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  1. Book ; Online: Optimistic Simulated Exploration as an Incentive for Real Exploration

    Danihelka, Ivo

    2009  

    Abstract: Many reinforcement learning exploration techniques are overly optimistic and try to explore every state. Such exploration is impossible in environments with the unlimited number of states. I propose to use simulated exploration with an optimistic model ... ...

    Abstract Many reinforcement learning exploration techniques are overly optimistic and try to explore every state. Such exploration is impossible in environments with the unlimited number of states. I propose to use simulated exploration with an optimistic model to discover promising paths for real exploration. This reduces the needs for the real exploration.

    Comment: accepted, noted that the initial path was 217 steps long
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Publishing date 2009-03-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: Muesli

    Hessel, Matteo / Danihelka, Ivo / Viola, Fabio / Guez, Arthur / Schmitt, Simon / Sifre, Laurent / Weber, Theophane / Silver, David / van Hasselt, Hado

    Combining Improvements in Policy Optimization

    2021  

    Abstract: We propose a novel policy update that combines regularized policy optimization with model learning as an auxiliary loss. The update (henceforth Muesli) matches MuZero's state-of-the-art performance on Atari. Notably, Muesli does so without using deep ... ...

    Abstract We propose a novel policy update that combines regularized policy optimization with model learning as an auxiliary loss. The update (henceforth Muesli) matches MuZero's state-of-the-art performance on Atari. Notably, Muesli does so without using deep search: it acts directly with a policy network and has computation speed comparable to model-free baselines. The Atari results are complemented by extensive ablations, and by additional results on continuous control and 9x9 Go.
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Publishing date 2021-04-13
    Publishing country us
    Document type Book ; Online
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  3. Article ; Online: Neural scene representation and rendering.

    Eslami, S M Ali / Jimenez Rezende, Danilo / Besse, Frederic / Viola, Fabio / Morcos, Ari S / Garnelo, Marta / Ruderman, Avraham / Rusu, Andrei A / Danihelka, Ivo / Gregor, Karol / Reichert, David P / Buesing, Lars / Weber, Theophane / Vinyals, Oriol / Rosenbaum, Dan / Rabinowitz, Neil / King, Helen / Hillier, Chloe / Botvinick, Matt /
    Wierstra, Daan / Kavukcuoglu, Koray / Hassabis, Demis

    Science (New York, N.Y.)

    2018  Volume 360, Issue 6394, Page(s) 1204–1210

    Abstract: Scene representation-the process of converting visual sensory data into concise descriptions-is a requirement for intelligent behavior. Recent work has shown that neural networks excel at this task when provided with large, labeled datasets. However, ... ...

    Abstract Scene representation-the process of converting visual sensory data into concise descriptions-is a requirement for intelligent behavior. Recent work has shown that neural networks excel at this task when provided with large, labeled datasets. However, removing the reliance on human labeling remains an important open problem. To this end, we introduce the Generative Query Network (GQN), a framework within which machines learn to represent scenes using only their own sensors. The GQN takes as input images of a scene taken from different viewpoints, constructs an internal representation, and uses this representation to predict the appearance of that scene from previously unobserved viewpoints. The GQN demonstrates representation learning without human labels or domain knowledge, paving the way toward machines that autonomously learn to understand the world around them.
    MeSH term(s) Machine Learning ; Neural Networks (Computer) ; Vision, Ocular
    Language English
    Publishing date 2018-06-14
    Publishing country United States
    Document type Journal Article
    ZDB-ID 128410-1
    ISSN 1095-9203 ; 0036-8075
    ISSN (online) 1095-9203
    ISSN 0036-8075
    DOI 10.1126/science.aar6170
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  4. Book ; Online: OpenSpiel

    Lanctot, Marc / Lockhart, Edward / Lespiau, Jean-Baptiste / Zambaldi, Vinicius / Upadhyay, Satyaki / Pérolat, Julien / Srinivasan, Sriram / Timbers, Finbarr / Tuyls, Karl / Omidshafiei, Shayegan / Hennes, Daniel / Morrill, Dustin / Muller, Paul / Ewalds, Timo / Faulkner, Ryan / Kramár, János / De Vylder, Bart / Saeta, Brennan / Bradbury, James /
    Ding, David / Borgeaud, Sebastian / Lai, Matthew / Schrittwieser, Julian / Anthony, Thomas / Hughes, Edward / Danihelka, Ivo / Ryan-Davis, Jonah

    A Framework for Reinforcement Learning in Games

    2019  

    Abstract: OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and sequential, ... ...

    Abstract OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and sequential, strictly turn-taking and simultaneous-move, perfect and imperfect information games, as well as traditional multiagent environments such as (partially- and fully- observable) grid worlds and social dilemmas. OpenSpiel also includes tools to analyze learning dynamics and other common evaluation metrics. This document serves both as an overview of the code base and an introduction to the terminology, core concepts, and algorithms across the fields of reinforcement learning, computational game theory, and search.
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computer Science and Game Theory ; Computer Science - Multiagent Systems
    Publishing date 2019-08-25
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Hybrid computing using a neural network with dynamic external memory.

