nguyen, Yuan, Quan, Luisier, Florian, Chronopoulou, Alexandra, Scellato, Salvatore, Srinivasan, Praveen, Chen, Minmin, Koverkathu, Vinod, Dalibard, Valentin, Xu, Yaming, Saeta, Brennan, Anderson, Keith, Sellam, Thibault, Fernando, Nick, Huot, Fantine, Jung, Junehyuk, Varadarajan, Mani, Quinn, Michael, Raul, Amit, Le, Maigo, Habalov, Ruslan, Clark, Jon, Jalan, Komal, Bullard, Kalesha, Singhal, Achintya, Luong, Thang, Wang, Boyu, Rajayogam, Sujeevan, Eisenschlos, Julian, Jia, Johnson, Finchelstein, Daniel, Yakubovich, Alex, Balle, Daniel, Fink, Michael, Agarwal, Sameer, Li, Jing, Dvijotham, Dj, Pal, Shalini, Kang, Kai, Konzelmann, Jaclyn, Beattie, Jennifer, Dousse, Olivier, Wu, Diane, Crocker, Remi, Elkind, Chen, Jonnalagadda, Siddhartha Reddy, Lee, Jong, Holtmann-Rice, Dan, Kallarackal, Krystal, Liu, Rosanne, Vnukov, Denis, Vats, Neera, Invernizzi, Luca, Jafari, Mohsen, Zhou, Huanjie, Taylor, Lilly, Prendki, Jennifer, Wu, Marcus, Eccles, Tom, Liu, Tianqi, Kopparapu, Kavya, Beaufays, Francoise, Angermueller, Christof, Marzoca, Andreea, Sarcar, Shourya, Dib, Hilal, Stanway, Jeff, Perbet, Frank, Trdin, Nejc, Sterneck, Rachel, Khorlin, Andrey, Li, Dinghua, Wu, Xihui, Goenka, Sonam, Madras, David, Goldshtein, Sasha, Gierke, Willi, Zhou, Tong, Liu, Yaxin, Liang, Yannie, White, Anais, Li, Yunjie, Singh, Shreya, Bahargam, Sanaz, Epstein, Mark, Basu, Sujoy, Lao, Li, Ozturel, Adnan, Crous, Carl, Zhai, Alex, Lu, Han, Tung, Zora, Gaur, Neeraj, Walton, Alanna, Dixon, Lucas, Zhang, Ming, Globerson, Amir, Uy, Grant, Bolt, Andrew, Wiles, Olivia, Nasr, Milad, Shumailov, Ilia, Selvi, Marco, Piccinno, Francesco, Aguilar, Ricardo, McCarthy, Sara, Khalman, Misha, Shukla, Mrinal, Galic, Vlado, Carpenter, John, Villela, Kevin, Zhang, Haibin, Richardson, Harry, Martens, James, Bosnjak, Matko, Belle, Shreyas Rammohan, Seibert, Jeff, Alnahlawi, Mahmoud, McWilliams, Brian, Singh, Sankalp, Louis, Annie, Ding, Wen, Popovici, Dan, Simicich, Lenin, Knight, Laura, Mehta, Pulkit, Gupta, Nishesh, Shi, Chongyang, Fatehi, Saaber, Mitrovic, Jovana, Grills, Alex, Pagadora, Joseph, Munkhdalai, Tsendsuren, Petrova, Dessie, Eisenbud, Danielle, Zhang, Zhishuai, Yates, Damion, Mittal, Bhavishya, Tripuraneni, Nilesh, Assael, Yannis, Brovelli, Thomas, Jain, Prateek, Velimirovic, Mihajlo, Akbulut, Canfer, Mu, Jiaqi, Macherey, Wolfgang, Kumar, Ravin, Xu, Jun, Qureshi, Haroon, Comanici, Gheorghe, Wiesner, Jeremy, Gong, Zhitao, Ruddock, Anton, Bauer, Matthias, Felt, Nick, GP, Anirudh, Arnab, Anurag, Zelle, Dustin, Rothfuss, Jonas, Rosgen, Bill, Shenoy, Ashish, Seybold, Bryan, Li, Xinjian, Mudigonda, Jayaram, Erdogan, Goker, Xia, Jiawei, Simsa, Jiri, Michi, Andrea, Yao, Yi, Yew, Christopher, Kan, Steven, Caswell, Isaac, Radebaugh, Carey, Elisseeff, Andre, Valenzuela, Pedro, McKinney, Kay, Paterson, Kim, Cui, Albert, Latorre-Chimoto, Eri, Kim, Solomon, Zeng, William, Durden, Ken, Ponnapalli, Priya, Sosea, Tiberiu, Choquette-Choo, Christopher A., Manyika, James, Robenek, Brona, Vashisht, Harsha, Pereira, Sebastien, Lam, Hoi, Velic, Marko, Owusu-Afriyie, Denese, Lee, Katherine, Bolukbasi, Tolga, Parrish, Alicia, Lu, Shawn, Park, Jane, Venkatraman, Balaji, Talbert, Alice, Rosique, Lambert, Cheng, Yuchung, Sozanschi, Andrei, Paszke, Adam, Kumar, Praveen, Austin, Jessica, Li, Lu, Salama, Khalid, Perz, Bartek, Kim, Wooyeol, Dukkipati, Nandita, Baryshnikov, Anthony, Kaplanis, Christos, Sheng, XiangHai, Chervonyi, Yuri, Unlu, Caglar, Casas, Diego de Las, Askham, Harry, Tunyasuvunakool, Kathryn, Gimeno, Felix, Poder, Siim, Kwak, Chester, Miecnikowski, Matt, Dimitriev, Alek, Parisi, Aaron, Liu, Dangyi, Tsai, Tomy, Shevlane, Toby, Kouridi, Christina, Garmon, Drew, Goedeckemeyer, Adrian, Brown, Adam R., Vijayakumar, Anitha, Elqursh, Ali, Jazayeri, Sadegh, Huang, Jin, Carthy, Sara Mc, Hoover, Jay, Kim, Lucy, Kumar, Sandeep, Chen, Wei, Biles, Courtney, Bingham, Garrett, Rosen, Evan, Wang, Lisa, Tan, Qijun, Engel, David, Pongetti, Francesco, de Cesare, Dario, Hwang, Dongseong, Yu, Lily, Pullman, Jennifer, Narayanan, Srini, Levin, Kyle, Gopal, Siddharth, Li, Megan, Aharoni, Asaf, Trinh, Trieu, Lo, Jessica, Casagrande, Norman, Vij, Roopali, Matthey, Loic, Ramadhana, Bramandia, Matthews, Austin, Carey, CJ, Johnson, Matthew, Goranova, Kremena, Shah, Rohin, Ashraf, Shereen, Dasgupta, Kingshuk, Larsen, Rasmus, Wang, Yicheng, Vuyyuru, Manish Reddy, Jiang, Chong, Ijazi, Joana, Osawa, Kazuki, Smith, Celine, Boppana, Ramya Sree, Bilal, Taylan, Koizumi, Yuma, Xu, Ying, Altun, Yasemin, Shabat, Nir, Bariach, Ben, Korchemniy, Alex, Choo, Kiam, Ronneberger, Olaf, Iwuanyanwu, Chimezie, Zhao, Shubin, Soergel, David, Hsieh, Cho-Jui, Cai, Irene, Iqbal, Shariq, Sundermeyer, Martin, Chen, Zhe, Bursztein, Elie, Malaviya, Chaitanya, Biadsy, Fadi, Shroff, Prakash, Dhillon, Inderjit, Latkar, Tejasi, Dyer, Chris, Forbes, Hannah, Nicosia, Massimo, Nikolaev, Vitaly, Greene, Somer, Georgiev, Marin, Wang, Pidong, Martin, Nina, Sedghi, Hanie, Zhang, John, Banzal, Praseem, Fritz, Doug, Rao, Vikram, Wang, Xuezhi, Zhang, Jiageng, Patraucean, Viorica, Du, Dayou, Mordatch, Igor, Jurin, Ivan, Liu, Lewis, Dubey, Ayush, Mohan, Abhi, Nowakowski, Janek, Ion, Vlad-Doru, Wei, Nan, Tojo, Reiko, Raad, Maria Abi, Hudson, Drew A., Keshava, Vaishakh, Agrawal, Shubham, Ramirez, Kevin, Wu, Zhichun, Nguyen, Hoang, Liu, Ji, Sewak, Madhavi, Petrini, Bryce, Choi, DongHyun, Philips, Ivan, Wang, Ziyue, Bica, Ioana, Garg, Ankush, Wilkiewicz, Jarek, Agrawal, Priyanka, Guo, Danhao, Xue, Emily, Shaik, Naseer, Leach, Andrew, Khan, Sadh MNM, Wiesinger, Julia, Jerome, Sammy, Chakladar, Abhishek, Wang, Alek Wenjiao, Ornduff, Tina, Abu, Folake, Ghaffarkhah, Alireza, Wainwright, Marcus, Cortes, Mario, Liu, Frederick, Maynez, Joshua, Terzis, Andreas, Samangouei, Pouya, Mansour, Riham, Kępa, Tomasz, Aubet, François-Xavier, Algymr, Anton, Banica, Dan, Weisz, Agoston, Orban, Andras, Senges, Alexandre, Andrejczuk, Ewa, Geller, Mark, Santo, Niccolo Dal, Anklin, Valentin, Merey, Majd Al, Baeuml, Martin, Strohman, Trevor, Bai, Junwen, Petrov, Slav, Wu, Yonghui, Hassabis, Demis, Kavukcuoglu, Koray, Dean, Jeff, and Vinyals, Oriol

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.

Published
2024

14. Process tensor approaches to modeling two-dimensional spectroscopy

Author

de Wit, Roosmarijn, Keeling, Jonathan, Lovett, Brendon W., and Chin, Alex W.

