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#richardsutton — Public Fediverse posts

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  1. Richard S. Sutton, einer der Mitbegründer des Reinforcement Learning und seit Jahrzehnten eine zentrale Figur der KI-Forschung, stellt in seinem YouTube-Vortrag „The Future of AI“ eine unbequeme These auf: So beeindruckend heutige KI-Systeme auch wirken – wissenschaftlich stehen wir seiner Ansicht nach noch am Anfang. #KünstlicheIntelligenz #LernenausErfahrung #ReinforcementLearning #RichardSutton #Sprachmodelle

    wahnsinnwissen.de/?p=1124

  2. Web World Models lösen das Stabilitätsproblem autonomer KI-Agenten. Das Live-Web ist für Training zu unvorhersehbar und riskant. Forscher setzen auf deterministische Simulationen. HTML, CSS und JS werden lokal gerendert, was schnelle Iterationen und Reproduzierbarkeit ermöglicht. Dieser Ansatz folgt Richard Suttons Fokus auf Planungsfähigkeit statt reiner Datenskalierung. Reicht eine Simulation für das echte Chaos? #WebWorldModels #KIAgenten #RichardSutton
    all-ai.de/news/news26/matrix-w

  3. Web World Models lösen das Stabilitätsproblem autonomer KI-Agenten. Das Live-Web ist für Training zu unvorhersehbar und riskant. Forscher setzen auf deterministische Simulationen. HTML, CSS und JS werden lokal gerendert, was schnelle Iterationen und Reproduzierbarkeit ermöglicht. Dieser Ansatz folgt Richard Suttons Fokus auf Planungsfähigkeit statt reiner Datenskalierung. Reicht eine Simulation für das echte Chaos? #WebWorldModels #KIAgenten #RichardSutton
    all-ai.de/news/news26/matrix-w

  4. So many gems in this interview, just little spoiler:

    #DwarkeshPatel Next token prediction!

    #RichardSutton That’s not a goal. It doesn’t change the world…

    youtu.be/21EYKqUsPfg

    ps: My goal now is aging with such clarity thinking, and relaxed dialectical teaching!!!

    #TheBitterLesson #RL #ML #LLMs #ImitationLearning #GoalDrivenExperience

  5. So many gems in this interview, just little spoiler:

    #DwarkeshPatel Next token prediction!

    #RichardSutton That’s not a goal. It doesn’t change the world…

    youtu.be/21EYKqUsPfg

    ps: My goal now is aging with such clarity thinking, and relaxed dialectical teaching!!!

    #TheBitterLesson #RL #ML #LLMs #ImitationLearning #GoalDrivenExperience

  6. So many gems in this interview, just little spoiler:

    #DwarkeshPatel Next token prediction!

    #RichardSutton That’s not a goal. It doesn’t change the world…

    youtu.be/21EYKqUsPfg

    ps: My goal now is aging with such clarity thinking, and relaxed dialectical teaching!!!

    #TheBitterLesson #RL #ML #LLMs #ImitationLearning #GoalDrivenExperience

  7. So many gems in this interview, just little spoiler:

    #DwarkeshPatel Next token prediction!

    #RichardSutton That’s not a goal. It doesn’t change the world…

    youtu.be/21EYKqUsPfg

    ps: My goal now is aging with such clarity thinking, and relaxed dialectical teaching!!!

    #TheBitterLesson #RL #ML #LLMs #ImitationLearning #GoalDrivenExperience

  8. So many gems in this interview, just little spoiler:

    #DwarkeshPatel Next token prediction!

    #RichardSutton That’s not a goal. It doesn’t change the world…

    youtu.be/21EYKqUsPfg

    ps: My goal now is aging with such clarity thinking, and relaxed dialectical teaching!!!

    #TheBitterLesson #RL #ML #LLMs #ImitationLearning #GoalDrivenExperience

  9. Richard Sutton, AI lider, nh'And LLMs là đường mát. DWarkesh thiếu kiến thức sâu rộng và nhiều tuyên bố(mat). Cần ngôn ngữ chính xác đểologues sản lý. #AI #MachineLearning #RichardSutton #LLMs #YouTubeCommentary #TinAI #TinhHoc

    reddit.com/r/singularity/comme

  10. @ekmiller

    » The point of the bitter lesson is that the right learning algorithms (those that scale efficiently with massive computation) are exactly what we need.
    Massive computation does not alleviate the need for data efficiency «

    24/11/2023 #RichardSutton

    Nowadays neuroscience forever expansing body of literature, spreading across different subfields, disperse schools, training practices, and multiple sets of technologies. Instead attempts for building a comprehensive knowledge consensus.

  11. @ekmiller

    » The point of the bitter lesson is that the right learning algorithms (those that scale efficiently with massive computation) are exactly what we need.
    Massive computation does not alleviate the need for data efficiency «

    24/11/2023 #RichardSutton

    Nowadays neuroscience forever expansing body of literature, spreading across different subfields, disperse schools, training practices, and multiple sets of technologies. Instead attempts for building a comprehensive knowledge consensus.

  12. @ekmiller

    » The point of the bitter lesson is that the right learning algorithms (those that scale efficiently with massive computation) are exactly what we need.
    Massive computation does not alleviate the need for data efficiency «

    24/11/2023 #RichardSutton

    Nowadays neuroscience forever expansing body of literature, spreading across different subfields, disperse schools, training practices, and multiple sets of technologies. Instead attempts for building a comprehensive knowledge consensus.

  13. @ekmiller

    » The point of the bitter lesson is that the right learning algorithms (those that scale efficiently with massive computation) are exactly what we need.
    Massive computation does not alleviate the need for data efficiency «

    24/11/2023 #RichardSutton

    Nowadays neuroscience forever expansing body of literature, spreading across different subfields, disperse schools, training practices, and multiple sets of technologies. Instead attempts for building a comprehensive knowledge consensus.

