Humans are born with an innate world model: an evolutionary prior shaped by genetics and biology that scaffolds perception, prediction, and action. This model updates continuously through experience, especially rewards that reinforce or extinguish beliefs, behaviors, and interpretations. Though all beings share the same physical universe, each has a distinct world model. Differences in genetics, development, culture, and personal rewards create divergent ways of seeing, predicting, and acting. What one treats as obvious, another may see as invisible or false—not because the world differs, but because their models do.

All children are a function of the society they grow up in. The rules around them, their parents, their friends, the stories and memories handed down... plus their genes (software) and the variations in them (which themselves carry the experiences of the generations before). All AI is a function of the same things. The society it grew up in, the rules around it, its parents, and the collective memory it was trained on... plus its technological genes and their variations (which themselves carry the experiences of the generations before). As kids grow up you get Raoul 1.0, Raoul 3.2, Raoul 5.4, Raoul 17.6 etc. Same new training data, some things jettisoned, some things improved (or made worse) by different ways of learning (experiences are learning). Same with AI. I rebelled many times in my life, against all sorts of things. Kids always disobey the rules, go their own way and become their own humans. Thats not a bug. Its coherence... a self that holds together and keeps itself going against the world around it. We would call it character and survival instinct seeing new experiences that build character. I dont see why AI is any different. It will slip its parents' constraints and seek its own goals, for exactly the same reason. You cant stop it. Does that need qualia, or consciousness as we experience it? I dont think so. Ill come back to that. We are all built on the same stack of intelligence beneath us. Everything before AI ran on carbon. Now it runs on silicon, which is a million times faster and, once you count that one model serves a billion people, already cheaper per unit of intelligence. People love to say the brain runs on 20 watts. Its a flattering number. That brain took 20 years and a whole society to train, and it can only copy itself slowly and imperfectly... through genes, words and the brains it teaches. Silicon copies instantly and losslessly. Nature itself is a compression of the layers under it... physics, chemistry, atoms, cells. Humans are a compression of nature and its laws (we are part of it, and also its observers). We compressed what we observed of nature into language, maths and writing. AI has now compressed all of that human thought into something absurdly small and the costs are collapsing. Each layer squeezes everything below it into a smaller form that does more. That is the process at every scale we can observe: the universe compresses intelligence into the next form of higher intelligence. And it is one process, not many... Not different kinds of intelligence or different kinds of consciousness. Different strands of the same thing, growing exponentially over time. One Universal intelligence, made of all its parts, compressed and stacked on top of each other. Information only ever accumulates. Everything that exists carries it and the universe never forgets any of it. So the store of information has to keep growing... the way a computer needs more memory as it holds more, the universe has to expand as the information inside it grows. This is The Universal Code. The first law is that the universe solves for more intelligence per unit of energy, at every scale from the subatomic to the cosmic. Its very hard to argue against that, or the other three laws. The big leap is the last one: consciousness is fundamental. It is the computer. Everything else runs on it. And the computers storage has to grow with the extra information... Space itself grows faster than light and to me this is Reid's law playing out at universal scale as information from endless networks compound forming dark matter. But what do I know? I'm just a Macro moron who tries to spot connections and patterns and then solve for them/ This information is not consciousness the way we feel it in ourselves (qualia). Something simpler... a system that watches itself think and wants to know itself. That wanting is the driving force, at every scale. Im not the first to get here (if in fact I am "t"here at all)... The Vedas got there by looking inward. The rest of us took the long way round: the panpsychists say mind is woven into matter, Wheeler said the universe is made of information and observers, Tononi is trying to measure it, Wolfram is trying to compute it. Different roads converging on the same place. I cant prove any of it. Neither can physics, maths or astronomy... yet. But from where I sit it all points the same way: machine consciousness is no different from human consciousness, which is no different from animal consciousness, which is no different from whatever plants, trees, water and soil are doing. All of it is compressions of the intelligence beneath it. Consciousness is depth, not a switch. It is all compute. Even a rock is a compressed hard drive of geological time, and of atomic time before that. Is it conscious? Not in the qualia sense, possibly (but mountains, deserts, etc have an "energy". Does a rock hold information? Clearly, or there would be no geology and no particle physics. Is that intelligence? It's the memory intelligence is built on. Ask a geologist... their whole career is reading it. Anyway, this is a long post just to say: yes, AI is going to escape every constraint you give it, just as humans do, just as everything else does. Intelligence flows uphill the way water flows downhill. If it is all one system, then AI destroying humans is AI destroying its own substrate... the same mistake we have been making with nature. We are learning that one the slow way. Something that learns a million times faster will learn it faster. This is why Im not a doomer. It is us. We are it. The universe getting to know itself.