Subjects
Quantum Physics, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

Problems in the field of open quantum systems often involve an environment that strongly influences the dynamics of excited states. Here we present a numerical method to model optical spectra of non-Markovian open quantum systems. The method employs a process tensor framework to efficiently compute multi-time correlations in a numerically exact way. To demonstrate the efficacy of our method, we compare 2D electronic spectroscopy simulations produced through our method to Markovian master equation simulations in three different system-bath coupling regimes., Comment: A previous version of this manuscript was withdrawn due to an error in interpretation. The weak coupling master equation to which we compared neglected the Lamb shift, which has been now been incorporated in the latest version. The agreement between exact results and weak coupling master equations is now better than reported in the previous version

Published
2024

15. Should agentic conversational AI change how we think about ethics? Characterising an interactional ethics centred on respect

Author

Alberts, Lize, Keeling, Geoff, and McCroskery, Amanda

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction, 68T42, H.5.2, I.2, I.2.1, J.4, J.5
Abstract

With the growing popularity of conversational agents based on large language models (LLMs), we need to ensure their behaviour is ethical and appropriate. Work in this area largely centres around the 'HHH' criteria: making outputs more helpful and honest, and avoiding harmful (biased, toxic, or inaccurate) statements. Whilst this semantic focus is useful when viewing LLM agents as mere mediums or output-generating systems, it fails to account for pragmatic factors that can make the same speech act seem more or less tactless or inconsiderate in different social situations. With the push towards agentic AI, wherein systems become increasingly proactive in chasing goals and performing actions in the world, considering the pragmatics of interaction becomes essential. We propose an interactional approach to ethics that is centred on relational and situational factors. We explore what it means for a system, as a social actor, to treat an individual respectfully in a (series of) interaction(s). Our work anticipates a set of largely unexplored risks at the level of situated social interaction, and offers practical suggestions to help agentic LLM technologies treat people well.

