#perceptron — Public Fediverse posts

In a mind-boggling twist, a self-proclaimed genius constructs a #perceptron in a 1999 video game 🎮 to prove... what exactly? 🤔 Apparently, Age of Empires II can teach us more about #AI than actual AI research can. Who knew medieval strategy games were the real gatekeepers of machine learning wisdom? 🏰💡
https://adewynter.github.io/notes/aoe2-circuits #AgeOfEmpires #MachineLearning #GamingWisdom #HackerNews #ngated

In a mind-boggling twist, a self-proclaimed genius constructs a #perceptron in a 1999 video game 🎮 to prove... what exactly? 🤔 Apparently, Age of Empires II can teach us more about #AI than actual AI research can. Who knew medieval strategy games were the real gatekeepers of machine learning wisdom? 🏰💡
https://adewynter.github.io/notes/aoe2-circuits #AgeOfEmpires #MachineLearning #GamingWisdom #HackerNews #ngated

A Perceptron in Age of Empires II

https://adewynter.github.io/notes/aoe2-circuits

#HackerNews #Perceptron #AgeOfEmpiresII #AI #MachineLearning #GamingInsights

A Perceptron in Age of Empires II

https://adewynter.github.io/notes/aoe2-circuits

#HackerNews #Perceptron #AgeOfEmpiresII #AI #MachineLearning #GamingInsights

"“If LLMs Have Human-Like Attributes, Then So Does Age of Empires II,” is the title of Adrian de Wynter’s paper showing his work. He told 404 Media that absurdity can be a powerful tool. “I have this tendency to dial up things to 11 when I really think I need to make a point,” he said. “I should also note that absurdism is pretty standard in philosophy and theoretical computer science.”

And so De Wynter built an LLM within AoEII using goats. “The point of the paper is to formally show that we anthropomorphise too readily, and that sometimes the claims we make with regards to LLM capabilities are too strong,” he told 404 Media. “It's not an easy task, given that ‘human-like attributes’ is a bit of an abstract term.”
AoEII has a scenario editor, a sandbox mode that allows players to craft their own maps and quests using the game’s assets, and De Wynter used that to build an operational NOT AND (NAND) gate and a 1-bit perceptron within the game. In this crude version of an LLM, grass is 0, bridges are 1, and goats are the bits. “Only one rail is active at a time, with a goat acting as the signal carrier. When the gate fires, the bit-goats are removed (they ded) and a new bit-goat is placed in its respective output rail,” Wynter explained on his GitHub.

A perceptron is the simplest form of a neural network, it’s an algorithm that sorts an input into binary classes. YouTube is littered with videos of players doing the same thing with redstone in Minecraft. But no one claims the goats of AoEII are neurons in a thinking machine or that the complicated tracks of NAND gates players build in Minecraft show emergent intelligence."

https://www.404media.co/if-ai-is-sentient-then-so-is-age-of-empires-ii/

#AI #GenerativeAI #LLMs #NeuralNetworks #VideoGames #AgeOfEmpires #Perceptron

"“If LLMs Have Human-Like Attributes, Then So Does Age of Empires II,” is the title of Adrian de Wynter’s paper showing his work. He told 404 Media that absurdity can be a powerful tool. “I have this tendency to dial up things to 11 when I really think I need to make a point,” he said. “I should also note that absurdism is pretty standard in philosophy and theoretical computer science.”

And so De Wynter built an LLM within AoEII using goats. “The point of the paper is to formally show that we anthropomorphise too readily, and that sometimes the claims we make with regards to LLM capabilities are too strong,” he told 404 Media. “It's not an easy task, given that ‘human-like attributes’ is a bit of an abstract term.”
AoEII has a scenario editor, a sandbox mode that allows players to craft their own maps and quests using the game’s assets, and De Wynter used that to build an operational NOT AND (NAND) gate and a 1-bit perceptron within the game. In this crude version of an LLM, grass is 0, bridges are 1, and goats are the bits. “Only one rail is active at a time, with a goat acting as the signal carrier. When the gate fires, the bit-goats are removed (they ded) and a new bit-goat is placed in its respective output rail,” Wynter explained on his GitHub.

A perceptron is the simplest form of a neural network, it’s an algorithm that sorts an input into binary classes. YouTube is littered with videos of players doing the same thing with redstone in Minecraft. But no one claims the goats of AoEII are neurons in a thinking machine or that the complicated tracks of NAND gates players build in Minecraft show emergent intelligence."

https://www.404media.co/if-ai-is-sentient-then-so-is-age-of-empires-ii/

#AI #GenerativeAI #LLMs #NeuralNetworks #VideoGames #AgeOfEmpires #Perceptron

🧠 Ah yes, the "smallest brain"—a #perceptron. It's so basic, even your cat could code it 🐾. But fear not—our trusty guide stumbles through #Python like a toddler on roller skates, promising "interactive demos" that redefine boredom one epoch at a time. 🙄
https://ranpara.net/posts/perceptron-explained-from-scratch/ #smallestbrain #coding #interactive #demos #AIlearning #HackerNews #ngated