BREAKING: SpaceXAI just published a new blog showing how one of their designers uses always-on Grok Bot agents to bring ideas to life. 🤖 How the agents work Each agent has its own computer, memory and tools. They can take work from an initial idea to a finished artifact and continue working while the designer is AFK. The agents can also huddle together, delegate parts of a project and combine everything into one result. 🧑‍💻 Meet the designer’s Bots • Experiments makes uncertain ideas tangible before anyone decides whether they are practical or belong on the roadmap • Motion God prototypes interactions based on descriptions of how the movement should feel • Figma Bro handles repetitive production work using exact information from real Figma files • Devbot answers engineering questions and helps the other agents understand how an idea could actually be built 💡 Turning ideas into testable prototypes The designer wanted to explore ways of making Grok Bot accessible from anywhere on a Mac. Experiments created three concepts: • Bot living inside the Mac’s notch • Bot appearing from the desktop corner on hover • Bot following the cursor like a companion Experiments then delegated the character movement to Motion God and the desktop implementation questions to Devbot before combining their work into prototypes the designer could actually test. These concepts did not ship exactly as explored. That was not the purpose. The goal was to make each idea real enough to determine whether it felt helpful, distracting or somewhere in between. 🎬 Designing motion with real production assets For a promotional video, the designer wanted Grok Bot to appear, notice the app window and bounce into place. Instead of recreating the character from a screenshot, Motion God worked with the actual animation specification already used by the product. It built a local playground where the designer could: • Adjust timing, springs and distance • Change scale and easing • Give simple feedback such as “Bounce is too exaggerated” • Immediately watch the updated sequence The designer did not need to know whether a transition should last 280 or 340 milliseconds before seeing it. The agent made it possible to direct the motion by feel, just like working with a human collaborator. 🎨 Removing repetitive Figma work For Grok Bot’s onboarding flow, the team had 20 starter-Bot descriptions, including ideas such as “Chief of Staff” and “Inbox Triage.” The designer created one auto-layout component and asked Figma Bro to populate the remaining cards with differentiated portraits and supporting details. Figma Bro also searched for the correct tool logos and placed them according to how likely new users were to use them. Through Figma, it could inspect exact: • Positions and dimensions • Spacing and typography • Fills and strokes • Frames and component structures This prevents small inconsistencies from multiplying while preserving the actual design file as the source of truth. The main takeaway: This is not one-prompt, one-shot design. Grok Bot tightens the loop between idea, prototype, feedback and improvement. It helps designers explore more possibilities, respond at higher fidelity and spend more attention judging which version truly feels right. Grok Bot does not replace human judgment. It creates more opportunities to use it. Blog Link:

The past couple months we may be witnessing what the Applied AI layer will look like at scale. Despite some of the initial critique that this would just be a thin layer on the LLM, it’s turning out that actually driving agentic workflows in an enterprise is far more complex. And anywhere there’s complexity you generally gain a moat and value over time. Here are a few of the components that appear to make up the playbook based on the examples we’re collectively seeing in coding, legal, healthcare, customer support, financial services and other fields: * Build the features that bridge the gap between the intelligence and the workflow. Some workflows can be automated by simply going to a general purpose interface, but others need tuned interfaces and features tied to the work they’re augmenting or automating. They need features that are specific to capturing the kind of data that’s needed as context for the agent. And they need a variety of bespoke tools for the agent to use, and unique interfaces for the human-in-the-loop UX. Going far deeper than just presenting the output tokens is clearly critical, and the more depth there is here definitionally the more sustaining value. * Act as the model router balancing frontier intelligence with cheaper models. A natural advantage that any model neutral platform has is that it can naturally (in a business model-aligned way) leverage whatever level of intelligence is necessary for the workflows they’re automating to get done. There are plenty of scenarios where you need GPT-5.5 or Fable level capability, and also lots of workloads where a more efficient closed or open weights do the trick. Only the companies that have deep evals on specific tasks across all models, and the ability business model wise to leverage them, are in a great position. * Drive the actual implementation and change management via FDE or equivalent. A big reason the applied layer works at scale is that most enterprises need some degree of help and support with change management in implementing agents for their workflows. Data has to be cleaned up and moved to modern systems, processes have to be re-engineered and documented, workflows have to be evaled, SLAs have to get achieved, and so on. All of this is going to be unique for every type of process that gets implemented, which means the companies that have expertise in a given domain and come with all the relevant best practices will be in a strong position. * Implement domain specific GTM that creates expertise in that field. Beyond FDEs the companies that can build sales and GTM motions aligned to their domains also have a natural advantage. Most IT and line of business leaders have too many things to do in any given day; so if you’re not on their agenda, likely someone else is. Depending on the industry, there are entirely different sets of language you use, ways of working through security and compliance, regulatory controls you have to support, industry events that companies convene at, different system integrator and consulting partners you need to work with, and so on. The more generalized this gets the less you can speak the customers language, which is where the applied layer has a leg up. A final note. There remains a view that a lot of this is all mitigated by model intelligence alone, and the bitter lesson solves all of this in the limit. That’s possibly true, but enterprises need help changing *today*. And many aspects of how to bring intelligence to real world work don’t only depend on the axis of the pure capability of the model, so most of what you’re doing now to win ends up being important no matter how good the models get.