    Graves, Alex / Wayne, Greg / Reynolds, Malcolm / Harley, Tim / Danihelka, Ivo / Grabska-Barwińska, Agnieszka / Colmenarejo, Sergio Gómez / Grefenstette, Edward / Ramalho, Tiago / Agapiou, John / Badia, Adrià Puigdomènech / Hermann, Karl Moritz / Zwols, Yori / Ostrovski, Georg / Cain, Adam / King, Helen / Summerfield, Christopher / Blunsom, Phil / Kavukcuoglu, Koray /
    Hassabis, Demis

    Nature

    2016  Volume 538, Issue 7626, Page(s) 471–476

    Abstract: Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of ... ...

    Abstract Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from data. When trained with supervised learning, we demonstrate that a DNC can successfully answer synthetic questions designed to emulate reasoning and inference problems in natural language. We show that it can learn tasks such as finding the shortest path between specified points and inferring the missing links in randomly generated graphs, and then generalize these tasks to specific graphs such as transport networks and family trees. When trained with reinforcement learning, a DNC can complete a moving blocks puzzle in which changing goals are specified by sequences of symbols. Taken together, our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural networks without external read-write memory.
    Language English
    Publishing date 2016--27
    Publishing country England
    Document type Journal Article
    ZDB-ID 120714-3
    ISSN 1476-4687 ; 0028-0836
    ISSN (online) 1476-4687
    ISSN 0028-0836
    DOI 10.1038/nature20101
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  6. Article ; Online: Grandmaster level in StarCraft II using multi-agent reinforcement learning.

    Vinyals, Oriol / Babuschkin, Igor / Czarnecki, Wojciech M / Mathieu, Michaël / Dudzik, Andrew / Chung, Junyoung / Choi, David H / Powell, Richard / Ewalds, Timo / Georgiev, Petko / Oh, Junhyuk / Horgan, Dan / Kroiss, Manuel / Danihelka, Ivo / Huang, Aja / Sifre, Laurent / Cai, Trevor / Agapiou, John P / Jaderberg, Max /
    Vezhnevets, Alexander S / Leblond, Rémi / Pohlen, Tobias / Dalibard, Valentin / Budden, David / Sulsky, Yury / Molloy, James / Paine, Tom L / Gulcehre, Caglar / Wang, Ziyu / Pfaff, Tobias / Wu, Yuhuai / Ring, Roman / Yogatama, Dani / Wünsch, Dario / McKinney, Katrina / Smith, Oliver / Schaul, Tom / Lillicrap, Timothy / Kavukcuoglu, Koray / Hassabis, Demis / Apps, Chris / Silver, David

    Nature

    2019  Volume 575, Issue 7782, Page(s) 350–354

    Abstract: Many real-world applications require artificial agents to compete and coordinate with other agents in complex environments. As a stepping stone to this goal, the domain of StarCraft has emerged as an important challenge for artificial intelligence ... ...

    Abstract Many real-world applications require artificial agents to compete and coordinate with other agents in complex environments. As a stepping stone to this goal, the domain of StarCraft has emerged as an important challenge for artificial intelligence research, owing to its iconic and enduring status among the most difficult professional esports and its relevance to the real world in terms of its raw complexity and multi-agent challenges. Over the course of a decade and numerous competitions
    MeSH term(s) Artificial Intelligence ; Humans ; Learning ; Reinforcement, Psychology ; Video Games
    Language English
    Publishing date 2019-10-30
    Publishing country England
    Document type Journal Article
    ZDB-ID 120714-3
    ISSN 1476-4687 ; 0028-0836
    ISSN (online) 1476-4687
    ISSN 0028-0836
    DOI 10.1038/s41586-019-1724-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Book ; Online: Gemini

