#neural-networks — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #neural-networks, aggregated by home.social.
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RT @MiaAI_lab: Schlechte Nachrichten
mehr auf Arint.info
#35B #KI #MaschinellesLernen #MoE #NeuralNetworks #Qwen #arint_info
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RT @MiaAI_lab: Schlechte Nachrichten
mehr auf Arint.info
#35B #KI #MaschinellesLernen #MoE #NeuralNetworks #Qwen #arint_info
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https://www.wired.com/story/ai-models-built-from-rat-brains-are-about-to-become-a-reality/
(Happy freaking #Halloween)
“We figured out a way to code information, like images for example, to the biological material,” says TBC cofounder Ksendzovsky. “We then observe how the biology processes that information, and then we build a tool that mimics that process.”
This is not the first biologically-derived computing platform that #Amazon is familiar with says Deap Ubhi, global director of technology for startups at #AmazonWebServices
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https://www.wired.com/story/ai-models-built-from-rat-brains-are-about-to-become-a-reality/
(Happy freaking #Halloween)
“We figured out a way to code information, like images for example, to the biological material,” says TBC cofounder Ksendzovsky. “We then observe how the biology processes that information, and then we build a tool that mimics that process.”
This is not the first biologically-derived computing platform that #Amazon is familiar with says Deap Ubhi, global director of technology for startups at #AmazonWebServices
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https://www.wired.com/story/ai-models-built-from-rat-brains-are-about-to-become-a-reality/
(Happy freaking #Halloween)
“We figured out a way to code information, like images for example, to the biological material,” says TBC cofounder Ksendzovsky. “We then observe how the biology processes that information, and then we build a tool that mimics that process.”
This is not the first biologically-derived computing platform that #Amazon is familiar with says Deap Ubhi, global director of technology for startups at #AmazonWebServices
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https://www.wired.com/story/ai-models-built-from-rat-brains-are-about-to-become-a-reality/
(Happy freaking #Halloween)
“We figured out a way to code information, like images for example, to the biological material,” says TBC cofounder Ksendzovsky. “We then observe how the biology processes that information, and then we build a tool that mimics that process.”
This is not the first biologically-derived computing platform that #Amazon is familiar with says Deap Ubhi, global director of technology for startups at #AmazonWebServices
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https://phys.org/news/2026-09-room-temperature-skyrmion-based-synapses.html
"Using…2D #Waals ferromagnet Fe₃GaTe₂, the researchers exploit a collective transformation of the magnetic state from a #skyrmion lattice…Rather than depending on the stochastic behavior of individual skyrmions, large populations of magnetic textures evolve collectively and deterministically.
This…produces a…highly reproducible change in the material's anomalous Hall resistance…enabling the multiply-accumulate operations that underpin modern #neuralnetworks."
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https://phys.org/news/2026-09-room-temperature-skyrmion-based-synapses.html
"Using…2D #Waals ferromagnet Fe₃GaTe₂, the researchers exploit a collective transformation of the magnetic state from a #skyrmion lattice…Rather than depending on the stochastic behavior of individual skyrmions, large populations of magnetic textures evolve collectively and deterministically.
This…produces a…highly reproducible change in the material's anomalous Hall resistance…enabling the multiply-accumulate operations that underpin modern #neuralnetworks."
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https://phys.org/news/2026-09-room-temperature-skyrmion-based-synapses.html
"Using…2D #Waals ferromagnet Fe₃GaTe₂, the researchers exploit a collective transformation of the magnetic state from a #skyrmion lattice…Rather than depending on the stochastic behavior of individual skyrmions, large populations of magnetic textures evolve collectively and deterministically.
This…produces a…highly reproducible change in the material's anomalous Hall resistance…enabling the multiply-accumulate operations that underpin modern #neuralnetworks."
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https://phys.org/news/2026-09-room-temperature-skyrmion-based-synapses.html
"Using…2D #Waals ferromagnet Fe₃GaTe₂, the researchers exploit a collective transformation of the magnetic state from a #skyrmion lattice…Rather than depending on the stochastic behavior of individual skyrmions, large populations of magnetic textures evolve collectively and deterministically.
This…produces a…highly reproducible change in the material's anomalous Hall resistance…enabling the multiply-accumulate operations that underpin modern #neuralnetworks."
