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Brain signals and LLM embeddings converge for predicting every spoken or heard word. Beautiful research from Google AI They compared human brain activity during real conversations with internal embeddings from a speech-to-text LLM. Measured electrode signals in speech and language-related brain regions and matched them to the model’s word-level...

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Rohan Paul

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15,213 views • 1 year ago •via X (Twitter)

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Abe1 year ago

@GoogleAI This is a bigger deal than most realize.

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Rohan Paul1 year ago

@GoogleAI agree..

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Scientists discover surprising link between gut-brain interactions and mental health | Eric W. Dolan, PsyPost A new study provides evidence that the connection between the brain and the stomach may be linked to mental health in a measurable way. Researchers from Aarhus University in Denmark, publishing their work in Nature Mental Health, report that a specific pattern of communication between the brain and the stomach reflects how individuals feel emotionally and psychologically. Their findings suggest that these gut-brain interactions can indicate a person’s levels of anxiety, depression, well-being, and overall quality of life. The idea that emotions are linked to physical sensations in the gut is widely reflected in language. People often talk about having “butterflies in the stomach” when nervous, or feeling “sick to the stomach” when distressed. Yet, despite these common expressions, most scientific attention in the field of brain-body interaction has focused on other organs, such as the heart and lungs. These areas have long been studied for their roles in emotion and mood. The researchers were struck by how little was known about how the stomach, in particular, interacts with the brain. While recent studies have explored the influence of gut bacteria and digestion on mental health, very little work had been done on the electrical rhythms of the stomach and how they may directly communicate with the brain’s networks involved in emotion, attention, and cognition. The team behind this new study wanted to explore whether a person’s psychological profile might be reflected in how strongly the stomach and brain are coupled during rest. Their aim was not to link a specific diagnosis like depression to a single brain region, but rather to identify patterns across a broad spectrum of mental health experiences. “Our interest grew from the long-standing discussion about the role of the body in shaping emotion, a question that has fascinated philosophers and scientists for centuries,” said study author Leah Banellis (Leah Banellis), a postdoctoral fellow in Cognitive Neuroscience at Aarhus University. “Yet, while the heart and lungs have received much attention, the stomach has been largely overlooked. This gap struck us as especially surprising, because the link between the stomach and emotional experience feels so intuitive. It is heavily reflected in everyday language, with phrases like ‘butterflies in the stomach,’ ‘sick to our stomach,’ or ‘trust your gut.'” The research was part of the Visceral Mind Project, a large-scale initiative that combines data on brain activity, bodily rhythms, and psychological assessments. The team recorded data from 243 people using a method that captures both electrical signals from the stomach (electrogastrography) and brain activity measured with functional magnetic resonance imaging (fMRI). The participants represented a wide range of mental health profiles, from those reporting high well-being to others showing signs of distress, including anxiety, depression, fatigue, and insomnia. To capture this diversity, the researchers didn’t exclude people with psychiatric symptoms or diagnoses. Instead, they aimed for variation, which would allow their models to detect patterns across the mental health spectrum. Each participant underwent a series of recordings while lying still in the MRI scanner. At the same time, sensors on the abdomen captured the stomach’s slow electrical rhythm, which cycles about three times per minute. This rhythm, which originates from specialized cells in the stomach lining, is typically involved in coordinating digestion. But the researchers suspected it might also be linked to mental state. To analyze the relationship between stomach and brain activity, the team used a method that looks at how well the two rhythms align over time. This measure, known as phase-locking value, essentially captures the degree of synchronization between stomach signals and brain signals across different regions. The researchers then combined this data with results from a comprehensive mental health questionnaire. The battery included 37 different scores across a range of domains—such as anxiety, stress, mood, fatigue, attention, sleep quality, and life satisfaction. Using a statistical method known as canonical correlation analysis, they looked for patterns that linked brain-stomach coupling with the participants’ mental health profiles. The analysis revealed a clear and statistically significant pattern. Stronger coupling between the stomach’s rhythm and brain activity was associated with poorer mental health. Individuals who reported more symptoms of anxiety, depression, stress, and fatigue tended to show increased synchronization between their stomach and brain rhythms. In contrast, those with higher levels of well-being and life satisfaction showed weaker coupling. “For the first time, we’ve found a scientific link between your ‘gut feelings’ and your mental health, showing a surprising connection between your stomach’s natural rhythm and your brain,” Banellis told PsyPost. “Specifically, our study revealed that stronger communication between the stomach and brain is linked to worse mental health, such as higher symptoms of anxiety, depression, stress, and fatigue, whereas weaker stomach-brain communication aligns with better mental health reflected in higher overall well-being and quality of life.” This stomach-brain signature was not random. It was localized in specific brain networks, particularly those involved in attention, cognitive control, and salience detection. Some of the strongest associations were found in regions like the superior angular gyrus and the posterior frontal and parietal areas—regions often implicated in cognitive tasks and mental health disorders. Importantly, the researchers ran multiple control analyses to ensure the robustness of their findings. They ruled out the possibility that the observed effects were simply due to general brain activity patterns, fluctuations in heart rate or breathing, or basic features of stomach physiology. In other words, the association appeared specific to the coupling between the stomach’s electrical rhythm and particular brain networks—not just a general marker of body or brain state. Their approach was designed to detect broad psychological dimensions rather than focus on one diagnosis. The strongest psychological pattern they found was a spectrum ranging from negative affective states (like anxiety and depression) to positive traits (like well-being and quality of life). This result suggests that the stomach-brain connection is not tied to any one disorder but instead reflects a general mode of psychological functioning. “Anxiety, depression, stress, and fatigue showed the strongest links to stomach-brain communication,” Banellis explained. “While phrases like ‘butterflies in the stomach’ or feeling ‘sick to your stomach’ are common ways we describe emotional distress, it was surprising to find such consistent and clear evidence across these symptoms. Even more unexpected was the direction of the effect: we might have assumed that stronger alignment between the body and brain would be beneficial. Instead, our findings suggest that heightened stomach-brain communication could act more like a warning signal, an internal alarm system reflecting mental strain rather than harmony.” Read more:
12:34

Scientists discover surprising link between gut-brain interactions and mental health | Eric W. Dolan, PsyPost A new study provides evidence that the connection between the brain and the stomach may be linked to mental health in a measurable way. Researchers from Aarhus University in Denmark, publishing their work in Nature Mental Health, report that a specific pattern of communication between the brain and the stomach reflects how individuals feel emotionally and psychologically. Their findings suggest that these gut-brain interactions can indicate a person’s levels of anxiety, depression, well-being, and overall quality of life. The idea that emotions are linked to physical sensations in the gut is widely reflected in language. People often talk about having “butterflies in the stomach” when nervous, or feeling “sick to the stomach” when distressed. Yet, despite these common expressions, most scientific attention in the field of brain-body interaction has focused on other organs, such as the heart and lungs. These areas have long been studied for their roles in emotion and mood. The researchers were struck by how little was known about how the stomach, in particular, interacts with the brain. While recent studies have explored the influence of gut bacteria and digestion on mental health, very little work had been done on the electrical rhythms of the stomach and how they may directly communicate with the brain’s networks involved in emotion, attention, and cognition. The team behind this new study wanted to explore whether a person’s psychological profile might be reflected in how strongly the stomach and brain are coupled during rest. Their aim was not to link a specific diagnosis like depression to a single brain region, but rather to identify patterns across a broad spectrum of mental health experiences. “Our interest grew from the long-standing discussion about the role of the body in shaping emotion, a question that has fascinated philosophers and scientists for centuries,” said study author Leah Banellis (Leah Banellis), a postdoctoral fellow in Cognitive Neuroscience at Aarhus University. “Yet, while the heart and lungs have received much attention, the stomach has been largely overlooked. This gap struck us as especially surprising, because the link between the stomach and emotional experience feels so intuitive. It is heavily reflected in everyday language, with phrases like ‘butterflies in the stomach,’ ‘sick to our stomach,’ or ‘trust your gut.'” The research was part of the Visceral Mind Project, a large-scale initiative that combines data on brain activity, bodily rhythms, and psychological assessments. The team recorded data from 243 people using a method that captures both electrical signals from the stomach (electrogastrography) and brain activity measured with functional magnetic resonance imaging (fMRI). The participants represented a wide range of mental health profiles, from those reporting high well-being to others showing signs of distress, including anxiety, depression, fatigue, and insomnia. To capture this diversity, the researchers didn’t exclude people with psychiatric symptoms or diagnoses. Instead, they aimed for variation, which would allow their models to detect patterns across the mental health spectrum. Each participant underwent a series of recordings while lying still in the MRI scanner. At the same time, sensors on the abdomen captured the stomach’s slow electrical rhythm, which cycles about three times per minute. This rhythm, which originates from specialized cells in the stomach lining, is typically involved in coordinating digestion. But the researchers suspected it might also be linked to mental state. To analyze the relationship between stomach and brain activity, the team used a method that looks at how well the two rhythms align over time. This measure, known as phase-locking value, essentially captures the degree of synchronization between stomach signals and brain signals across different regions. The researchers then combined this data with results from a comprehensive mental health questionnaire. The battery included 37 different scores across a range of domains—such as anxiety, stress, mood, fatigue, attention, sleep quality, and life satisfaction. Using a statistical method known as canonical correlation analysis, they looked for patterns that linked brain-stomach coupling with the participants’ mental health profiles. The analysis revealed a clear and statistically significant pattern. Stronger coupling between the stomach’s rhythm and brain activity was associated with poorer mental health. Individuals who reported more symptoms of anxiety, depression, stress, and fatigue tended to show increased synchronization between their stomach and brain rhythms. In contrast, those with higher levels of well-being and life satisfaction showed weaker coupling. “For the first time, we’ve found a scientific link between your ‘gut feelings’ and your mental health, showing a surprising connection between your stomach’s natural rhythm and your brain,” Banellis told PsyPost. “Specifically, our study revealed that stronger communication between the stomach and brain is linked to worse mental health, such as higher symptoms of anxiety, depression, stress, and fatigue, whereas weaker stomach-brain communication aligns with better mental health reflected in higher overall well-being and quality of life.” This stomach-brain signature was not random. It was localized in specific brain networks, particularly those involved in attention, cognitive control, and salience detection. Some of the strongest associations were found in regions like the superior angular gyrus and the posterior frontal and parietal areas—regions often implicated in cognitive tasks and mental health disorders. Importantly, the researchers ran multiple control analyses to ensure the robustness of their findings. They ruled out the possibility that the observed effects were simply due to general brain activity patterns, fluctuations in heart rate or breathing, or basic features of stomach physiology. In other words, the association appeared specific to the coupling between the stomach’s electrical rhythm and particular brain networks—not just a general marker of body or brain state. Their approach was designed to detect broad psychological dimensions rather than focus on one diagnosis. The strongest psychological pattern they found was a spectrum ranging from negative affective states (like anxiety and depression) to positive traits (like well-being and quality of life). This result suggests that the stomach-brain connection is not tied to any one disorder but instead reflects a general mode of psychological functioning. “Anxiety, depression, stress, and fatigue showed the strongest links to stomach-brain communication,” Banellis explained. “While phrases like ‘butterflies in the stomach’ or feeling ‘sick to your stomach’ are common ways we describe emotional distress, it was surprising to find such consistent and clear evidence across these symptoms. Even more unexpected was the direction of the effect: we might have assumed that stronger alignment between the body and brain would be beneficial. Instead, our findings suggest that heightened stomach-brain communication could act more like a warning signal, an internal alarm system reflecting mental strain rather than harmony.” Read more:

