#alphazero — Public Fediverse posts

5/
The Future of the Infinity Machine

DeepMind positions AI as a fundamental force of nature designed to uncover the discoverable patterns of the universe. While this "infinity machine" offers the potential to cure diseases and solve climate change, it also presents a central tension: the challenge of managing an intelligence that may eventually surpass human ability to understand or control it.

https://youtu.be/2nTuBwvp1Is

#DemisHassabis #DeepMind #AI #AGI #AlphaZero #AlphaFold #InfinityMachine

5/
The Future of the Infinity Machine

DeepMind positions AI as a fundamental force of nature designed to uncover the discoverable patterns of the universe. While this "infinity machine" offers the potential to cure diseases and solve climate change, it also presents a central tension: the challenge of managing an intelligence that may eventually surpass human ability to understand or control it.

https://youtu.be/2nTuBwvp1Is

#DemisHassabis #DeepMind #AI #AGI #AlphaZero #AlphaFold #InfinityMachine

5/
The Future of the Infinity Machine

DeepMind positions AI as a fundamental force of nature designed to uncover the discoverable patterns of the universe. While this "infinity machine" offers the potential to cure diseases and solve climate change, it also presents a central tension: the challenge of managing an intelligence that may eventually surpass human ability to understand or control it.

https://youtu.be/2nTuBwvp1Is

#DemisHassabis #DeepMind #AI #AGI #AlphaZero #AlphaFold #InfinityMachine

5/
The Future of the Infinity Machine

DeepMind positions AI as a fundamental force of nature designed to uncover the discoverable patterns of the universe. While this "infinity machine" offers the potential to cure diseases and solve climate change, it also presents a central tension: the challenge of managing an intelligence that may eventually surpass human ability to understand or control it.

https://youtu.be/2nTuBwvp1Is

#DemisHassabis #DeepMind #AI #AGI #AlphaZero #AlphaFold #InfinityMachine

5/
The Future of the Infinity Machine

DeepMind positions AI as a fundamental force of nature designed to uncover the discoverable patterns of the universe. While this "infinity machine" offers the potential to cure diseases and solve climate change, it also presents a central tension: the challenge of managing an intelligence that may eventually surpass human ability to understand or control it.

https://youtu.be/2nTuBwvp1Is

#DemisHassabis #DeepMind #AI #AGI #AlphaZero #AlphaFold #InfinityMachine

4/
#AlphaZero: A breakthrough agent that learned only from the rules of a game, eventually discovering strategies previously unknown to humans.

AlphaFold: A massive pivot to biology that solved the 50-year-old "protein folding" challenge. AlphaFold2 achieved accuracy levels comparable to years of expensive lab work, allowing scientists to map antibiotic-resistant bugs and design disease-resistant crops in minutes.

https://youtu.be/2nTuBwvp1Is

#DeepMind #AI #AGI #AlphaFold #InfinityMachine

4/
#AlphaZero: A breakthrough agent that learned only from the rules of a game, eventually discovering strategies previously unknown to humans.

AlphaFold: A massive pivot to biology that solved the 50-year-old "protein folding" challenge. AlphaFold2 achieved accuracy levels comparable to years of expensive lab work, allowing scientists to map antibiotic-resistant bugs and design disease-resistant crops in minutes.

https://youtu.be/2nTuBwvp1Is

#DeepMind #AI #AGI #AlphaFold #InfinityMachine

4/
#AlphaZero: A breakthrough agent that learned only from the rules of a game, eventually discovering strategies previously unknown to humans.

AlphaFold: A massive pivot to biology that solved the 50-year-old "protein folding" challenge. AlphaFold2 achieved accuracy levels comparable to years of expensive lab work, allowing scientists to map antibiotic-resistant bugs and design disease-resistant crops in minutes.

https://youtu.be/2nTuBwvp1Is

#DeepMind #AI #AGI #AlphaFold #InfinityMachine

4/
#AlphaZero: A breakthrough agent that learned only from the rules of a game, eventually discovering strategies previously unknown to humans.

AlphaFold: A massive pivot to biology that solved the 50-year-old "protein folding" challenge. AlphaFold2 achieved accuracy levels comparable to years of expensive lab work, allowing scientists to map antibiotic-resistant bugs and design disease-resistant crops in minutes.

https://youtu.be/2nTuBwvp1Is

#DeepMind #AI #AGI #AlphaFold #InfinityMachine

4/
#AlphaZero: A breakthrough agent that learned only from the rules of a game, eventually discovering strategies previously unknown to humans.

AlphaFold: A massive pivot to biology that solved the 50-year-old "protein folding" challenge. AlphaFold2 achieved accuracy levels comparable to years of expensive lab work, allowing scientists to map antibiotic-resistant bugs and design disease-resistant crops in minutes.

https://youtu.be/2nTuBwvp1Is

#DeepMind #AI #AGI #AlphaFold #InfinityMachine

Founding Vision and Philosophy

DeepMind's founder, Demis Hassabis, is a former chess prodigy who transitioned from mastering games to studying the rules of intelligence through neuroscience. His core philosophy is built on two primary beliefs:

Intelligence as a Lens:
To truly understand reality, one must first understand the intelligence through which we perceive it.

https://youtu.be/2nTuBwvp1Is

#DemisHassabis #DeepMind #AI #AGI #AlphaZero #AlphaFold #InfinityMachine #Gemini

Founding Vision and Philosophy

DeepMind's founder, Demis Hassabis, is a former chess prodigy who transitioned from mastering games to studying the rules of intelligence through neuroscience. His core philosophy is built on two primary beliefs:

Intelligence as a Lens:
To truly understand reality, one must first understand the intelligence through which we perceive it.

https://youtu.be/2nTuBwvp1Is

#DemisHassabis #DeepMind #AI #AGI #AlphaZero #AlphaFold #InfinityMachine #Gemini

Founding Vision and Philosophy

DeepMind's founder, Demis Hassabis, is a former chess prodigy who transitioned from mastering games to studying the rules of intelligence through neuroscience. His core philosophy is built on two primary beliefs:

Intelligence as a Lens:
To truly understand reality, one must first understand the intelligence through which we perceive it.