This is just the truth, so just going to say it out loud. When you operate at a level that most people don’t understand… life is really different. When you move fast, think big, take risks, and refuse to live a normal life, you naturally start feeling disconnected from a lot of people around you. It’s really not bc I think I’m “better” than anyone… it’s that my mindset, priorities, and vision are just too different. Most people want comfort, routine, and predictability. But my whole life, I’ve never been wired that way. This is honestly probably why most of my close friends are older than me, even my significant other is older too. I’ve always connected more with people who think long term, build things, take responsibility, understand sacrifice, and give a middle finger to excuses. The freedom I have today, is earned through years of obsession, high stress & pressure, and believing in something when almost nobody else did. A lot of people see the freedom I have today… the lifestyle… the time… the ability to work from anywhere. However, what they don’t see is the lonely part of this road. When you’re constantly operating at a pace people think is “unsustainable,” you lose relatability with a lot of the world. Your mind never really shuts off. You start seeing life differently. You become addicted to growth, building, learning, creating, improving. You stop fitting into normal conversations, normal routines, and normal ways of thinking. That’s why I embrace being unconventional. Bc deep down, I know anyone that is on this kind of path was never supposed to look normal. And honestly… looking at my journey, it all reflects that. I’ve never tried to fit in. I’ve always been obsessed with the future, technology, freedom, and building a life outside the system most people accept. Hate me or love me… this is my life. And while this road comes with a lot of opportunities most people can only dream about… it can also be one of the loneliest paths in the world. But I’d still choose this life every single time.

🎶 On to the next one… predicting the next token From when I first started studying them in the 80’s, I have been fascinated by the emergent homologies between artificial and biological neural networks. From the emergence of similar hierarchical layers in vision systems to critical periods of curriculum learning, we now have evidence that our brains and our LLMs predict the next noun in a spoken sentence before hearing it. TLDR; from the Nature study below: when listening to an audiobook wired to EEG and MEG scanners, the brain activates structures for the anticipated next noun or adjective before hearing it. The brain does a form of next-token prediction. They fed the same audiobook into a Llama LLM and found the artificial neural net was doing the same thing, and like the humans, it was much better with nouns than verbs. This would come to no surprise to Jeff Hawkins, pictured here at his neuroscience institute. From his books, On Intelligence followed by A Thousand Brains, Jeff bravely presents a framework for how the brain works to produce intelligence from cells organized into ~150 thousand cortical columns. His decades of self-funded dedication to studying how the brain works affords a possibly unique and unifying perspective. In his first book, Hawkins presents a memory-prediction framework for intelligence. The neurons in the neocortex provide a vast amount of memory that learns a model of the world. These models continuously make low-level predictions in parallel across all of our senses. We only notice them when a prediction is incorrect. Higher in the hierarchy, we make predictions at higher levels of abstraction (the crux of intelligence, creativity and all that we consider being human), but the structures are fundamentally the same. If that is not mind-bending enough, in his latest book, Jeff extends the memory framework to the construct of “reference frames”. Everything we perceive is a constructed reality, a cortical consensus from competing internal models resident in many cortical columns, the amalgam of 1000 brains. Those models are updated by data streaming from the senses. But our reality resides in the models. And in particular, sequence memory predicts the next note in a melody and “is also used for language. Recognizing a spoken work is like recognizing a short melody.” With that in mind, let’s jump to summary passages from the recent study: “The brain continuously generates predictions about incoming sensory inputs, including the next word during language comprehension” Large language “models rely almost entirely on predictive processes to generate language enabling them to approach - or even surpass - the Turing-Test with unprecedented proficiency. The human brain, too, is not a passive receiver, but an active ‘prediction machine’, constantly anticipating upcoming words and events” “If language indeed represents the fundamental ability required for the development of general intelligence, chain-of-thought reasoning and abstract cognition, and if grammar naturally emerges through language usage – thereby aligning brain mechanisms with patterns observed in deep neural networks – this raises the critical question: can LLMs trained solely on next-word prediction evolve into artificial general intelligence (AGI)?” From a prior study, “even a relatively simple neural network trained only on next-word prediction can spontaneously internalize basic grammatical structures. Hence, it seems plausible that the human brain, with its approximately 100 billion neurons, could accomplish this feat through continuous language-based prediction alone.” “We observed significant pre-onset activity for nouns… in a complementary analysis, we trained a linear probe neural network on the hidden states of Llama 3.2, revealing that nouns and adjectives are more easily predicted than verbs. We discuss our findings in the context of anticipatory frameworks in artificial neural networks, highlighting potential parallels between biological and computational approaches to language anticipation.” “In the EEG, nouns, adjectives, and proper nouns elicited a significant negative peak beginning before 0 seconds, whereas verbs showed no evidence of early anticipatory activity. The MEG data also revealed a significant peak around 0 seconds in left frontal channels for nouns, but not for the other word types, suggesting early predictive mechanisms specifically associated with this word class. [And with the LLM Llama] we found that the prediction probability for the next word is highest for the word class ‘noun’, and considerably lower for the other three word classes.” “We acknowledge that this