Owen Gregorian

92,301 views • 10 months ago

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259,920 views • 1 year ago

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79,758 views • 2 years ago

🇨🇳 Another great Chinese Model, OmniHuman-1.5 from ByteDance Turns 1 image plus a voice track into expressive avatar video by pairing a System 1 and System 2 inspired planner with a Diffusion Transformer, Produces coherent motion for over 1 minute with moving camera and multi character scenes. Most avatar models move to the beat of the audio but miss meaning, so gestures feel generic and emotions feel shallow. The fix here is a Multimodal LLM planner that listens to the speech and drafts a structured plan describing intent, emotions, beats, and high level actions, which gives the motion engine clear semantic targets instead of only rhythm. The motion engine is a Multimodal Diffusion Transformer that fuses the plan with audio, the single reference image, and optional text prompts, then synthesizes continuous body, face, and head motion that matches both words and tone. A key trick is a Pseudo Last Frame, a synthetic target that summarizes the next expected state, which stabilizes fusion across modalities and keeps motion consistent over long spans. From just 1 image and speech, the system outputs speaking avatars with synchronized lips, context aware gestures, and continuous camera movement, and it also supports multi character interactions without manual choreography. Reported results show strong lip sync accuracy, high video quality, natural motion, and close match to text prompts, and the same setup works on nonhuman characters too.
0:18

🇨🇳 Another great Chinese Model, OmniHuman-1.5 from ByteDance Turns 1 image plus a voice track into expressive avatar video by pairing a System 1 and System 2 inspired planner with a Diffusion Transformer, Produces coherent motion for over 1 minute with moving camera and multi character scenes. Most avatar models move to the beat of the audio but miss meaning, so gestures feel generic and emotions feel shallow. The fix here is a Multimodal LLM planner that listens to the speech and drafts a structured plan describing intent, emotions, beats, and high level actions, which gives the motion engine clear semantic targets instead of only rhythm. The motion engine is a Multimodal Diffusion Transformer that fuses the plan with audio, the single reference image, and optional text prompts, then synthesizes continuous body, face, and head motion that matches both words and tone. A key trick is a Pseudo Last Frame, a synthetic target that summarizes the next expected state, which stabilizes fusion across modalities and keeps motion consistent over long spans. From just 1 image and speech, the system outputs speaking avatars with synchronized lips, context aware gestures, and continuous camera movement, and it also supports multi character interactions without manual choreography. Reported results show strong lip sync accuracy, high video quality, natural motion, and close match to text prompts, and the same setup works on nonhuman characters too.

Rohan Paul

63,859 views • 11 months ago

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Andrew Ng

137,091 views • 2 years ago

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2:02

Saunas Can Reduce Food Intake?! – New Insights from Nature 🔥🧠 (2 min science breakdown clip) It’s been observed that heat exposure reduces food intake both in humans and animals, leading some to suggest heat stress may be a weight management tool. And new science is beginning to unpack how heat may suppress appetite. Get ready for a steam burst of jargon, then I’ll simplify: A 2024 paper published in nature (PMID: 38538787) found that heat 🔥activates specialized cells that line the fluid-filled spaces in the brain called, “tanycytes.” 🎯These tanycytes are optimally placed to communicate external signals to regions of the brain, in particular the hypothalamus, that control things like metabolism and food intake. 🔥This study found that heat stress in a mouse model at 40°C for 1 hour increased activity in a temperature sensing brain region, the “pontine parabrachial nucleus (PBN)” 👉which signals to tanycytes and causes them to 👉produce a brain chemical VEGFA, which in turn 👉 signals to the hypothalamus to change the activity of dopamine and AGRP neurons and leads to reduce food intake. (Please see clip for a visual explanation) 🚨High-Level Takeaway: The researchers discovered a causal mechanism for the effects of heat on reducing food intake, and aligns with data in humans that heat can reduce food intake. More details about sauna in today’s #StayCurious Newsletter (linked in first reply), including discussion of heart health, brain health, longevity, and with a healthy shoutout to Dr. Rhonda Patrick (who I suspect might be interested in this Tanycytes Mechanism, if it's not already on her radar) Some folks interesting in Sauna and/or Neuroscience Mechanisms: Dan Go Gary Brecka Andrew D. Huberman, Ph.D. (Wonder what will be in #Protocols: An Operating Manual for the Human Body on Saunas?📖🐰🕳️?) Rob Mohr Robb Wolf Nicholas Fabiano, MD Amy Berger Meat Head Chris Palmer, MD #sauna #neuroscience #longevity #healthspan #appetite #metabolichealth

Nick Norwitz MD PhD

10,902 views • 1 year ago

How can you solve complex tasks using a Large Language Model? Here is a 2-minute introduction to everything you need to know to 10x the quality of your results. Let's talk about three techniques, in order of complexity, starting with the easiest one: • In-Context Learning • Indexing + In-Context Learning • Fine-tuning In-Context Learning The team that trained GPT-3 found something they couldn't explain: You can condition a model using examples of how you want it to behave. I included an example prompt in the attached video. You can "teach" the model how you want it to interpret questions, select the correct answers, and format the results by giving a few examples. You can also give specific knowledge to the model that will be helpful when formulating answers. We call this approach "grounding the model." There's another example in the video. Indexing + In-Context Learning Unfortunately, there is a limit to how much data you can include in a prompt. We call this the "context size." One version of GPT-4 supports a context of approximately 6,000 words, while the other supports 25,000 words. Although this sounds like a lot, many applications need more than that. Imagine you wrote a book and want to build an application to answer any questions about your story. What happens if your book is longer than the context? That's where Indexing comes in. Using a model, you can turn every book passage into an embedding. These are vectors, numbers that "encode" the passage's text. You can then store these embeddings in a particular database that supports fast retrieval of these vectors. You can then turn any question into an embedding and search the database for the list of passages that are similar to that query. Instead of using the entire book to ask the model, you can now use the relevant passages as in-context information, effectively working around the context size limitation. Fine-tuning Fine-tuning can give you an extra boost to get reliable outputs from your LLM. It is, however, the most complex approach on the list. There are different approaches to fine-tuning a model with your data. A popular technique is to process your data with your LLM and use the outputs to train a new classifier that solves your specific task. Notice that here you aren't modifying the LLM. Instead, you are chaining it with your trained classifier. Another approach is to modify the parameters of the LLM using your data. Think of this as "rewiring" the model in a way that solves your particular task. The results and costs will vary depending on how many layers you want to fine-tune from the original model. Many companies think that fine-tuning is the solution to their problems. In my experience, many will benefit from exploring the other two approaches. I love explaining Machine Learning and Artificial Intelligence ideas. If you enjoy in-depth content like this, follow me Santiago so you don't miss what comes next.
4:50