https://youtu.be/2nTuBwvp1Is

#DemisHassabis #DeepMind #AI #AGI #AlphaZero #AlphaFold #InfinityMachine #Gemini

Founding Vision and Philosophy

DeepMind's founder, Demis Hassabis, is a former chess prodigy who transitioned from mastering games to studying the rules of intelligence through neuroscience. His core philosophy is built on two primary beliefs:

Intelligence as a Lens:
To truly understand reality, one must first understand the intelligence through which we perceive it.

https://youtu.be/2nTuBwvp1Is

#DemisHassabis #DeepMind #AI #AGI #AlphaZero #AlphaFold #InfinityMachine #Gemini

Founding Vision and Philosophy

DeepMind's founder, Demis Hassabis, is a former chess prodigy who transitioned from mastering games to studying the rules of intelligence through neuroscience. His core philosophy is built on two primary beliefs:

Intelligence as a Lens:
To truly understand reality, one must first understand the intelligence through which we perceive it.

https://youtu.be/2nTuBwvp1Is

#DemisHassabis #DeepMind #AI #AGI #AlphaZero #AlphaFold #InfinityMachine #Gemini

THE INFINITY MACHINE

Introduction: The Quest for Super Intelligence

The quest to build artificial general intelligence (AGI) is a modern pursuit with high stakes, echoing the awe and apprehension felt by early atomic scientists. At the center of this effort is DeepMind, a company founded on the grand vision of creating an "infinity machine"—a general-purpose tool for pure discovery.

https://youtu.be/2nTuBwvp1Is

#DemisHassabis #DeepMind #AI #AGI #AlphaZero #AlphaFold #InfinityMachine #Gemini

THE INFINITY MACHINE

Introduction: The Quest for Super Intelligence

The quest to build artificial general intelligence (AGI) is a modern pursuit with high stakes, echoing the awe and apprehension felt by early atomic scientists. At the center of this effort is DeepMind, a company founded on the grand vision of creating an "infinity machine"—a general-purpose tool for pure discovery.

https://youtu.be/2nTuBwvp1Is

#DemisHassabis #DeepMind #AI #AGI #AlphaZero #AlphaFold #InfinityMachine #Gemini

THE INFINITY MACHINE

Introduction: The Quest for Super Intelligence

The quest to build artificial general intelligence (AGI) is a modern pursuit with high stakes, echoing the awe and apprehension felt by early atomic scientists. At the center of this effort is DeepMind, a company founded on the grand vision of creating an "infinity machine"—a general-purpose tool for pure discovery.

https://youtu.be/2nTuBwvp1Is

#DemisHassabis #DeepMind #AI #AGI #AlphaZero #AlphaFold #InfinityMachine #Gemini

THE INFINITY MACHINE

Introduction: The Quest for Super Intelligence

The quest to build artificial general intelligence (AGI) is a modern pursuit with high stakes, echoing the awe and apprehension felt by early atomic scientists. At the center of this effort is DeepMind, a company founded on the grand vision of creating an "infinity machine"—a general-purpose tool for pure discovery.

https://youtu.be/2nTuBwvp1Is

#DemisHassabis #DeepMind #AI #AGI #AlphaZero #AlphaFold #InfinityMachine #Gemini

THE INFINITY MACHINE

Introduction: The Quest for Super Intelligence

The quest to build artificial general intelligence (AGI) is a modern pursuit with high stakes, echoing the awe and apprehension felt by early atomic scientists. At the center of this effort is DeepMind, a company founded on the grand vision of creating an "infinity machine"—a general-purpose tool for pure discovery.

https://youtu.be/2nTuBwvp1Is

#DemisHassabis #DeepMind #AI #AGI #AlphaZero #AlphaFold #InfinityMachine #Gemini

13-Mar-2026
#AI’s #gamePlaying still has flaws: #AlphaZero-style self-play tested on #Nim
Despite heavy training, agents show blind spots and can miss optimal moves

https://www.eurekalert.org/news-releases/1119906

#science #technology

13-Mar-2026
#AI’s #gamePlaying still has flaws: #AlphaZero-style self-play tested on #Nim
Despite heavy training, agents show blind spots and can miss optimal moves

https://www.eurekalert.org/news-releases/1119906

#science #technology

13-Mar-2026
#AI’s #gamePlaying still has flaws: #AlphaZero-style self-play tested on #Nim
Despite heavy training, agents show blind spots and can miss optimal moves

https://www.eurekalert.org/news-releases/1119906

#science #technology

13-Mar-2026
#AI’s #gamePlaying still has flaws: #AlphaZero-style self-play tested on #Nim
Despite heavy training, agents show blind spots and can miss optimal moves

https://www.eurekalert.org/news-releases/1119906

#science #technology

13-Mar-2026
#AI’s #gamePlaying still has flaws: #AlphaZero-style self-play tested on #Nim
Despite heavy training, agents show blind spots and can miss optimal moves

https://www.eurekalert.org/news-releases/1119906

#science #technology

Абсолютный ноль: как ИИ учится без данных

​Absolute Zero Reasoner отличается от традиционных подходов к обучению ИИ, позволяя ИИ обучаться с нуля, без необходимости использования заранее предоставленных человеком данных.

Absolute Zero Reasoner (AZR) представляет собой революционную концепцию в области искусственного...

#DST #DSTGlobal #ДСТ #ДСТГлобал #Абсолютныйноль #искусственныйинтеллект #AbsoluteZero #ИИ #AZR #AlphaZero #DeepMind #парадигмы #Модель

Источник: https://dstglobal.ru/club/1109-absolyutnyi-nol-kak-ii-uchitsja-bez-dannyh

Абсолютный ноль: как ИИ учится без данных

​Absolute Zero Reasoner отличается от традиционных подходов к обучению ИИ, позволяя ИИ обучаться с нуля, без необходимости использования заранее предоставленных человеком данных.

Absolute Zero Reasoner (AZR) представляет собой революционную концепцию в области искусственного...

#DST #DSTGlobal #ДСТ #ДСТГлобал #Абсолютныйноль #искусственныйинтеллект #AbsoluteZero #ИИ #AZR #AlphaZero #DeepMind #парадигмы #Модель

Источник: https://dstglobal.ru/club/1109-absolyutnyi-nol-kak-ii-uchitsja-bez-dannyh

Абсолютный ноль: как ИИ учится без данных

​Absolute Zero Reasoner отличается от традиционных подходов к обучению ИИ, позволяя ИИ обучаться с нуля, без необходимости использования заранее предоставленных человеком данных.