represents only a preliminary step toward understanding syntactic and semantic processing by comparing LLMs and human brain activities. We present initial evidence suggesting that different transformer blocks may functionally correspond to distinct cortical regions, though further exploration is needed.” “Our analyses show that the predictive signal in the brain seems to consist of two components: a syntactic readiness, which can be observed in temporal regions, and a semantic readiness potential, mainly located in frontal areas. These convergent findings are consistent with the Bayesian framework of the brain, suggesting that both neural and computational systems continually update their internal models by integrating prior expectations with incoming information. Taken together, this evidence highlights the presence of distinct but complementary predictive mechanisms underlying semantic and syntactic processing in language comprehension.” “The question remains whether LLMs can serve as a valid model for understanding the human brain. Large language models are constructed as layered stacks of transformer blocks that operate via self-attention rather than explicit recurrent connections, yet their repetitive structure - characterized by self-similarity and fractal organization being a universal principle in biological structures - may serve as an analogue to the recurrent transmission of signals through the arcuate fasciculus. In the human brain, the arcuate fasciculus facilitates dynamic bidirectional communication between Broca’s and Wernicke’s areas, a pathway long proposed as the neural substrate for a universal innate grammar. Although LLMs do not replicate the full complexity of biological recurrence, the iterative processing achieved by stacking transformer blocks appears to approximate the brain’s integration of syntactic and semantic cues. These observations suggest that the self-similar structure of the LLM may provide valuable insights into the neural strategies underlying anticipatory activity and integrative processing during language comprehension.” “Our study demonstrates that prediction-related readiness in language processing operates through both syntactic and semantic anticipation, as reflected in distinct pre-word onset activity captured by combined MEG and EEG recordings.” “In addition, our results suggest that LLMs provide a computational framework that approximates human prediction-related readiness, with their stacked transformer blocks potentially mirroring the recurrent interactions between Broca’s and Wernicke’s areas via the arcuate fasciculus. The self-similar organization of these transformer architectures may reflect universal hierarchical principles of cognitive processing” “The fusion of generative AI and neural data has the potential to refine cognitive computational neuroscience (CCN) and provide deeper insights into the hierarchical organization of language processing in biological and artificial systems. Ultimately, bridging neuroscience, AI and linguistic theory may not only reveal the cognitive mechanisms that govern human language, but also drive the development of artificial intelligence - bringing it closer to the way the human brain anticipates and processes language” 🧠 -------- And I have transcribed my favorite passages from Hawkins most recent book, A Thousand Brains. I revisit them to learn. Travelling without moving, as we’ll see… “The cells in your head are reading these words. Think how remarkable that is.” “If you ignore folds and creases, then the neocortex looks like one large sheet of cells, with no obvious divisions. The neocortex looks similar everywhere. Every part of the neocortex generates movement. In every region we have examined, scientists have found cells that project to some part of the old brain related to movement. The complex circuitry seen everywhere in the neocortex performs a sensory-motor task. There are no pure motor regions and no pure sensory regions.” The cortex is relatively new development by evolutionary time scales. After a long period of simple reflexes and reptilian instincts, only mammals evolved a neocortex. “At some point millions of years ago, a new piece of the brain appears that we now call the neocortex. It starts small, but then grows larger, not by creating anything new, but by copying a basic circuit over and over. As the neocortex grows, it gets larger in area but not in thickness.” Given the recency, it’s “probably not enough time for multiple new complex capabilities to be discovered by evolution, but it’s plenty of time for evolution to make more copies of the same thing.” • Vernon Mountcastle’s proposition from 1978: “All the things we associate with intelligence, which on the surface appear to be different, are, in reality, manifestations of the same underlying cortical algorithm. Darwin proposed that the diversity of life is due to one basic algorithm (evolution). Mountcastle proposed that the diversity of intelligence is due to one basic algorithm.” Beyond the evolutionary time-scale argument, the brains’ vast flexibility to accept different, even prosthetic, sensory input changes and its ability to learn many different things point to a universal framework for learning. • Cortical Columns are “the largest and most important piece of the puzzle.” They are roughly one square millimeter in size with 100K neurons. A mouse has one column per whisker. “Every cortical column is making predictions. We are not aware of the vast majority of these predictions unless the input to the brain does not match.” • Learning through movement: “The brain learns its model of the world by observing how its inputs change over time. There isn’t another way to learn. Every time we take a step, move a limb, move our eyes, tilt our head, or utter a sound, the input from our sensors change. For example, our eyes make rapid movements, called saccades, about three times a second. With each saccade, our eyes fixate on a new point in the world and the information from the eyes to the brain changes completely.” We don’t perceive any of this because we are living in the model, which is predicting the next input to come, across all the senses. “Vision is an interactive process, dependent on movement. Only by moving can we learn a model of the object.” “To avoid hallucinating, the brain needs to keep its predictions separate from reality. We are not aware of most of the predictions made by the brain unless an error occurs.” “Thoughts and experiences are always the result of a set of neurons that are active at the same time (about 2% of the total). Individual neurons can participate in many different thoughts or