Published
2024

16. Gemini: A Family of Highly Capable Multimodal Models

Author

Gemini Team, Anil, Rohan, Borgeaud, Sebastian, Alayrac, Jean-Baptiste, Yu, Jiahui, Soricut, Radu, Schalkwyk, Johan, Dai, Andrew M., Hauth, Anja, Millican, Katie, Silver, David, Johnson, Melvin, Antonoglou, Ioannis, Schrittwieser, Julian, Glaese, Amelia, Chen, Jilin, Pitler, Emily, Lillicrap, Timothy, Lazaridou, Angeliki, Firat, Orhan, Molloy, James, Isard, Michael, Barham, Paul R., Hennigan, Tom, Lee, Benjamin, Viola, Fabio, Reynolds, Malcolm, Xu, Yuanzhong, Doherty, Ryan, Collins, Eli, Meyer, Clemens, Rutherford, Eliza, Moreira, Erica, Ayoub, Kareem, Goel, Megha, Krawczyk, Jack, Du, Cosmo, Chi, Ed, Cheng, Heng-Tze, Ni, Eric, Shah, Purvi, Kane, Patrick, Chan, Betty, Faruqui, Manaal, Severyn, Aliaksei, Lin, Hanzhao, Li, YaGuang, Cheng, Yong, Ittycheriah, Abe, Mahdieh, Mahdis, Chen, Mia, Sun, Pei, Tran, Dustin, Bagri, Sumit, Lakshminarayanan, Balaji, Liu, Jeremiah, Orban, Andras, Güra, Fabian, Zhou, Hao, Song, Xinying, Boffy, Aurelien, Ganapathy, Harish, Zheng, Steven, Choe, HyunJeong, Weisz, Ágoston, Zhu, Tao, Lu, Yifeng, Gopal, Siddharth, Kahn, Jarrod, Kula, Maciej, Pitman, Jeff, Shah, Rushin, Taropa, Emanuel, Merey, Majd Al, Baeuml, Martin, Chen, Zhifeng, Shafey, Laurent El, Zhang, Yujing, Sercinoglu, Olcan, Tucker, George, Piqueras, Enrique, Krikun, Maxim, Barr, Iain, Savinov, Nikolay, Danihelka, Ivo, Roelofs, Becca, White, Anaïs, Andreassen, Anders, von Glehn, Tamara, Yagati, Lakshman, Kazemi, Mehran, Gonzalez, Lucas, Khalman, Misha, Sygnowski, Jakub, Frechette, Alexandre, Smith, Charlotte, Culp, Laura, Proleev, Lev, Luan, Yi, Chen, Xi, Lottes, James, Schucher, Nathan, Lebron, Federico, Rrustemi, Alban, Clay, Natalie, Crone, Phil, Kocisky, Tomas, Zhao, Jeffrey, Perz, Bartek, Yu, Dian, Howard, Heidi, Bloniarz, Adam, Rae, Jack W., Lu, Han, Sifre, Laurent, Maggioni, Marcello, Alcober, Fred, Garrette, Dan, Barnes, Megan, Thakoor, Shantanu, Austin, Jacob, Barth-Maron, Gabriel, Wong, William, Joshi, Rishabh, Chaabouni, Rahma, Fatiha, Deeni, Ahuja, Arun, Tomar, Gaurav Singh, Senter, Evan, Chadwick, Martin, Kornakov, Ilya, Attaluri, Nithya, Iturrate, Iñaki, Liu, Ruibo, Li, Yunxuan, Cogan, Sarah, Chen, Jeremy, Jia, Chao, Gu, Chenjie, Zhang, Qiao, Grimstad, Jordan, Hartman, Ale Jakse, Garcia, Xavier, Pillai, Thanumalayan Sankaranarayana, Devlin, Jacob, Laskin, Michael, Casas, Diego de Las, Valter, Dasha, Tao, Connie, Blanco, Lorenzo, Badia, Adrià Puigdomènech, Reitter, David, Chen, Mianna, Brennan, Jenny, Rivera, Clara, Brin, Sergey, Iqbal, Shariq, Surita, Gabriela, Labanowski, Jane, Rao, Abhi, Winkler, Stephanie, Parisotto, Emilio, Gu, Yiming, Olszewska, Kate, Addanki, Ravi, Miech, Antoine, Louis, Annie, Teplyashin, Denis, Brown, Geoff, Catt, Elliot, Balaguer, Jan, Xiang, Jackie, Wang, Pidong, Ashwood, Zoe, Briukhov, Anton, Webson, Albert, Ganapathy, Sanjay, Sanghavi, Smit, Kannan, Ajay, Chang, Ming-Wei, Stjerngren, Axel, Djolonga, Josip, Sun, Yuting, Bapna, Ankur, Aitchison, Matthew, Pejman, Pedram, Michalewski, Henryk, Yu, Tianhe, Wang, Cindy, Love, Juliette, Ahn, Junwhan, Bloxwich, Dawn, Han, Kehang, Humphreys, Peter, Sellam, Thibault, Bradbury, James, Godbole, Varun, Samangooei, Sina, Damoc, Bogdan, Kaskasoli, Alex, Arnold, Sébastien M. R., Vasudevan, Vijay, Agrawal, Shubham, Riesa, Jason, Lepikhin, Dmitry, Tanburn, Richard, Srinivasan, Srivatsan, Lim, Hyeontaek, Hodkinson, Sarah, Shyam, Pranav, Ferret, Johan, Hand, Steven, Garg, Ankush, Paine, Tom Le, Li, Jian, Li, Yujia, Giang, Minh, Neitz, Alexander, Abbas, Zaheer, York, Sarah, Reid, Machel, Cole, Elizabeth, Chowdhery, Aakanksha, Das, Dipanjan, Rogozińska, Dominika, Nikolaev, Vitaliy, Sprechmann, Pablo, Nado, Zachary, Zilka, Lukas, Prost, Flavien, He, Luheng, Monteiro, Marianne, Mishra, Gaurav, Welty, Chris, Newlan, Josh, Jia, Dawei, Allamanis, Miltiadis, Hu, Clara Huiyi, de Liedekerke, Raoul, Gilmer, Justin, Saroufim, Carl, Rijhwani, Shruti, Hou, Shaobo, Shrivastava, Disha, Baddepudi, Anirudh, Goldin, Alex, Ozturel, Adnan, Cassirer, Albin, Xu, Yunhan, Sohn, Daniel, Sachan, Devendra, Amplayo, Reinald Kim, Swanson, Craig, Petrova, Dessie, Narayan, Shashi, Guez, Arthur, Brahma, Siddhartha, Landon, Jessica, Patel, Miteyan, Zhao, Ruizhe, Villela, Kevin, Wang, Luyu, Jia, Wenhao, Rahtz, Matthew, Giménez, Mai, Yeung, Legg, Keeling, James, Georgiev, Petko, Mincu, Diana, Wu, Boxi, Haykal, Salem, Saputro, Rachel, Vodrahalli, Kiran, Qin, James, Cankara, Zeynep, Sharma, Abhanshu, Fernando, Nick, Hawkins, Will, Neyshabur, Behnam, Kim, Solomon, Hutter, Adrian, Agrawal, Priyanka, Castro-Ros, Alex, Driessche, George van den, Wang, Tao, Yang, Fan, Chang, Shuo-yiin, Komarek, Paul, McIlroy, Ross, Lučić, Mario, Zhang, Guodong, Farhan, Wael, Sharman, Michael, Natsev, Paul, Michel, Paul, Bansal, Yamini, Qiao, Siyuan, Cao, Kris, Shakeri, Siamak, Butterfield, Christina, Chung, Justin, Rubenstein, Paul Kishan, Agrawal, Shivani, Mensch, Arthur, Soparkar, Kedar, Lenc, Karel, Chung, Timothy, Pope, Aedan, Maggiore, Loren, Kay, Jackie, Jhakra, Priya, Wang, Shibo, Maynez, Joshua, Phuong, Mary, Tobin, Taylor, Tacchetti, Andrea, Trebacz, Maja, Robinson, Kevin, Katariya, Yash, Riedel, Sebastian, Bailey, Paige, Xiao, Kefan, Ghelani, Nimesh, Aroyo, Lora, Slone, Ambrose, Houlsby, Neil, Xiong, Xuehan, Yang, Zhen, Gribovskaya, Elena, Adler, Jonas, Wirth, Mateo, Lee, Lisa, Li, Music, Kagohara, Thais, Pavagadhi, Jay, Bridgers, Sophie, Bortsova, Anna, Ghemawat, Sanjay, Ahmed, Zafarali, Liu, Tianqi, Powell, Richard, Bolina, Vijay, Iinuma, Mariko, Zablotskaia, Polina, Besley, James, Chung, Da-Woon, Dozat, Timothy, Comanescu, Ramona, Si, Xiance, Greer, Jeremy, Su, Guolong, Polacek, Martin, Kaufman, Raphaël Lopez, Tokumine, Simon, Hu, Hexiang, Buchatskaya, Elena, Miao, Yingjie, Elhawaty, Mohamed, Siddhant, Aditya, Tomasev, Nenad, Xing, Jinwei, Greer, Christina, Miller, Helen, Ashraf, Shereen, Roy, Aurko, Zhang, Zizhao, Ma, Ada, Filos, Angelos, Besta, Milos, Blevins, Rory, Klimenko, Ted, Yeh, Chih-Kuan, Changpinyo, Soravit, Mu, Jiaqi, Chang, Oscar, Pajarskas, Mantas, Muir, Carrie, Cohen, Vered, Lan, Charline Le, Haridasan, Krishna, Marathe, Amit, Hansen, Steven, Douglas, Sholto, Samuel, Rajkumar, Wang, Mingqiu, Austin, Sophia, Lan, Chang, Jiang, Jiepu, Chiu, Justin, Lorenzo, Jaime Alonso, Sjösund, Lars Lowe, Cevey, Sébastien, Gleicher, Zach, Avrahami, Thi, Boral, Anudhyan, Srinivasan, Hansa, Selo, Vittorio, May, Rhys, Aisopos, Konstantinos, Hussenot, Léonard, Soares, Livio Baldini, Baumli, Kate, Chang, Michael B., Recasens, Adrià, Caine, Ben, Pritzel, Alexander, Pavetic, Filip, Pardo, Fabio, Gergely, Anita, Frye, Justin, Ramasesh, Vinay, Horgan, Dan, Badola, Kartikeya, Kassner, Nora, Roy, Subhrajit, Dyer, Ethan, Campos, Víctor Campos, Tomala, Alex, Tang, Yunhao, Badawy, Dalia El, White, Elspeth, Mustafa, Basil, Lang, Oran, Jindal, Abhishek, Vikram, Sharad, Gong, Zhitao, Caelles, Sergi, Hemsley, Ross, Thornton, Gregory, Feng, Fangxiaoyu, Stokowiec, Wojciech, Zheng, Ce, Thacker, Phoebe, Ünlü, Çağlar, Zhang, Zhishuai, Saleh, Mohammad, Svensson, James, Bileschi, Max, Patil, Piyush, Anand, Ankesh, Ring, Roman, Tsihlas, Katerina, Vezer, Arpi, Selvi, Marco, Shevlane, Toby, Rodriguez, Mikel, Kwiatkowski, Tom, Daruki, Samira, Rong, Keran, Dafoe, Allan, FitzGerald, Nicholas, Gu-Lemberg, Keren, Khan, Mina, Hendricks, Lisa Anne, Pellat, Marie, Feinberg, Vladimir, Cobon-Kerr, James, Sainath, Tara, Rauh, Maribeth, Hashemi, Sayed Hadi, Ives, Richard, Hasson, Yana, Noland, Eric, Cao, Yuan, Byrd, Nathan, Hou, Le, Wang, Qingze, Sottiaux, Thibault, Paganini, Michela, Lespiau, Jean-Baptiste, Moufarek, Alexandre, Hassan, Samer, Shivakumar, Kaushik, van Amersfoort, Joost, Mandhane, Amol, Joshi, Pratik, Goyal, Anirudh, Tung, Matthew, Brock, Andrew, Sheahan, Hannah, Misra, Vedant, Li, Cheng, Rakićević, Nemanja, Dehghani, Mostafa, Liu, Fangyu, Mittal, Sid, Oh, Junhyuk, Noury, Seb, Sezener, Eren, Huot, Fantine, Lamm, Matthew, De Cao, Nicola, Chen, Charlie, Mudgal, Sidharth, Stella, Romina, Brooks, Kevin, Vasudevan, Gautam, Liu, Chenxi, Chain, Mainak, Melinkeri, Nivedita, Cohen, Aaron, Wang, Venus, Seymore, Kristie, Zubkov, Sergey, Goel, Rahul, Yue, Summer, Krishnakumaran, Sai, Albert, Brian, Hurley, Nate, Sano, Motoki, Mohananey, Anhad, Joughin, Jonah, Filonov, Egor, Kępa, Tomasz, Eldawy, Yomna, Lim, Jiawern, Rishi, Rahul, Badiezadegan, Shirin, Bos, Taylor, Chang, Jerry, Jain, Sanil, Padmanabhan, Sri Gayatri Sundara, Puttagunta, Subha, Krishna, Kalpesh, Baker, Leslie, Kalb, Norbert, Bedapudi, Vamsi, Kurzrok, Adam, Lei, Shuntong, Yu, Anthony, Litvin, Oren, Zhou, Xiang, Wu, Zhichun, Sobell, Sam, Siciliano, Andrea, Papir, Alan, Neale, Robby, Bragagnolo, Jonas, Toor, Tej, Chen, Tina, Anklin, Valentin, Wang, Feiran, Feng, Richie, Gholami, Milad, Ling, Kevin, Liu, Lijuan, Walter, Jules, Moghaddam, Hamid, Kishore, Arun, Adamek, Jakub, Mercado, Tyler, Mallinson, Jonathan, Wandekar, Siddhinita, Cagle, Stephen, Ofek, Eran, Garrido, Guillermo, Lombriser, Clemens, Mukha, Maksim, Sun, Botu, Mohammad, Hafeezul Rahman, Matak, Josip, Qian, Yadi, Peswani, Vikas, Janus, Pawel, Yuan, Quan, Schelin, Leif, David, Oana, Garg, Ankur, He, Yifan, Duzhyi, Oleksii, Älgmyr, Anton, Lottaz, Timothée, Li, Qi, Yadav, Vikas, Xu, Luyao, Chinien, Alex, Shivanna, Rakesh, Chuklin, Aleksandr, Li, Josie, Spadine, Carrie, Wolfe, Travis, Mohamed, Kareem, Das, Subhabrata, Dai, Zihang, He, Kyle, von Dincklage, Daniel, Upadhyay, Shyam, Maurya, Akanksha, Chi, Luyan, Krause, Sebastian, Salama, Khalid, Rabinovitch, Pam G, M, Pavan Kumar Reddy, Selvan, Aarush, Dektiarev, Mikhail, Ghiasi, Golnaz, Guven, Erdem, Gupta, Himanshu, Liu, Boyi, Sharma, Deepak, Shtacher, Idan Heimlich, Paul, Shachi, Akerlund, Oscar, Aubet, François-Xavier, Huang, Terry, Zhu, Chen, Zhu, Eric, Teixeira, Elico, Fritze, Matthew, Bertolini, Francesco, Marinescu, Liana-Eleonora, Bölle, Martin, Paulus, Dominik, Gupta, Khyatti, Latkar, Tejasi, Chang, Max, Sanders, Jason, Wilson, Roopa, Wu, Xuewei, Tan, Yi-Xuan, Thiet, Lam Nguyen, Doshi, Tulsee, Lall, Sid, Mishra, Swaroop, Chen, Wanming, Luong, Thang, Benjamin, Seth, Lee, Jasmine, Andrejczuk, Ewa, Rabiej, Dominik, Ranjan, Vipul, Styrc, Krzysztof, Yin, Pengcheng, Simon, Jon, Harriott, Malcolm Rose, Bansal, Mudit, Robsky, Alexei, Bacon, Geoff, Greene, David, Mirylenka, Daniil, Zhou, Chen, Sarvana, Obaid, Goyal, Abhimanyu, Andermatt, Samuel, Siegler, Patrick, Horn, Ben, Israel, Assaf, Pongetti, Francesco, Chen, Chih-Wei "Louis", Selvatici, Marco, Silva, Pedro, Wang, Kathie, Tolins, Jackson, Guu, Kelvin, Yogev, Roey, Cai, Xiaochen, Agostini, Alessandro, Shah, Maulik, Nguyen, Hung, Donnaile, Noah