Absolute Zero Reasoner (AZR) представляет собой революционную концепцию в области искусственного...

#DST #DSTGlobal #ДСТ #ДСТГлобал #Абсолютныйноль #искусственныйинтеллект #AbsoluteZero #ИИ #AZR #AlphaZero #DeepMind #парадигмы #Модель

Источник: https://dstglobal.ru/club/1109-absolyutnyi-nol-kak-ii-uchitsja-bez-dannyh

Абсолютный ноль: как ИИ учится без данных

​Absolute Zero Reasoner отличается от традиционных подходов к обучению ИИ, позволяя ИИ обучаться с нуля, без необходимости использования заранее предоставленных человеком данных.

Absolute Zero Reasoner (AZR) представляет собой революционную концепцию в области искусственного...

#DST #DSTGlobal #ДСТ #ДСТГлобал #Абсолютныйноль #искусственныйинтеллект #AbsoluteZero #ИИ #AZR #AlphaZero #DeepMind #парадигмы #Модель

Источник: https://dstglobal.ru/club/1109-absolyutnyi-nol-kak-ii-uchitsja-bez-dannyh

Абсолютный ноль: как ИИ учится без данных

​Absolute Zero Reasoner отличается от традиционных подходов к обучению ИИ, позволяя ИИ обучаться с нуля, без необходимости использования заранее предоставленных человеком данных.

Absolute Zero Reasoner (AZR) представляет собой революционную концепцию в области искусственного...

#DST #DSTGlobal #ДСТ #ДСТГлобал #Абсолютныйноль #искусственныйинтеллект #AbsoluteZero #ИИ #AZR #AlphaZero #DeepMind #парадигмы #Модель

Источник: https://dstglobal.ru/club/1109-absolyutnyi-nol-kak-ii-uchitsja-bez-dannyh

Oops, I think I've gone a bit too deep into the #AI rabbit hole today 😳 (a thread 🧵):

Did you know why AI systems like #AlphaGo or #AlphaZero performed so well?
It was because of their _objective function_:
-1 for loosing, +1 for winning ¯\_(ツ)_/¯

Why Artificial Intelligence Like AlphaZero Has Trouble With the Real World (February 2018)

https://www.quantamagazine.org/why-artificial-intelligence-like-alphazero-has-trouble-with-the-real-world-20180221/

Try to design an objective function for a self-driving car...

1/3

#ArtificialIntelligence #RabbitHole

Oops, I think I've gone a bit too deep into the #AI rabbit hole today 😳 (a thread 🧵):

Did you know why AI systems like #AlphaGo or #AlphaZero performed so well?
It was because of their _objective function_:
-1 for loosing, +1 for winning ¯\_(ツ)_/¯

Why Artificial Intelligence Like AlphaZero Has Trouble With the Real World (February 2018)

https://www.quantamagazine.org/why-artificial-intelligence-like-alphazero-has-trouble-with-the-real-world-20180221/

Try to design an objective function for a self-driving car...

1/3

#ArtificialIntelligence #RabbitHole

Oops, I think I've gone a bit too deep into the #AI rabbit hole today 😳 (a thread 🧵):

Did you know why AI systems like #AlphaGo or #AlphaZero performed so well?
It was because of their _objective function_:
-1 for loosing, +1 for winning ¯\_(ツ)_/¯

Why Artificial Intelligence Like AlphaZero Has Trouble With the Real World (February 2018)

https://www.quantamagazine.org/why-artificial-intelligence-like-alphazero-has-trouble-with-the-real-world-20180221/

Try to design an objective function for a self-driving car...

1/3

#ArtificialIntelligence #RabbitHole

Oops, I think I've gone a bit too deep into the #AI rabbit hole today 😳 (a thread 🧵):

Did you know why AI systems like #AlphaGo or #AlphaZero performed so well?
It was because of their _objective function_:
-1 for loosing, +1 for winning ¯\_(ツ)_/¯

Why Artificial Intelligence Like AlphaZero Has Trouble With the Real World (February 2018)

https://www.quantamagazine.org/why-artificial-intelligence-like-alphazero-has-trouble-with-the-real-world-20180221/

Try to design an objective function for a self-driving car...

1/3

#ArtificialIntelligence #RabbitHole

Oops, I think I've gone a bit too deep into the #AI rabbit hole today 😳 (a thread 🧵):

Did you know why AI systems like #AlphaGo or #AlphaZero performed so well?
It was because of their _objective function_:
-1 for loosing, +1 for winning ¯\_(ツ)_/¯

Why Artificial Intelligence Like AlphaZero Has Trouble With the Real World (February 2018)

https://www.quantamagazine.org/why-artificial-intelligence-like-alphazero-has-trouble-with-the-real-world-20180221/

Try to design an objective function for a self-driving car...

1/3

#ArtificialIntelligence #RabbitHole

A Implementation of Alpha Zero for Chess in MLX

https://github.com/koogle/mlx-playground/tree/main/chesszero

#HackerNews #AlphaZero #Chess #MLX #MachineLearning #GitHub

A Implementation of Alpha Zero for Chess in MLX

https://github.com/koogle/mlx-playground/tree/main/chesszero

#HackerNews #AlphaZero #Chess #MLX #MachineLearning #GitHub

A Implementation of Alpha Zero for Chess in MLX

https://github.com/koogle/mlx-playground/tree/main/chesszero

#HackerNews #AlphaZero #Chess #MLX #MachineLearning #GitHub

A Implementation of Alpha Zero for Chess in MLX

https://github.com/koogle/mlx-playground/tree/main/chesszero

#HackerNews #AlphaZero #Chess #MLX #MachineLearning #GitHub

A Implementation of Alpha Zero for Chess in MLX

https://github.com/koogle/mlx-playground/tree/main/chesszero

#HackerNews #AlphaZero #Chess #MLX #MachineLearning #GitHub

Pequeños y grandes pasos hacia el imperio de la inteligencia artificial

Traducción de la infografía:

• 1943 – McCullock y Pitts publican un artículo titulado Un cálculo lógico de ideas inmanentes en la actividad nerviosa, en el que proponen las bases para las redes neuronales.

• 1950 – Turing publica Computing Machinery and Intelligence, proponiendo el Test de Turing como forma de medir la capacidad de una máquina.