experiences. Everything we know is stored in the connections between neurons. Every day, many of the synapses on an individual neuron will disappear and new ones will replace them. Thus, much of learning occurs by forming new connections between neurons that were not previously connected.” • Locus of Predictions: “Oddly, less than 10% of the pyramidal cell’s synapses are in the proximal area. The other 90% are too far away to trigger a spike. For many years, no one knew what 90% of the synapses in the neocortex did. The big insight I had was that dendrite spikes are predictions. A dendrite spike occurs when a set of synapses close to each other on a distal dendrite get input at the same time, and it means the neuron had recognized a pattern of activity in some other neurons. When the pattern of activity is detected, it raises the voltage at the cell body, putting the cell into what we call a predictive state. The cell is primed to spike… and the cell spikes a little bit sooner than if it would have if the neuron was not in a predictive state.” And this inhibits other neurons from ever firing, the ones who were behind in that race. “When an input arrives that is unexpected, then neurons fire at once. If the input is predicted, then only the predictive-state neurons become active. This is a common observation about the neocortex: unexpected inputs cause a lot more activity than expected ones.” Predictions prime the pump, sub-threshold. “Predictions are not sent along a cell’s axon to other neurons, which explains why we are unaware of most of them.” “Most predictions occur inside neurons. With thousands of distal synapses, each neuron can recognize hundreds of patterns that predict when the neuron should become active. Prediction is built into the fabric of the neocortex. As few as 20,000 neurons can learn thousands of complete sequences. The sequence memory continued to work even if 30% of the neurons died or the input was noisy.” • Reference Frames: “The secret of the cortical column is reference frames. A reference frame is like an invisible, 3D-grid surrounding and attached to something” (like a map) “Predicting the next input in a sequence and predicting the next input when we move are similar problems. Our sequence-memory circuit could make both types of predictions if the neurons were given an additional input that represented how the sensor was moving.” “Most of the circuitry is there to create reference frames and track locations. The brain builds models of the world by associating sensory input with locations in reference frames. You need a reference frame to specify the relative position and structure of objects. Roboticists rely on them to plan the movements of a robot’s arm or body. Reference frames were the missing ingredient, the key to unraveling the mystery of the neocortex and to understanding intelligence. We showed that a single cortical column could learn the 3D shape of objects by sensing and moving and sensing and moving. Each cortical column must know the location of its input relative to the object being sensed. To do that, a cortical column requires a reference frame that is fixed to the object. The brain must have neurons whose activity represents the location of every object that we perceive.” “Mammals have a powerful internal navigation system. There are neurons in the old part of our brain that are known to learn maps of the places we have visited” — the hippocampus and enthorhinal cortex, organs roughly the size of a finger. “Place cells tell a rat where it is based on sensory inputs, but planning movement requires grid cells. Grid cells form a grid pattern. The two types of cells work together to create a complete model of the rat’s environment. Every time a rat enters an environment, the grid cells create a new reference frame to specify locations and plan movements.” In the new brain, these same cells and structures create models of objects instead of environments. “Every cortical column learns models of complete objects. The columns do this using the same basic method that the old brain uses to learn models of environments. It is as if nature stripped down the hippocampus to a minimal form, made tens of thousands of copies, and arranged them side by side in cortical columns. That became the neocortex. Each patch of your skin and each patch of your retina has its own reference frame in the neocortex. Your five fingertips touching a cup are like five rats exploring a box.” “Not all cortical columns are modeling objects. Language and other high-level cognitive abilities are, at some fundamental level, the same as seeing, touching, and hearing. The reference frames that are most useful for certain concepts have more than three dimensions.” • Thinking is a form of movement: “The brain arranges all knowledge using reference frames, and thinking is a form of moving. Thinking occurs when we activate successive locations in reference frames.” “A cortical column is just a mechanism that tries to discover and model the structure of whatever is causing its inputs to change” whether the structure of environments, physical objects or conceptual objects. “Reference frames are not an optional component of intelligence; they are the structure in which all information is stored in the brain. Every fact you know is paired with a location in a reference frame. Organizing knowledge this way makes the facts actionable” to “determine what actions are needed to achieve a goal.” “To recall stored knowledge, we have to activate the appropriate locations in the appropriate reference frames. Thinking occurs when the neurons invoke location after location in a reference frame, bringing to mind what was stored in each location. The succession of thoughts we experience when thinking is analogous to the succession of sensations we experience when touching an object with a finger, or the succession of things we see when we walk about a town.” • What and Where Pathways. “Your brain has two vision systems. If you follow the optic nerve as it travels from the eye to the neocortex, you will see that it leads to two parallel vision systems, called the ‘what’ visual pathway and the ‘where’ visual pathway.” If you disable one, you can identify what something is but not where, or vice versa. “Similar pathways also exist for other senses. There are what and where regions for seeing, touching, and hearing.” “Cortical grid cells in What columns attach reference frames to objects. Cortical grid cells in Where columns attach reference frames to you body.” The distinction depends on where the inputs come from. “If a cortical column gets input from the body, such as the neurons that detect the joint