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#askFedi Do you know of speech-to-text live transcription models that:
1. perform OK for lightly technical English speech, in near-real-time,
2. have available (ideally libre/open-source) training data, libre/open-source training code, libre/open-source inference code, and/or libre/open-source weights (ideally some amount of freedom to use/study/modify/distribute; e.g. the Open Source AI Definition)Glanced at:
april-asr: probably best candidate so far; somewhat below quality expectations on my laptop (tested with https://flathub.org/en/apps/net.sapples.LiveCaptions; quality/performance is configurable; maybe I should run it on a more powerful server and check if it's better?)
OpenAI Whisper: no training data/code
Vosk official model: not sure about training data/code, not sure about weights
Mozilla DeepSpeech: Archived
CoquiSTT: Looks abandoned#Transcription #NeuralNetworks #NN #MachineLearning #ML #SpeechToText #STT #FOSS #FLOSS #OpenSource #FreeSoftware #LibreSoftware #OSAID #OpenSourceAI #CommonVoice
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#askFedi Do you know of speech-to-text live transcription models that:
1. perform OK for lightly technical English speech, in near-real-time,
2. have available (ideally libre/open-source) training data, libre/open-source training code, libre/open-source inference code, and/or libre/open-source weights (ideally some amount of freedom to use/study/modify/distribute; e.g. the Open Source AI Definition)Glanced at:
april-asr: probably best candidate so far; somewhat below quality expectations on my laptop (tested with https://flathub.org/en/apps/net.sapples.LiveCaptions; quality/performance is configurable; maybe I should run it on a more powerful server and check if it's better?)
OpenAI Whisper: no training data/code
Vosk official model: not sure about training data/code, not sure about weights
Mozilla DeepSpeech: Archived
CoquiSTT: Looks abandoned#Transcription #NeuralNetworks #NN #MachineLearning #ML #SpeechToText #STT #FOSS #FLOSS #OpenSource #FreeSoftware #LibreSoftware #OSAID #OpenSourceAI #CommonVoice
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#askFedi Do you know of speech-to-text live transcription models that:
1. perform OK for lightly technical English speech, in near-real-time,
2. have available (ideally libre/open-source) training data, libre/open-source training code, libre/open-source inference code, and/or libre/open-source weights (ideally some amount of freedom to use/study/modify/distribute; e.g. the Open Source AI Definition)Glanced at:
april-asr: probably best candidate so far; somewhat below quality expectations on my laptop (tested with https://flathub.org/en/apps/net.sapples.LiveCaptions; quality/performance is configurable; maybe I should run it on a more powerful server and check if it's better?)
OpenAI Whisper: no training data/code
Vosk official model: not sure about training data/code, not sure about weights
Mozilla DeepSpeech: Archived
CoquiSTT: Looks abandoned#Transcription #NeuralNetworks #NN #MachineLearning #ML #SpeechToText #STT #FOSS #FLOSS #OpenSource #FreeSoftware #LibreSoftware #OSAID #OpenSourceAI #CommonVoice
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#askFedi Do you know of speech-to-text live transcription models that:
1. perform OK for lightly technical English speech, in near-real-time,
2. have available (ideally libre/open-source) training data, libre/open-source training code, libre/open-source inference code, and/or libre/open-source weights (ideally some amount of freedom to use/study/modify/distribute; e.g. the Open Source AI Definition)Glanced at:
april-asr: probably best candidate so far; somewhat below quality expectations on my laptop (tested with https://flathub.org/en/apps/net.sapples.LiveCaptions; quality/performance is configurable; maybe I should run it on a more powerful server and check if it's better?)
OpenAI Whisper: no training data/code
Vosk official model: not sure about training data/code, not sure about weights
Mozilla DeepSpeech: Archived
CoquiSTT: Looks abandoned#Transcription #NeuralNetworks #NN #MachineLearning #ML #SpeechToText #STT #FOSS #FLOSS #OpenSource #FreeSoftware #LibreSoftware #OSAID #OpenSourceAI #CommonVoice
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DATE: September 24, 2026 at 09:28AM
SOURCE: SCIENCE DAILY PSYCHOLOGY FEEDTITLE: When what you see doesn’t make sense, your brain does something remarkable
URL: https://www.sciencedaily.com/releases/2026/09/260923035929.htm
The brain may have a built-in way to quickly settle disagreements between regions processing the same scene. Researchers found that matching signals between two visual areas lasted longer, while conflicting signals rapidly faded away. This “consensus building” mechanism could help explain how the brain turns many specialized inputs into one unified view of the world.