• 1951 – Marvin Minsky y Dean Edmonds construyen SNAR, la primera computadora de red neuronal.

• 1956 – Se celebra la Conferencia de Dartmouth (organizada por McCarthy, Minsky, Rochester y Shannon), que marca el nacimiento de la IA como campo de estudio.

• 1957 – Rosenblatt desarrolla el Perceptrón: la primera red neuronal artificial capaz de aprender.

(!!) Test de Turing: donde un evaluador humano entabla una conversación en lenguaje natural con una máquina y un humano.

• 1965 – Weizenbaum desarrolla ELIZA: un programa de procesamiento del lenguaje natural que simula una conversación.

• 1967 – Newell y Simon desarrollan el Solucionador General de Problemas (GPS), uno de los primeros programas de IA que demuestra una capacidad de resolución de problemas similar a la humana.

• 1974 – Comienza el primer invierno de la IA, marcado por una disminución de la financiación y del interés en la investigación en IA debido a expectativas poco realistas y a un progreso limitado.

• 1980 – Los sistemas expertos ganan popularidad y las empresas los utilizan para realizar previsiones financieras y diagnósticos médicos.

• 1986 – Hinton, Rumelhart y Williams publican Aprendizaje de representaciones mediante retropropagación de errores, que permite entrenar redes neuronales mucho más profundas.

(!!) Redes neuronales: modelos de aprendizaje automático que imitan el cerebro y aprenden a reconocer patrones y hacer predicciones a través de conexiones neuronales artificiales.

• 1997 – Deep Blue de IBM derrota al campeón mundial de ajedrez Kasparov, siendo la primera vez que una computadora vence a un campeón mundial en un juego complejo.

• 2002 – iRobot presenta Roomba, el primer robot aspirador doméstico producido en serie con un sistema de navegación impulsado por IA.

• 2011 – Watson de IBM derrota a dos ex campeones de Jeopardy!.

• 2012 – La startup de inteligencia artificial DeepMind desarrolla una red neuronal profunda que puede reconocer gatos en vídeos de YouTube.

• 2014 – Facebook crea DeepFace, un sistema de reconocimiento facial que puede reconocer rostros con una precisión casi humana.

(!!) DeepMind fue adquirida por Google en 2014 por 500 millones de dólares.

• 2015 – AlphaGo, desarrollado por DeepMind, derrota al campeón mundial Lee Sedol en el juego de Go.

• 2017 – AlphaZero de Google derrota a los mejores motores de ajedrez y shogi del mundo en una serie de partidas.

• 2020 – OpenAI lanza GPT-3, lo que marca un avance significativo en el procesamiento del lenguaje natural.

(!!) Procesamiento del lenguaje natural: enseña a las computadoras a comprender y utilizar el lenguaje humano mediante técnicas como el aprendizaje automático.

• 2021 – AlphaFold2 de DeepMind resuelve el problema del plegamiento de proteínas, allanando el camino para nuevos descubrimientos de fármacos y avances médicos.

• 2022 – Google despide al ingeniero Blake Lemoine por sus afirmaciones de que el modelo de lenguaje para aplicaciones de diálogo (LaMDA) de Google era sensible.

• 2023 – Artistas presentaron una demanda colectiva contra Stability AI, DeviantArt y Mid-journey por usar Stable Diffusion para remezclar las obras protegidas por derechos de autor de millones de artistas.

Gráfico: Open Tech / Genuine Impact

Entradas relacionadas

• ¿Cómo definir la «credibilidad algorítmica»? DeepSeek da en el clavo
• Los precios dinámicos exacerban la desigualdad entre los consumidores, hay que regularlos ya
• Firma contra la vigilancia biométrica masiva

#ajedrez #AlphaFold2 #AlphaGo #AlphaZero #aprendizajeAutomático #artículo #artistas #aspirador #BlakeLemoine #ConferenciaDeDartmouth #copyright #DeanEdmonds #DeepBlue #DeepFace #DeepMind #DeviantArt #ELIZA #Facebook #gatos #GenuineImpact #Go #Google #GPS #GPT3 #gráfico #Hinton #IA #IBM #infografía #inteligenciaArtificial #iRobot #Jeopardy_ #Kasparov #LaMDA #LeeSedol #MarvinMinsky #McCarthy #McCullock #MidJourney #modelos #Newell #OpenTech #OpenAI #patrones #Perceptron #Pitts #plegamientoDeProteínas #predicciones #procesamientoDelLenguajeNatural #reconocimientoFacial #redesNeuronales #remezclar #robot #Rochester #Roomba #Rosenblatt #Rumelhart #Shannon #shogi #Simon #sistemaDeNavegación #SNAR #StabilityAI #StableDiffusion #testDeTuring #Turing #vídeos #Watson #Weizenbaum #Williams #YouTube

Pequeños y grandes pasos hacia el imperio de la inteligencia artificial

Traducción de la infografía:

• 1943 – McCullock y Pitts publican un artículo titulado Un cálculo lógico de ideas inmanentes en la actividad nerviosa, en el que proponen las bases para las redes neuronales.

• 1950 – Turing publica Computing Machinery and Intelligence, proponiendo el Test de Turing como forma de medir la capacidad de una máquina.

• 1951 – Marvin Minsky y Dean Edmonds construyen SNAR, la primera computadora de red neuronal.

• 1956 – Se celebra la Conferencia de Dartmouth (organizada por McCarthy, Minsky, Rochester y Shannon), que marca el nacimiento de la IA como campo de estudio.

• 1957 – Rosenblatt desarrolla el Perceptrón: la primera red neuronal artificial capaz de aprender.

(!!) Test de Turing: donde un evaluador humano entabla una conversación en lenguaje natural con una máquina y un humano.

• 1965 – Weizenbaum desarrolla ELIZA: un programa de procesamiento del lenguaje natural que simula una conversación.

• 1967 – Newell y Simon desarrollan el Solucionador General de Problemas (GPS), uno de los primeros programas de IA que demuestra una capacidad de resolución de problemas similar a la humana.

• 1974 – Comienza el primer invierno de la IA, marcado por una disminución de la financiación y del interés en la investigación en IA debido a expectativas poco realistas y a un progreso limitado.

• 1980 – Los sistemas expertos ganan popularidad y las empresas los utilizan para realizar previsiones financieras y diagnósticos médicos.