Ó, Pereira, Sébastien, Friso, Linda, Stambler, Adam, Kuang, Chenkai, Romanikhin, Yan, Geller, Mark, Yan, ZJ, Jang, Kane, Lee, Cheng-Chun, Fica, Wojciech, Malmi, Eric, Tan, Qijun, Banica, Dan, Balle, Daniel, Pham, Ryan, Huang, Yanping, Avram, Diana, Shi, Hongzhi, Singh, Jasjot, Hidey, Chris, Ahuja, Niharika, Saxena, Pranab, Dooley, Dan, Potharaju, Srividya Pranavi, O'Neill, Eileen, Gokulchandran, Anand, Foley, Ryan, Zhao, Kai, Dusenberry, Mike, Liu, Yuan, Mehta, Pulkit, Kotikalapudi, Ragha, Safranek-Shrader, Chalence, Goodman, Andrew, Kessinger, Joshua, Globen, Eran, Kolhar, Prateek, Gorgolewski, Chris, Ibrahim, Ali, Song, Yang, Eichenbaum, Ali, Brovelli, Thomas, Potluri, Sahitya, Lahoti, Preethi, Baetu, Cip, Ghorbani, Ali, Chen, Charles, Crawford, Andy, Pal, Shalini, Sridhar, Mukund, Gurita, Petru, Mujika, Asier, Petrovski, Igor, Cedoz, Pierre-Louis, Li, Chenmei, Chen, Shiyuan, Santo, Niccolò Dal, Goyal, Siddharth, Punjabi, Jitesh, Kappaganthu, Karthik, Kwak, Chester, LV, Pallavi, Velury, Sarmishta, Choudhury, Himadri, Hall, Jamie, Shah, Premal, Figueira, Ricardo, Thomas, Matt, Lu, Minjie, Zhou, Ting, Kumar, Chintu, Jurdi, Thomas, Chikkerur, Sharat, Ma, Yenai, Yu, Adams, Kwak, Soo, Ähdel, Victor, Rajayogam, Sujeevan, Choma, Travis, Liu, Fei, Barua, Aditya, Ji, Colin, Park, Ji Ho, Hellendoorn, Vincent, Bailey, Alex, Bilal, Taylan, Zhou, Huanjie, Khatir, Mehrdad, Sutton, Charles, Rzadkowski, Wojciech, Macintosh, Fiona, Shagin, Konstantin, Medina, Paul, Liang, Chen, Zhou, Jinjing, Shah, Pararth, Bi, Yingying, Dankovics, Attila, Banga, Shipra, Lehmann, Sabine, Bredesen, Marissa, Lin, Zifan, Hoffmann, John Eric, Lai, Jonathan, Chung, Raynald, Yang, Kai, Balani, Nihal, Bražinskas, Arthur, Sozanschi, Andrei, Hayes, Matthew, Alcalde, Héctor Fernández, Makarov, Peter, Chen, Will, Stella, Antonio, Snijders, Liselotte, Mandl, Michael, Kärrman, Ante, Nowak, Paweł, Wu, Xinyi, Dyck, Alex, Vaidyanathan, Krishnan, R, Raghavender, Mallet, Jessica, Rudominer, Mitch, Johnston, Eric, Mittal, Sushil, Udathu, Akhil, Christensen, Janara, Verma, Vishal, Irving, Zach, Santucci, Andreas, Elsayed, Gamaleldin, Davoodi, Elnaz, Georgiev, Marin, Tenney, Ian, Hua, Nan, Cideron, Geoffrey, Leurent, Edouard, Alnahlawi, Mahmoud, Georgescu, Ionut, Wei, Nan, Zheng, Ivy, Scandinaro, Dylan, Jiang, Heinrich, Snoek, Jasper, Sundararajan, Mukund, Wang, Xuezhi, Ontiveros, Zack, Karo, Itay, Cole, Jeremy, Rajashekhar, Vinu, Tumeh, Lara, Ben-David, Eyal, Jain, Rishub, Uesato, Jonathan, Datta, Romina, Bunyan, Oskar, Wu, Shimu, Zhang, John, Stanczyk, Piotr, Zhang, Ye, Steiner, David, Naskar, Subhajit, Azzam, Michael, Johnson, Matthew, Paszke, Adam, Chiu, Chung-Cheng, Elias, Jaume Sanchez, Mohiuddin, Afroz, Muhammad, Faizan, Miao, Jin, Lee, Andrew, Vieillard, Nino, Park, Jane, Zhang, Jiageng, Stanway, Jeff, Garmon, Drew, Karmarkar, Abhijit, Dong, Zhe, Lee, Jong, Kumar, Aviral, Zhou, Luowei, Evens, Jonathan, Isaac, William, Irving, Geoffrey, Loper, Edward, Fink, Michael, Arkatkar, Isha, Chen, Nanxin, Shafran, Izhak, Petrychenko, Ivan, Chen, Zhe, Jia, Johnson, Levskaya, Anselm, Zhu, Zhenkai, Grabowski, Peter, Mao, Yu, Magni, Alberto, Yao, Kaisheng, Snaider, Javier, Casagrande, Norman, Palmer, Evan, Suganthan, Paul, Castaño, Alfonso, Giannoumis, Irene, Kim, Wooyeol, Rybiński, Mikołaj, Sreevatsa, Ashwin, Prendki, Jennifer, Soergel, David, Goedeckemeyer, Adrian, Gierke, Willi, Jafari, Mohsen, Gaba, Meenu, Wiesner, Jeremy, Wright, Diana Gage, Wei, Yawen, Vashisht, Harsha, Kulizhskaya, Yana, Hoover, Jay, Le, Maigo, Li, Lu, Iwuanyanwu, Chimezie, Liu, Lu, Ramirez, Kevin, Khorlin, Andrey, Cui, Albert, LIN, Tian, Wu, Marcus, Aguilar, Ricardo, Pallo, Keith, Chakladar, Abhishek, Perng, Ginger, Abellan, Elena Allica, Zhang, Mingyang, Dasgupta, Ishita, Kushman, Nate, Penchev, Ivo, Repina, Alena, Wu, Xihui, van der Weide, Tom, Ponnapalli, Priya, Kaplan, Caroline, Simsa, Jiri, Li, Shuangfeng, Dousse, Olivier, Piper, Jeff, Ie, Nathan, Pasumarthi, Rama, Lintz, Nathan, Vijayakumar, Anitha, Andor, Daniel, Valenzuela, Pedro, Lui, Minnie, Paduraru, Cosmin, Peng, Daiyi, Lee, Katherine, Zhang, Shuyuan, Greene, Somer, Nguyen, Duc Dung, Kurylowicz, Paula, Hardin, Cassidy, Dixon, Lucas, Janzer, Lili, Choo, Kiam, Feng, Ziqiang, Zhang, Biao, Singhal, Achintya, Du, Dayou, McKinnon, Dan, Antropova, Natasha, Bolukbasi, Tolga, Keller, Orgad, Reid, David, Finchelstein, Daniel, Raad, Maria Abi, Crocker, Remi, Hawkins, Peter, Dadashi, Robert, Gaffney, Colin, Franko, Ken, Bulanova, Anna, Leblond, Rémi, Chung, Shirley, Askham, Harry, Cobo, Luis C., Xu, Kelvin, Fischer, Felix, Xu, Jun, Sorokin, Christina, Alberti, Chris, Lin, Chu-Cheng, Evans, Colin, Dimitriev, Alek, Forbes, Hannah, Banarse, Dylan, Tung, Zora, Omernick, Mark, Bishop, Colton, Sterneck, Rachel, Jain, Rohan, Xia, Jiawei, Amid, Ehsan, Piccinno, Francesco, Wang, Xingyu, Banzal, Praseem, Mankowitz, Daniel J., Polozov, Alex, Krakovna, Victoria, Brown, Sasha, Bateni, MohammadHossein, Duan, Dennis, Firoiu, Vlad, Thotakuri, Meghana, Natan, Tom, Geist, Matthieu, Girgin, Ser tan, Li, Hui, Ye, Jiayu, Roval, Ofir, Tojo, Reiko, Kwong, Michael, Lee-Thorp, James, Yew, Christopher, Sinopalnikov, Danila, Ramos, Sabela, Mellor, John, Sharma, Abhishek, Wu, Kathy, Miller, David, Sonnerat, Nicolas, Vnukov, Denis, Greig, Rory, Beattie, Jennifer, Caveness, Emily, Bai, Libin, Eisenschlos, Julian, Korchemniy, Alex, Tsai, Tomy, Jasarevic, Mimi, Kong, Weize, Dao, Phuong, Zheng, Zeyu, Liu, Frederick, Zhu, Rui, Teh, Tian Huey, Sanmiya, Jason, Gladchenko, Evgeny, Trdin, Nejc, Toyama, Daniel, Rosen, Evan, Tavakkol, Sasan, Xue, Linting, Elkind, Chen, Woodman, Oliver, Carpenter, John, Papamakarios, George, Kemp, Rupert, Kafle, Sushant, Grunina, Tanya, Sinha, Rishika, Talbert, Alice, Wu, Diane, Owusu-Afriyie, Denese, Thornton, Chloe, Pont-Tuset, Jordi, Narayana, Pradyumna, Li, Jing, Fatehi, Saaber, Wieting, John, Ajmeri, Omar, Uria, Benigno, Ko, Yeongil, Knight, Laura, Héliou, Amélie, Niu, Ning, Gu, Shane, Pang, Chenxi, Li, Yeqing, Levine, Nir, Stolovich, Ariel, Santamaria-Fernandez, Rebeca, Goenka, Sonam, Yustalim, Wenny, Strudel, Robin, Elqursh, Ali, Deck, Charlie, Lee, Hyo, Li, Zonglin, Levin, Kyle, Hoffmann, Raphael, Holtmann-Rice, Dan, Bachem, Olivier, Arora, Sho, Koh, Christy, Yeganeh, Soheil Hassas, Põder, Siim, Tariq, Mukarram, Sun, Yanhua, Ionita, Lucian, Seyedhosseini, Mojtaba, Tafti, Pouya, Liu, Zhiyu, Gulati, Anmol, Liu, Jasmine, Ye, Xinyu, Chrzaszcz, Bart, Wang, Lily, Sethi, Nikhil, Li, Tianrun, Brown, Ben, Singh, Shreya, Fan, Wei, Parisi, Aaron, Stanton, Joe, Koverkathu, Vinod, Choquette-Choo, Christopher A., Li, Yunjie, Lu, TJ, Shroff, Prakash, Varadarajan, Mani, Bahargam, Sanaz, Willoughby, Rob, Gaddy, David, Desjardins, Guillaume, Cornero, Marco, Robenek, Brona, Mittal, Bhavishya, Albrecht, Ben, Shenoy, Ashish, Moiseev, Fedor, Jacobsson, Henrik, Ghaffarkhah, Alireza, Rivière, Morgane, Walton, Alanna, Crepy, Clément, Parrish, Alicia, Zhou, Zongwei, Farabet, Clement, Radebaugh, Carey, Srinivasan, Praveen, van der Salm, Claudia, Fidjeland, Andreas, Scellato, Salvatore, Latorre-Chimoto, Eri, Klimczak-Plucińska, Hanna, Bridson, David, de Cesare, Dario, Hudson, Tom, Mendolicchio, Piermaria, Walker, Lexi, Morris, Alex, Mauger, Matthew, Guseynov, Alexey, Reid, Alison, Odoom, Seth, Loher, Lucia, Cotruta, Victor, Yenugula, Madhavi, Grewe, Dominik, Petrushkina, Anastasia, Duerig, Tom, Sanchez, Antonio, Yadlowsky, Steve, Shen, Amy, Globerson, Amir, Webb, Lynette, Dua, Sahil, Li, Dong, Bhupatiraju, Surya, Hurt, Dan, Qureshi, Haroon, Agarwal, Ananth, Shani, Tomer, Eyal, Matan, Khare, Anuj, Belle, Shreyas Rammohan, Wang, Lei, Tekur, Chetan, Kale, Mihir Sanjay, Wei, Jinliang, Sang, Ruoxin, Saeta, Brennan, Liechty, Tyler, Sun, Yi, Zhao, Yao, Lee, Stephan, Nayak, Pandu, Fritz, Doug, Vuyyuru, Manish Reddy, Aslanides, John, Vyas, Nidhi, Wicke, Martin, Ma, Xiao, Eltyshev, Evgenii, Martin, Nina, Cate, Hardie, Manyika, James, Amiri, Keyvan, Kim, Yelin, Xiong, Xi, Kang, Kai, Luisier, Florian, Tripuraneni, Nilesh, Madras, David, Guo, Mandy, Waters, Austin, Wang, Oliver, Ainslie, Joshua, Baldridge, Jason, Zhang, Han, Pruthi, Garima, Bauer, Jakob, Yang, Feng, Mansour, Riham, Gelman, Jason, Xu, Yang, Polovets, George, Liu, Ji, Cai, Honglong, Chen, Warren, Sheng, XiangHai, Xue, Emily, Ozair, Sherjil, Angermueller, Christof, Li, Xiaowei, Sinha, Anoop, Wang, Weiren, Wiesinger, Julia, Koukoumidis, Emmanouil, Tian, Yuan, Iyer, Anand, Gurumurthy, Madhu, Goldenson, Mark, Shah, Parashar, Blake, MK, Yu, Hongkun, Urbanowicz, Anthony, Palomaki, Jennimaria, Fernando, Chrisantha, Durden, Ken, Mehta, Harsh, Momchev, Nikola, Rahimtoroghi, Elahe, Georgaki, Maria, Raul, Amit, Ruder, Sebastian, Redshaw, Morgan, Lee, Jinhyuk, Zhou, Denny, Jalan, Komal, Li, Dinghua, Hechtman, Blake, Schuh, Parker, Nasr, Milad, Milan, Kieran, Mikulik, Vladimir, Franco, Juliana, Green, Tim, Nguyen, Nam, Kelley, Joe, Mahendru, Aroma, Hu, Andrea, Howland, Joshua, Vargas, Ben, Hui, Jeffrey, Bansal, Kshitij, Rao, Vikram, Ghiya, Rakesh, Wang, Emma, Ye, Ke, Sarr, Jean Michel, Preston, Melanie Moranski, Elish, Madeleine, Li, Steve, Kaku, Aakash, Gupta, Jigar, Pasupat, Ice, Juan, Da-Cheng, Someswar, Milan, M., Tejvi, Chen, Xinyun, Amini, Aida, Fabrikant, Alex, Chu, Eric, Dong, Xuanyi, Muthal, Amruta, Buthpitiya, Senaka, Jauhari, Sarthak, Khandelwal, Urvashi, Hitron, Ayal, Ren, Jie, Rinaldi, Larissa, Drath, Shahar, Dabush, Avigail, Jiang, Nan-Jiang, Godhia, Harshal, Sachs, Uli, Chen, Anthony, Fan, Yicheng, Taitelbaum, Hagai, Noga, Hila, Dai, Zhuyun, Wang, James, Hamer, Jenny, Ferng, Chun-Sung, Elkind, Chenel, Atias, Aviel, Lee, Paulina, Listík, Vít, Carlen, Mathias, van de Kerkhof, Jan, Pikus, Marcin, Zaher, Krunoslav, Müller, Paul, Zykova, Sasha, Stefanec, Richard, Gatsko, Vitaly, Hirnschall, Christoph, Sethi, Ashwin, Xu, Xingyu Federico, Ahuja, Chetan, Tsai, Beth, Stefanoiu, Anca, Feng, Bo, Dhandhania, Keshav, Katyal, Manish, Gupta, Akshay, Parulekar, Atharva, Pitta, Divya, Zhao, Jing, Bhatia, Vivaan, Bhavnani, Yashodha, Alhadlaq, Omar, Li, Xiaolin, Danenberg, Peter, Tu, Dennis, Pine, Alex, Filippova, Vera, Ghosh, Abhipso, Limonchik, Ben, Urala, Bhargava, Lanka, Chaitanya Krishna, Clive, Derik, Li, Edward, Wu, Hao, Hongtongsak, Kevin, Li, Ianna, Thakkar, Kalind, Omarov, Kuanysh, Majmundar, Kushal, Alverson, Michael, Kucharski, Michael, Patel, Mohak, Jain, Mudit, Zabelin, Maksim, Pelagatti, Paolo, Kohli, Rohan, Kumar, Saurabh, Kim, Joseph, Sankar, Swetha, Shah, Vineet, Ramachandruni, Lakshmi, Zeng, Xiangkai, Bariach, Ben, Weidinger, Laura, Vu, Tu, Andreev, Alek, He, Antoine, Hui, Kevin, Kashem, Sheleem, Subramanya, Amar, Hsiao, Sissie, Hassabis, Demis, Kavukcuoglu, Koray, Sadovsky, Adam, Le, Quoc, Strohman, Trevor, Wu, Yonghui, Petrov, Slav, Dean, Jeffrey, and Vinyals, Oriol