🧠 Ah yes, the "smallest brain"—a #perceptron. It's so basic, even your cat could code it 🐾. But fear not—our trusty guide stumbles through #Python like a toddler on roller skates, promising "interactive demos" that redefine boredom one epoch at a time. 🙄
https://ranpara.net/posts/perceptron-explained-from-scratch/ #smallestbrain #coding #interactive #demos #AIlearning #HackerNews #ngated

The Smallest Brain You Can Build: A Perceptron in Python

https://ranpara.net/posts/perceptron-explained-from-scratch/

#HackerNews #Perceptron #Python #NeuralNetworks #MachineLearning #AI

The Smallest Brain You Can Build: A Perceptron in Python

https://ranpara.net/posts/perceptron-explained-from-scratch/

#HackerNews #Perceptron #Python #NeuralNetworks #MachineLearning #AI

In our #KDAI2026 lecture this week we were taking a tour de force from perceptrons to transformers, 60 years of neural networks in a ninety minutes lecture.

#lecture @fiz_karlsruhe @fizise @KIT_Karlsruhe #AI #llms #perceptron #neuralnetwork #transformer #bert #gpt #lstm #rnn #transferlearning #machinelearning #cowboybebop

In our #KDAI2026 lecture this week we were taking a tour de force from perceptrons to transformers, 60 years of neural networks in a ninety minutes lecture.

#lecture @fiz_karlsruhe @fizise @KIT_Karlsruhe #AI #llms #perceptron #neuralnetwork #transformer #bert #gpt #lstm #rnn #transferlearning #machinelearning #cowboybebop

In our #KDAI2026 lecture this week we were taking a tour de force from perceptrons to transformers, 60 years of neural networks in a ninety minutes lecture.

#lecture @fiz_karlsruhe @fizise @KIT_Karlsruhe #AI #llms #perceptron #neuralnetwork #transformer #bert #gpt #lstm #rnn #transferlearning #machinelearning #cowboybebop

In our #KDAI2026 lecture this week we were taking a tour de force from perceptrons to transformers, 60 years of neural networks in a ninety minutes lecture.

#lecture @fiz_karlsruhe @fizise @KIT_Karlsruhe #AI #llms #perceptron #neuralnetwork #transformer #bert #gpt #lstm #rnn #transferlearning #machinelearning #cowboybebop

Human #perceptron #il

Human #perceptron #il

The perceptron, introduced by Frank Rosenblatt in 1957, was the first neural network model to gain wide attention. Initially hailed as a step toward machines that could “see, talk, and think,” it soon revealed limits: single-layer perceptrons could only solve linearly separable problems. After Minsky & Papert’s critique (1969), research funding dried up—until multilayer perceptrons and backpropagation revived neural networks in the 1980s.
https://en.m.wikipedia.org/wiki/Perceptron
#NeuralNetworks #Perceptron

The perceptron, introduced by Frank Rosenblatt in 1957, was the first neural network model to gain wide attention. Initially hailed as a step toward machines that could “see, talk, and think,” it soon revealed limits: single-layer perceptrons could only solve linearly separable problems. After Minsky & Papert’s critique (1969), research funding dried up—until multilayer perceptrons and backpropagation revived neural networks in the 1980s.
https://en.m.wikipedia.org/wiki/Perceptron
#NeuralNetworks #Perceptron

A couple weeks ago @aeonofdiscord showed me "The Hitch-hiker's Guide to Artificial Intelligence" from 1986, a book by Richard Forsyth and Chris Naylor, which teaches readers the state-of-the-art in AI using... BASIC programming type-ins. There's some extremely minimal examples of expert systems, A* maze path search, alpha-beta game trees, etc. but one that made me curious was a "Perceptron image classifier" for machine vision. Image classification in 16kb? I had to see for myself if it would work, so I made a #Processing version. And, amazingly, it actually does - giving about 7 examples of a smile vs frown face, it can then distinguish between the two. Not bad for an idea from 1957!

#computervision #perceptron

На дворе LLM, а книгу о перцептроне так никто и не открыл!?

Сложно следить за околонаучными темами, и понимать, что ветка эволюции научного направления пошла не туда. Сейчас случился некий бум псевдонаучного взлета LLM, и я приведу в качестве современной статьи на хабре лишь одну, но это по прежнему массовое явление. Например, в статье компании Friflex за 2024 год История LLM-агентов: 10 ярких моментов по прежнему утверждается " На смену однослойному перцептрону Розэнблатта пришел многослойный. В статье Learning representations by back-propagating errors («Обучение представлений с помощью обратного распространения ошибки») Румельхарт и Хинтон показали, что многослойный перцептрон справляется с задачами, которые были не под силу его однослойному предшественнику. Например, с XOR. ". Совершенно излишне говорить, что это полное вранье, а авторы статьи даже не потрудились открыть эту статью, чтобы её прочитать. Это стало массовым явлением, и я его наблюдаю как минимум 20 лет, я когда то написал тут на хабре цикл статей объясняющих детали, лучше всего посмотреть эту Какова роль первого «случайного» слоя в перцептроне Розенблатта . Поэтому к этому возвращаться не будем. Я не знаю почему, может это массовая культура так влияет на людей, а порог вхождения в тематику ИИ слишком сложный? Не знаю, но не важно. Чтобы продемонстрировать скорость обучения перцептрона я написал несколько реализаций перцептрона Розенблатта и выложил их на гитхабе. А затем мы коснемся LLM.