URL: https://www.sciencedaily.com/releases/2026/09/260923035929.htm
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Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #BrainScience #Neuroscience #ConsensusBuilding #VisualProcessing #BrainRecognition #Cognition #NeuralNetworks #Perception #UnifiedView #ResearchNews
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DATE: September 24, 2026 at 09:28AM
SOURCE: SCIENCE DAILY PSYCHOLOGY FEEDTITLE: When what you see doesn’t make sense, your brain does something remarkable
URL: https://www.sciencedaily.com/releases/2026/09/260923035929.htm
The brain may have a built-in way to quickly settle disagreements between regions processing the same scene. Researchers found that matching signals between two visual areas lasted longer, while conflicting signals rapidly faded away. This “consensus building” mechanism could help explain how the brain turns many specialized inputs into one unified view of the world.
URL: https://www.sciencedaily.com/releases/2026/09/260923035929.htm
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Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #BrainScience #Neuroscience #ConsensusBuilding #VisualProcessing #BrainRecognition #Cognition #NeuralNetworks #Perception #UnifiedView #ResearchNews
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DATE: September 24, 2026 at 09:28AM
SOURCE: SCIENCE DAILY PSYCHOLOGY FEEDTITLE: When what you see doesn’t make sense, your brain does something remarkable
URL: https://www.sciencedaily.com/releases/2026/09/260923035929.htm
The brain may have a built-in way to quickly settle disagreements between regions processing the same scene. Researchers found that matching signals between two visual areas lasted longer, while conflicting signals rapidly faded away. This “consensus building” mechanism could help explain how the brain turns many specialized inputs into one unified view of the world.
URL: https://www.sciencedaily.com/releases/2026/09/260923035929.htm
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #BrainScience #Neuroscience #ConsensusBuilding #VisualProcessing #BrainRecognition #Cognition #NeuralNetworks #Perception #UnifiedView #ResearchNews
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AI Models Built From Rat Brains Just Got Closer to Reality
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AI Models Built From Rat Brains Just Got Closer to Reality
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AI Models Built From Rat Brains Just Got Closer to Reality
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AI Models Built From Rat Brains Just Got Closer to Reality
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Egy oktatási segédanyagként is használható, szemléltető, érdekes neurális hálózaton dolgozom. Ebben segít az Opus-5.
...valamiért úgy érzem, hogy itt dolgozni kell a paraméterekkel, mert ez nem úgy tűnik, mintha megtanulna járni / egyensúlyozni, pedig az lenne a cél.
#ReinforcementLearning #NeuralNetwork #MachineLearning #ArtificialIntelligence #AI #DeepLearning #Simulation #PhysicsSimulation #NeuralNetworks #AIResearch #MachineLearningExperiment #Robotics #Locomotion #LearningToWalk #NeuralWalker #TechDemo #Coding #Programming
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Egy oktatási segédanyagként is használható, szemléltető, érdekes neurális hálózaton dolgozom. Ebben segít az Opus-5.
...valamiért úgy érzem, hogy itt dolgozni kell a paraméterekkel, mert ez nem úgy tűnik, mintha megtanulna járni / egyensúlyozni, pedig az lenne a cél.
#ReinforcementLearning #NeuralNetwork #MachineLearning #ArtificialIntelligence #AI #DeepLearning #Simulation #PhysicsSimulation #NeuralNetworks #AIResearch #MachineLearningExperiment #Robotics #Locomotion #LearningToWalk #NeuralWalker #TechDemo #Coding #Programming
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I Trained a Fly’s Brain to Generate WIRED Story Ideas
https://fed.brid.gy/r/https://www.wired.com/story/i-trained-a-fly-on-wired-story-ideas/
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I Trained a Fly’s Brain to Generate WIRED Story Ideas
https://fed.brid.gy/r/https://www.wired.com/story/i-trained-a-fly-on-wired-story-ideas/
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I Trained a Fly’s Brain to Generate WIRED Story Ideas
https://fed.brid.gy/r/https://www.wired.com/story/i-trained-a-fly-on-wired-story-ideas/
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I Trained a Fly’s Brain to Generate WIRED Story Ideas
https://fed.brid.gy/r/https://www.wired.com/story/i-trained-a-fly-on-wired-story-ideas/
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Terence Tao @tao :
The math behind today’s LLMs is actually simple. Training and running them mostly uses linear algebra, matrix multiplication, and a bit of calculus, material an undergraduate can handle. We understand how to build and operate these models.