    Gemini Team / Anil, Rohan / Borgeaud, Sebastian / Wu, Yonghui / Alayrac, Jean-Baptiste / Yu, Jiahui / Soricut, Radu / Schalkwyk, Johan / Dai, Andrew M. / Hauth, Anja / Millican, Katie / Silver, David / Petrov, Slav / 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 / 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 / Liu, Ruibo / Li, Yunxuan / Cogan, Sarah / Chen, Jeremy / Jia, Chao / Gu, Chenjie / Zhang, Qiao / Grimstad, Jordan / Hartman, Ale Jakse / Chadwick, Martin / Tomar, Gaurav Singh / Garcia, Xavier / Senter, Evan / Taropa, Emanuel / 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 / Zhang, Yujing / Addanki, Ravi / Miech, Antoine / Louis, Annie / Shafey, Laurent El / Teplyashin, Denis / Brown, Geoff / Catt, Elliot / Attaluri, Nithya / 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, Vitaly / 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 / Lin, Hanzhao / 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 / Cheng, Yong / 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 / 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 / Li, YaGuang / 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 / Elsayed, Gamaleldin / Chi, Ed / Mahdieh, Mahdis / Tenney, Ian / Hua, Nan / Petrychenko, Ivan / Kane, Patrick / Scandinaro, Dylan / Jain, Rishub / Uesato, Jonathan / Datta, Romina / Sadovsky, Adam / Bunyan, Oskar / Rabiej, Dominik / Wu, Shimu / Zhang, John / Vasudevan, Gautam / Leurent, Edouard / Alnahlawi, Mahmoud / Georgescu, Ionut / Wei, Nan / Zheng, Ivy / Chan, Betty / Rabinovitch, Pam G / 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 / Potluri, Sahitya / Park, Jane / Davoodi, Elnaz / Zhang, Jiageng / Stanway, Jeff / Garmon, Drew / Karmarkar, Abhijit / Dong, Zhe / Lee, Jong / Kumar, Aviral / Zhou, Luowei / Evens, Jonathan / Isaac, William / Chen, Zhe / Jia, Johnson / Levskaya, Anselm / Zhu, Zhenkai / Gorgolewski, Chris / Grabowski, Peter / Mao, Yu / Magni, Alberto / Yao, Kaisheng / Snaider, Javier / Casagrande, Norman / Suganthan, Paul / Palmer, Evan / Irving, Geoffrey / Loper, Edward / Faruqui, Manaal / Arkatkar, Isha / Chen, Nanxin / Shafran, Izhak / Fink, Michael / 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 / Georgiev, Marin / Wu, Marcus / Aguilar, Ricardo / Pallo, Keith / Chakladar, Abhishek / Repina, Alena / Wu, Xihui / van der Weide, Tom / Ponnapalli, Priya / Kaplan, Caroline / Simsa, Jiri / Li, Shuangfeng / Dousse, Olivier / Piper, Jeff / Ie, Nathan / Lui, Minnie / Pasumarthi, Rama / Lintz, Nathan / Vijayakumar, Anitha / Thiet, Lam Nguyen / Andor, Daniel / Valenzuela, Pedro / Paduraru, Cosmin / Peng, Daiyi / Lee, Katherine / Zhang, Shuyuan / Greene, Somer / Nguyen, Duc Dung / Kurylowicz, Paula / Velury, Sarmishta / Krause, Sebastian / Hardin, Cassidy / Dixon, Lucas / Janzer, Lili / Choo, Kiam / Feng, Ziqiang / Zhang, Biao / Singhal, Achintya / Latkar, Tejasi / Zhang, Mingyang / Le, Quoc / Abellan, Elena Allica / 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 / Lall, Sid / Franko, Ken / Filonov, Egor / Bulanova, Anna / Leblond, Rémi / Yadav, Vikas / Chung, Shirley / Askham, Harry / Cobo, Luis C. / Xu, Kelvin / Fischer, Felix / Xu, Jun / Sorokin, Christina / Alberti, Chris / Lin, Chu-Cheng / Evans, Colin / Zhou, Hao / Dimitriev, Alek / Forbes, Hannah / Banarse, Dylan / Tung, Zora / Liu, Jeremiah / Omernick, Mark / Bishop, Colton / Kumar, Chintu / Sterneck, Rachel / Foley, Ryan / Jain, Rohan / Mishra, Swaroop / Xia, Jiawei / Bos, Taylor / Cideron, Geoffrey / Amid, Ehsan / Piccinno, Francesco / Wang, Xingyu / Banzal, Praseem / Gurita, Petru / Noga, Hila / Shah, Premal / Mankowitz, Daniel J. / Polozov, Alex / Kushman, Nate / Krakovna, Victoria / Brown, Sasha / Bateni, MohammadHossein / Duan, Dennis / Firoiu, Vlad / Thotakuri, Meghana / Natan, Tom / Mohananey, Anhad / Geist, Matthieu / Mudgal, Sidharth / Girgin, Sertan / Li, Hui / Ye, Jiayu / Roval, Ofir / Tojo, Reiko / Kwong, Michael / Lee-Thorp, James / Yew, Christopher / Yuan, Quan / Bagri, Sumit / Sinopalnikov, Danila / Ramos, Sabela / Mellor, John / Sharma, Abhishek / Severyn, Aliaksei / Lai, Jonathan / Wu, Kathy / Cheng, Heng-Tze / 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 / Geller, Mark / Teh, Tian Huey / 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    A Family of Highly Capable Multimodal Models

    2023  

    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 ... ...

    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 Gemini models in cross-modal reasoning and language understanding will enable a wide variety of use cases and we discuss our approach toward deploying them responsibly to users.
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition
    Subject code 004
    Publishing date 2023-12-18
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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