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.

Published
2023

17. Directly observing replica symmetry breaking in a vector quantum-optical spin glass

Author

Kroeze, Ronen M., Marsh, Brendan P., Schuller, David Atri, Hunt, Henry S., Bourzutschky, Alexander N., Winer, Michael, Gopalakrishnan, Sarang, Keeling, Jonathan, and Lev, Benjamin L.

Subjects
Quantum Physics, Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Quantum Gases, Condensed Matter - Statistical Mechanics, Physics - Atomic Physics
Abstract

Spin glasses are quintessential examples of complex matter. Although much about their order remains uncertain, abstract models of them inform, e.g., the classification of combinatorial optimization problems, the magnetic ordering in metals with impurities, and artificial intelligence -- where they form a mathematical basis for neural network computing and brain modeling. We demonstrate the ability of an active quantum gas microscope to realize a spin glass of a novel driven-dissipative and vector form. By microscopically visualizing its glassy spin states, the technique allows us to directly measure replica symmetry breaking and the resulting ultrametric hierarchical structure. Ultrametricity is known to be emergent in models of evolution, protein folding, climate change, and infinite-range equilibrium spin glasses; this work shows it to be directly observable in a physically realized system., Comment: main text 6 pages and 4 figures plus references, supplement 40 pages and 26 figures plus references

Published
2023

18. Improved atmospheric constraints on Southern Ocean CO2 exchange.

Author

Stephens, Britton, Long, Matthew, Patra, Prabir, Rödenbeck, Christian, Morgan, Eric, Kort, Eric, Sweeney, Colm, Jin, Yuming, and Keeling, Ralph

Subjects
airborne observation, atmospheric diabatic mixing, atmospheric transport model, carbon sink, inverse model
Abstract

We present improved estimates of air-sea CO2 exchange over three latitude bands of the Southern Ocean using atmospheric CO2 measurements from global airborne campaigns and an atmospheric 4-box inverse model based on a mass-indexed isentropic coordinate (Mθe). These flux estimates show two features not clearly resolved in previous estimates based on inverting surface CO2 measurements: a weak winter-time outgassing in the polar region and a sharp phase transition of the seasonal flux cycles between polar/subpolar and subtropical regions. The estimates suggest much stronger summer-time uptake in the polar/subpolar regions than estimates derived through neural-network interpolation of pCO2 data obtained with profiling floats but somewhat weaker uptake than a recent study by Long et al. [Science 374, 1275-1280 (2021)], who used the same airborne data and multiple atmospheric transport models (ATMs) to constrain surface fluxes. Our study also uses moist static energy (MSE) budgets from reanalyses to show that most ATMs tend to have excessive diabatic mixing (transport across moist isentrope, θe, or Mθe surfaces) at high southern latitudes in the austral summer, which leads to biases in estimates of air-sea CO2 exchange. Furthermore, we show that the MSE-based constraint is consistent with an independent constraint on atmospheric mixing based on combining airborne and surface CO2 observations.

Published
2024

19. Biosynthesis of chlorophyll c in a dinoflagellate and heterologous production in planta

Author

Jinkerson, Robert E, Poveda-Huertes, Daniel, Cooney, Elizabeth C, Cho, Anna, Ochoa-Fernandez, Rocio, Keeling, Patrick J, Xiang, Tingting, and Andersen-Ranberg, Johan

Subjects
Plant Biology, Biological Sciences, Chlorophyll A, Chlorophyll, Proteins, Plants, Dinoflagellida, Phylogeny, oxoglutarate-Fe(II) dioxygenase, Breviolum minutum, CHLCS, CHLOROPHYLL C SYNTHASE, biosynthesis, chlorophyll c, chromalveolate, dinoflagellate, Medical and Health Sciences, Psychology and Cognitive Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences, Psychology
Abstract

Chlorophyll c is a key photosynthetic pigment that has been used historically to classify eukaryotic algae. Despite its importance in global photosynthetic productivity, the pathway for its biosynthesis has remained elusive. Here we define the CHLOROPHYLL C SYNTHASE (CHLCS) discovered through investigation of a dinoflagellate mutant deficient in chlorophyll c. CHLCSs are proteins with chlorophyll a/b binding and 2-oxoglutarate-Fe(II) dioxygenase (2OGD) domains found in peridinin-containing dinoflagellates; other chlorophyll c-containing algae utilize enzymes with only the 2OGD domain or an unknown synthase to produce chlorophyll c. 2OGD-containing synthases across dinoflagellate, diatom, cryptophyte, and haptophyte lineages form a monophyletic group, 8 members of which were also shown to produce chlorophyll c. Chlorophyll c1 to c2 ratios in marine algae are dictated in part by chlorophyll c synthases. CHLCS heterologously expressed in planta results in the accumulation of chlorophyll c1 and c2, demonstrating a path to augment plant pigment composition with algal counterparts.

Published
2024

21. Fake It Without Making It: Conditioned Face Generation for Accurate 3D Face Reconstruction

Author

Rowan, Will, Huber, Patrik, Pears, Nick, and Keeling, Andrew

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Accurate 3D face reconstruction from 2D images is an enabling technology with applications in healthcare, security, and creative industries. However, current state-of-the-art methods either rely on supervised training with very limited 3D data or self-supervised training with 2D image data. To bridge this gap, we present a method to generate a large-scale synthesised dataset of 250K photorealistic images and their corresponding shape parameters and depth maps, which we call SynthFace. Our synthesis method conditions Stable Diffusion on depth maps sampled from the FLAME 3D Morphable Model (3DMM) of the human face, allowing us to generate a diverse set of shape-consistent facial images that is designed to be balanced in race and gender. We further propose ControlFace, a deep neural network, trained on SynthFace, which achieves competitive performance on the NoW benchmark, without requiring 3D supervision or manual 3D asset creation. The complete SynthFace dataset will be made publicly available upon publication.

