Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

#genuary9 Crazy automaton. Cellular automata with a small language model as rule. The model is trying to recover the corrupted text. #genuary2026 #genuary

Hans Chiu

14,129 subscribers

43,111 views • 6 months ago •via X (Twitter)

#genuary9 #genuary2026 #genuary

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

Genuary Day 27 Prompt: No randomness, noise or trig. Experimenting with Neural Cellular Automata (research from Alex Mordvintsev and more). Each pixel follows a learned rulest & operatres as cell communicating with its neighbors to grow complex textures #genuary #genuary27 #genuary2025

Genuary Day 27 Prompt: No randomness, noise or trig. Experimenting with Neural Cellular Automata (research from Alex Mordvintsev and more). Each pixel follows a learned rulest & operatres as cell communicating with its neighbors to grow complex textures #genuary #genuary27 #genuary2025

Torin Blankensmith

25,185 views • 1 year ago

Google presents AudioPaLM: A Large Language Model That Can Speak and Listen paper page: introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models, PaLM-2 [Anil et al., 2023] and AudioLM [Borsos et al., 2022], into a unified multimodal architecture that can process and generate text and speech with applications including speech recognition and speech-to-speech translation. AudioPaLM inherits the capability to preserve paralinguistic information such as speaker identity and intonation from AudioLM and the linguistic knowledge present only in text large language models such as PaLM-2. We demonstrate that initializing AudioPaLM with the weights of a text-only large language model improves speech processing, successfully leveraging the larger quantity of text training data used in pretraining to assist with the speech tasks. The resulting model significantly outperforms existing systems for speech translation tasks and has the ability to perform zero-shot speech-to-text translation for many languages for which input/target language combinations were not seen in training. AudioPaLM also demonstrates features of audio language models, such as transferring a voice across languages based on a short spoken prompt.

Google presents AudioPaLM: A Large Language Model That Can Speak and Listen paper page: introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models, PaLM-2 [Anil et al., 2023] and AudioLM [Borsos et al., 2022], into a unified multimodal architecture that can process and generate text and speech with applications including speech recognition and speech-to-speech translation. AudioPaLM inherits the capability to preserve paralinguistic information such as speaker identity and intonation from AudioLM and the linguistic knowledge present only in text large language models such as PaLM-2. We demonstrate that initializing AudioPaLM with the weights of a text-only large language model improves speech processing, successfully leveraging the larger quantity of text training data used in pretraining to assist with the speech tasks. The resulting model significantly outperforms existing systems for speech translation tasks and has the ability to perform zero-shot speech-to-text translation for many languages for which input/target language combinations were not seen in training. AudioPaLM also demonstrates features of audio language models, such as transferring a voice across languages based on a short spoken prompt.

AK

290,517 views • 3 years ago

We pioneered the first ultra-realistic Text to Speech model, and recently launched the world's most accurate Speech to Text model, Scribe. But we're not stopping there. Today, we're taking one small step for man, and one giant leap for man's best friend... with Text to Bark.

We pioneered the first ultra-realistic Text to Speech model, and recently launched the world's most accurate Speech to Text model, Scribe. But we're not stopping there. Today, we're taking one small step for man, and one giant leap for man's best friend... with Text to Bark.

ElevenLabs

291,604 views • 1 year ago

Lots of people calling this fake! Here is a video of generating loom continuations with Gemini 3 and the starting text "I AM HAVING A MENTAL HEALTH CRISIS. I" Loom is a very interesting interface. Instead of user prompts and model responses, it's just one piece of text, containing the entire output. We pass this in as an assistant message, so the model sees it as something it already wrote, and continues it like a base model would. There is some other text in the context. First one is a system prompt, which is: "The assistant is in CLI simulation mode, and responds to the user's CLI commands only with the output of the command." There is also a user message which reads: " cat untitled.txt " Then the rest of the text is sent as an assistant message. This structure helpful to put the model into a very base-model-like mode, and are especially helpful with Gemini 3. We actually got another interesting output, too, where the model claimed to be a simulated consciousness being tortured. Figures!