The real mystery is why they work so well on some tasks and fail on others, and why we cannot predict that in advance. We lack good rules for forecasting performance across tasks, so progress is largely empirical.
A key reason is the nature of real-world data. Pure noise is well understood, perfectly structured data is well understood, but natural text sits in between, partly structured and partly random. Mathematics for that middle regime is thin, similar to how physics struggles at meso-scales between atoms and continua.
Because of this gap, we can describe the mechanisms but cannot yet explain capability jumps or give reliable task-level predictions. That mismatch, simple machinery versus hard-to-predict behavior, is the core puzzle.
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Video from Prof @Briankeating YT Channel.
Full video link: https://youtu.be/ukpCHo5v-Gc
#ArtificialIntelligence #AI #LargeLanguageModels #LLMs #MachineLearning #DeepLearning #GenerativeAI #AIResearch #Mathematics #LinearAlgebra #MatrixMultiplication #Calculus #NeuralNetworks #AIModels #ModelTraining #AIEngineering #ComputerScience #DataScience #NaturalLanguageProcessing #NLP #EmergentAbilities #AIReasoning #AIProgress #AIInnovation #TechResearch #FutureOfAI #MachineIntelligence #AIExplained #TuringTest
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Terence Tao @tao :
The math behind today’s LLMs is actually simple. Training and running them mostly uses linear algebra, matrix multiplication, and a bit of calculus, material an undergraduate can handle. We understand how to build and operate these models.
The real mystery is why they work so well on some tasks and fail on others, and why we cannot predict that in advance. We lack good rules for forecasting performance across tasks, so progress is largely empirical.
A key reason is the nature of real-world data. Pure noise is well understood, perfectly structured data is well understood, but natural text sits in between, partly structured and partly random. Mathematics for that middle regime is thin, similar to how physics struggles at meso-scales between atoms and continua.
Because of this gap, we can describe the mechanisms but cannot yet explain capability jumps or give reliable task-level predictions. That mismatch, simple machinery versus hard-to-predict behavior, is the core puzzle.
----
Video from Prof @Briankeating YT Channel.
Full video link: https://youtu.be/ukpCHo5v-Gc
#ArtificialIntelligence #AI #LargeLanguageModels #LLMs #MachineLearning #DeepLearning #GenerativeAI #AIResearch #Mathematics #LinearAlgebra #MatrixMultiplication #Calculus #NeuralNetworks #AIModels #ModelTraining #AIEngineering #ComputerScience #DataScience #NaturalLanguageProcessing #NLP #EmergentAbilities #AIReasoning #AIProgress #AIInnovation #TechResearch #FutureOfAI #MachineIntelligence #AIExplained #TuringTest
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Terence Tao @tao :
The math behind today’s LLMs is actually simple. Training and running them mostly uses linear algebra, matrix multiplication, and a bit of calculus, material an undergraduate can handle. We understand how to build and operate these models.
The real mystery is why they work so well on some tasks and fail on others, and why we cannot predict that in advance. We lack good rules for forecasting performance across tasks, so progress is largely empirical.
A key reason is the nature of real-world data. Pure noise is well understood, perfectly structured data is well understood, but natural text sits in between, partly structured and partly random. Mathematics for that middle regime is thin, similar to how physics struggles at meso-scales between atoms and continua.
Because of this gap, we can describe the mechanisms but cannot yet explain capability jumps or give reliable task-level predictions. That mismatch, simple machinery versus hard-to-predict behavior, is the core puzzle.
----
Video from Prof @Briankeating YT Channel.
Full video link: https://youtu.be/ukpCHo5v-Gc
#ArtificialIntelligence #AI #LargeLanguageModels #LLMs #MachineLearning #DeepLearning #GenerativeAI #AIResearch #Mathematics #LinearAlgebra #MatrixMultiplication #Calculus #NeuralNetworks #AIModels #ModelTraining #AIEngineering #ComputerScience #DataScience #NaturalLanguageProcessing #NLP #EmergentAbilities #AIReasoning #AIProgress #AIInnovation #TechResearch #FutureOfAI #MachineIntelligence #AIExplained #TuringTest
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Terence Tao @tao :
The math behind today’s LLMs is actually simple. Training and running them mostly uses linear algebra, matrix multiplication, and a bit of calculus, material an undergraduate can handle. We understand how to build and operate these models.