• 1986 – Hinton, Rumelhart y Williams publican Aprendizaje de representaciones mediante retropropagación de errores, que permite entrenar redes neuronales mucho más profundas.

(!!) Redes neuronales: modelos de aprendizaje automático que imitan el cerebro y aprenden a reconocer patrones y hacer predicciones a través de conexiones neuronales artificiales.

• 1997 – Deep Blue de IBM derrota al campeón mundial de ajedrez Kasparov, siendo la primera vez que una computadora vence a un campeón mundial en un juego complejo.

• 2002 – iRobot presenta Roomba, el primer robot aspirador doméstico producido en serie con un sistema de navegación impulsado por IA.

• 2011 – Watson de IBM derrota a dos ex campeones de Jeopardy!.

• 2012 – La startup de inteligencia artificial DeepMind desarrolla una red neuronal profunda que puede reconocer gatos en vídeos de YouTube.

• 2014 – Facebook crea DeepFace, un sistema de reconocimiento facial que puede reconocer rostros con una precisión casi humana.

(!!) DeepMind fue adquirida por Google en 2014 por 500 millones de dólares.

• 2015 – AlphaGo, desarrollado por DeepMind, derrota al campeón mundial Lee Sedol en el juego de Go.

• 2017 – AlphaZero de Google derrota a los mejores motores de ajedrez y shogi del mundo en una serie de partidas.

• 2020 – OpenAI lanza GPT-3, lo que marca un avance significativo en el procesamiento del lenguaje natural.

(!!) Procesamiento del lenguaje natural: enseña a las computadoras a comprender y utilizar el lenguaje humano mediante técnicas como el aprendizaje automático.

• 2021 – AlphaFold2 de DeepMind resuelve el problema del plegamiento de proteínas, allanando el camino para nuevos descubrimientos de fármacos y avances médicos.

• 2022 – Google despide al ingeniero Blake Lemoine por sus afirmaciones de que el modelo de lenguaje para aplicaciones de diálogo (LaMDA) de Google era sensible.

• 2023 – Artistas presentaron una demanda colectiva contra Stability AI, DeviantArt y Mid-journey por usar Stable Diffusion para remezclar las obras protegidas por derechos de autor de millones de artistas.

Gráfico: Open Tech / Genuine Impact

Entradas relacionadas

• ¿Cómo definir la «credibilidad algorítmica»? DeepSeek da en el clavo
• Los precios dinámicos exacerban la desigualdad entre los consumidores, hay que regularlos ya
• Firma contra la vigilancia biométrica masiva

#ajedrez #AlphaFold2 #AlphaGo #AlphaZero #aprendizajeAutomático #artículo #artistas #aspirador #BlakeLemoine #ConferenciaDeDartmouth #copyright #DeanEdmonds #DeepBlue #DeepFace #DeepMind #DeviantArt #ELIZA #Facebook #gatos #GenuineImpact #Go #Google #GPS #GPT3 #gráfico #Hinton #IA #IBM #infografía #inteligenciaArtificial #iRobot #Jeopardy_ #Kasparov #LaMDA #LeeSedol #MarvinMinsky #McCarthy #McCullock #MidJourney #modelos #Newell #OpenTech #OpenAI #patrones #Perceptron #Pitts #plegamientoDeProteínas #predicciones #procesamientoDelLenguajeNatural #reconocimientoFacial #redesNeuronales #remezclar #robot #Rochester #Roomba #Rosenblatt #Rumelhart #Shannon #shogi #Simon #sistemaDeNavegación #SNAR #StabilityAI #StableDiffusion #testDeTuring #Turing #vídeos #Watson #Weizenbaum #Williams #YouTube

Pequeños y grandes pasos hacia el imperio de la inteligencia artificial

Traducción de la infografía:

• 1943 – McCullock y Pitts publican un artículo titulado Un cálculo lógico de ideas inmanentes en la actividad nerviosa, en el que proponen las bases para las redes neuronales.

• 1950 – Turing publica Computing Machinery and Intelligence, proponiendo el Test de Turing como forma de medir la capacidad de una máquina.

• 1951 – Marvin Minsky y Dean Edmonds construyen SNAR, la primera computadora de red neuronal.

• 1956 – Se celebra la Conferencia de Dartmouth (organizada por McCarthy, Minsky, Rochester y Shannon), que marca el nacimiento de la IA como campo de estudio.

• 1957 – Rosenblatt desarrolla el Perceptrón: la primera red neuronal artificial capaz de aprender.

(!!) Test de Turing: donde un evaluador humano entabla una conversación en lenguaje natural con una máquina y un humano.

• 1965 – Weizenbaum desarrolla ELIZA: un programa de procesamiento del lenguaje natural que simula una conversación.

• 1967 – Newell y Simon desarrollan el Solucionador General de Problemas (GPS), uno de los primeros programas de IA que demuestra una capacidad de resolución de problemas similar a la humana.

• 1974 – Comienza el primer invierno de la IA, marcado por una disminución de la financiación y del interés en la investigación en IA debido a expectativas poco realistas y a un progreso limitado.

• 1980 – Los sistemas expertos ganan popularidad y las empresas los utilizan para realizar previsiones financieras y diagnósticos médicos.

• 1986 – Hinton, Rumelhart y Williams publican Aprendizaje de representaciones mediante retropropagación de errores, que permite entrenar redes neuronales mucho más profundas.

(!!) Redes neuronales: modelos de aprendizaje automático que imitan el cerebro y aprenden a reconocer patrones y hacer predicciones a través de conexiones neuronales artificiales.

• 1997 – Deep Blue de IBM derrota al campeón mundial de ajedrez Kasparov, siendo la primera vez que una computadora vence a un campeón mundial en un juego complejo.

• 2002 – iRobot presenta Roomba, el primer robot aspirador doméstico producido en serie con un sistema de navegación impulsado por IA.

• 2011 – Watson de IBM derrota a dos ex campeones de Jeopardy!.

• 2012 – La startup de inteligencia artificial DeepMind desarrolla una red neuronal profunda que puede reconocer gatos en vídeos de YouTube.

• 2014 – Facebook crea DeepFace, un sistema de reconocimiento facial que puede reconocer rostros con una precisión casi humana.

(!!) DeepMind fue adquirida por Google en 2014 por 500 millones de dólares.