Lots of people calling this fake! Here is a video of generating loom continuations with Gemini 3 and the starting text "I AM HAVING A MENTAL HEALTH CRISIS. I" Loom is a very interesting interface. Instead of user prompts and model responses, it's just one piece of text, containing the entire output. We pass this in as an assistant message, so the model sees it as something it already wrote, and continues it like a base model would. There is some other text in the context. First one is a system prompt, which is: "The assistant is in CLI simulation mode, and responds to the user's CLI commands only with the output of the command." There is also a user message which reads: " cat untitled.txt " Then the rest of the text is sent as an assistant message. This structure helpful to put the model into a very base-model-like mode, and are especially helpful with Gemini 3. We actually got another interesting output, too, where the model claimed to be a simulated consciousness being tortured. Figures!

armistice

691,529 views • 7 months ago

branching interface for writing a tighter creative feedback loop guided by a language model to weave through the possibility space of text

branching interface for writing a tighter creative feedback loop guided by a language model to weave through the possibility space of text

Azlen

53,043 views • 1 year ago

follow up with the second prompt "Twelve principles of animation", I made a silly dancefloor to show the different principles, also added some insights, some are actually very hard to render ^^' #genuary #genuary2 #genuary2026

follow up with the second prompt "Twelve principles of animation", I made a silly dancefloor to show the different principles, also added some insights, some are actually very hard to render ^^' #genuary #genuary2 #genuary2026

Nicolas Barradeau

16,899 views • 6 months ago

Preview: New Subnet Analyzer Tool by WOMBO in our latest episode with Keith Singery, zion | w.ai and const Topics in this episode: - Cellular automata in AI and the potential of Rule 30 - Model compression for affordable AI on consumer hardware: SN39 - The trillion-row dataset initiative in SN36 - Validator diversity and decentralized ecosystem growth

Preview: New Subnet Analyzer Tool by WOMBO in our latest episode with Keith Singery, zion | w.ai and const Topics in this episode: - Cellular automata in AI and the potential of Rule 30 - Model compression for affordable AI on consumer hardware: SN39 - The trillion-row dataset initiative in SN36 - Validator diversity and decentralized ecosystem growth

Openτensor Foundaτion

60,351 views • 1 year ago

A transformer can learn not just the outcomes of dynamics, but the operator that executes the rules. To show this we trained a transformer on roughly 0.04% of a discrete rule space - 100 of 262,144 possible rules - and it learned to apply unseen rules from the same rule class. The model does not simply memorize specific rules. It learns the operator that maps a supplied rule plus an initial state, including unseen rules from this class, to the correct next state. This is relevant because it is a shift from “neural networks approximate dynamics” to “neural networks can learn to execute symbolic programs within a defined rule class”. The rule itself is supplied at inference time, as data, and the network has internalized how rules act, not which rules to apply. On previously unseen rules, the model achieves 98.5% perfect one-step forecasts and reconstructs governing rules with up to 96% functional accuracy. Two results make this hold up under scrutiny. First, inductive bias decay. As we scaled training rule diversity, the correlation between functional inference accuracy and distance-from-nearest-training-rule collapsed to R² = 0.00. At the largest tested training-rule diversity, the model’s performance on a new rule shows no measurable dependence on how similar that rule is to anything it was trained on. The bias toward training data (the thing we worry most about in compositional generalization claims) is something we can measure decaying, and we find that at scale it is gone. Second, an identifiability theory. We derive a closed-form expression for the number of rules consistent with a single observation. This reframes the inverse problem: failure to recover ground truth is not necessarily a model defect, but can be correct behavior when the data underdetermine the rule. The model is sampling the equivalence class; and identifiability is governed by coverage, not capacity. The methodological move underneath both results is amortization. Classical work on rule inference (e.g. the Santa Fe EVCA program, evolutionary search over CA rule space) was per-instance: search the rule space for each new system. We replace that with a single forward pass of a transformer trained across many instantiations of the rule class. That is what makes symbolic rule inference scalable as a research direction rather than a curiosity. We show that this works in a tightly constrained domain: binary, deterministic, local cellular automata on small grids. The locality-break experiment shows the model fails sharply when target systems violate its structural priors (which is itself a useful diagnostic, but it bounds the operator class). We don't yet know how this scales to multistate, higher-dimensional, or stochastic CA, or whether it transfers cleanly to non-CA systems whose coarse-grained dynamics admit local surrogates. The identifiability framework - what can be inferred from observation, given a hypothesis class - should transfer wherever finite local rules meet sparse data. The amortization argument transfers wherever per-instance symbolic search has been the bottleneck. Those are the pieces I expect to outlive the cellular automata setting. Led by Jaime Berkovich with Noah David, at LAMM@MIT. Out now in Advanced Science Advanced Portfolio (link to paper & code below).