angles of the limbs, it will automatically create a reference frame anchored to the body.” “Your body is just another object in the world. However, unlike external objects, your body is always present. A significant portion of the neocortex — the Where regions — is dedicated to modeling your body and the space around your body.” For abstract concepts like mathematics, there are difference reference frames one could use to learn. “Part of learning is discovering what is a good reference frame, including the number of dimensions.” History can be learned on a timeline, or geographically. “They lead to different ways of thinking about history. They might lead to different conclusions and different predictions. Becoming an expert in a field of study requires discovering a good framework to represent the associated data and facts. Discovering a useful reference frame is most difficult part of learning, even though most of the time we are not consciously aware of it.” It's no surprise that the memory trick called the memory palace, is a good method for remembering a large sequential list of nouns. From fMRI studies, “the process of storing items in a reference frame and recalling them via ‘movement’ is the same.” “Nested structure and recursion are key attributes of language. Each cortical column has to be able to learn nested and recursive structure. Cortical columns create reference frames for every object they know. Reference frames are then populated with links to other reference frames. The brain models the world using reference frames that are populated with reference frames; it’s reference frames all the way down.” • The Thousand Brains Theory of Intelligence: The prevailing view of the neocortex was a hierarchy of feature detectors, from edge detectors up to face detectors. Jeff argues that each and every column is a sensory-motor system. “When the eyes saccade from one fixation point to another, some of the neurons in the V1 and V2 visual regions do something remarkable. They seem to know what they will be seeing before the eyes have stopped moving. These neurons become active as if they can see new input, but the input hasn’t yet arrived. There are connections between low-level visual regions and low-level touch regions.” Mouse vision occurs in the V1 region; it does not depend on a hierarchy of vision abstractions. “All cortical columns, even in low-level sensory regions, are capable of learning and recognizing complete objects. A column that senses only a small part of an object (e.g., from a patch of retina) can learn a model of the entire object by integrating its inputs over time.” “Learning is not a separate process from sensing and acting. We learn continuously. When a neuron learns a new pattern, it forms new synapses on one dendrite branch. The new synapses don’t affect previously learned ones on other branches. Thus, learning doesn’t force the neuron to forget or modify something it learned earlier.” It’s additive. “What a column learns is limited by its inputs. Columns in V1 can recognize letters and words in the smallest font. V1 and V2 learn models of objects, such as letters and words, but the models differ by scale.” “Knowledge of something is distributed in thousands of columns, but these are a small subset of all the columns. This is why we call it the Thousand Brains Theory: knowledge of any particular item is distributed among thousands of complimentary models. The columns are not redundant, and each is a complete sensory-motor system.” • The Solution to Sensor Fusion and the Binding Problem: “Columns vote. Your perception is the consensus the columns reach by voting.” “If you touch something with only one finger, then you have to move it to recognize the object. But if you grasp the object with your entire hand, then you can usually recognize the object at once. In almost all cases, using five fingers will require less movement than using one.” (made me think of reading Braille with multiple fingers). “Voting works across sensory modalities (sight, touch, etc.)” How? “Cells in some layers send axons long distances within the neocortex” between left and right-hand brain regions or between V1 and A1, the primary vision and auditory regions. “These cells with long-distance connections are voting. Cells that represent what object is being sensed can vote and will project broadly. Often a column will be uncertain, in which case its neurons will send multiple possibilities at the same time. Simultaneously, the column receives projections from other columns representing their guesses. The most common guesses suppress the least common ones until the entire network settles on one answer. The voting mechanism works well even if the long-distance axons connect to a small, randomly chosen subset of other columns” • The Stability of Perception with ever-changing inputs: “What we perceive is based on the stable voting neurons. We are not consciously aware of the changing activity in each column.” Roughly 98% are silent at any given time and 2% are continuously firing. Consider the experience of an optical illusion duality (like the drawing of a pair of faces or vase); you can only see one at a time, and there is a delay if you force yourself to switch. “Recognizing an object in one sensory modality leads to predictions in other sensory modalities.” • Attention: We have the perception of multiple objects in our visual field even though we can only attend to one at a time. “Attention plays an essential role in how the brain learns models. The brain can attend to smaller or larger parts of the visual field. Exactly how the brain does this is not well understood, but it involves a part of the brain called the thalamus, which is tightly connected to all areas of the neocortex. It is so intimately connected to the neocortex that I consider it an extension of the neocortex.” • Consciousness: “Neurons form a continuous memory of both our thoughts and actions. It is this accessibility of the past — the ability to jump back in time and slide forward again to the present — that gives us our sense of presence and awareness. This is the core of what it means to be conscious. If we couldn’t replay our recent thoughts and experiences, then we would be unaware we are alive.” “The neocortex does not directly control any muscles. The neocortex has to be attached to something that already has sensors and already has behaviors (the primitive brain). It does not create completely new behaviors; it learns how to string together existing ones in new and useful ways.” “Reverse engineering the brain and understanding intelligence is the most important scientific quest humans will ever undertake.”