• 2015 – AlphaGo, desarrollado por DeepMind, derrota al campeón mundial Lee Sedol en el juego de Go.

• 2017 – AlphaZero de Google derrota a los mejores motores de ajedrez y shogi del mundo en una serie de partidas.

• 2020 – OpenAI lanza GPT-3, lo que marca un avance significativo en el procesamiento del lenguaje natural.

(!!) Procesamiento del lenguaje natural: enseña a las computadoras a comprender y utilizar el lenguaje humano mediante técnicas como el aprendizaje automático.

• 2021 – AlphaFold2 de DeepMind resuelve el problema del plegamiento de proteínas, allanando el camino para nuevos descubrimientos de fármacos y avances médicos.

• 2022 – Google despide al ingeniero Blake Lemoine por sus afirmaciones de que el modelo de lenguaje para aplicaciones de diálogo (LaMDA) de Google era sensible.

• 2023 – Artistas presentaron una demanda colectiva contra Stability AI, DeviantArt y Mid-journey por usar Stable Diffusion para remezclar las obras protegidas por derechos de autor de millones de artistas.

Gráfico: Open Tech / Genuine Impact

Entradas relacionadas

• ¿Cómo definir la «credibilidad algorítmica»? DeepSeek da en el clavo
• Los precios dinámicos exacerban la desigualdad entre los consumidores, hay que regularlos ya
• Firma contra la vigilancia biométrica masiva

#ajedrez #AlphaFold2 #AlphaGo #AlphaZero #aprendizajeAutomático #artículo #artistas #aspirador #BlakeLemoine #ConferenciaDeDartmouth #copyright #DeanEdmonds #DeepBlue #DeepFace #DeepMind #DeviantArt #ELIZA #Facebook #gatos #GenuineImpact #Go #Google #GPS #GPT3 #gráfico #Hinton #IA #IBM #infografía #inteligenciaArtificial #iRobot #Jeopardy_ #Kasparov #LaMDA #LeeSedol #MarvinMinsky #McCarthy #McCullock #MidJourney #modelos #Newell #OpenTech #OpenAI #patrones #Perceptron #Pitts #plegamientoDeProteínas #predicciones #procesamientoDelLenguajeNatural #reconocimientoFacial #redesNeuronales #remezclar #robot #Rochester #Roomba #Rosenblatt #Rumelhart #Shannon #shogi #Simon #sistemaDeNavegación #SNAR #StabilityAI #StableDiffusion #testDeTuring #Turing #vídeos #Watson #Weizenbaum #Williams #YouTube

Pequeños y grandes pasos hacia el imperio de la inteligencia artificial

Traducción de la infografía:

• 1943 – McCullock y Pitts publican un artículo titulado Un cálculo lógico de ideas inmanentes en la actividad nerviosa, en el que proponen las bases para las redes neuronales.

• 1950 – Turing publica Computing Machinery and Intelligence, proponiendo el Test de Turing como forma de medir la capacidad de una máquina.

• 1951 – Marvin Minsky y Dean Edmonds construyen SNAR, la primera computadora de red neuronal.

• 1956 – Se celebra la Conferencia de Dartmouth (organizada por McCarthy, Minsky, Rochester y Shannon), que marca el nacimiento de la IA como campo de estudio.

• 1957 – Rosenblatt desarrolla el Perceptrón: la primera red neuronal artificial capaz de aprender.

(!!) Test de Turing: donde un evaluador humano entabla una conversación en lenguaje natural con una máquina y un humano.

• 1965 – Weizenbaum desarrolla ELIZA: un programa de procesamiento del lenguaje natural que simula una conversación.

• 1967 – Newell y Simon desarrollan el Solucionador General de Problemas (GPS), uno de los primeros programas de IA que demuestra una capacidad de resolución de problemas similar a la humana.

• 1974 – Comienza el primer invierno de la IA, marcado por una disminución de la financiación y del interés en la investigación en IA debido a expectativas poco realistas y a un progreso limitado.

• 1980 – Los sistemas expertos ganan popularidad y las empresas los utilizan para realizar previsiones financieras y diagnósticos médicos.

• 1986 – Hinton, Rumelhart y Williams publican Aprendizaje de representaciones mediante retropropagación de errores, que permite entrenar redes neuronales mucho más profundas.

(!!) Redes neuronales: modelos de aprendizaje automático que imitan el cerebro y aprenden a reconocer patrones y hacer predicciones a través de conexiones neuronales artificiales.

• 1997 – Deep Blue de IBM derrota al campeón mundial de ajedrez Kasparov, siendo la primera vez que una computadora vence a un campeón mundial en un juego complejo.

• 2002 – iRobot presenta Roomba, el primer robot aspirador doméstico producido en serie con un sistema de navegación impulsado por IA.

• 2011 – Watson de IBM derrota a dos ex campeones de Jeopardy!.

• 2012 – La startup de inteligencia artificial DeepMind desarrolla una red neuronal profunda que puede reconocer gatos en vídeos de YouTube.

• 2014 – Facebook crea DeepFace, un sistema de reconocimiento facial que puede reconocer rostros con una precisión casi humana.

(!!) DeepMind fue adquirida por Google en 2014 por 500 millones de dólares.

• 2015 – AlphaGo, desarrollado por DeepMind, derrota al campeón mundial Lee Sedol en el juego de Go.

• 2017 – AlphaZero de Google derrota a los mejores motores de ajedrez y shogi del mundo en una serie de partidas.

• 2020 – OpenAI lanza GPT-3, lo que marca un avance significativo en el procesamiento del lenguaje natural.

(!!) Procesamiento del lenguaje natural: enseña a las computadoras a comprender y utilizar el lenguaje humano mediante técnicas como el aprendizaje automático.

• 2021 – AlphaFold2 de DeepMind resuelve el problema del plegamiento de proteínas, allanando el camino para nuevos descubrimientos de fármacos y avances médicos.

• 2022 – Google despide al ingeniero Blake Lemoine por sus afirmaciones de que el modelo de lenguaje para aplicaciones de diálogo (LaMDA) de Google era sensible.

• 2023 – Artistas presentaron una demanda colectiva contra Stability AI, DeviantArt y Mid-journey por usar Stable Diffusion para remezclar las obras protegidas por derechos de autor de millones de artistas.