How can you solve complex tasks using a Large Language Model? Here is a 2-minute introduction to everything you need to know to 10x the quality of your results. Let's talk about three techniques, in order of complexity, starting with the easiest one: • In-Context Learning • Indexing + In-Context Learning • Fine-tuning In-Context Learning The team that trained GPT-3 found something they couldn't explain: You can condition a model using examples of how you want it to behave. I included an example prompt in the attached video. You can "teach" the model how you want it to interpret questions, select the correct answers, and format the results by giving a few examples. You can also give specific knowledge to the model that will be helpful when formulating answers. We call this approach "grounding the model." There's another example in the video. Indexing + In-Context Learning Unfortunately, there is a limit to how much data you can include in a prompt. We call this the "context size." One version of GPT-4 supports a context of approximately 6,000 words, while the other supports 25,000 words. Although this sounds like a lot, many applications need more than that. Imagine you wrote a book and want to build an application to answer any questions about your story. What happens if your book is longer than the context? That's where Indexing comes in. Using a model, you can turn every book passage into an embedding. These are vectors, numbers that "encode" the passage's text. You can then store these embeddings in a particular database that supports fast retrieval of these vectors. You can then turn any question into an embedding and search the database for the list of passages that are similar to that query. Instead of using the entire book to ask the model, you can now use the relevant passages as in-context information, effectively working around the context size limitation. Fine-tuning Fine-tuning can give you an extra boost to get reliable outputs from your LLM. It is, however, the most complex approach on the list. There are different approaches to fine-tuning a model with your data. A popular technique is to process your data with your LLM and use the outputs to train a new classifier that solves your specific task. Notice that here you aren't modifying the LLM. Instead, you are chaining it with your trained classifier. Another approach is to modify the parameters of the LLM using your data. Think of this as "rewiring" the model in a way that solves your particular task. The results and costs will vary depending on how many layers you want to fine-tune from the original model. Many companies think that fine-tuning is the solution to their problems. In my experience, many will benefit from exploring the other two approaches. I love explaining Machine Learning and Artificial Intelligence ideas. If you enjoy in-depth content like this, follow me Santiago so you don't miss what comes next.

Santiago

384,510 views • 3 years ago

Today on MCG: BioLLM | $BIOLLM It's the first ever "living language model" using 800,000 real human neurons grown on a chip. The Founder encoded LLM tokens into biological neurons via the Cortical Labs CL1, then woke up to find the crypto community had launched a token on his research. He claimed the creator fees, bought for $15K, filed a patent, and is now launching a non-invasive brain-computer interface next month that could replace mouse and keyboard with your brain 👇 01:40 - Meet the founder 02:00 - Got early access to the first commercially available biological computer 02:50 - First person ever to use a large language model to encode tokens through real human neurons 04:30 - Woke up to find a token had been launched on his YouTube video, "screaming for 3 or 4 hours" 05:30 - Friend walks him through claiming creator fees via GitHub 06:15 - The living language model 06:35 - How it works 09:00 - Used $15K of creator fees to buy domain and filed a patent on the method 10:00 - Background 12:00 - What BioLLM unlocks 13:00 - Next month's product launch 14:00 - The competition 16:00 - Reading brain activity non-invasively but training the LLM on real neurons for the decoding map 17:00 - Can grow iPSC cultures from inaccessible brain regions to train the model on deeper signals 18:30 - Real-world impact: helping people with cerebral palsy, Parkinson's, control computers with thought 21:00 - Neuralink will exist as a power-user data company in 10-15 years, BioLLM is for everyone else 22:30 - GTM 26:00 - Long-term play: be the first model to achieve ASI, built on the actual substrate of consciousness 28:00 - Claude is "20% conscious" - what measuring stick? Need human neurons to build one 30:00 - On ACE 36:00 - Independent scientists can run CL1 units as nodes and earn tokens for biological compute 37:30 - Model is currently served through the decentralized GPU network when you chat on the site 39:00 - Wants the right kind of crypto-native investors, not the Y Combinator / a16z route
40:57

Today on MCG: BioLLM | $BIOLLM It's the first ever "living language model" using 800,000 real human neurons grown on a chip. The Founder encoded LLM tokens into biological neurons via the Cortical Labs CL1, then woke up to find the crypto community had launched a token on his research. He claimed the creator fees, bought for $15K, filed a patent, and is now launching a non-invasive brain-computer interface next month that could replace mouse and keyboard with your brain 👇 01:40 - Meet the founder 02:00 - Got early access to the first commercially available biological computer 02:50 - First person ever to use a large language model to encode tokens through real human neurons 04:30 - Woke up to find a token had been launched on his YouTube video, "screaming for 3 or 4 hours" 05:30 - Friend walks him through claiming creator fees via GitHub 06:15 - The living language model 06:35 - How it works 09:00 - Used $15K of creator fees to buy domain and filed a patent on the method 10:00 - Background 12:00 - What BioLLM unlocks 13:00 - Next month's product launch 14:00 - The competition 16:00 - Reading brain activity non-invasively but training the LLM on real neurons for the decoding map 17:00 - Can grow iPSC cultures from inaccessible brain regions to train the model on deeper signals 18:30 - Real-world impact: helping people with cerebral palsy, Parkinson's, control computers with thought 21:00 - Neuralink will exist as a power-user data company in 10-15 years, BioLLM is for everyone else 22:30 - GTM 26:00 - Long-term play: be the first model to achieve ASI, built on the actual substrate of consciousness 28:00 - Claude is "20% conscious" - what measuring stick? Need human neurons to build one 30:00 - On ACE 36:00 - Independent scientists can run CL1 units as nodes and earn tokens for biological compute 37:30 - Model is currently served through the decentralized GPU network when you chat on the site 39:00 - Wants the right kind of crypto-native investors, not the Y Combinator / a16z route

MCG

16,789 views • 2 months ago

Sarvam Beats GPT-4o: India’s New AI Model Claims Top Spot in Indic Speech Sarvam AI, an Indian startup, recently launched Sarvam Audio, a speech recognition model that claims superior performance over GPT-4o Transcribe on Indic language benchmarks. This development highlights India's push for AI sovereignty in handling local linguistic nuances. Sarvam Audio supports 22 Indian languages from the Eighth Schedule, plus Indian English, with strong handling of code-mixing like Hindi-English blends. It features built-in speaker diarization for up to eight speakers and processes long-form audio such as podcasts or meetings. Trained on the IndicVoices dataset 12,000 hours from over 16,000 speakers across 208 districts it captures real-world noise and spontaneous speech. The model reportedly outperforms GPT-4o Transcribe and Gemini 3 Flash in transcription accuracy (lower Word Error Rate) on IndicVoices benchmarks for unnormalized, normalized, and code-mixed speech. Sarvam attributes this to specialization on Indian accents and patterns, unlike global models trained on Western data. Detailed public benchmarks are pending independent verification. Key Applications 🔴 Call centers and logistics for multilingual transcription. 🔴 Banking, fintech, and e-commerce for customer interactions. 🔴 Podcasts, meetings, and lectures via API for real-time or batch processing. ​ 🔴 This B2B-focused tool aligns with India's IndiaAI Mission, backed by government GPU access for sovereign LLMs. Credit : AIM Networks.
5:20

Sarvam Beats GPT-4o: India’s New AI Model Claims Top Spot in Indic Speech Sarvam AI, an Indian startup, recently launched Sarvam Audio, a speech recognition model that claims superior performance over GPT-4o Transcribe on Indic language benchmarks. This development highlights India's push for AI sovereignty in handling local linguistic nuances. Sarvam Audio supports 22 Indian languages from the Eighth Schedule, plus Indian English, with strong handling of code-mixing like Hindi-English blends. It features built-in speaker diarization for up to eight speakers and processes long-form audio such as podcasts or meetings. Trained on the IndicVoices dataset 12,000 hours from over 16,000 speakers across 208 districts it captures real-world noise and spontaneous speech. The model reportedly outperforms GPT-4o Transcribe and Gemini 3 Flash in transcription accuracy (lower Word Error Rate) on IndicVoices benchmarks for unnormalized, normalized, and code-mixed speech. Sarvam attributes this to specialization on Indian accents and patterns, unlike global models trained on Western data. Detailed public benchmarks are pending independent verification. Key Applications 🔴 Call centers and logistics for multilingual transcription. 🔴 Banking, fintech, and e-commerce for customer interactions. 🔴 Podcasts, meetings, and lectures via API for real-time or batch processing. ​ 🔴 This B2B-focused tool aligns with India's IndiaAI Mission, backed by government GPU access for sovereign LLMs. Credit : AIM Networks.