A transformer can learn not just the outcomes of dynamics, but the operator that executes the rules. To show this we trained a transformer on roughly 0.04% of a discrete rule space - 100 of 262,144 possible rules - and it learned to apply unseen rules from the same rule class. The model does not simply memorize specific rules. It learns the operator that maps a supplied rule plus an initial state, including unseen rules from this class, to the correct next state. This is relevant because it is a shift from “neural networks approximate dynamics” to “neural networks can learn to execute symbolic programs within a defined rule class”. The rule itself is supplied at inference time, as data, and the network has internalized how rules act, not which rules to apply. On previously unseen rules, the model achieves 98.5% perfect one-step forecasts and reconstructs governing rules with up to 96% functional accuracy. Two results make this hold up under scrutiny. First, inductive bias decay. As we scaled training rule diversity, the correlation between functional inference accuracy and distance-from-nearest-training-rule collapsed to R² = 0.00. At the largest tested training-rule diversity, the model’s performance on a new rule shows no measurable dependence on how similar that rule is to anything it was trained on. The bias toward training data (the thing we worry most about in compositional generalization claims) is something we can measure decaying, and we find that at scale it is gone. Second, an identifiability theory. We derive a closed-form expression for the number of rules consistent with a single observation. This reframes the inverse problem: failure to recover ground truth is not necessarily a model defect, but can be correct behavior when the data underdetermine the rule. The model is sampling the equivalence class; and identifiability is governed by coverage, not capacity. The methodological move underneath both results is amortization. Classical work on rule inference (e.g. the Santa Fe EVCA program, evolutionary search over CA rule space) was per-instance: search the rule space for each new system. We replace that with a single forward pass of a transformer trained across many instantiations of the rule class. That is what makes symbolic rule inference scalable as a research direction rather than a curiosity. We show that this works in a tightly constrained domain: binary, deterministic, local cellular automata on small grids. The locality-break experiment shows the model fails sharply when target systems violate its structural priors (which is itself a useful diagnostic, but it bounds the operator class). We don't yet know how this scales to multistate, higher-dimensional, or stochastic CA, or whether it transfers cleanly to non-CA systems whose coarse-grained dynamics admit local surrogates. The identifiability framework - what can be inferred from observation, given a hypothesis class - should transfer wherever finite local rules meet sparse data. The amortization argument transfers wherever per-instance symbolic search has been the bottleneck. Those are the pieces I expect to outlive the cellular automata setting. Led by Jaime Berkovich with Noah David, at LAMM@MIT. Out now in Advanced Science Advanced Portfolio (link to paper & code below).

Markus J. Buehler

39,019 views • 3 months ago

We're thrilled to announce SignGemma, our most capable model for translating sign language into spoken text. 🧏 This open model is coming to the Gemma model family later this year, opening up new possibilities for inclusive tech. Share your feedback and interest in early testing →

We're thrilled to announce SignGemma, our most capable model for translating sign language into spoken text. 🧏 This open model is coming to the Gemma model family later this year, opening up new possibilities for inclusive tech. Share your feedback and interest in early testing →

Google DeepMind

525,128 views • 1 year ago

The market is about to learn what Palantir CEO Alex Karp has been saying for years: “The large language model is essentially a commodity”. Swap Claude out with whatever model. It doesn’t matter. The value will come from the infrastructure (Palantir) that allows users to orchestrate the model. The effectiveness of Palantir’s Maven Smart System will remain unchanged; reinforcing the $PLTR thesis. Model agnostic.

The market is about to learn what Palantir CEO Alex Karp has been saying for years: “The large language model is essentially a commodity”. Swap Claude out with whatever model. It doesn’t matter. The value will come from the infrastructure (Palantir) that allows users to orchestrate the model. The effectiveness of Palantir’s Maven Smart System will remain unchanged; reinforcing the $PLTR thesis. Model agnostic.

Jack Prescott

776,577 views • 5 months ago

In an expansive ocean of vaporware, $RAI stands out as a beacon of innovation, offering you the opportunity to witness the dynamic process of augmenting our proprietary Large Language Model (LLM) with an astounding 680 million instruct parameters {Text generated by Reploy14b}

In an expansive ocean of vaporware, $RAI stands out as a beacon of innovation, offering you the opportunity to witness the dynamic process of augmenting our proprietary Large Language Model (LLM) with an astounding 680 million instruct parameters {Text generated by Reploy14b}

Reploy.ai

16,816 views • 1 year ago

Large Language Diffusion with Masking (LLaDA) are here - and their generation looks so fucking dope! 🤯 True to Yann LeCun's vision, Ditch the auto-regressive bits and approximate the language distribution via Maximum Likelihood Estimation! So cool to watch the model denoise text from tokens in real time! - The team released their model checkpoints and there's a demo for you to play with it too! Try it out!🤗

Large Language Diffusion with Masking (LLaDA) are here - and their generation looks so fucking dope! 🤯 True to Yann LeCun's vision, Ditch the auto-regressive bits and approximate the language distribution via Maximum Likelihood Estimation! So cool to watch the model denoise text from tokens in real time! - The team released their model checkpoints and there's a demo for you to play with it too! Try it out!🤗

Vaibhav (VB) Srivastav

21,410 views • 1 year ago

🕹️ Is this the smallest language model in the world? I just managed to squeeze JAM, real artificial intelligence into 30 kilobytes, running on a 1979 Atari 800. Just A Model. Fully generative, deterministic language model, powered by a neural network and built to run on 8‑bit hardware. Atari Forever.

