Difference between revisions of "Gato"
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* [http://ai.google/tools/ Google's Tools and Resources] | * [http://ai.google/tools/ Google's Tools and Resources] | ||
* [[Google]] | * [[Google]] | ||
| − | * [http://storage.googleapis.com/deepmind-media/A%20Generalist%20Agent/Generalist%20Agent.pdf A Generalist Agent | S. Reed, K. Żołna, E. Parisotto, S. Gómez Colmenarejo, A. Novikov, G. Barth-Maron, M. Giménez, Y. Sulsky, J. Kay, J. Springenberg, T. Eccles, J. Bruce, A. Razavi, A. Edwards, N. Heess, Y. Chen, R. Hadsell, O. Vinyals, M. Bordbar and N. de Freitas - DeepMind | + | * [http://storage.googleapis.com/deepmind-media/A%20Generalist%20Agent/Generalist%20Agent.pdf A Generalist] [[Agents|Agent]] | S. Reed, K. Żołna, E. Parisotto, S. Gómez Colmenarejo, A. Novikov, G. Barth-Maron, M. Giménez, Y. Sulsky, J. Kay, J. Springenberg, T. Eccles, J. Bruce, A. Razavi, A. Edwards, N. Heess, Y. Chen, R. Hadsell, O. Vinyals, M. Bordbar and N. de Freitas - DeepMind |
* [http://www.louisbouchard.ai/deepmind-gato/ Deepmind's new model Gato is amazing! | Louis Bouchard] | * [http://www.louisbouchard.ai/deepmind-gato/ Deepmind's new model Gato is amazing! | Louis Bouchard] | ||
| − | DeepMind's “generalist” AI model inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens. | + | DeepMind's “generalist” AI model inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist [[Agents|agent]] beyond the realm of text outputs. The [[Agents|agent]], which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens. |
Gato has 16 [[Attention]] Heads... | Gato has 16 [[Attention]] Heads... | ||
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<youtube>zO49vZ31xb0</youtube> | <youtube>zO49vZ31xb0</youtube> | ||
<b>This AI Can Solve 604 Tasks [Paper Analysis of Gato by DeepMind] | <b>This AI Can Solve 604 Tasks [Paper Analysis of Gato by DeepMind] | ||
| − | </b><br>DeepMind published a revolutionary paper 🔥 They introduced Gato, a generalist AI agent that can carry out more than 600 tasks with a single transformer neural architecture. The tasks are varied, from playing Atari games to providing captions to images. | + | </b><br>DeepMind published a revolutionary paper 🔥 They introduced Gato, a generalist AI [[Agents|agent]] that can carry out more than 600 tasks with a single transformer neural architecture. The tasks are varied, from playing Atari games to providing captions to images. |
This paper demonstrates that: | This paper demonstrates that: | ||
| − | 📌 Generalist | + | 📌 Generalist [[Agents|agent]] can perform reasonably well on many tasks / embodiments / modalities |
| − | 📌 Generalist agents have the potential to learn new tasks with few data points | + | 📌 Generalist [[Agents|agents]] have the potential to learn new tasks with few data points |
| − | 📌 By scaling up the parameter size, we can build a general-purpose agent | + | 📌 By scaling up the parameter size, we can build a general-purpose [[Agents|agent]] |
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<youtube>wSQJZHfAg18</youtube> | <youtube>wSQJZHfAg18</youtube> | ||
<b>Is Gato Really the Future of AI? | <b>Is Gato Really the Future of AI? | ||
| − | </b><br>DeepMind has released "A Generalist | + | </b><br>DeepMind has released "A Generalist [[Agents|agent]]", a paper that introduces their new multi-modal model Gato. But is Gato truly a generalist [[Agents|agent]]? It is a transformer based model with the goal of generalizing over new tasks. It is trained fully autoregressively with supervised learning (no reinforcement learning) on a total of 603 different tasks. The tasks include robotics, Atari, DM Lab, Procgen, and a lot more. It also includes text and image tasks. This video is a paper review / explanation where I also give my thoughts on the paper. |
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<youtube>xZKSWNv6Esc</youtube> | <youtube>xZKSWNv6Esc</youtube> | ||
<b>Gato: A single Transformer to RuLe them all! ([[Google]]'s Deepmind's new model) | <b>Gato: A single Transformer to RuLe them all! ([[Google]]'s Deepmind's new model) | ||
| − | </b><br>Deepmind's new model Gato is amazing! The first generalist RL agent using transformers! | + | </b><br>Deepmind's new model Gato is amazing! The first generalist RL [[Agents|agent]] using transformers! |
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Revision as of 18:19, 4 February 2023
YouTube search... ...Google search
- Google's Tools and Resources
- A Generalist Agent | S. Reed, K. Żołna, E. Parisotto, S. Gómez Colmenarejo, A. Novikov, G. Barth-Maron, M. Giménez, Y. Sulsky, J. Kay, J. Springenberg, T. Eccles, J. Bruce, A. Razavi, A. Edwards, N. Heess, Y. Chen, R. Hadsell, O. Vinyals, M. Bordbar and N. de Freitas - DeepMind
- Deepmind's new model Gato is amazing! | Louis Bouchard
DeepMind's “generalist” AI model inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens.
Gato has 16 Attention Heads...
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