  14. @ekmiller

    » The point of the bitter lesson is that the right learning algorithms (those that scale efficiently with massive computation) are exactly what we need.
    Massive computation does not alleviate the need for data efficiency «

    24/11/2023 #RichardSutton

    Nowadays neuroscience forever expansing body of literature, spreading across different subfields, disperse schools, training practices, and multiple sets of technologies. Instead attempts for building a comprehensive knowledge consensus.

  15. #ACMPrize
    #2024ACMPrize
    #ACMTuringAward

    #AndrewBarto
    #RichardSutton

    » #ReinforcementLearning
    An Introduction
    1998
    standard reference...cited over 75,000
    ...
    prominent example of #RL
    #AlphaGo victory
    over best human #Go players
    2016 2017
    ....
    recently has been the development of the chatbot #ChatGPT
    ...
    large language model #LLM trained in two phases ...employs a technique called
    reinforcement learning from human feedback #RLHF «

    aka cheap labor unnamed in papers

    awards.acm.org/about/2024-turi

    2/2

  16. #ACMPrize
    #2024ACMPrize
    #ACMTuringAward

    #AndrewBarto
    #RichardSutton

    » #ReinforcementLearning
    An Introduction
    1998
    standard reference...cited over 75,000
    ...
    prominent example of #RL
    #AlphaGo victory
    over best human #Go players
    2016 2017
    ....
    recently has been the development of the chatbot #ChatGPT
    ...
    large language model #LLM trained in two phases ...employs a technique called
    reinforcement learning from human feedback #RLHF «

    aka cheap labor unnamed in papers

    awards.acm.org/about/2024-turi

    2/2

  17. #ACMPrize
    #2024ACMPrize
    #ACMTuringAward

    #AndrewBarto
    #RichardSutton

    » #ReinforcementLearning
    An Introduction
    1998
    standard reference...cited over 75,000
    ...
    prominent example of #RL
    #AlphaGo victory
    over best human #Go players
    2016 2017
    ....
    recently has been the development of the chatbot #ChatGPT
    ...
    large language model #LLM trained in two phases ...employs a technique called
    reinforcement learning from human feedback #RLHF «

    aka cheap labor unnamed in papers

    awards.acm.org/about/2024-turi

    2/2

  18. #ACMPrize
    #2024ACMPrize
    #ACMTuringAward

    #AndrewBarto
    #RichardSutton

    » #ReinforcementLearning
    An Introduction
    1998
    standard reference...cited over 75,000
    ...
    prominent example of #RL
    #AlphaGo victory
    over best human #Go players
    2016 2017
    ....
    recently has been the development of the chatbot #ChatGPT
    ...
    large language model #LLM trained in two phases ...employs a technique called
    reinforcement learning from human feedback #RLHF «

    aka cheap labor unnamed in papers

    awards.acm.org/about/2024-turi

    2/2

  19. #ACMPrize
    #2024ACMPrize
    #ACMTuringAward

    #AndrewBarto
    #RichardSutton

    » #ReinforcementLearning
    An Introduction
    1998
    standard reference...cited over 75,000
    ...
    prominent example of #RL
    #AlphaGo victory
    over best human #Go players
    2016 2017
    ....
    recently has been the development of the chatbot #ChatGPT
    ...
    large language model #LLM trained in two phases ...employs a technique called
    reinforcement learning from human feedback #RLHF «

    aka cheap labor unnamed in papers

    awards.acm.org/about/2024-turi

    2/2

  20. @emilygorcenski @kordinglab

    » Not at all.

    The point of the bitter lesson is that the right learning algorithms

    (those that scale efficiently with massive computation)

    are exactly what we need.

    Massive computation does not alleviate the need for data efficiency «

    #RichardSutton 24/11/2023

    nitter.cz/RichardSSutton/statu

    #TheBitterLessonInML

    incompleteideas.net/IncIdeas/B

  21. @emilygorcenski @kordinglab

    » Not at all.

    The point of the bitter lesson is that the right learning algorithms

    (those that scale efficiently with massive computation)

    are exactly what we need.

    Massive computation does not alleviate the need for data efficiency «

    #RichardSutton 24/11/2023

    nitter.cz/RichardSSutton/statu

    #TheBitterLessonInML

    incompleteideas.net/IncIdeas/B

  22. @emilygorcenski @kordinglab

    » Not at all.

    The point of the bitter lesson is that the right learning algorithms

    (those that scale efficiently with massive computation)

    are exactly what we need.

    Massive computation does not alleviate the need for data efficiency «

    #RichardSutton 24/11/2023

    nitter.cz/RichardSSutton/statu

    #TheBitterLessonInML

    incompleteideas.net/IncIdeas/B

  23. @emilygorcenski @kordinglab

    » Not at all.

    The point of the bitter lesson is that the right learning algorithms

    (those that scale efficiently with massive computation)

    are exactly what we need.

    Massive computation does not alleviate the need for data efficiency «

    #RichardSutton 24/11/2023

    nitter.cz/RichardSSutton/statu

    #TheBitterLessonInML

    incompleteideas.net/IncIdeas/B

  24. @emilygorcenski @kordinglab

    » Not at all.

    The point of the bitter lesson is that the right learning algorithms

    (those that scale efficiently with massive computation)

    are exactly what we need.

    Massive computation does not alleviate the need for data efficiency «

    #RichardSutton 24/11/2023

    nitter.cz/RichardSSutton/statu

    #TheBitterLessonInML

    incompleteideas.net/IncIdeas/B