The real mystery is why they work so well on some tasks and fail on others, and why we cannot predict that in advance. We lack good rules for forecasting performance across tasks, so progress is largely empirical.
A key reason is the nature of real-world data. Pure noise is well understood, perfectly structured data is well understood, but natural text sits in between, partly structured and partly random. Mathematics for that middle regime is thin, similar to how physics struggles at meso-scales between atoms and continua.
Because of this gap, we can describe the mechanisms but cannot yet explain capability jumps or give reliable task-level predictions. That mismatch, simple machinery versus hard-to-predict behavior, is the core puzzle.
----
Video from Prof @Briankeating YT Channel.
Full video link: https://youtu.be/ukpCHo5v-Gc
#ArtificialIntelligence #AI #LargeLanguageModels #LLMs #MachineLearning #DeepLearning #GenerativeAI #AIResearch #Mathematics #LinearAlgebra #MatrixMultiplication #Calculus #NeuralNetworks #AIModels #ModelTraining #AIEngineering #ComputerScience #DataScience #NaturalLanguageProcessing #NLP #EmergentAbilities #AIReasoning #AIProgress #AIInnovation #TechResearch #FutureOfAI #MachineIntelligence #AIExplained #TuringTest
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Crunch to 8-bit is not very much fan-out. Crunch to 4 bit? How could you even encode a giraffe?
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Crunch to 8-bit is not very much fan-out. Crunch to 4 bit? How could you even encode a giraffe?
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Crunch to 8-bit is not very much fan-out. Crunch to 4 bit? How could you even encode a giraffe?
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FYI: Explaining regurgitation: Regurgitation is an AI model reproducing its training data verbatim in output. How memorisation happens, how courts count it, and why the rate is disputed. https://ppc.land/regurgitation/ #AI #MachineLearning #DataScience #neuralnetworks #ArtificialIntelligence
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FYI: Explaining regurgitation: Regurgitation is an AI model reproducing its training data verbatim in output. How memorisation happens, how courts count it, and why the rate is disputed. https://ppc.land/regurgitation/ #AI #MachineLearning #DataScience #neuralnetworks #ArtificialIntelligence
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FYI: Explaining regurgitation: Regurgitation is an AI model reproducing its training data verbatim in output. How memorisation happens, how courts count it, and why the rate is disputed. https://ppc.land/regurgitation/ #AI #MachineLearning #DataScience #neuralnetworks #ArtificialIntelligence
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FYI: Explaining regurgitation: Regurgitation is an AI model reproducing its training data verbatim in output. How memorisation happens, how courts count it, and why the rate is disputed. https://ppc.land/regurgitation/ #AI #MachineLearning #DataScience #neuralnetworks #ArtificialIntelligence
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“The Neural Network Zoo” [2016], The Asimov Institute (https://www.asimovinstitute.org/neural-network-zoo/).
On HN: https://news.ycombinator.com/item?id=21701058
#AI #NN #NeuralNetworks #ANN #ML #MachineLearning #Visualization
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“The Neural Network Zoo” [2016], The Asimov Institute (https://www.asimovinstitute.org/neural-network-zoo/).
On HN: https://news.ycombinator.com/item?id=21701058
#AI #NN #NeuralNetworks #ANN #ML #MachineLearning #Visualization
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“The Neural Network Zoo” [2016], The Asimov Institute (https://www.asimovinstitute.org/neural-network-zoo/).