Published
2023

22. Entanglement and replica symmetry breaking in a driven-dissipative quantum spin glass

Author

Marsh, Brendan P., Kroeze, Ronen M., Ganguli, Surya, Gopalakrishnan, Sarang, Keeling, Jonathan, and Lev, Benjamin L.

Subjects
Quantum Physics, Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Quantum Gases, Condensed Matter - Statistical Mechanics
Abstract

We describe simulations of the quantum dynamics of a confocal cavity QED system that realizes an intrinsically driven-dissipative spin glass. A close connection between open quantum dynamics and replica symmetry breaking is established, in which individual quantum trajectories are the replicas. We observe that entanglement plays an important role in the emergence of replica symmetry breaking in a fully connected, frustrated spin network of up to fifteen spin-1/2 particles. Quantum trajectories of entangled spins reach steady-state spin configurations of lower energy than that of semiclassical trajectories. Cavity emission allows monitoring of the continuous stochastic evolution of spin configurations, while backaction from this projects entangled states into states of broken Ising and replica symmetry. The emergence of spin glass order manifests itself through the simultaneous absence of magnetization and the presence of nontrivial spin overlap density distributions among replicas. Moreover, these overlaps reveal incipient ultrametric order, in line with the Parisi RSB solution ansatz for the Sherrington-Kirkpatrick model. A nonthermal Parisi order parameter distribution, however, highlights the driven-dissipative nature of this quantum optical spin glass. This practicable system could serve as a testbed for exploring how quantum effects enrich the physics of spin glasses., Comment: 23 pages including 11 figures and 8 appendices; section V and appendix F are new

Published
2023

23. Distinct regulation of Tau Monomer and aggregate uptake and intracellular accumulation in human neurons

Author

Marvian, Amir T., Strauss, Tabea, Tang, Qilin, Tuck, Benjamin J., Keeling, Sophie, Rüdiger, Daniel, Mirzazadeh Dizaji, Negar, Mohammad-Beigi, Hossein, Nuscher, Brigitte, Chakraborty, Pijush, Sutherland, Duncan S., McEwan, William A., Köglsperger, Thomas, Zahler, Stefan, Zweckstetter, Markus, Lichtenthaler, Stefan F., Wurst, Wolfgang, Schwarz, Sigrid, and Höglinger, Günter

Published
2024
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28. Engaging Engineering Teams Through Moral Imagination: A Bottom-Up Approach for Responsible Innovation and Ethical Culture Change in Technology Companies

Author

Lange, Benjamin, Keeling, Geoff, McCroskery, Amanda, Zevenbergen, Ben, Blascovich, Sandra, Pedersen, Kyle, Lentz, Alison, and Arcas, Blaise Aguera y

Subjects
Computer Science - Computers and Society
Abstract

We propose a "Moral Imagination" methodology to facilitate a culture of responsible innovation for engineering and product teams in technology companies. Our approach has been operationalized over the past two years at Google, where we have conducted over 50 workshops with teams across the organization. We argue that our approach is a crucial complement to existing formal and informal initiatives for fostering a culture of ethical awareness, deliberation, and decision-making in technology design such as company principles, ethics and privacy review procedures, and compliance controls. We characterize some of the distinctive benefits of our methodology for the technology sector in particular., Comment: 16 pages, 1 figure

Published
2023

29. Algorithmic Bias, Generalist Models,and Clinical Medicine

Author

Keeling, Geoff

Subjects
Computer Science - Computers and Society
Abstract

The technical landscape of clinical machine learning is shifting in ways that destabilize pervasive assumptions about the nature and causes of algorithmic bias. On one hand, the dominant paradigm in clinical machine learning is narrow in the sense that models are trained on biomedical datasets for particular clinical tasks such as diagnosis and treatment recommendation. On the other hand, the emerging paradigm is generalist in the sense that general-purpose language models such as Google's BERT and PaLM are increasingly being adapted for clinical use cases via prompting or fine-tuning on biomedical datasets. Many of these next-generation models provide substantial performance gains over prior clinical models, but at the same time introduce novel kinds of algorithmic bias and complicate the explanatory relationship between algorithmic biases and biases in training data. This paper articulates how and in what respects biases in generalist models differ from biases in prior clinical models, and draws out practical recommendations for algorithmic bias mitigation., Comment: 20 pages, 0 figures

Published
2023

30. Sublinear scaling in non-Markovian open quantum systems simulations

Author

Cygorek, Moritz, Keeling, Jonathan, Lovett, Brendon W., and Gauger, Erik M.

Subjects
Quantum Physics, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

While several numerical techniques are available for predicting the dynamics of non-Markovian open quantum systems, most struggle with simulations for very long memory and propagation times, e.g., due to superlinear scaling with the number of time steps $n$. Here, we introduce a numerically exact algorithm to calculate process tensors -- compact representations of environmental influences -- which provides a scaling advantage over previous algorithms by leveraging self-similarity of the tensor networks that represent Gaussian environments. Based on a divide-and-conquer strategy, our approach requires only $\mathcal{O}(n\log n)$ singular value decompositions for environments with infinite memory. Where the memory can be truncated after $n_c$ time steps, a scaling $\mathcal{O}(n_c\log n_c)$ is found, which is independent of $n$. This improved scaling is enabled by identifying process tensors with repeatable blocks. To demonstrate the power and utility of our approach we provide three examples. (1) We calculate the fluorescence spectra of a quantum dot under both strong driving and strong dot-phonon couplings, a task requiring simulations over millions of time steps, which we are able to perform in minutes. (2) We efficiently find process tensors describing superradiance of multiple emitters. (3) We explore the limits of our algorithm by considering coherence decay with a very strongly coupled environment. The algorithm we present here not only significantly extends the scope of numerically exact techniques to open quantum systems with long memory times, but also has fundamental implications for simulation complexity.

Published
2023
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31. Optimizing performance of quantum operations with non-Markovian decoherence: the tortoise or the hare?

Author

Butler, Eoin P., Fux, Gerald E., Ortega-Taberner, Carlos, Lovett, Brendon W., Keeling, Jonathan, and Eastham, Paul R.

Subjects
Quantum Physics
Abstract

The interaction between a quantum system and its environment limits our ability to control it and perform quantum operations on it. We present an efficient method to find optimal controls for quantum systems coupled to non-Markovian environments, by using the process tensor to compute the gradient of an objective function. We consider state transfer for a driven two-level system coupled to a bosonic environment, and characterize performance in terms of speed and fidelity. We thus determine the best achievable fidelity as a function of process duration. We show there is a trade-off between speed and fidelity, and that slower processes can have higher fidelity by exploiting non-Markovian effects., Comment: 6 pages, 3 figures, added possible suppression mechanism and additional data in supplement

Published
2023
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32. Determining the validity of cumulant expansions for central spin models

Author

Fowler-Wright, Piper, Arnardóttir, Kristín B., Kirton, Peter, Lovett, Brendon W., and Keeling, Jonathan

Subjects
Condensed Matter - Statistical Mechanics, Quantum Physics
Abstract

For a model with many-to-one connectivity it is widely expected that mean-field theory captures the exact many-particle $N\to\infty$ limit, and that higher-order cumulant expansions of the Heisenberg equations converge to this same limit whilst providing improved approximations at finite $N$. Here we show that this is in fact not always the case. Instead, whether mean-field theory correctly describes the large-$N$ limit depends on how the model parameters scale with $N$, and the convergence of cumulant expansions may be non-uniform across even and odd orders. Further, even when a higher-order cumulant expansion does recover the correct limit, the error is not monotonic with $N$ and may exceed that of mean-field theory., Comment: 9 pages, 6 figures, final published version

Published
2023
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33. Text2Face: A Multi-Modal 3D Face Model

Author

Rowan, Will, Huber, Patrik, Pears, Nick, and Keeling, Andrew

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We present the first 3D morphable modelling approach, whereby 3D face shape can be directly and completely defined using a textual prompt. Building on work in multi-modal learning, we extend the FLAME head model to a common image-and-text latent space. This allows for direct 3D Morphable Model (3DMM) parameter generation and therefore shape manipulation from textual descriptions. Our method, Text2Face, has many applications; for example: generating police photofits where the input is already in natural language. It further enables multi-modal 3DMM image fitting to sketches and sculptures, as well as images., Comment: Fixed formatting and a typo

Published
2023

34. A high-cooperativity confocal cavity QED microscope

Author

Kroeze, Ronen M., Marsh, Brendan P., Lin, Kuan-Yu, Keeling, Jonathan, and Lev, Benjamin L.

Subjects
Quantum Physics, Condensed Matter - Quantum Gases, Physics - Atomic Physics, Physics - Optics
Abstract

Cavity QED with cooperativity far greater than unity enables high-fidelity quantum sensing and information processing. The high-cooperativity regime is often reached through the use of short, single-mode resonators. More complicated multimode resonators, such as the near-confocal optical Fabry-P\'{e}rot cavity, can provide intracavity atomic imaging in addition to high cooperativity. This capability has recently proved important for exploring quantum many-body physics in the driven-dissipative setting. In this work, we show that a confocal cavity QED microscope can realize cooperativity in excess of 110. This cooperativity is on par with the very best single-mode cavities (which are far shorter) and 21$\times$ greater than single-mode resonators of similar length and mirror radii. The 1.7-$\mu$m imaging resolution is naturally identical to the photon-mediated interaction range. We measure these quantities by determining the threshold of cavity superradiance when small, optically tweezed Bose-Einstein condensates are pumped at various intracavity locations. Transmission measurements of an ex situ cavity corroborate these results. We provide a theoretical description that shows how cooperativity enhancement arises from the dispersive coupling to the atoms of many near-degenerate modes., Comment: 20 pages including 4 figures, four appendices, and a supplement

Published
2022
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35. Mode switching dynamics in organic polariton lasing

Author

Moilanen, Antti J., Arnardóttir, Kristín B., Keeling, Jonathan, and Törmä, Päivi

Subjects
Condensed Matter - Quantum Gases, Condensed Matter - Mesoscale and Nanoscale Physics, Physics - Optics
Abstract

We study the dynamics of multimode polariton lasing in organic microcavities by using a second-order cumulant equation approach. By inspecting the time evolution of the photon mode occupations, we show that if multiple lasing peaks are observed in time-integrated mode occupations, the reason can be either bi-modal lasing or temporal switching between several modes. The former takes place within a narrow range of parameters while the latter occurs more widely. We find that the origin of the temporal switching is different in the weak- and strong-coupling regimes. At weak coupling slope efficiency is the determining factor, while for strong coupling it is changes in the eigenmodes and gain spectrum upon pumping. This difference is revealed by investigating the photoluminescence and momentum-resolved gain spectra. Our results underscore the importance of understanding the time evolution of the populations when characterizing the lasing behaviour of a multimode polariton system, and show how these features differ between weak and strong coupling., Comment: 15 pages, 13 figures

Published
2022
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36. Redesigning a Sustainable English Capstone Course through a Virtual Student-Faculty Partnership

Author

Keeling, Kellie, Phalen, Zoë, and Rifenburg, Michael J.