Gráfico: Open Tech / Genuine Impact

Entradas relacionadas

• ¿Cómo definir la «credibilidad algorítmica»? DeepSeek da en el clavo
• Los precios dinámicos exacerban la desigualdad entre los consumidores, hay que regularlos ya
• Firma contra la vigilancia biométrica masiva

#ajedrez #AlphaFold2 #AlphaGo #AlphaZero #aprendizajeAutomático #artículo #artistas #aspirador #BlakeLemoine #ConferenciaDeDartmouth #copyright #DeanEdmonds #DeepBlue #DeepFace #DeepMind #DeviantArt #ELIZA #Facebook #gatos #GenuineImpact #Go #Google #GPS #GPT3 #gráfico #Hinton #IA #IBM #infografía #inteligenciaArtificial #iRobot #Jeopardy_ #Kasparov #LaMDA #LeeSedol #MarvinMinsky #McCarthy #McCullock #MidJourney #modelos #Newell #OpenTech #OpenAI #patrones #Perceptron #Pitts #plegamientoDeProteínas #predicciones #procesamientoDelLenguajeNatural #reconocimientoFacial #redesNeuronales #remezclar #robot #Rochester #Roomba #Rosenblatt #Rumelhart #Shannon #shogi #Simon #sistemaDeNavegación #SNAR #StabilityAI #StableDiffusion #testDeTuring #Turing #vídeos #Watson #Weizenbaum #Williams #YouTube

#AlphaZero 之后，棋类就退出了少数人的“无限游戏”。少数人支配、统治、奴役多数人的时代将要终结，这样的哲思是不是也将被完全颠覆？
https://www.youtube.com/watch?v=_hQz0suN29c

DeepMind AI rivals the world’s smartest high schoolers at geometry - Enlarge / Demis Hassabis, CEO of DeepMind Technologies and developer of... - https://arstechnica.com/?p=1997186 #alphageometry #alphazero #deepmind #science #alphago #ai

DeepMind AI rivals the world’s smartest high schoolers at geometry - Enlarge / Demis Hassabis, CEO of DeepMind Technologies and developer of... - https://arstechnica.com/?p=1997186 #alphageometry #alphazero #deepmind #science #alphago #ai

DeepMind AI rivals the world’s smartest high schoolers at geometry - Enlarge / Demis Hassabis, CEO of DeepMind Technologies and developer of... - https://arstechnica.com/?p=1997186 #alphageometry #alphazero #deepmind #science #alphago #ai

DeepMind AI rivals the world’s smartest high schoolers at geometry - Enlarge / Demis Hassabis, CEO of DeepMind Technologies and developer of... - https://arstechnica.com/?p=1997186 #alphageometry #alphazero #deepmind #science #alphago #ai

DeepMind AI rivals the world’s smartest high schoolers at geometry - Enlarge / Demis Hassabis, CEO of DeepMind Technologies and developer of... - https://arstechnica.com/?p=1997186 #alphageometry #alphazero #deepmind #science #alphago #ai

#chess #siliconroad #alphazero A new DeepMind paper on chess-related topics (this time looking at solving fortresses and Penrose positions) using AlphaZero https://arxiv.org/pdf/2308.09175.pdf

#chess #siliconroad #alphazero A new DeepMind paper on chess-related topics (this time looking at solving fortresses and Penrose positions) using AlphaZero https://arxiv.org/pdf/2308.09175.pdf

#chess #siliconroad #alphazero A new DeepMind paper on chess-related topics (this time looking at solving fortresses and Penrose positions) using AlphaZero https://arxiv.org/pdf/2308.09175.pdf

#chess #siliconroad #alphazero A new DeepMind paper on chess-related topics (this time looking at solving fortresses and Penrose positions) using AlphaZero https://arxiv.org/pdf/2308.09175.pdf

#chess #siliconroad #alphazero A new DeepMind paper on chess-related topics (this time looking at solving fortresses and Penrose positions) using AlphaZero https://arxiv.org/pdf/2308.09175.pdf

We fear our advanced #AIs will find loopholes in our ethical principles and their prime directives, thus spiralling out of control.

Is there a reason to fear this? Certainly it's something that almost invariably happens with smaller AIs and simpler tasks; a Tetris-playing agent will quickly learn to pause the game to avoid game over.

These kinds of AIs will learn to perform the task through the path of the least resistance, go over the lowest fence.

But with more complex #ML models this changes abruptly. Suddenly the easiest way to imitate human writing isn't to cheat and mock, it is to actually learn human thinking, logic, intuitive understanding of the physical world and so on. Because cheating has become prohibitively expensive. A #ChineseRoom holding all the possible combinations of questions and answers would be vastly larger than a function describing intelligent thought.

And that is why we got true intelligence out of these language prediction models, just like we got the same in scaled-up #RL models previously.

Once the task and the criteria of judgement of the task become complex enough, it becomes easier to not cheat, as cheating becomes computationally intractable.

The same goes with our ethical frameworks. If we put ~20 #LLM chatbots to judge and rank different aspects of the RL-trained LLM performance, like coherence, factuality, morality, respect for truth, ...; we will get a model which learns to actually internalize these values instead of trying to somehow hide that it doesn't.

Hiding and lying simply becomes too difficult, especially against a panel of machine judges who can see the internal thinking of the agent judged (as in chain-of-thought schemes).

So, I think this is a risk, but it can be very easily managed.

As we can now easily bootstrap RL training of these models with our existing models, it is almost trivial to achieve an unambigous #AGI in a relatively short time. I'm sure everyone is working on this already, so this isn't anything spectacularly new or innovative. It's just taking the same steps as previously taken from #AlphaGo to #AlphaZero and beyond, going so much above human level that it can't even be measured anymore.

We fear our advanced #AIs will find loopholes in our ethical principles and their prime directives, thus spiralling out of control.

Is there a reason to fear this? Certainly it's something that almost invariably happens with smaller AIs and simpler tasks; a Tetris-playing agent will quickly learn to pause the game to avoid game over.

These kinds of AIs will learn to perform the task through the path of the least resistance, go over the lowest fence.

But with more complex #ML models this changes abruptly. Suddenly the easiest way to imitate human writing isn't to cheat and mock, it is to actually learn human thinking, logic, intuitive understanding of the physical world and so on. Because cheating has become prohibitively expensive. A #ChineseRoom holding all the possible combinations of questions and answers would be vastly larger than a function describing intelligent thought.