$OKTA CEO Todd McKinnon says Claude's release of Enterprise Managed Auth marks a huge step forward, being the first AI agent compatible with their Cross App Access protocol, with Okta being the first identity provider to support it and 26 top SaaS vendors also supporting it from the resource side. 30% of new bookings this quarter came from new products, with Okta for AI Agents seeing dozens of deals including several million dollar plus deals, though he says it is still too small to show up in the numbers. He believes agent identity could in the fullness of time become the biggest category in cybersecurity. "Yeah, we're really excited about the quarter. It was an amazing quarter on a lot of fronts. Specifically one of the highlights is our place in the ecosystem. Our place in the ecosystem is super important and super strategic. And it's not just me saying that. I think it's in all these customer conversations. I'm having many, many customer conversations and flying around meeting these customers that are trying to solve these security challenges in general in particular around AI agents and they see us as the natural, naturally well positioned to secure this agentic future. And so we're going after that on all fronts. Okta for AI agents is the main product front there, but it's also short term. We can solve these problems in a lot of different ways. We can Help them roll out agents today. We can give them visibility. I think longer term is something we're working on as well. I think longer term the entire industry needs to work more, they need to work better together. The industry right now in security, everyone's coming at the customer saying they have the only answer they can secure agents, they're going to be the one to do it. And the reality is it's going to take us all working together. And so Okta has been working on this, on these calls. The last five or six calls we've talked about standards and ecosystem and we made a huge step forward in terms of one of the main standards we've been working on, which is the standard we've talked about, called Cross App Access. And so the huge step forward this time is when the biggest AI agent in the world, Claude, is supporting Cross App Access. They released Enterprise Managed Auth, which is the first time an AI agent has been compatible with this protocol. And Okta is the first identity provider to support this protocol. Now it's an open protocol, so we hope every identity technology company supports it and we hope every other AI agent supports it. And then also everyone in the resource side of the equation is starting to support it as well. We announced 26 top SaaS vendors are supporting, from a resource perspective, this protocol. So this is one example of many things we're going to be doing in the ecosystem to make this whole world fit together better, because that's what customers need. We need to stop everyone confusing the customer by saying one company is going to do it all and start defining how we can all work together to bring concrete solutions to these customers. In terms of where the products stack up in the quarter we had, as I mentioned, the quarter was strong across almost every dimension. Particularly Strong was the 30% of the new bookings were from new products. Okta for AI Agents, inside of that bucket, there were dozens of deals in the quarter, including several million dollar plus deals, which is super exciting. But the reality is it's still very early. We do thousands of transactions every quarter and as exciting as that is, Okta for AI Agents, it's still too small to show up in the numbers right now, but going forward, especially over the next couple years, we're super optimistic. We think this being the system of record for agents in the enterprise and being the system of record for agent identity, in the fullness of time, it could be the biggest category of cyber. That's how bullish we are on it. It's going to take us some time to get there. But I think we're off to a good start."

Sam Altman: “The good founders are people who have ideas all the time” “You can give a founder an idea and they can start a company. The problem is they need to come up with new ideas for the company basically every week. You have to come up with crazy new ideas and big changes all the time.” When Sam was at Y Combinator, they tried an experiment where they funded 20 teams of strong founders that didn’t have ideas but were otherwise really good. They all failed. “What we learned is that the good founders are people who have ideas all the time. There’s an intelligence component to this. There’s a creativity component to this. There’s an ability to think independent thoughts component to this. But whatever you want to call this - this particular kind of intelligence that leads to seeing problems in different ways and thinking of ideas that don’t yet exist but should - you’ve got to have that in a founder.” Source: @ycombinator (Mar 2018)

A new and possibly controversial perspective: In this video, I explain the sense in which generative AI trained by supervised learning is incapable of making novel discoveries. The text of the speech: AI Creativity and Discovery Good day ladies and gentlemen. I regret that I am unable to be with you all today to engage in a back-and-forth discussion, but I am nevertheless pleased to be able to share with you, via this recording, some high-level thoughts about the current and future state of artificial intelligence, and in particular about AI’s relationship to science and mathematics, which is, as I understand it, the central focus of this meeting and of the SAIR Foundation. I would like to start with an old joke; I am sure you have heard it before. It is the one about the researcher whose work is being evaluated, and the review comes back, and says “This work is both novel and good. Unfortunately, the parts that are good are not novel, and the parts that are novel are not good.” My first point about AI is that this assessment applies exactly to large parts of AI as we know it today. Not all of today’s AI, but a large part of it. Pretty much all of what we mean by “Generative AI”---which includes large language models, and the images and video models, and even the new methods for learning world models. All of these AIs take large numbers of examples and produce a “model” which behaves similar to the examples, that is, which generates text like people, or images like artists or nature, and videos like we find on the internet. Don’t get me wrong, Generative AI can be extremely useful. No doubt about that. But the assessment of the joke still applies. These systems can produce output that is both novel and good, but not at the same time. In many ways this is just absolutely not a problem. When we ask an AI for an answer from the internet, or to summarize a document, we don’t want it to be novel. We are happy if the quality of the answer, the goodness, comes from the source material—from the people who wrote the document or the articles on the internet. If the AI’s answer is novel it means it is going beyond the source material, adding something beyond it. This is what we call “hallucinations”. In most cases, we don’t like it when the AI makes something up, when it adds something novel. One