https://habr.com/ru/articles/941754/

#chatgpt #backpropagation #perceptron

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

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#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

We started our #ISE2024 lecture on Basic Machine Learning today with "A (very) brief History of AI", which - in the end - took longer than expected ;-) ....stories and anecdotes time again

lecture slides: https://drive.google.com/file/d/1smo2qdVbXIVWqE9JHrtD6Ao6LDj1q9Jn/view?usp=drive_link

@enorouzi @sourisnumerique @fizise @fiz_karlsruhe #AI #HistoryOfAI #perceptron #expertsystem #knowledgebase #symbolicAI #subsymbolicAI #neuralnetworks #connectivism #generativeAI #creativeAI #AIart #astronaut

We started our #ISE2024 lecture on Basic Machine Learning today with "A (very) brief History of AI", which - in the end - took longer than expected ;-) ....stories and anecdotes time again

lecture slides: https://drive.google.com/file/d/1smo2qdVbXIVWqE9JHrtD6Ao6LDj1q9Jn/view?usp=drive_link

@enorouzi @sourisnumerique @fizise @fiz_karlsruhe #AI #HistoryOfAI #perceptron #expertsystem #knowledgebase #symbolicAI #subsymbolicAI #neuralnetworks #connectivism #generativeAI #creativeAI #AIart #astronaut

Кто знает, что значит GPT в названии ChatGPT, могут дальше не читать!.

В настоящее время искусственный интеллект (ИИ) стремительно развивается. Мы являемся свидетелями интеллектуальной мощи таких нейросетей, как GPT-4 Turbo от OpenAI и Gemini Ultra от Google . В Интернете появляется огромное количество научных и популярных публикаций. Зачем же нужна еще одна статья про ИИ? Играя с ребенком в ChatGPT, я неожиданно осознал, что не понимаю значения аббревиатуры GPT. И, казалось бы, простая задача для айтишника, неожиданно превратилась в нетривиальное исследование архитектур современных нейросетей, которым я и хочу поделиться. Сгенерированная ИИ картинка, будет еще долго напоминать мою задумчивость при взгляде на многообразие и сложность современных нейросетей.

https://habr.com/ru/articles/785080/

#нейросети #нейронные_сети #cnn #rnn #трансформеры #gpt #chatgpt #gan #nlp #perceptron

Check out my new essay "Memorization and Memory Devices in Early Machine Learning" in Interfaces on memory in (historical) artificial neural networks: https://drive.google.com/file/d/1cQ5sjThMLqcHerm3ptwssy7U43ddlJXQ/view #ml #ai #perceptron

@glaroc It absolutely is fun, and the thing is it makes a real effort to try and teach you about real nural networks and how they really operate. And I tried. I really did. I even watched part of the video they linked. But it is so far beyond my ability to comprehend... It made me a little sad. Yet I love the game, so there. Haha. #Perceptron

@glaroc It absolutely is fun, and the thing is it makes a real effort to try and teach you about real nural networks and how they really operate. And I tried. I really did. I even watched part of the video they linked. But it is so far beyond my ability to comprehend... It made me a little sad. Yet I love the game, so there. Haha. #Perceptron

@remenca @spla En #Vapnik va escriure un llibre que déu n'hi do: "The nature of statistical learning theory" http://lib.ysu.am/disciplines_bk/22cca8eefb24af29d10bbc661e3a5ebf.pdf que introdueix de tot, incloguent-hi #SVM #ANN #perceptron i molt més.

@remenca @spla En #Vapnik va escriure un llibre que déu n'hi do: "The nature of statistical learning theory" http://lib.ysu.am/disciplines_bk/22cca8eefb24af29d10bbc661e3a5ebf.pdf que introdueix de tot, incloguent-hi #SVM #ANN #perceptron i molt més.

Having good Git commits is a really good way to be able to do controlled experiments on your software.

For example, I changed how my asteroids AIs are mutated and crossed over. How do I know that *both* of the changes are required for high achievement?

git revert, baby.

[ #git #programming #perceptron #machinelearning ]

Having good Git commits is a really good way to be able to do controlled experiments on your software.

For example, I changed how my asteroids AIs are mutated and crossed over. How do I know that *both* of the changes are required for high achievement?

git revert, baby.

[ #git #programming #perceptron #machinelearning ]