Gato - Revision history

BPeat at 12:36, 16 September 2026

2026-09-16T12:36:31Z

BPeat at 01:49, 18 May 2023

2023-05-18T01:49:06Z

BPeat at 17:46, 3 May 2023

2023-05-03T17:46:08Z

BPeat at 20:45, 16 April 2023

2023-04-16T20:45:23Z

BPeat: Text replacement - "http:" to "https:"

2023-03-28T19:31:11Z

Text replacement - "http:" to "https:"

BPeat at 15:29, 19 March 2023

2023-03-19T15:29:20Z

BPeat at 22:19, 4 February 2023

2023-02-04T22:19:41Z

BPeat at 11:40, 24 May 2022

2022-05-24T11:40:48Z

BPeat at 11:39, 24 May 2022

2022-05-24T11:39:56Z

BPeat at 11:38, 24 May 2022

2022-05-24T11:38:39Z

@@ Line 11: / Line 11: @@
 * [[Google]]
 * [[Attention]] Mechanism  ...[[Transformer]] ...[[Generative Pre-trained Transformer (GPT)]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
-* [https://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
+* [https://storage.googleapis.com/deepmind-media/A%20Generalist%20Agent/Generalist%20Agent.pdf A Generalist] [[Agents/Assistants|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
 * [https://www.louisbouchard.ai/deepmind-gato/ Deepmind's new model Gato is amazing! | Louis Bouchard]
 * [[Policy]]   ... [[Policy vs Plan]] ... [[Constitutional AI]] ... [[Trust Region Policy Optimization (TRPO)]] ... [[Policy Gradient (PG)]] ... [[Proximal Policy Optimization (PPO)]]
-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.
+DeepMind's “generalist” AI model inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist [[Agents/Assistants|Agent]] beyond the realm of text outputs. The [[Agents/Assistants|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...
@@ Line 27: / Line 27: @@
 <youtube>zO49vZ31xb0</youtube>
 <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 [[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.
+</b><br>DeepMind published a revolutionary paper 🔥 They introduced Gato, a generalist AI [[Agents/Assistants|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:
-📌 Generalist [[Agents|agent]] can perform reasonably well on many tasks / embodiments / modalities
+📌 Generalist [[Agents/Assistants|Agent]] can perform reasonably well on many tasks / embodiments / modalities
-📌 Generalist [[Agents|agents]] have the potential to learn new tasks with few data points
+📌 Generalist [[Agents/Assistants|Agents]] have the potential to learn new tasks with few data points
-📌 By scaling up the parameter size, we can build a general-purpose [[Agents|agent]]
+📌 By scaling up the parameter size, we can build a general-purpose [[Agents/Assistants|Agent]]
 |}
 |<!-- M -->
@@ Line 41: / Line 41: @@
 <youtube>wSQJZHfAg18</youtube>
 <b>Is Gato Really the Future of AI?
-</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.
+</b><br>DeepMind has released "A Generalist [[Agents/Assistants|Agent]]", a paper that introduces their new multi-modal model Gato. But is Gato truly a generalist [[Agents/Assistants|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.
 |}
 |}<!-- B -->
@@ Line 59: / Line 59: @@
 <youtube>xZKSWNv6Esc</youtube>
 <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 [[Agents|agent]] using transformers!
+</b><br>Deepmind's new model Gato is amazing! The first generalist RL [[Agents/Assistants|Agents]] using transformers!
 |}
 |}<!-- B -->

@@ Line 15: / Line 15: @@
 * [[Policy]]   ... [[Policy vs Plan]] ... [[Constitutional AI]] ... [[Trust Region Policy Optimization (TRPO)]] ... [[Policy Gradient (PG)]] ... [[Proximal Policy Optimization (PPO)]]
-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.
+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...

@@ Line 10: / Line 10: @@
 * [https://ai.google/tools/ Google's Tools and Resources]
 * [[Google]]
-* [[Attention]] Mechanism  ...[[Transformer]] Model   ...[[Generative Pre-trained Transformer (GPT)]]
+* [[Attention]] Mechanism  ...[[Transformer]] ...[[Generative Pre-trained Transformer (GPT)]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
 * [https://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
 * [https://www.louisbouchard.ai/deepmind-gato/ Deepmind's new model Gato is amazing! | Louis Bouchard]

@@ Line 13: / Line 13: @@
 * [https://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
 * [https://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 [[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.
+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...

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=Google+Gato YouTube search...]
+[https://www.youtube.com/results?search_query=Google+Gato YouTube search...]
-[http://www.google.com/search?q=Google+Gato ...Google search]
+[https://www.google.com/search?q=Google+Gato ...Google search]
-* [http://ai.google/tools/ Google's Tools and Resources]
+* [https://ai.google/tools/ Google's Tools and Resources]
 * [[Google]]
 * [[Attention]] Mechanism  ...[[Transformer]] Model   ...[[Generative Pre-trained Transformer (GPT)]]
-* [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
+* [https://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]
+* [https://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 [[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.
@@ Line 18: / Line 18: @@
 Gato has 16 [[Attention]] Heads...
-<img src="http://i.gzn.jp/img/2022/05/18/deepmind-gato/gato_m.png" width="800">
+<img src="https://i.gzn.jp/img/2022/05/18/deepmind-gato/gato_m.png" width="800">
 {|<!-- T -->
@@ Line 50: / Line 50: @@
 <youtube>6fWEHrXN9zo</youtube>
 <b>Integrated AI - Gato by DeepMind (May/2022) 1.2B + Asimo, GPT-3, Tesla Optimus, Boston Dynamics
-</b><br>Dr Alan D. Thompson is a world expert in artificial intelligence (AI), specializing in the augmentation of human intelligence, and advancing the evolution of ‘integrated AI’. Alan’s applied AI research and visualizations are featured across major international media, including citations in the University of Oxford’s debate on AI Ethics in December 2021. http://lifearchitect.ai/
+</b><br>Dr Alan D. Thompson is a world expert in artificial intelligence (AI), specializing in the augmentation of human intelligence, and advancing the evolution of ‘integrated AI’. Alan’s applied AI research and visualizations are featured across major international media, including citations in the University of Oxford’s debate on AI Ethics in December 2021. https://lifearchitect.ai/
 |}
 |<!-- M -->

← Older revision		Revision as of 11:40, 24 May 2022
Line 16:		Line 16:

	Gato has 16 [[Attention]] Heads...		Gato has 16 [[Attention]] Heads...
		+
	<img src="http://i.gzn.jp/img/2022/05/18/deepmind-gato/gato_m.png" width="800">		<img src="http://i.gzn.jp/img/2022/05/18/deepmind-gato/gato_m.png" width="800">

← Older revision		Revision as of 11:38, 24 May 2022
Line 14:		Line 14:

	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 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...
		+	http://i.gzn.jp/img/2022/05/18/deepmind-gato/gato_m.png
		+

	{\|<!-- T -->		{\|<!-- T -->