On HN: https://news.ycombinator.com/item?id=21701058
#AI #NN #NeuralNetworks #ANN #ML #MachineLearning #Visualization
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Midnight Rambler by Main St Audio Labs 🎸
Neural amp sim (Fender Tweed Deluxe 5E3), dual chans, 3-way cab conv, tuner, TPT filters, noise gate.💻 Mac/Win/Linux (VST3/AU/Standalone)
🎁 FREE https://mainstaudiolabs.github.io/midnight-rambler.htmlMore freeware 👉 https://linktr.ee/legalvst
#freeplugin #ampsim #guitar #neuralnetworks #convolution #tuner
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Midnight Rambler by Main St Audio Labs 🎸
Neural amp sim (Fender Tweed Deluxe 5E3), dual chans, 3-way cab conv, tuner, TPT filters, noise gate.💻 Mac/Win/Linux (VST3/AU/Standalone)
🎁 FREE https://mainstaudiolabs.github.io/midnight-rambler.htmlMore freeware 👉 https://linktr.ee/legalvst
#freeplugin #ampsim #guitar #neuralnetworks #convolution #tuner
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Midnight Rambler by Main St Audio Labs 🎸
Neural amp sim (Fender Tweed Deluxe 5E3), dual chans, 3-way cab conv, tuner, TPT filters, noise gate.💻 Mac/Win/Linux (VST3/AU/Standalone)
🎁 FREE https://mainstaudiolabs.github.io/midnight-rambler.htmlMore freeware 👉 https://linktr.ee/legalvst
#freeplugin #ampsim #guitar #neuralnetworks #convolution #tuner
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Midnight Rambler by Main St Audio Labs 🎸
Neural amp sim (Fender Tweed Deluxe 5E3), dual chans, 3-way cab conv, tuner, TPT filters, noise gate.💻 Mac/Win/Linux (VST3/AU/Standalone)
🎁 FREE https://mainstaudiolabs.github.io/midnight-rambler.htmlMore freeware 👉 https://linktr.ee/legalvst
#freeplugin #ampsim #guitar #neuralnetworks #convolution #tuner
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These Russian Mathematicians Taught AI Models How to Talk to Each Other Without Using Words
I recently met with some brilliant Russian mathematicians who showed me a way for artificial intelligence models to…
#NewsBeep #News #Artificialintelligence #AI #ailab #Anthropic #ArtificialIntelligence #Mathematics #neuralnetworks #opensource #OpenAI #Technology #UK #UnitedKingdom
https://www.newsbeep.com/uk/758478/ -
https://www.europesays.com/uk/1183388/ These Russian Mathematicians Taught AI Models How to Talk to Each Other Without Using Words #AI #AiLab #Anthropic #ArtificialIntelligence #Mathematics #NeuralNetworks #OpenSource #openai #Technology #UK #UnitedKingdom
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These Russian Mathematicians Taught AI Models How to Talk to Each Other Without Using Words
https://fed.brid.gy/r/https://www.wired.com/story/russian-startup-mostik-ai-models-communication/
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These Russian Mathematicians Taught AI Models How to Talk to Each Other Without Using Words
https://fed.brid.gy/r/https://www.wired.com/story/russian-startup-mostik-ai-models-communication/
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These Russian Mathematicians Taught AI Models How to Talk to Each Other Without Using Words
https://fed.brid.gy/r/https://www.wired.com/story/russian-startup-mostik-ai-models-communication/
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These Russian Mathematicians Taught AI Models How to Talk to Each Other Without Using Words
https://fed.brid.gy/r/https://www.wired.com/story/russian-startup-mostik-ai-models-communication/
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The Emergent Symbolic Structure of Artificial Neural Networks
https://arxiv.org/abs/2608.29530
Comments: https://news.ycombinator.com/item?id=49531651
#HackerNews #EmergentSymbolicStructure #ArtificialNeuralNetworks #MachineLearning #AIResearch #NeuralNetworks
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The Emergent Symbolic Structure of Artificial Neural Networks
https://arxiv.org/abs/2608.29530
Comments: https://news.ycombinator.com/item?id=49531651
#HackerNews #EmergentSymbolicStructure #ArtificialNeuralNetworks #MachineLearning #AIResearch #NeuralNetworks
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The Emergent Symbolic Structure of Artificial Neural Networks
https://arxiv.org/abs/2608.29530
Comments: https://news.ycombinator.com/item?id=49531651
#HackerNews #EmergentSymbolicStructure #ArtificialNeuralNetworks #MachineLearning #AIResearch #NeuralNetworks
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The Emergent Symbolic Structure of Artificial Neural Networks
https://arxiv.org/abs/2608.29530
Comments: https://news.ycombinator.com/item?id=49531651
#HackerNews #EmergentSymbolicStructure #ArtificialNeuralNetworks #MachineLearning #AIResearch #NeuralNetworks
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Reflective Interpretive Frameworks • Incident 2
• https://inquiryintoinquiry.com/2026/08/27/reflective-interpretive-frameworks-incident-2/Re: Terence Tao • Modular Arithmetic Challenge
• https://terrytao.wordpress.com/2026/06/08/modular-arithmetic-challenge/
• https://competition.sair.foundation/competitions/modular-arithmetic-challenge/overviewThe Modular Arithmetic Challenge asks a simple question:
• Can a neural network learn to do modular multiplication efficiently?