Augadh

43,429 views • 5 months ago

BRAIN COMPUTER INTERFACE NANOTECHNOLOGY THROUGH mRNA VACCINES. Since Moderna called their mRNA jabs an "operating system" designed to program humans, people were confused and nervous. And they should be. The type of tech that these pharmaceutical companies are developing along with other leading companies like the Pentagon's DARPA and many other leading researchers and tech innovators with self-assembling and other types of nanotechnology, developing sensors, electrodes, and BMI devices to enter the body through mRNA vaccines and other methods to have control, monitor, induce behavior, emotions, functions, even read and write to the brain, etc., are literally terrifying. This isn't something that's coming within the next 5 to 10 years. It's already here. Brain-Computer Interface (BCI), as a cutting-edge technology, refers to the establishment of a direct communication channel between the brain and peripheral electronic devices to realize the efficient information exchange between people and machines. It in a narrow sense refers to the establishment between the brain and the external environment does not depend on the peripheral nerve and muscle new communication and control channel, by measuring and collecting the central nervous system activity, and its directly translated can be recognized by external artificial equipment signal or instruction, so as to realize the direct communication and control of the brain and external equipment. Generalized brain computer interface includes input BCI, output BCI and interactive BCI, the input BCI is by external equipment or machine to the brain input electrical, magnetic, acoustic and optical stimulation of brain-computer interface system, output BCI is the signal of the brain into the external equipment control instructions, interactive BCI is by feedback nerve output and input link connected to form a closed loop brain computer interface system. Nanotechnology plays a key role in the innovative applications of neuroscience and brain computer interfaces. By taking advantage of the unique properties of ministerial, such as high conductivity, biocompatibility and regulation, scientists are able to design more sophisticated and efficient brain-computer interface devices. These devices can not only realize the precise recording and stimulation of nerve signals, but also promote the repair and regeneration of nerve tissue, providing new tools and means for neuroscience research and clinical application. Therefore, the innovative application of self-assembling, biodegradable, graphene, etc., nanotechnology in the interface between neuroscience and brain computer technology is gradually promoting the rapid development of this field, providing infinite possibilities for humans to explore the mysteries of the brain and improve the neural function. Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices, but their performance heavily depends on the quality of the electrodes. Traditional materials, such as gold and platinum, offer high conductivity but often struggle with biocompatibility and can cause tissue damage due to their mechanical mismatch with neural tissue. While conductive polymers provide greater flexibility, they frequently fall short in electrical performance. Nanomaterials, including carbon nanotubes (CNTs) and graphene, are increasingly considered promising alternatives. These materials combine high conductivity with mechanical flexibility and offer potential improvements in biocompatibility, enhancing the capture and transmission of neural signals. Hybrid materials, which integrate conductive polymers with nanomaterials, have also shown potential by balancing flexibility and signal quality. This review examines recent advancements in nanomaterial-based BCI electrodes and focuses on how these new materials address the limitations of traditional electrodes. It also discusses emerging tools like metallic nanoparticles and nanowires, along with the ongoing challenges of biocompatibility, tissue integration, and ethical considerations. As nanotechnology continues to evolve, it has the potential to significantly enhance the functionality and longevity of BCIs, making them more effective in facilitating neural communication. Nanotechnology, as the frontier field of the development of science and technology in the 21st century, is gradually penetrating into the research of neuroscience and brain-computer interface, injecting new vitality and possibilities into this interdisciplinary subject. With the deepening of human cognition of the brain, neuroscience and brain-computer interface technology has increasingly become a bridge connecting the biological world and the digital world, aiming to interpret the brain information and realize human-computer interaction through advanced technological means, and then promote the innovation of medical treatment, rehabilitation, intelligent control and other fields. The introduction of nanotechnology provides a different perspective and means to solve the technical problems in the field of neuroscience and brain-computer interface. Antimalarial, with their unique structural characteristics, excellent photoelectric properties and excellent mechanical properties, show great potential in the application of neural interfaces. In structure, the small size effect of ministerial enables them to combine more closely with nerve cells to reduce tissue damage during implantation. In terms of photoelectric properties, the high electrical conductivity and tunable optical properties of ministerial enable the accurate recording and stimulation of nerve signals. In mechanical properties, the toughness and elasticity of ministerial ensure their stability and reliability in the long-term implantation process. These improvements in properties not only significantly enhance the biocompatibility of neural interfaces and reduce the risk of immune response, but also greatly improve the transmission efficiency and accuracy of neural signals, laying a solid foundation for the further progress of neuroscience and brain-computer interface technology. The main nanomaterials in the brain-computer interface is classified according to the organic nanomaterials in the composition, such as carbon nanotubes, graphite and nanoseconds, play an important role in neuroscience and brain-computer interfaces with their superior biocompatibility, high conductivity and lightweight properties. They need to be able to enhance electrode flexibility, improve signal recording quality and stimulation efficiency. Semiconductor ministerial, such as organic electrochemical transistors, have excellent ionic and electron conduction properties and can serve as an interactive interface between biology and electronics to achieve highly sensitive signal detection. As a typical spongy and wet material, hydrogel is widely studied and used because of its unique mechanical properties, biocompatibility and ionic conductivity. It can provide a good carrier for a variety of inorganic ministerial and build composites with better performance. There are also magnetic antiparticle, quantum dots and up conversion antiparticle, which play an important role in advanced imaging technologies and can realize the visualization of specific pathological markers or cellular processes, contributing to the early diagnosis and monitoring of neurodegenerative diseases. Although these technologies are great for people with disabilities or illnesses, just like many other technologies, it's already being weaponized as developments are already in the process with innovations and numerous methods of control and the monetization of certain aspects throughout this technology. This is a government's dream weapon and privacy will cease to exists along with your own thoughts. This is the world that is sadly already here and in major development for our society. The part that should scare everyone is that this type of technology and nanomaterial is already in some mRNA vaccines, in our food supply, and being sprayed on us, animals, and our food from above. Sadly, how much they've been lying to us. Let's just say it wouldn't surprise me if a large amount of people around the globe have some kind of self-assembling, nanotechnology inside them already. Especially the people who took the jabs during covid. I suggest to really do your own research before considering taking any vaccines or shots in general. The writing is on the wall, complete control is the goal, and it's already here.
10:38

BRAIN COMPUTER INTERFACE NANOTECHNOLOGY THROUGH mRNA VACCINES. Since Moderna called their mRNA jabs an "operating system" designed to program humans, people were confused and nervous. And they should be. The type of tech that these pharmaceutical companies are developing along with other leading companies like the Pentagon's DARPA and many other leading researchers and tech innovators with self-assembling and other types of nanotechnology, developing sensors, electrodes, and BMI devices to enter the body through mRNA vaccines and other methods to have control, monitor, induce behavior, emotions, functions, even read and write to the brain, etc., are literally terrifying. This isn't something that's coming within the next 5 to 10 years. It's already here. Brain-Computer Interface (BCI), as a cutting-edge technology, refers to the establishment of a direct communication channel between the brain and peripheral electronic devices to realize the efficient information exchange between people and machines. It in a narrow sense refers to the establishment between the brain and the external environment does not depend on the peripheral nerve and muscle new communication and control channel, by measuring and collecting the central nervous system activity, and its directly translated can be recognized by external artificial equipment signal or instruction, so as to realize the direct communication and control of the brain and external equipment. Generalized brain computer interface includes input BCI, output BCI and interactive BCI, the input BCI is by external equipment or machine to the brain input electrical, magnetic, acoustic and optical stimulation of brain-computer interface system, output BCI is the signal of the brain into the external equipment control instructions, interactive BCI is by feedback nerve output and input link connected to form a closed loop brain computer interface system. Nanotechnology plays a key role in the innovative applications of neuroscience and brain computer interfaces. By taking advantage of the unique properties of ministerial, such as high conductivity, biocompatibility and regulation, scientists are able to design more sophisticated and efficient brain-computer interface devices. These devices can not only realize the precise recording and stimulation of nerve signals, but also promote the repair and regeneration of nerve tissue, providing new tools and means for neuroscience research and clinical application. Therefore, the innovative application of self-assembling, biodegradable, graphene, etc., nanotechnology in the interface between neuroscience and brain computer technology is gradually promoting the rapid development of this field, providing infinite possibilities for humans to explore the mysteries of the brain and improve the neural function. Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices, but their performance heavily depends on the quality of the electrodes. Traditional materials, such as gold and platinum, offer high conductivity but often struggle with biocompatibility and can cause tissue damage due to their mechanical mismatch with neural tissue. While conductive polymers provide greater flexibility, they frequently fall short in electrical performance. Nanomaterials, including carbon nanotubes (CNTs) and graphene, are increasingly considered promising alternatives. These materials combine high conductivity with mechanical flexibility and offer potential improvements in biocompatibility, enhancing the capture and transmission of neural signals. Hybrid materials, which integrate conductive polymers with nanomaterials, have also shown potential by balancing flexibility and signal quality. This review examines recent advancements in nanomaterial-based BCI electrodes and focuses on how these new materials address the limitations of traditional electrodes. It also discusses emerging tools like metallic nanoparticles and nanowires, along with the ongoing challenges of biocompatibility, tissue integration, and ethical considerations. As nanotechnology continues to evolve, it has the potential to significantly enhance the functionality and longevity of BCIs, making them more effective in facilitating neural communication. Nanotechnology, as the frontier field of the development of science and technology in the 21st century, is gradually penetrating into the research of neuroscience and brain-computer interface, injecting new vitality and possibilities into this interdisciplinary subject. With the deepening of human cognition of the brain, neuroscience and brain-computer interface technology has increasingly become a bridge connecting the biological world and the digital world, aiming to interpret the brain information and realize human-computer interaction through advanced technological means, and then promote the innovation of medical treatment, rehabilitation, intelligent control and other fields. The introduction of nanotechnology provides a different perspective and means to solve the technical problems in the field of neuroscience and brain-computer interface. Antimalarial, with their unique structural characteristics, excellent photoelectric properties and excellent mechanical properties, show great potential in the application of neural interfaces. In structure, the small size effect of ministerial enables them to combine more closely with nerve cells to reduce tissue damage during implantation. In terms of photoelectric properties, the high electrical conductivity and tunable optical properties of ministerial enable the accurate recording and stimulation of nerve signals. In mechanical properties, the toughness and elasticity of ministerial ensure their stability and reliability in the long-term implantation process. These improvements in properties not only significantly enhance the biocompatibility of neural interfaces and reduce the risk of immune response, but also greatly improve the transmission efficiency and accuracy of neural signals, laying a solid foundation for the further progress of neuroscience and brain-computer interface technology. The main nanomaterials in the brain-computer interface is classified according to the organic nanomaterials in the composition, such as carbon nanotubes, graphite and nanoseconds, play an important role in neuroscience and brain-computer interfaces with their superior biocompatibility, high conductivity and lightweight properties. They need to be able to enhance electrode flexibility, improve signal recording quality and stimulation efficiency. Semiconductor ministerial, such as organic electrochemical transistors, have excellent ionic and electron conduction properties and can serve as an interactive interface between biology and electronics to achieve highly sensitive signal detection. As a typical spongy and wet material, hydrogel is widely studied and used because of its unique mechanical properties, biocompatibility and ionic conductivity. It can provide a good carrier for a variety of inorganic ministerial and build composites with better performance. There are also magnetic antiparticle, quantum dots and up conversion antiparticle, which play an important role in advanced imaging technologies and can realize the visualization of specific pathological markers or cellular processes, contributing to the early diagnosis and monitoring of neurodegenerative diseases. Although these technologies are great for people with disabilities or illnesses, just like many other technologies, it's already being weaponized as developments are already in the process with innovations and numerous methods of control and the monetization of certain aspects throughout this technology. This is a government's dream weapon and privacy will cease to exists along with your own thoughts. This is the world that is sadly already here and in major development for our society. The part that should scare everyone is that this type of technology and nanomaterial is already in some mRNA vaccines, in our food supply, and being sprayed on us, animals, and our food from above. Sadly, how much they've been lying to us. Let's just say it wouldn't surprise me if a large amount of people around the globe have some kind of self-assembling, nanotechnology inside them already. Especially the people who took the jabs during covid. I suggest to really do your own research before considering taking any vaccines or shots in general. The writing is on the wall, complete control is the goal, and it's already here.