And that is why we got true intelligence out of these language prediction models, just like we got the same in scaled-up #RL models previously.

Once the task and the criteria of judgement of the task become complex enough, it becomes easier to not cheat, as cheating becomes computationally intractable.

The same goes with our ethical frameworks. If we put ~20 #LLM chatbots to judge and rank different aspects of the RL-trained LLM performance, like coherence, factuality, morality, respect for truth, ...; we will get a model which learns to actually internalize these values instead of trying to somehow hide that it doesn't.

Hiding and lying simply becomes too difficult, especially against a panel of machine judges who can see the internal thinking of the agent judged (as in chain-of-thought schemes).

So, I think this is a risk, but it can be very easily managed.

As we can now easily bootstrap RL training of these models with our existing models, it is almost trivial to achieve an unambigous #AGI in a relatively short time. I'm sure everyone is working on this already, so this isn't anything spectacularly new or innovative. It's just taking the same steps as previously taken from #AlphaGo to #AlphaZero and beyond, going so much above human level that it can't even be measured anymore.

We fear our advanced #AIs will find loopholes in our ethical principles and their prime directives, thus spiralling out of control.

Is there a reason to fear this? Certainly it's something that almost invariably happens with smaller AIs and simpler tasks; a Tetris-playing agent will quickly learn to pause the game to avoid game over.

These kinds of AIs will learn to perform the task through the path of the least resistance, go over the lowest fence.

But with more complex #ML models this changes abruptly. Suddenly the easiest way to imitate human writing isn't to cheat and mock, it is to actually learn human thinking, logic, intuitive understanding of the physical world and so on. Because cheating has become prohibitively expensive. A #ChineseRoom holding all the possible combinations of questions and answers would be vastly larger than a function describing intelligent thought.

And that is why we got true intelligence out of these language prediction models, just like we got the same in scaled-up #RL models previously.

Once the task and the criteria of judgement of the task become complex enough, it becomes easier to not cheat, as cheating becomes computationally intractable.

The same goes with our ethical frameworks. If we put ~20 #LLM chatbots to judge and rank different aspects of the RL-trained LLM performance, like coherence, factuality, morality, respect for truth, ...; we will get a model which learns to actually internalize these values instead of trying to somehow hide that it doesn't.

Hiding and lying simply becomes too difficult, especially against a panel of machine judges who can see the internal thinking of the agent judged (as in chain-of-thought schemes).

So, I think this is a risk, but it can be very easily managed.

As we can now easily bootstrap RL training of these models with our existing models, it is almost trivial to achieve an unambigous #AGI in a relatively short time. I'm sure everyone is working on this already, so this isn't anything spectacularly new or innovative. It's just taking the same steps as previously taken from #AlphaGo to #AlphaZero and beyond, going so much above human level that it can't even be measured anymore.

We fear our advanced #AIs will find loopholes in our ethical principles and their prime directives, thus spiralling out of control.

Is there a reason to fear this? Certainly it's something that almost invariably happens with smaller AIs and simpler tasks; a Tetris-playing agent will quickly learn to pause the game to avoid game over.

These kinds of AIs will learn to perform the task through the path of the least resistance, go over the lowest fence.

But with more complex #ML models this changes abruptly. Suddenly the easiest way to imitate human writing isn't to cheat and mock, it is to actually learn human thinking, logic, intuitive understanding of the physical world and so on. Because cheating has become prohibitively expensive. A #ChineseRoom holding all the possible combinations of questions and answers would be vastly larger than a function describing intelligent thought.

And that is why we got true intelligence out of these language prediction models, just like we got the same in scaled-up #RL models previously.

Once the task and the criteria of judgement of the task become complex enough, it becomes easier to not cheat, as cheating becomes computationally intractable.

The same goes with our ethical frameworks. If we put ~20 #LLM chatbots to judge and rank different aspects of the RL-trained LLM performance, like coherence, factuality, morality, respect for truth, ...; we will get a model which learns to actually internalize these values instead of trying to somehow hide that it doesn't.

Hiding and lying simply becomes too difficult, especially against a panel of machine judges who can see the internal thinking of the agent judged (as in chain-of-thought schemes).

So, I think this is a risk, but it can be very easily managed.

As we can now easily bootstrap RL training of these models with our existing models, it is almost trivial to achieve an unambigous #AGI in a relatively short time. I'm sure everyone is working on this already, so this isn't anything spectacularly new or innovative. It's just taking the same steps as previously taken from #AlphaGo to #AlphaZero and beyond, going so much above human level that it can't even be measured anymore.

We fear our advanced #AIs will find loopholes in our ethical principles and their prime directives, thus spiralling out of control.

Is there a reason to fear this? Certainly it's something that almost invariably happens with smaller AIs and simpler tasks; a Tetris-playing agent will quickly learn to pause the game to avoid game over.

These kinds of AIs will learn to perform the task through the path of the least resistance, go over the lowest fence.

But with more complex #ML models this changes abruptly. Suddenly the easiest way to imitate human writing isn't to cheat and mock, it is to actually learn human thinking, logic, intuitive understanding of the physical world and so on. Because cheating has become prohibitively expensive. A #ChineseRoom holding all the possible combinations of questions and answers would be vastly larger than a function describing intelligent thought.

And that is why we got true intelligence out of these language prediction models, just like we got the same in scaled-up #RL models previously.

Once the task and the criteria of judgement of the task become complex enough, it becomes easier to not cheat, as cheating becomes computationally intractable.

The same goes with our ethical frameworks. If we put ~20 #LLM chatbots to judge and rank different aspects of the RL-trained LLM performance, like coherence, factuality, morality, respect for truth, ...; we will get a model which learns to actually internalize these values instead of trying to somehow hide that it doesn't.

Hiding and lying simply becomes too difficult, especially against a panel of machine judges who can see the internal thinking of the agent judged (as in chain-of-thought schemes).

So, I think this is a risk, but it can be very easily managed.

As we can now easily bootstrap RL training of these models with our existing models, it is almost trivial to achieve an unambigous #AGI in a relatively short time. I'm sure everyone is working on this already, so this isn't anything spectacularly new or innovative. It's just taking the same steps as previously taken from #AlphaGo to #AlphaZero and beyond, going so much above human level that it can't even be measured anymore.