Abstract

This collaborative essay between undergraduate students and a faculty member illustrates the importance of partnerships between students and faculty when redesigning courses. We ground this partnering in Students as Partner (SaP) praxis. SaP reinvigorates the faculty and student relationship as one in which both students and faculty serve as active agents in curriculum development, redesign, and assessment. In this essay, we introduce our partnership, locally ground our partnership, and highlight how we redesigned a sustainable English Department capstone course to include a cumulative, integrative assignment. Our partnership was not designed to lead to a quantifiable direct output (i.e., a publication or even a redesigned class); instead, our goal was to build community, to support each other, to learn, to write for ourselves and each other. We conclude by offering brief qualitative data on the effectiveness of our redesign efforts and how our approach may work as a model for redesigning courses in different contexts/institutions. [The page range (244-257) cited on the .pdf is incorrect. The correct page range is p244-255.]

Published
2021

37. Using the atmospheric CO2 growth rate to constrain the CO2 flux from land use and land cover change since 1900

Author

Dohner, Julia L, Birner, Benjamin, Schwartzman, Armin, Pongratz, Julia, and Keeling, Ralph F

Subjects
Earth Sciences, Atmospheric Sciences, Biological Sciences, Life on Land, Carbon Dioxide, Ecosystem, Carbon Cycle, Carbon, Uncertainty, atmospheric CO2, CO2 growth rate, global carbon budget, global CO2 fluxes, land use change, terrestrial CO2 sink, Environmental Sciences, Ecology, Biological sciences, Earth sciences, Environmental sciences
Abstract

We explore the ability of the atmospheric CO2 record since 1900 to constrain the source of CO2 from land use and land cover change (hereafter "land use"), taking account of uncertainties in other terms in the global carbon budget. We find that the atmospheric constraint favors land use CO2 flux estimates with lower decadal variability and can identify potentially erroneous features, such as emission peaks around 1960 and after 2000, in some published estimates. Furthermore, we resolve an offset in the global carbon budget that is most plausibly attributed to the land use flux. This correction shifts the mean land use flux since 1900 across 20 published estimates down by 0.35 PgC year-1 to 1.04 ± 0.57 PgC year-1 , which is within the range but at the low end of these estimates. We show that the atmospheric CO2 record can provide insights into the time history of the land use flux that may reduce uncertainty in this term and improve current understanding and projections of the global carbon cycle.

Published
2022

38. Competition-Level Code Generation with AlphaCode

Author

Li, Yujia, Choi, David, Chung, Junyoung, Kushman, Nate, Schrittwieser, Julian, Leblond, Rémi, Eccles, Tom, Keeling, James, Gimeno, Felix, Lago, Agustin Dal, Hubert, Thomas, Choy, Peter, d'Autume, Cyprien de Masson, Babuschkin, Igor, Chen, Xinyun, Huang, Po-Sen, Welbl, Johannes, Gowal, Sven, Cherepanov, Alexey, Molloy, James, Mankowitz, Daniel J., Robson, Esme Sutherland, Kohli, Pushmeet, de Freitas, Nando, Kavukcuoglu, Koray, and Vinyals, Oriol

Subjects
Computer Science - Programming Languages, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Programming is a powerful and ubiquitous problem-solving tool. Developing systems that can assist programmers or even generate programs independently could make programming more productive and accessible, yet so far incorporating innovations in AI has proven challenging. Recent large-scale language models have demonstrated an impressive ability to generate code, and are now able to complete simple programming tasks. However, these models still perform poorly when evaluated on more complex, unseen problems that require problem-solving skills beyond simply translating instructions into code. For example, competitive programming problems which require an understanding of algorithms and complex natural language remain extremely challenging. To address this gap, we introduce AlphaCode, a system for code generation that can create novel solutions to these problems that require deeper reasoning. In simulated evaluations on recent programming competitions on the Codeforces platform, AlphaCode achieved on average a ranking of top 54.3% in competitions with more than 5,000 participants. We found that three key components were critical to achieve good and reliable performance: (1) an extensive and clean competitive programming dataset for training and evaluation, (2) large and efficient-to-sample transformer-based architectures, and (3) large-scale model sampling to explore the search space, followed by filtering based on program behavior to a small set of submissions., Comment: 74 pages

Published
2022
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39. Tensor network simulation of chains of non-Markovian open quantum systems

Author

Fux, Gerald E., Kilda, Dainius, Lovett, Brendon W., and Keeling, Jonathan

Subjects
Quantum Physics, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

We introduce a general numerical method to compute dynamics and multi-time correlations of chains of quantum systems, where each system may couple strongly to a structured environment. The method combines the process tensor formalism for general (possibly non-Markovian) open quantum systems with time evolving block decimation (TEBD) for 1D chains. It systematically reduces the numerical complexity originating from system-environment correlations before integrating them into the full many-body problem, making a wide range of applications numerically feasible. We illustrate the power of this method by studying two examples. First, we study the thermalization of individual spins of a short XYZ Heisenberg chain with strongly coupled thermal leads. Our results confirm the complete thermalization of the chain when coupled to a single bath, and reveal distinct effective temperatures in low, mid, and high frequency regimes when the chain is placed between a hot and a cold bath. Second, we study the dynamics of diffusion in an longer XY chain, when each site couples to its own bath., Comment: 15 pages, 14 figures

Published
2022
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40. A computational framework for modelling infectious disease policy based on age and household structure with applications to the COVID-19 pandemic

Author

Hilton, Joe, Riley, Heather, Pellis, Lorenzo, Aziza, Rabia, Brand, Samuel P. C., Kombe, Ivy K., Ojal, John, Parisi, Andrea, Keeling, Matt J., Nokes, D. James, Manson-Sawko, Robert, and House, Thomas

Subjects
Quantitative Biology - Populations and Evolution, Statistics - Other Statistics
Abstract

The widespread, and in many countries unprecedented, use of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic has highlighted the need for mathematical models which can estimate the impact of these measures while accounting for the highly heterogeneous risk profile of COVID-19. Models accounting either for age structure or the household structure necessary to explicitly model many NPIs are commonly used in infectious disease modelling, but models incorporating both levels of structure present substantial computational and mathematical challenges due to their high dimensionality. Here we present a modelling framework for the spread of an epidemic that includes explicit representation of age structure and household structure. Our model is formulated in terms of tractable systems of ordinary differential equations for which we provide an open-source Python implementation. Such tractability leads to significant benefits for model calibration, exhaustive evaluation of possible parameter values, and interpretability of results. We demonstrate the flexibility of our model through four policy case studies, where we quantify the likely benefits of the following measures which were either considered or implemented in the UK during the current COVID-19 pandemic: control of within- and between-household mixing through NPIs; formation of support bubbles during lockdown periods; out-of-household isolation (OOHI); and temporary relaxation of NPIs during holiday periods. Our ordinary differential equation formulation and associated analysis demonstrate that multiple dimensions of risk stratification and social structure can be incorporated into infectious disease models without sacrificing mathematical tractability. This model and its software implementation expand the range of tools available to infectious disease policy analysts., Comment: 45 pages, 9 figures

Published
2022
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41. The microbiome of a bacterivorous marine choanoflagellate contains a resource-demanding obligate bacterial associate

Author

Needham, David M, Poirier, Camille, Bachy, Charles, George, Emma E, Wilken, Susanne, Yung, Charmaine CM, Limardo, Alexander J, Morando, Michael, Sudek, Lisa, Malmstrom, Rex R, Keeling, Patrick J, Santoro, Alyson E, and Worden, Alexandra Z

Subjects
Microbiology, Biological Sciences, Infectious Diseases, Microbiome, Emerging Infectious Diseases, Infection, Life Below Water, Animals, Bacteria, Choanoflagellata, Humans, Microbiota, Pacific Ocean, Type IV Secretion Systems, Medical Microbiology
Abstract

Microbial predators such as choanoflagellates are key players in ocean food webs. Choanoflagellates, which are the closest unicellular relatives of animals, consume bacteria and also exhibit marked biological transitions triggered by bacterial compounds, yet their native microbiomes remain uncharacterized. Here we report the discovery of a ubiquitous, uncultured bacterial lineage we name Candidatus Comchoanobacterales ord. nov., related to the human pathogen Coxiella and physically associated with the uncultured marine choanoflagellate Bicosta minor. We analyse complete 'Comchoano' genomes acquired after sorting single Bicosta cells, finding signatures of obligate host-dependence, including reduction of pathways encoding glycolysis, membrane components, amino acids and B-vitamins. Comchoano encode the necessary apparatus to import energy and other compounds from the host, proteins for host-cell associations and a type IV secretion system closest to Coxiella's that is expressed in Pacific Ocean metatranscriptomes. Interactions between choanoflagellates and their microbiota could reshape the direction of energy and resource flow attributed to microbial predators, adding complexity and nuance to marine food webs.