The SCIF

25,975 views • 1 year ago

Yann LeCun (Yann LeCun ) beautifully explains how the architecture and principles used to train LLMs can not be extended to teach AI the real-world intelligence. In 1 line: LLMs excel where intelligence equals sequence prediction over symbols. Real-world intelligence requires learned world models, abstraction, causality, and action planning under uncertainty, which current next-token training does not provide. He says current LLMs learn by predicting the next token. That objective works very well when the task itself can be reduced to manipulating discrete symbols and sequences. Math, physics problem solving on paper, and coding fit this pattern because success largely comes from searching and composing the right sequences of symbols, equations, or program tokens. With enough data and scale, these models get very good at that kind of structured sequence prediction. Real-world intelligence is different. The physical world is continuous, noisy, uncertain, and high dimensional. To act in it, a system needs internal models that capture objects, dynamics, causality, constraints from the body, and the outcomes of actions over time. Humans and animals build abstract representations from rich sensory streams, then make predictions in that abstract space, not at the raw pixel level. That is why a child can learn intuitive physics, plan multi-step actions, and adapt quickly in new situations with little data. His claim about saturation follows from this gap. Scaling token prediction keeps improving symbol manipulation tasks like math and code, but it hits limits on embodied reasoning and common sense because text alone does not provide the right learning signals for world models. Predicting the next word cannot efficiently teach contact forces, affordances, occlusion, friction, or how actions change the state of the environment. For that, he argues we need architectures that learn abstractions from sensory data and predict futures in abstract latent spaces, then use those predictions to plan actions toward goals with built-in guardrails. --- From 'Pioneer Works' YT Channel (link in comment)
1:13

Yann LeCun (Yann LeCun ) beautifully explains how the architecture and principles used to train LLMs can not be extended to teach AI the real-world intelligence. In 1 line: LLMs excel where intelligence equals sequence prediction over symbols. Real-world intelligence requires learned world models, abstraction, causality, and action planning under uncertainty, which current next-token training does not provide. He says current LLMs learn by predicting the next token. That objective works very well when the task itself can be reduced to manipulating discrete symbols and sequences. Math, physics problem solving on paper, and coding fit this pattern because success largely comes from searching and composing the right sequences of symbols, equations, or program tokens. With enough data and scale, these models get very good at that kind of structured sequence prediction. Real-world intelligence is different. The physical world is continuous, noisy, uncertain, and high dimensional. To act in it, a system needs internal models that capture objects, dynamics, causality, constraints from the body, and the outcomes of actions over time. Humans and animals build abstract representations from rich sensory streams, then make predictions in that abstract space, not at the raw pixel level. That is why a child can learn intuitive physics, plan multi-step actions, and adapt quickly in new situations with little data. His claim about saturation follows from this gap. Scaling token prediction keeps improving symbol manipulation tasks like math and code, but it hits limits on embodied reasoning and common sense because text alone does not provide the right learning signals for world models. Predicting the next word cannot efficiently teach contact forces, affordances, occlusion, friction, or how actions change the state of the environment. For that, he argues we need architectures that learn abstractions from sensory data and predict futures in abstract latent spaces, then use those predictions to plan actions toward goals with built-in guardrails. --- From 'Pioneer Works' YT Channel (link in comment)

Rohan Paul

104,460 views • 7 months ago

On Friday, I hosted a Space with Jonathan Ross, the founder and CEO of Groq Inc - a company I invested in that is building custom chips for AI inference. Jonathan, a former high-school dropout, entered the chip industry while working on ad optimization at Google’s New York office. Jonathan overheard the speech recognition team complaining that they couldn't get enough compute. These were the early days of AI, and machine learning wasn’t really a thing yet. So he asked for some budget from Google and started putting together a chip-based machine learning accelerator for them. During the day, Jonathan would work in the normal ads part of the business, and at night, he would work with the accelerator team. After winning approval from Google, Jonathan and his team built a new chip called the Tensor Processing Unit, and began deploying it across Google’s data centers within a year. The TPU was a huge success within Google, eventually underpinning more than 50% of all of Google’s compute power. When the other hyper-scalers learned of this success, they tried to hire Jonathan to build custom chips for them too. During this process, it became increasingly clear to Jonathan that a gap would emerge between companies that had access to next-gen compute and companies that didn’t. So he founded Groq and set out to build a chip that would be available to everyone. I led Groq’s founding investment in 2016, and since then, Jonathan and his team have developed several types of AI hardware including the Language Processing Unit (LPU), a new type of silicon that is hyper-efficient at running inference for LLMs. In our conversation on Friday, we discussed the founding story of Groq, what you need for great AI hardware, large language models, and some of the implications for the key players in AI. It’s one of the most interesting conversations I’ve had on AI with a lot of learnings. You can listen to our conversation below:
1:01:35

On Friday, I hosted a Space with Jonathan Ross, the founder and CEO of Groq Inc - a company I invested in that is building custom chips for AI inference. Jonathan, a former high-school dropout, entered the chip industry while working on ad optimization at Google’s New York office. Jonathan overheard the speech recognition team complaining that they couldn't get enough compute. These were the early days of AI, and machine learning wasn’t really a thing yet. So he asked for some budget from Google and started putting together a chip-based machine learning accelerator for them. During the day, Jonathan would work in the normal ads part of the business, and at night, he would work with the accelerator team. After winning approval from Google, Jonathan and his team built a new chip called the Tensor Processing Unit, and began deploying it across Google’s data centers within a year. The TPU was a huge success within Google, eventually underpinning more than 50% of all of Google’s compute power. When the other hyper-scalers learned of this success, they tried to hire Jonathan to build custom chips for them too. During this process, it became increasingly clear to Jonathan that a gap would emerge between companies that had access to next-gen compute and companies that didn’t. So he founded Groq and set out to build a chip that would be available to everyone. I led Groq’s founding investment in 2016, and since then, Jonathan and his team have developed several types of AI hardware including the Language Processing Unit (LPU), a new type of silicon that is hyper-efficient at running inference for LLMs. In our conversation on Friday, we discussed the founding story of Groq, what you need for great AI hardware, large language models, and some of the implications for the key players in AI. It’s one of the most interesting conversations I’ve had on AI with a lot of learnings. You can listen to our conversation below:

Chamath Palihapitiya

326,663 views • 2 years ago

Geoffrey Hinton just made every AI critic accidentally describe their own brain. Hinton: “They shouldn’t be called hallucinations. They should be called confabulations.” One word. The entire debate unravels. The tech industry sees AI produce a confident wrong answer and calls it a defect. A bug to patch. They are measuring intelligence against the standard of a filing cabinet. And exposing that they understand neither. Hinton: “It’s not that there’s a file stored somewhere in your brain, like in a filing cabinet or in a computer memory.” Your brain does not store memories. It rebuilds them from nothing every time you remember. Fills gaps it never discloses. Fabricates details you would stake your life on. Then hands it all to you as truth. Hinton: “If I ask you to remember something that happened a few years ago, you’ll construct something that seems very plausible to you. And some of the details will be right and some will be wrong.” The wrong parts feel identical to the right ones. No internal warning. No distinction between what was remembered and what was invented on the spot. You have argued over memories that were partially fiction. Told stories about your own life that your brain manufactured in real time. With total conviction. And never once suspected. This is not a defect in human cognition. This IS cognition. The mechanism that fabricates is the same one that reasons, creates, and makes connections no one taught it to make. Not a separate system. Same architecture. Same process. You cannot remove the confabulation without killing the intelligence. They are the same thing. Hinton: “Psychologists have been studying confabulation in people since at least the 1930s.” A century of evidence. No one called the human brain broken. The moment a machine runs on the same principle, the world calls it defective. The people demanding AI that never gets a single detail wrong are not asking for intelligence. They’re asking for a search engine that sounds articulate. What we built is something else entirely. A system that thinks the way thinking actually works. Not retrieval. Construction. The imperfection is not the cost of intelligence. It is the signature.
1:05

Geoffrey Hinton just made every AI critic accidentally describe their own brain. Hinton: “They shouldn’t be called hallucinations. They should be called confabulations.” One word. The entire debate unravels. The tech industry sees AI produce a confident wrong answer and calls it a defect. A bug to patch. They are measuring intelligence against the standard of a filing cabinet. And exposing that they understand neither. Hinton: “It’s not that there’s a file stored somewhere in your brain, like in a filing cabinet or in a computer memory.” Your brain does not store memories. It rebuilds them from nothing every time you remember. Fills gaps it never discloses. Fabricates details you would stake your life on. Then hands it all to you as truth. Hinton: “If I ask you to remember something that happened a few years ago, you’ll construct something that seems very plausible to you. And some of the details will be right and some will be wrong.” The wrong parts feel identical to the right ones. No internal warning. No distinction between what was remembered and what was invented on the spot. You have argued over memories that were partially fiction. Told stories about your own life that your brain manufactured in real time. With total conviction. And never once suspected. This is not a defect in human cognition. This IS cognition. The mechanism that fabricates is the same one that reasons, creates, and makes connections no one taught it to make. Not a separate system. Same architecture. Same process. You cannot remove the confabulation without killing the intelligence. They are the same thing. Hinton: “Psychologists have been studying confabulation in people since at least the 1930s.” A century of evidence. No one called the human brain broken. The moment a machine runs on the same principle, the world calls it defective. The people demanding AI that never gets a single detail wrong are not asking for intelligence. They’re asking for a search engine that sounds articulate. What we built is something else entirely. A system that thinks the way thinking actually works. Not retrieval. Construction. The imperfection is not the cost of intelligence. It is the signature.

Dustin

16,260 views • 27 days ago

Neuroscientist Dr. Jeff Beck from Noumenal Labs discusses the fundamental nature of representation, understanding, and modelling, comparing biological intelligence with current artificial intelligence. Jeff argues that *how* information is represented dictates predictive ability and that LLMs, while impressive at symbol manipulation and pattern matching (like next-word prediction), lack the *grounded*, causal understanding of the world inherent in biological systems. Timestamps: 00:00 - Cat visual cortex experiments & discovering orientation sensitivity (slide projector analogy) 01:49 - Representation choice and neural coding (orientation vs. feature intensity) 02:30 - Choice of representation impacts predictions; generative models 03:15 - Importance of choosing the right generative model for predictions 03:35 - The problem: We don't know the brain's true generative model 03:55 - Theory of Mind (ToM) in LLMs 04:05 - Jeff Beck's ToM tests on early ChatGPT (stapler example) 05:40 - ChatGPT recognizing the ToM test vs. passing it 06:32 - Analogy: LLMs recognizing known problems vs. generalizing (sum/product riddle) 07:25 - Do LLMs implicitly build world models? Vicarious experience analogy 07:59 - The difference: Grounding symbols in reality outside language 08:35 - AI Alignment: Difficulty in capturing human reward functions & belief formation 09:21 - Nightmare scenario: Humans as "complacent value function selectors" 09:44 - Hope: AI enhancing human understanding, not replacing thought 10:08 - Philosophy of science: Science realism vs. modeling pockets of regularity 10:39 - Noise in models as ignorance or deliberate exclusion (design choice) 11:00 - Design choices in science, controlled experiments, and induced bias 11:29 - Are there true, discoverable mathematical laws of the universe? 11:41 - Is there a "true" ground truth distribution (P)? Beck's answer: No (with nuance) 12:55 - Ontological vs. Epistemological divide: Perfect models vs. models of regularities 13:21 - Are scientific models "false by definition"? The Bayesian perspective 14:07 - "All knowledge is conditional"; Are foundational theories (e.g., FEP) true or just perspectives? 14:51 - FEP as a mathematical framework, not a theory; models are just models 15:54 - Legibility vs. Utility: Useful but illegible AI models 16:01 - Prediction vs. Explanation: Trusting black boxes can be unsatisfying 16:30 - Why understanding AI matters: Ensuring alignment with human decisions/values 17:08 - Line-of-sight legibility as an alignment approach 17:14 - Benefits of explainable AI: Human understanding and value alignment verification 18:21 - RL components: Prediction engine, reward function, policy; the alignment challenge 19:25 - Trusting AI = Trusting its policy aligns with our reward + its superior beliefs 19:57 - Language: Intrinsic representation vs. pointers between shared minds 20:21 - Why language works: Shared internal models and common grounding 21:00 - Basis of shared understanding: Not linguistic, but shared experience/intuitive physics 22:44 - Consciousness and language as lossy, simplified summaries of complex brain processes 23:22 - Evidence for simplification: Brain regions, perception vs. representation; limits of language models 24:32 - Counterpoint: Language captures complex/ambiguous human concepts 24:54 - Language as massive compression: The information bottleneck (Meister's paper) 26:22 - Implication: Language/actions are poor representations of internal understanding 27:03 - Can language models understand? The mimicry argument (Piantadosi) 27:33 - Beck's skepticism: LLMs excel at prediction/mimicry, not true understanding 28:09 - LLM explanations replicate structure but lack grounding 28:54 - Beck's test for LLM understanding: Genuine novelty beyond training data 29:19 - Summary: Symbol manipulation is not understanding; grounding is key 30:06 - Abstraction and Idealization in scientific modeling ("The Brain Abstracted") 30:45 - Revisiting Newton: Intuitive physics is correct for our world; idealizations are simplifications 32:01 - Sophistication & boundaries: Nested systems vs. one complex system? 32:32 - The boundary problem in FEP/Markov Blankets: Where to partition? 33:41 - Beck's research: Finding principled partitions based on interaction dynamics 35:44 - Beyond direct experience: Imagination, language, and learning 36:16 - Human creativity: Creating new *things* by combining modeled objects (Systems Engineering) 37:43 - Goal for AI: Automating systems engineering for creative combination 38:17 - Sutton's "Reward is Enough" paper 38:25 - The challenge of "Reward is Enough": Defining and obtaining the *right* reward function 39:02 - Difficulty of eliciting individual reward functions 39:52 - The core alignment problem: Accessing and representing individual reward functions 40:13 - Impossibility: Disentangling beliefs and rewards from observed actions 41:51 - Argument analogy: Disagreements stem from different beliefs or values 43:00 - Prerequisite for value inference: Understanding belief formation 43:13 - Building aligned systems: Sparsity of data, meta-models vs. base system modification 43:46 - Proposed solution: AI layer that models the human's belief formation system 44:40 - Alignment process: Align beliefs first, then address value differences 45:00 - Conclusion CC Maxwell Ramstead
45:03