Incidental Reflection 1 —
There are alternative models of neural networks which do not depend on threshold neurons and endlessly fiddling with weights.
Incidental Reflection 2 —
The series of three blog posts linked below present a case study comparing two ways of handling a classic example from the Parallel Distributed Processing paradigm, namely, the “Jets and Sharks” database problem, first taking up the original treatment by McClelland and Rumelhart and then proceeding according to a program I developed for propositional logic modeling. The latter method makes use of ideas from Grossberg's competition‑cooperation and winner‑take‑all dynamics, but is purely propositional‑logic based, involving no extraneous weights.
Theme One Program • Jets and Sharks
(1) https://inquiryintoinquiry.com/2024/06/20/theme-one-program-jets-and-sharks-1-a/
(2) https://inquiryintoinquiry.com/2024/06/22/theme-one-program-jets-and-sharks-2-a/
(3) https://inquiryintoinquiry.com/2024/06/23/theme-one-program-jets-and-sharks-3-a/Resources —
Survey of Theme One Program
• https://inquiryintoinquiry.com/2025/05/06/survey-of-theme-one-program-7/Differential Analytic Turing Automata
• https://oeis.org/wiki/Differential_Analytic_Turing_Automata_%E2%80%A2_Overview#Peirce #HigherOrderSignRelations #Inquiry #InquiryIntoInquiry #Logic #Mathematics
#Recursion #Reflection #RelationTheory #Semiotics #SignRelations #TriadicRelations
#PropositionalModels #MinimalNegationOperators #DifferentialAnalyticTuringAutomata
#CactusGraphs #CactusLanguage #DifferentialLogic #NeuralNetworks #SequenceLearning -
Reflective Interpretive Frameworks • Incident 2
• https://inquiryintoinquiry.com/2026/08/27/reflective-interpretive-frameworks-incident-2/Re: Terence Tao • Modular Arithmetic Challenge
• https://terrytao.wordpress.com/2026/06/08/modular-arithmetic-challenge/
• https://competition.sair.foundation/competitions/modular-arithmetic-challenge/overviewThe Modular Arithmetic Challenge asks a simple question:
• Can a neural network learn to do modular multiplication efficiently?
Incidental Reflection 1 —
There are alternative models of neural networks which do not depend on threshold neurons and endlessly fiddling with weights.
Incidental Reflection 2 —
The series of three blog posts linked below present a case study comparing two ways of handling a classic example from the Parallel Distributed Processing paradigm, namely, the “Jets and Sharks” database problem, first taking up the original treatment by McClelland and Rumelhart and then proceeding according to a program I developed for propositional logic modeling. The latter method makes use of ideas from Grossberg's competition‑cooperation and winner‑take‑all dynamics, but is purely propositional‑logic based, involving no extraneous weights.
Theme One Program • Jets and Sharks
(1) https://inquiryintoinquiry.com/2024/06/20/theme-one-program-jets-and-sharks-1-a/
(2) https://inquiryintoinquiry.com/2024/06/22/theme-one-program-jets-and-sharks-2-a/
(3) https://inquiryintoinquiry.com/2024/06/23/theme-one-program-jets-and-sharks-3-a/Resources —
Survey of Theme One Program
• https://inquiryintoinquiry.com/2025/05/06/survey-of-theme-one-program-7/Differential Analytic Turing Automata
• https://oeis.org/wiki/Differential_Analytic_Turing_Automata_%E2%80%A2_Overview#Peirce #HigherOrderSignRelations #Inquiry #InquiryIntoInquiry #Logic #Mathematics
#Recursion #Reflection #RelationTheory #Semiotics #SignRelations #TriadicRelations
#PropositionalModels #MinimalNegationOperators #DifferentialAnalyticTuringAutomata
#CactusGraphs #CactusLanguage #DifferentialLogic #NeuralNetworks #SequenceLearning -
Reflective Interpretive Frameworks • Incident 2
• https://inquiryintoinquiry.com/2026/08/27/reflective-interpretive-frameworks-incident-2/Re: Terence Tao • Modular Arithmetic Challenge
• https://terrytao.wordpress.com/2026/06/08/modular-arithmetic-challenge/
• https://competition.sair.foundation/competitions/modular-arithmetic-challenge/overviewThe Modular Arithmetic Challenge asks a simple question:
• Can a neural network learn to do modular multiplication efficiently?