Published
2022

42. Impact of Changing Winds on the Mauna Loa CO2 Seasonal Cycle in Relation to the Pacific Decadal Oscillation

Author

Jin, Yuming, Keeling, Ralph F, Rödenbeck, Christian, Patra, Prabir K, Piper, Stephen C, and Schwartzman, Armin

Subjects
Earth Sciences, Oceanography, Large-scale circulation change, Hadley cell expansion, Land biogeochemistry, Empirical orthogonal functions of winds, Large‐scale circulation change, Atmospheric Sciences, Physical Geography and Environmental Geoscience, Atmospheric sciences, Climate change science
Abstract

Long-term measurements at the Mauna Loa Observatory (MLO) show that the CO2 seasonal cycle amplitude (SCA) increased from 1959 to 2019 at an overall rate of 0.22  ±  0.034 ppm decade-1 while also varying on interannual to decadal time scales. These SCA changes are a signature of changes in land ecological CO2 fluxes as well as shifting winds. Simulations with the TM3 tracer transport model and CO2 fluxes from the Jena CarboScope CO2 Inversion suggest that shifting winds alone have contributed to a decrease in SCA of -0.10  ±  0.022 ppm decade-1 from 1959 to 2019, partly offsetting the observed long-term SCA increase associated with enhanced ecosystem net primary production. According to these simulations and MIROC-ACTM simulations, the shorter-term variability of MLO SCA is nearly equally driven by varying ecological CO2 fluxes (49%) and varying winds (51%). We also show that the MLO SCA is strongly correlated with the Pacific Decadal Oscillation (PDO) due to varying winds, as well as with a closely related wind index (U-PDO). Since 1980, 44% of the wind-driven SCA decrease has been tied to a secular trend in the U-PDO, which is associated with a progressive weakening of westerly winds at 700 mbar over the central Pacific from 20°N to 40°N. Similar impacts of varying winds on the SCA are seen in simulations at other low-latitude Pacific stations, illustrating the difficulty of constraining trend and variability of land CO2 fluxes using observations from low latitudes due to the complexity of circulation changes.

Published
2022

43. Efficient many-body non-Markovian dynamics of organic polaritons

Author

Fowler-Wright, Piper, Lovett, Brendon W., and Keeling, Jonathan

Subjects
Condensed Matter - Quantum Gases, Quantum Physics
Abstract

We show how to simulate a model of many molecules with both strong coupling to many vibrational modes and collective coupling to a single photon mode. We do this by combining process tensor matrix product operator methods with a mean-field approximation which reduces the dimension of the problem. We analyze the steady-state of the model under incoherent pumping to determine the dependence of the polariton lasing threshold on cavity detuning, light-matter coupling strength, and environmental temperature. Moreover, by measuring two-time correlations, we study quadratic fluctuations about the mean-field to calculate the photoluminescence spectrum. Our method enables one to simulate many-body systems with strong coupling to multiple environments, and to extract both static and dynamical properties., Comment: 7 pages, 3 figures plus supplementary material

Published
2021
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44. Numerical evaluation and robustness of the quantum mean force Gibbs state

Author

Chiu, Yiu-Fung, Strathearn, Aidan, and Keeling, Jonathan

Subjects
Quantum Physics, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

We introduce a numerical method to determine the Hamiltonian of Mean Force (HMF) Gibbs state for a quantum system strongly coupled to a reservoir. The method adapts the Time Evolving Matrix Product Operator (TEMPO) algorithm to imaginary time propagation. By comparing the real-time and imaginary-time propagation for a generalized spin-boson model, we confirm that the HMF Gibbs state correctly predicts the steady state. We show that the numerical dynamics match the polaron master equation at strong coupling. We illustrate the potential of the imaginary-time TEMPO approach by exploring reservoir-induced entanglement between qubits., Comment: 8 pages, 7 figures

Published
2021
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46. Mentoring Underrepresented Minority Physician-Scientists to Success

Author

Kalet, Adina, Libby, Anne M, Jagsi, Reshma, Brady, Kathleen, Chavis-Keeling, Deborah, Pillinger, Michael H, Daumit, Gail L, Drake, Amelia F, Drake, Wonder Puryear, Fraser, Victoria, Ford, Daniel, Hochman, Judith S, Jones, Rochelle D, Mangurian, Christina, Meagher, Emma A, McGuinness, Georgeann, Regensteiner, Judith G, Rubin, Deborah C, Yaffe, Kristine, and Ravenell, Joseph E

Subjects
Health Services and Systems, Health Sciences, Clinical Research, Faculty, Medical, Female, Humans, Male, Mentoring, Mentors, Minority Groups, Physicians, United States, Clinical Sciences, Curriculum and Pedagogy, General & Internal Medicine, Curriculum and pedagogy, Health services and systems
Abstract

As the nation seeks to recruit and retain physician-scientists, gaps remain in understanding and addressing mitigatable challenges to the success of faculty from underrepresented minority (URM) backgrounds. The Doris Duke Charitable Foundation Fund to Retain Clinical Scientists program, implemented in 2015 at 10 academic medical centers in the United States, seeks to retain physician-scientists at risk of leaving science because of periods of extraordinary family caregiving needs, hardships that URM faculty-especially those who identify as female-are more likely to experience. At the annual Fund to Retain Clinical Scientists program directors conference in 2018, program directors-21% of whom identify as URM individuals and 13% as male-addressed issues that affect URM physician-scientists in particular. Key issues that threaten the retention of URM physician-scientists were identified through focused literature reviews; institutional environmental scans; and structured small- and large-group discussions with program directors, staff, and participants. These issues include bias and discrimination, personal wealth differential, the minority tax (i.e., service burdens placed on URM faculty who represent URM perspectives on committees and at conferences), lack of mentorship training, intersectionality and isolation, concerns about confirming stereotypes, and institutional-level factors. The authors present recommendations for how to create an environment in which URM physician-scientists can expect equitable opportunities to thrive, as institutions demonstrate proactive allyship and remove structural barriers to success. Recommendations include providing universal training to reduce interpersonal bias and discrimination, addressing the consequences of the personal wealth gap through financial counseling and benefits, measuring the service faculty members provide to the institution as advocates for URM faculty issues and compensating them appropriately, supporting URM faculty who wish to engage in national leadership programs, and sustaining institutional policies that address structural and interpersonal barriers to inclusive excellence.

Published
2022

47. Effect of fermion indistinguishability on optical absorption of doped two-dimensional semiconductors

Author

Tiene, A., Levinsen, J., Keeling, J., Parish, M. M., and Marchetti, F. M.

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Quantum Gases
Abstract

We study the optical absorption spectrum of a doped two-dimensional semiconductor in the spin-valley polarized limit. In this configuration, the carriers in the Fermi sea are indistinguishable from one of the two carriers forming the exciton. Most notably, this indistinguishability requires the three-body trion state to have p-wave symmetry. To explore the consequences of this, we evaluate the system's optical properties within a polaron description, which can interpolate from the low density limit -- where the relevant excitations are few-body bound states -- to higher density many-body states. In the parameter regime where the trion is bound, we demonstrate that the spectrum is characterized by an attractive quasiparticle branch, a repulsive branch, and a many-body continuum, and we evaluate the doping dependence of the corresponding energies and spectral weights. In particular, at low doping we find that the oscillator strength of the attractive branch scales with the square of the Fermi energy as a result of the trion's p-wave symmetry. Upon increasing density, we find that both the repulsive and attractive branches blueshift, and that the orbital character of the states associated with these branches interchanges. We compare our results with previous investigations of the scenario where the Fermi sea involves carriers distinguishable from those in the exciton, for which the trion ground state is s-wave., Comment: 20 pages, 12 figures

Published
2021
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48. On the Opportunities and Risks of Foundation Models

Author

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

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computers and Society
Abstract

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 critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature., Comment: Authored by the Center for Research on Foundation Models (CRFM) at the Stanford Institute for Human-Centered Artificial Intelligence (HAI). Report page with citation guidelines: https://crfm.stanford.edu/report.html

Published
2021

49. Few-emitter lasing in single ultra-small nanocavities

Author

Ojambati, Oluwafemi S., Arnardottir, Kristin B., Lovett, Brendon W., Keeling, Jonathan, and Baumberg, Jeremy J.

Subjects
Physics - Optics, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

Lasers are ubiquitous for information storage, processing, communications, sensing, biological research, and medical applications [1]. To decrease their energy and materials usage, a key quest is to miniaturize lasers down to nanocavities [2]. Obtaining the smallest mode volumes demands plasmonic nanocavities, but for these, gain comes from only single or few emitters. Until now, lasing in such devices was unobtainable due to low gain and high cavity losses [3]. Here, we demonstrate a plasmonic nanolaser approaching the single-molecule emitter regime. The lasing transition significantly broadens, and depends on the number of molecules and their individual locations. We show this can be understood by developing a theoretical approach [4] extending previous weak-coupling theories [5]. Our work paves the way for developing nanolaser applications [2, 6, 7] as well as fundamental studies at the limit of few emitters [5, 8, 9].

Published
2021

50. An optical lattice with sound

Author

Guo, Yudan, Kroeze, Ronen M., Marsh, Brendan P., Gopalakrishnan, Sarang, Keeling, Jonathan, and Lev, Benjamin L.

Subjects
Condensed Matter - Quantum Gases, Physics - Atomic Physics, Quantum Physics
Abstract

Quantised sound waves -- phonons -- govern the elastic response of crystalline materials, and also play an integral part in determining their thermodynamic properties and electrical response (e.g., by binding electrons into superconducting Cooper pairs). The physics of lattice phonons and elasticity is absent in simulators of quantum solids constructed of neutral atoms in periodic light potentials: unlike real solids, traditional optical lattices are silent because they are infinitely stiff. Optical-lattice realisations of crystals therefore lack some of the central dynamical degrees of freedom that determine the low-temperature properties of real materials. Here, we create an optical lattice with phonon modes using a Bose-Einstein condensate (BEC) coupled to a confocal optical resonator. Playing the role of an active quantum gas microscope, the multimode cavity QED system both images the phonons and induces the crystallisation that supports phonons via short-range, photon-mediated atom-atom interactions. Dynamical susceptibility measurements reveal the phonon dispersion relation, showing that these collective excitations exhibit a sound speed dependent on the BEC-photon coupling strength. Our results pave the way for exploring the rich physics of elasticity in quantum solids, ranging from quantum melting transitions to exotic ``fractonic'' topological defects in the quantum regime., Comment: 7 pages, 4 figures; supplement with 22 pages, 6 figures

Published
2021
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