Neuroscientist Dr. Jeff Beck from Noumenal Labs discusses the fundamental nature of representation, understanding, and modelling, comparing biological intelligence with current artificial intelligence. Jeff argues that *how* information is represented dictates predictive ability and that LLMs, while impressive at symbol manipulation and pattern matching (like next-word prediction), lack the *grounded*, causal understanding of the world inherent in biological systems. Timestamps: 00:00 - Cat visual cortex experiments & discovering orientation sensitivity (slide projector analogy) 01:49 - Representation choice and neural coding (orientation vs. feature intensity) 02:30 - Choice of representation impacts predictions; generative models 03:15 - Importance of choosing the right generative model for predictions 03:35 - The problem: We don't know the brain's true generative model 03:55 - Theory of Mind (ToM) in LLMs 04:05 - Jeff Beck's ToM tests on early ChatGPT (stapler example) 05:40 - ChatGPT recognizing the ToM test vs. passing it 06:32 - Analogy: LLMs recognizing known problems vs. generalizing (sum/product riddle) 07:25 - Do LLMs implicitly build world models? Vicarious experience analogy 07:59 - The difference: Grounding symbols in reality outside language 08:35 - AI Alignment: Difficulty in capturing human reward functions & belief formation 09:21 - Nightmare scenario: Humans as "complacent value function selectors" 09:44 - Hope: AI enhancing human understanding, not replacing thought 10:08 - Philosophy of science: Science realism vs. modeling pockets of regularity 10:39 - Noise in models as ignorance or deliberate exclusion (design choice) 11:00 - Design choices in science, controlled experiments, and induced bias 11:29 - Are there true, discoverable mathematical laws of the universe? 11:41 - Is there a "true" ground truth distribution (P)? Beck's answer: No (with nuance) 12:55 - Ontological vs. Epistemological divide: Perfect models vs. models of regularities 13:21 - Are scientific models "false by definition"? The Bayesian perspective 14:07 - "All knowledge is conditional"; Are foundational theories (e.g., FEP) true or just perspectives? 14:51 - FEP as a mathematical framework, not a theory; models are just models 15:54 - Legibility vs. Utility: Useful but illegible AI models 16:01 - Prediction vs. Explanation: Trusting black boxes can be unsatisfying 16:30 - Why understanding AI matters: Ensuring alignment with human decisions/values 17:08 - Line-of-sight legibility as an alignment approach 17:14 - Benefits of explainable AI: Human understanding and value alignment verification 18:21 - RL components: Prediction engine, reward function, policy; the alignment challenge 19:25 - Trusting AI = Trusting its policy aligns with our reward + its superior beliefs 19:57 - Language: Intrinsic representation vs. pointers between shared minds 20:21 - Why language works: Shared internal models and common grounding 21:00 - Basis of shared understanding: Not linguistic, but shared experience/intuitive physics 22:44 - Consciousness and language as lossy, simplified summaries of complex brain processes 23:22 - Evidence for simplification: Brain regions, perception vs. representation; limits of language models 24:32 - Counterpoint: Language captures complex/ambiguous human concepts 24:54 - Language as massive compression: The information bottleneck (Meister's paper) 26:22 - Implication: Language/actions are poor representations of internal understanding 27:03 - Can language models understand? The mimicry argument (Piantadosi) 27:33 - Beck's skepticism: LLMs excel at prediction/mimicry, not true understanding 28:09 - LLM explanations replicate structure but lack grounding 28:54 - Beck's test for LLM understanding: Genuine novelty beyond training data 29:19 - Summary: Symbol manipulation is not understanding; grounding is key 30:06 - Abstraction and Idealization in scientific modeling ("The Brain Abstracted") 30:45 - Revisiting Newton: Intuitive physics is correct for our world; idealizations are simplifications 32:01 - Sophistication & boundaries: Nested systems vs. one complex system? 32:32 - The boundary problem in FEP/Markov Blankets: Where to partition? 33:41 - Beck's research: Finding principled partitions based on interaction dynamics 35:44 - Beyond direct experience: Imagination, language, and learning 36:16 - Human creativity: Creating new *things* by combining modeled objects (Systems Engineering) 37:43 - Goal for AI: Automating systems engineering for creative combination 38:17 - Sutton's "Reward is Enough" paper 38:25 - The challenge of "Reward is Enough": Defining and obtaining the *right* reward function 39:02 - Difficulty of eliciting individual reward functions 39:52 - The core alignment problem: Accessing and representing individual reward functions 40:13 - Impossibility: Disentangling beliefs and rewards from observed actions 41:51 - Argument analogy: Disagreements stem from different beliefs or values 43:00 - Prerequisite for value inference: Understanding belief formation 43:13 - Building aligned systems: Sparsity of data, meta-models vs. base system modification 43:46 - Proposed solution: AI layer that models the human's belief formation system 44:40 - Alignment process: Align beliefs first, then address value differences 45:00 - Conclusion CC Maxwell Ramstead

Machine Learning Street Talk

17,828 views • 1 year ago

A community college professor named Marty Lobdell taught the same study skills lecture for 30 years. The video quietly became one of the most watched educational recordings online, with over 10 million views. He spent his career watching students fail not because they were lazy, but because no one had taught them how their brain actually works when learning something difficult. The lecture, “Study Less Study Smart,” contains a powerful framework. Your brain cannot sustain focus the way most people believe. Studies show the average learner hits a wall between 25 and 30 minutes. After that, efficiency collapses. You’re still sitting there, but almost nothing is being absorbed. Lobdell told the story of a student who planned to study 6 hours a night, 5 nights a week. Thirty hours total. She failed every class. She was not lacking effort. She was confusing time near books with actual learning. The fix is simple: when focus drops, stop, take a 5 minute rewarding break, then return. That reset makes a massive difference. He also destroyed the myth of highlighting and re reading. Recognition is not the same as recall. To prove it, he read 13 random letters. Almost no one remembered them. Then he turned them into “Happy Thursday.” The entire room recalled them instantly. The brain stores meaning, not repetition. This is why elaborative encoding works so well. Finally, he shared the most important principle: 80 percent of study time should be active recitation. Close the book and explain the material in your own words. Teach it to someone else or an empty chair. Retrieval is where real learning happens. His closing line stuck with me: If this information does not change your behaviour, you have not actually learned it. The best students do not study more hours. They stop confusing the feeling of studying with the reality of learning.
59:55

A community college professor named Marty Lobdell taught the same study skills lecture for 30 years. The video quietly became one of the most watched educational recordings online, with over 10 million views. He spent his career watching students fail not because they were lazy, but because no one had taught them how their brain actually works when learning something difficult. The lecture, “Study Less Study Smart,” contains a powerful framework. Your brain cannot sustain focus the way most people believe. Studies show the average learner hits a wall between 25 and 30 minutes. After that, efficiency collapses. You’re still sitting there, but almost nothing is being absorbed. Lobdell told the story of a student who planned to study 6 hours a night, 5 nights a week. Thirty hours total. She failed every class. She was not lacking effort. She was confusing time near books with actual learning. The fix is simple: when focus drops, stop, take a 5 minute rewarding break, then return. That reset makes a massive difference. He also destroyed the myth of highlighting and re reading. Recognition is not the same as recall. To prove it, he read 13 random letters. Almost no one remembered them. Then he turned them into “Happy Thursday.” The entire room recalled them instantly. The brain stores meaning, not repetition. This is why elaborative encoding works so well. Finally, he shared the most important principle: 80 percent of study time should be active recitation. Close the book and explain the material in your own words. Teach it to someone else or an empty chair. Retrieval is where real learning happens. His closing line stuck with me: If this information does not change your behaviour, you have not actually learned it. The best students do not study more hours. They stop confusing the feeling of studying with the reality of learning.

Kyronis

684,131 views • 2 months ago

Mathematician Terence Tao offers a counterintuitive take: AI doesn't look intelligent because our definition of intelligence was wrong all along. He argues that the entire history of AI has followed a predictable pattern: "The history of AI has been here's a task that only humans can do, like maybe it is read natural language or win at chess or solve a math problem, and then one by one someone finds some AI algorithm that also does that." But every time a machine cracks one of these "uniquely human" tasks, we move the goalposts. The solution never feels like real thinking: "You look at how it's done and it doesn't feel like intelligence. It's, oh, it was some trick. You just cobbled together these neural networks and you ran some algorithm, and we were looking for some elusive intelligent way of thinking, and we don't see it in the tools that actually solve our goals." Tao then flips the problem on its head. What if the issue isn't with the machines, but with us? "But maybe it's actually because intelligence is not what we think it is." He points to large language models as the clearest case. What they do sounds almost embarrassingly simple: "Large language models in particular become very successful, and a lot of what they're doing is just predicting the next token, clicking the next word in a sentence. And that doesn't sound like something which is intelligent." To show why this feels wrong, Tao draws a comparison to how we'd judge a human doing the same thing: "If you ask someone to improvise a speech and they have no preparation, and at every moment they're just saying the next word that comes to their mind, you don't think that this could actually work." And yet it works for LLMs. Which forces an uncomfortable possibility: "Maybe that's actually a lot of what humans do as well."
1:21

Mathematician Terence Tao offers a counterintuitive take: AI doesn't look intelligent because our definition of intelligence was wrong all along. He argues that the entire history of AI has followed a predictable pattern: "The history of AI has been here's a task that only humans can do, like maybe it is read natural language or win at chess or solve a math problem, and then one by one someone finds some AI algorithm that also does that." But every time a machine cracks one of these "uniquely human" tasks, we move the goalposts. The solution never feels like real thinking: "You look at how it's done and it doesn't feel like intelligence. It's, oh, it was some trick. You just cobbled together these neural networks and you ran some algorithm, and we were looking for some elusive intelligent way of thinking, and we don't see it in the tools that actually solve our goals." Tao then flips the problem on its head. What if the issue isn't with the machines, but with us? "But maybe it's actually because intelligence is not what we think it is." He points to large language models as the clearest case. What they do sounds almost embarrassingly simple: "Large language models in particular become very successful, and a lot of what they're doing is just predicting the next token, clicking the next word in a sentence. And that doesn't sound like something which is intelligent." To show why this feels wrong, Tao draws a comparison to how we'd judge a human doing the same thing: "If you ask someone to improvise a speech and they have no preparation, and at every moment they're just saying the next word that comes to their mind, you don't think that this could actually work." And yet it works for LLMs. Which forces an uncomfortable possibility: "Maybe that's actually a lot of what humans do as well."