Incidental Reflection 1 —
There are alternative models of neural networks which do not depend on threshold neurons and endlessly fiddling with weights.
Incidental Reflection 2 —
The series of three blog posts linked below present a case study comparing two ways of handling a classic example from the Parallel Distributed Processing paradigm, namely, the “Jets and Sharks” database problem, first taking up the original treatment by McClelland and Rumelhart and then proceeding according to a program I developed for propositional logic modeling. The latter method makes use of ideas from Grossberg's competition‑cooperation and winner‑take‑all dynamics, but is purely propositional‑logic based, involving no extraneous weights.
Theme One Program • Jets and Sharks
(1) https://inquiryintoinquiry.com/2024/06/20/theme-one-program-jets-and-sharks-1-a/
(2) https://inquiryintoinquiry.com/2024/06/22/theme-one-program-jets-and-sharks-2-a/
(3) https://inquiryintoinquiry.com/2024/06/23/theme-one-program-jets-and-sharks-3-a/Resources —
Survey of Theme One Program
• https://inquiryintoinquiry.com/2025/05/06/survey-of-theme-one-program-7/Differential Analytic Turing Automata
• https://oeis.org/wiki/Differential_Analytic_Turing_Automata_%E2%80%A2_Overview#Peirce #HigherOrderSignRelations #Inquiry #InquiryIntoInquiry #Logic #Mathematics
#Recursion #Reflection #RelationTheory #Semiotics #SignRelations #TriadicRelations
#PropositionalModels #MinimalNegationOperators #DifferentialAnalyticTuringAutomata
#CactusGraphs #CactusLanguage #DifferentialLogic #NeuralNetworks #SequenceLearning -
Reflective Interpretive Frameworks • Incident 2
• https://inquiryintoinquiry.com/2026/08/27/reflective-interpretive-frameworks-incident-2/Re: Terence Tao • Modular Arithmetic Challenge
• https://terrytao.wordpress.com/2026/06/08/modular-arithmetic-challenge/
• https://competition.sair.foundation/competitions/modular-arithmetic-challenge/overviewThe Modular Arithmetic Challenge asks a simple question:
• Can a neural network learn to do modular multiplication efficiently?
Incidental Reflection 1 —
There are alternative models of neural networks which do not depend on threshold neurons and endlessly fiddling with weights.
Incidental Reflection 2 —
The series of three blog posts linked below present a case study comparing two ways of handling a classic example from the Parallel Distributed Processing paradigm, namely, the “Jets and Sharks” database problem, first taking up the original treatment by McClelland and Rumelhart and then proceeding according to a program I developed for propositional logic modeling. The latter method makes use of ideas from Grossberg's competition‑cooperation and winner‑take‑all dynamics, but is purely propositional‑logic based, involving no extraneous weights.
Theme One Program • Jets and Sharks
(1) https://inquiryintoinquiry.com/2024/06/20/theme-one-program-jets-and-sharks-1-a/
(2) https://inquiryintoinquiry.com/2024/06/22/theme-one-program-jets-and-sharks-2-a/
(3) https://inquiryintoinquiry.com/2024/06/23/theme-one-program-jets-and-sharks-3-a/Resources —
Survey of Theme One Program
• https://inquiryintoinquiry.com/2025/05/06/survey-of-theme-one-program-7/Differential Analytic Turing Automata
• https://oeis.org/wiki/Differential_Analytic_Turing_Automata_%E2%80%A2_Overview#Peirce #HigherOrderSignRelations #Inquiry #InquiryIntoInquiry #Logic #Mathematics
#Recursion #Reflection #RelationTheory #Semiotics #SignRelations #TriadicRelations
#PropositionalModels #MinimalNegationOperators #DifferentialAnalyticTuringAutomata
#CactusGraphs #CactusLanguage #DifferentialLogic #NeuralNetworks #SequenceLearning -
Israel Is Running a Synthetic Think Tank to Influence AI Search Results…
https://www.404media.co/israel-is-running-a-synthetic-think-tank-to-influence-ai-search-results/
"First spotted by Politico, the Hanover Institute for Public Policy is run by the American advertising firm Piro Inc, paid for by Israel, and appears designed to generate content for LLMs that are continually scanning the internet to inform their responses, with the goal of tweaking chatbot answers in favor of Israel." #neuralnetworks #aiagents #aitools #syntheticsocialmedia