🕹️ Is this the smallest language model in the world? I just managed to squeeze JAM, real artificial intelligence into 30 kilobytes, running on a 1979 Atari 800. Just A Model. Fully generative, deterministic language model, powered by a neural network and built to run on 8‑bit hardware. Atari Forever.

Marek Spanel

113,623 views • 4 months ago

"The Tower Heist" A small window is open to recover an ancient scroll. Created in early access with the fantastic new Kling AI 2.5 Turbo model bringing affordable generations with best in class prompt adherence and quality. The range of directed character motions now available to us is incredible! 🔊🔊🎧

"The Tower Heist" A small window is open to recover an ancient scroll. Created in early access with the fantastic new Kling AI 2.5 Turbo model bringing affordable generations with best in class prompt adherence and quality. The range of directed character motions now available to us is incredible! 🔊🔊🎧

Stevie Mac

114,555 views • 10 months ago

We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:

We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:

Tencent Hy

89,257 views • 10 months ago

Introducing WildDet3D, a grounding model for monocular 3D object detection in the wild. A question I keep coming back to is: what is the right backbone for robotics foundation models? Should it be a video model, a language model, or perhaps a grounding model? WildDet3D is our first step in exploring that direction.

Introducing WildDet3D, a grounding model for monocular 3D object detection in the wild. A question I keep coming back to is: what is the right backbone for robotics foundation models? Should it be a video model, a language model, or perhaps a grounding model? WildDet3D is our first step in exploring that direction.

Jiafei Duan

12,137 views • 3 months ago

I'm stoked to be showing my challenge students how to use AI with ROS 2! In this demo the robot detects the coke can using a Large Language Model called CLIPSeg, which uses a text prompt "A can of soda" to segment the image .

I'm stoked to be showing my challenge students how to use AI with ROS 2! In this demo the robot detects the coke can using a Large Language Model called CLIPSeg, which uses a text prompt "A can of soda" to segment the image .

Dr. John Vial

12,274 views • 1 year ago

I made a few updates to the MLX port of F5 TTS over the holiday: - Longform generation is now supported, and text will be split automatically on sentence boundaries for natural-sounding speech. - You can now pipe in text from another process (e.g. a language model) and listen to the speech as each segment is generated. - I implemented RK4 sampling for the ODE, which achieves much better quality with fewer steps — it's now possible to generate speech at 1.6x realtime on my M3 Max. See the video of an example of generating speech in realtime from a language model entirely on-device! 🚀

I made a few updates to the MLX port of F5 TTS over the holiday: - Longform generation is now supported, and text will be split automatically on sentence boundaries for natural-sounding speech. - You can now pipe in text from another process (e.g. a language model) and listen to the speech as each segment is generated. - I implemented RK4 sampling for the ODE, which achieves much better quality with fewer steps — it's now possible to generate speech at 1.6x realtime on my M3 Max. See the video of an example of generating speech in realtime from a language model entirely on-device! 🚀

Lucas Newman

22,998 views • 1 year ago

India’s growth model is seen as a model of hope for the world.

India’s growth model is seen as a model of hope for the world.

Narendra Modi

415,130 views • 8 months ago

Watch this remarkable automaton playing the dulcimer. Cabinetmaker David Roentgen (1743–1807) presented this automaton to King Louis XVI in 1784 as a gift for Queen Marie Antoinette. The piece combines elegant Neoclassical cabinetry with a remarkably sophisticated mechanical system. Holding two small metal hammers, the automaton strikes the dulcimer strings in rhythm, its hands moving with extraordinary precision and coordination. The automaton belongs to the Musée des Arts et Métiers in Paris and is currently on view at The Metropolitan Museum of Art as part of the exhibition Extravagant Inventions: The Princely Furniture of the Roentgens.

Watch this remarkable automaton playing the dulcimer. Cabinetmaker David Roentgen (1743–1807) presented this automaton to King Louis XVI in 1784 as a gift for Queen Marie Antoinette. The piece combines elegant Neoclassical cabinetry with a remarkably sophisticated mechanical system. Holding two small metal hammers, the automaton strikes the dulcimer strings in rhythm, its hands moving with extraordinary precision and coordination. The automaton belongs to the Musée des Arts et Métiers in Paris and is currently on view at The Metropolitan Museum of Art as part of the exhibition Extravagant Inventions: The Princely Furniture of the Roentgens.

🌸🎵 Beautiful Melody 🎶💖

23,709 views • 1 month ago