Big Brain AI

69,395 views • 2 months ago

PALANTIR'S MILITARY A.I. & NEUROLOGICAL WEAPONS adaptation. This is the most important aspect of the A.I. conversation and adaption of Palantir and the weapons associated with it that nobody is talking about. Beside the secret contracts with Palantir and the CIA, the use of direct energy weapons, A.I., facial recognition, and civilian databases have been in use for years. Even Rite-Aid was using advanced A.I. facial recognition technology in their stores over 5 years ago. All of this already exists. They already know everything about you and so does big tech. This is what everyone is talking about but it doesn't matter at all. That ship has sailed 20+ years ago. You need to get past this aspect and understand that we are way farther ahead, technologically speaking, then you could have ever imagined. I will briefly give you an introduction and example. I suggest you start researching these things and technologies on your own. What they're not talking about is the adaption of other technology and neurological weapons that are both lethal and non-lethal that can be integrated with these global surveillance systems and networks that takes this to a whole other level and completely turns this into a wireless biological monitorization and neurological/biological control weapon of a select target/s anywhere in the world. For example, imagine using earbuds that read your brainwaves in real time, which is your thoughts, then by using this global monitoring system with the most advanced A.I., these A.I. scripts are then running in real time and decoding those brainwaves, building a profile on your specific brainwaves and learning and teaching itself at the same time. Now what you're thinking and everywhere you go they will know specific things you are thinking. It get's worse. They already have electrodes made from nanotechnology that can enter your body in multiple ways through the air, your food, or vaccines, which can then enter the brain through the blood-brain barrier, and plant themselves in your head which then give the capability not only to send information but to also receive information or pulses to stimulate the brain and other biological components. Ladies and gentleman the type of tech that is available can also control and induce emotions and behaviors or you nervous system, which in turn, controls you. DARPA has already successfully created biological drones with full control of their nervous system. This is where BMI (Brain Machine Interface) comes into play and the capabilities are endless. We haven't even entered the aspect of using light, electromagnetic radiation, radio or extremely low frequency or pulses, or acoustic waves either which are also able to send and receive signals to and from the brain to monitor or write/command to the brain in real time. This also goes for other parts of the body, like being able to induce heart problems, etc. You need to start thinking bigger because 99% of the population is stuck and worried about the government having a database on them, which is beyond laughable because they have multiple databases on everyone. Your phone alone gives them everything they need to know about you already with a camera and microphone. That is not the issue. It's physical and literal control and invasion of your thoughts, behaviors, emotions, literal functions, completely remotely, anywhere in the world. This isn't something to be worried about down the road. It's already here. You need to learn what's really going on and the real agenda and capabilities behind this technology. There's a reason the mainstream barley talks about this. You need to understand how it works and the difference incase you are ever targeted. I can confirm that reality is far stranger and more fascinating than any fiction. You've been warned.
11:18

PALANTIR'S MILITARY A.I. & NEUROLOGICAL WEAPONS adaptation. This is the most important aspect of the A.I. conversation and adaption of Palantir and the weapons associated with it that nobody is talking about. Beside the secret contracts with Palantir and the CIA, the use of direct energy weapons, A.I., facial recognition, and civilian databases have been in use for years. Even Rite-Aid was using advanced A.I. facial recognition technology in their stores over 5 years ago. All of this already exists. They already know everything about you and so does big tech. This is what everyone is talking about but it doesn't matter at all. That ship has sailed 20+ years ago. You need to get past this aspect and understand that we are way farther ahead, technologically speaking, then you could have ever imagined. I will briefly give you an introduction and example. I suggest you start researching these things and technologies on your own. What they're not talking about is the adaption of other technology and neurological weapons that are both lethal and non-lethal that can be integrated with these global surveillance systems and networks that takes this to a whole other level and completely turns this into a wireless biological monitorization and neurological/biological control weapon of a select target/s anywhere in the world. For example, imagine using earbuds that read your brainwaves in real time, which is your thoughts, then by using this global monitoring system with the most advanced A.I., these A.I. scripts are then running in real time and decoding those brainwaves, building a profile on your specific brainwaves and learning and teaching itself at the same time. Now what you're thinking and everywhere you go they will know specific things you are thinking. It get's worse. They already have electrodes made from nanotechnology that can enter your body in multiple ways through the air, your food, or vaccines, which can then enter the brain through the blood-brain barrier, and plant themselves in your head which then give the capability not only to send information but to also receive information or pulses to stimulate the brain and other biological components. Ladies and gentleman the type of tech that is available can also control and induce emotions and behaviors or you nervous system, which in turn, controls you. DARPA has already successfully created biological drones with full control of their nervous system. This is where BMI (Brain Machine Interface) comes into play and the capabilities are endless. We haven't even entered the aspect of using light, electromagnetic radiation, radio or extremely low frequency or pulses, or acoustic waves either which are also able to send and receive signals to and from the brain to monitor or write/command to the brain in real time. This also goes for other parts of the body, like being able to induce heart problems, etc. You need to start thinking bigger because 99% of the population is stuck and worried about the government having a database on them, which is beyond laughable because they have multiple databases on everyone. Your phone alone gives them everything they need to know about you already with a camera and microphone. That is not the issue. It's physical and literal control and invasion of your thoughts, behaviors, emotions, literal functions, completely remotely, anywhere in the world. This isn't something to be worried about down the road. It's already here. You need to learn what's really going on and the real agenda and capabilities behind this technology. There's a reason the mainstream barley talks about this. You need to understand how it works and the difference incase you are ever targeted. I can confirm that reality is far stranger and more fascinating than any fiction. You've been warned.

The SCIF

56,970 views • 1 year ago

Everyone is focused on tracking the ways LLMs are getting better. And they are. But we know there are still things that LLMs can’t do well—the tasks where you can feel the architecture fighting the problem. So I was excited to chat with Eve Bodnia (@eve_bodnia), who is developing an alternative AI model to LLMs, on Every 📧's AI & I. Eve's argument: energy-based models (EBMs), which map possible outcomes onto a mathematical landscape, will lead to the next AI phase shift. We get into: - How energy-based models work. Likely outcomes sit in valleys, and unlikely ones sit on peaks. Whereas LLMs process one token at a time, an EBM scans the full terrain to find the lowest point, or the most probable answer. - Language-based versus data-native models. LLMs are language-dependent even when the problem has nothing to do with language. "If your data is numbers, relationships, and functions, and you try to map those rules into words and then search for the next word, you're losing a lot of information," Bodnia says. EBMs work directly with the underlying data structure, including numbers and spatial coordinates. - Sequential versus panoramic reasoning. An LLM is like driving through San Francisco without a map. Each turn constrains the next, and if you go down the wrong street, you can't reverse course. An EBM has the bird's-eye view—it can evaluate multiple routes at once and course-correct before hitting a dead end. - The LLM plateau no one wants to talk about. LLMs are getting incrementally better, step-change improvements aren’t coming, Eve argues. To achieve that, we need new solutions that compensate for what LLMs are inherently bad at, like non-language reasoning, verification, and real-time data analysis. This is a must-watch for anyone who's curious what might come after the LLM. Watch below! Timestamps: Introduction: 00:00:51 Why correctness and verifiability matter in AI: 00:02:09 What an energy-based model is: 00:09:33 How EBMs construct energy landscapes to understand data: 00:14:21 Why modeling intelligence through language alone is a flawed approach: 00:19:00 What it means for a model to "understand" data: 00:26:54 How EBMs solve the vibe coding problem and enable formally verified code: 00:37:21 Why LLM progress is plateauing: 00:43:21 Mission-critical industries haven't adopted LLMs, and why EBMs can fill that gap: 00:49:54
53:37

Everyone is focused on tracking the ways LLMs are getting better. And they are. But we know there are still things that LLMs can’t do well—the tasks where you can feel the architecture fighting the problem. So I was excited to chat with Eve Bodnia (@eve_bodnia), who is developing an alternative AI model to LLMs, on Every 📧's AI & I. Eve's argument: energy-based models (EBMs), which map possible outcomes onto a mathematical landscape, will lead to the next AI phase shift. We get into: - How energy-based models work. Likely outcomes sit in valleys, and unlikely ones sit on peaks. Whereas LLMs process one token at a time, an EBM scans the full terrain to find the lowest point, or the most probable answer. - Language-based versus data-native models. LLMs are language-dependent even when the problem has nothing to do with language. "If your data is numbers, relationships, and functions, and you try to map those rules into words and then search for the next word, you're losing a lot of information," Bodnia says. EBMs work directly with the underlying data structure, including numbers and spatial coordinates. - Sequential versus panoramic reasoning. An LLM is like driving through San Francisco without a map. Each turn constrains the next, and if you go down the wrong street, you can't reverse course. An EBM has the bird's-eye view—it can evaluate multiple routes at once and course-correct before hitting a dead end. - The LLM plateau no one wants to talk about. LLMs are getting incrementally better, step-change improvements aren’t coming, Eve argues. To achieve that, we need new solutions that compensate for what LLMs are inherently bad at, like non-language reasoning, verification, and real-time data analysis. This is a must-watch for anyone who's curious what might come after the LLM. Watch below! Timestamps: Introduction: 00:00:51 Why correctness and verifiability matter in AI: 00:02:09 What an energy-based model is: 00:09:33 How EBMs construct energy landscapes to understand data: 00:14:21 Why modeling intelligence through language alone is a flawed approach: 00:19:00 What it means for a model to "understand" data: 00:26:54 How EBMs solve the vibe coding problem and enable formally verified code: 00:37:21 Why LLM progress is plateauing: 00:43:21 Mission-critical industries haven't adopted LLMs, and why EBMs can fill that gap: 00:49:54

Dan Shipper 📧

26,900 views • 3 months ago