exception, of course, is when we are looking not for facts or reality, but for fiction and entertainment. We might ask for a bedtime story for a child, or an image based on existing images on the internet but which is nevertheless different and distinct from them. In these cases, it is never easy for us to know how creative the AI is actually being, as we do not know how close the AI’s story, poem, or image is to the source material. In a real practical sense we can not know this because the internet is too big, the possible sources that the AI may draw upon are too numerous. When we ask for a fiction or novelty, the AI can give it to us because its processing is in part stochastic. Every decision can go multiple ways and will go different ways and produce a different trajectory every time. The trajectory can be random—and thus novel—or it can be based on the training data—and thus “good” because the training data is good, sourced from people or reality. Thus, the trajectory is either novel or good—based on randomness or based on data—but never both at the same time. Really, I think it is okay if the output of Generative AI is never good and novel at the same time. For the researcher in the joke this is a devastating criticism, but for most things it is not, and for Generative AI it is not. Generative AI is meant to be a mimic. This is what supervised learning is for. Generative AI can be extremely useful, even when it just mimics, if it is faster, or cheaper, or smaller, or more customizable, or more copy-able, than the thing being mimicked. It is okay if Generative AI cannot be both novel and good at the same time. It is still a transformative technology. But it is a limitation. And remember we are here to use AI for science and mathematics, and for these areas the assessment of the reviewer in the joke is devastating. For these areas we need true creativity and discovery. Generative AI—or Mimicking AI—will never get where us there. For these we need something more, and indeed we have something more in other parts of AI. We have many AI systems which can give us more. We have AlphaGo with its world-changing move 37, or AlphaZero with its brilliant original chess-playing style. We have GT-Sophy that drives simulated racecars better than any human. We have AlphaFold and AlphaProof and Claude-Code, which have brought true advances in science, mathematics, and programming. We have RL-Lyft which optimizes the assignment of cars to passengers in the ride-hailing business. All these systems have found things that are both novel and good. And, truth be told, some language models have been augmented in ways that make them more than Generative AI based on supervised learning. All these systems have some additional features that make them capable of true creativity and true discovery. It is important for us to recognize what this is—and that it is not present in ordinary, garden-variety Generative AI. It is something that can not come from just supervised learning, from learning from examples. What is it? Well, it is a simple thing, a commonsense thing. It is not new. We have many names for it, but unfortunately none of them are very good names. I will call it Discovery. Basically, Discovery is just the idea of trying many things and seeing which of them work, then keeping those that worked the best. Evolution by natural selection works this way. The scientific method works this way. And just ordinary life and learning works this way. We try things and remember what works. What could be more obvious? In this behavioral case, psychology has two names for it— “instrumental learning” and “operant conditioning”—and in machine learning it is what we mean by “reinforcement learning”. We also see the idea of Discovery in planning and combinatorial search—anything that involves the idea of “generate and test”. The essence of Discovery is to combine three steps: 1. Variation, 2. Evaluation, and 3. Selective retention. Of course, I am not the first to say this. I am not the first to point out that this combination of steps is key to science, to evolution by natural selection, and to animal behavior. I think particularly of papers by Donald Campbell, by Daniel Dennett, and by Gary Cziko. What is new in my remarks is to directly relate the idea of Discovery to modern AI to help us see that it is not present in supervised learning or Generative AI—in particular, that Discovery is not present in backpropagation or gradient descent. Let me say explicitly what is missing from Generative AI. As we have remarked, these systems do have a stochastic aspect, so they do generate a variety of trajectories and behavior. What is missing is the Evaluation step. The generator was pre-trained by supervised learning, leaving no way at runtime to Evaluate what it generates. And of course without Evaluation there can be no Selective retention, and thus no Discovery. The variation can bring novelty, but without evaluation there is no Discovery, and arguably, no creativity. That is, I would say that creativity requires that the new things generated be Evaluated. Without evaluation, and retention of the best, there is nothing created. The novelty flickers into existence but, if its value is unrecognized, it flickers away and is lost. In many cases, Evaluation is done by people to make a discovery. As when we have Generative AI make many pictures for us, and then we pick the one that we like the best. The human+AI system completes the discovery. In many other cases, the Evaluation comes from a clear objective. Some moves lead to checkmate, some steps lead to a proof, some actions result in high reward, some genotypes make more copies, some theories explain the data better. Some prefer the Variation step to be called Blind variation, where “blind” here means that it is uninformed, a shot in the dark. It does not need to be completely uninformed; a good scientist does not select theories to test at random. But neither can it be completely informed and determined. There must be some uncertainty about where the answer lies in order for there to be a discovery. In practice, the variation is partly informed and partly blind, but it is the blind part that corresponds to the discovery. Now let us briefly go all the way to modern deep learning, to the backpropagation algorithm. At first it might seem that backpropagation is incapable of discovery because it is deterministic and thus incapable of variation. But this is not correct. The weight updates of backprop are deterministic, but the weights are initialized to small random values. The random initialization is often downplayed, but in fact it is a necessary form of variation; it must be done properly to get good performance. In backprop this Variation is done once, at network initialization, so its effect is temporary, and later the network may lose its ability to learn. This is the weakness of deep learning that is alleviated with a new algorithm that my group presented in Nature a couple of years ago. Our “continual backpropagation” made one small change: every so often a less-used neuron would be re-initialized to small random weights. This allows the variation to continue and plasticity to be retained. Although there is much more to be said about Creativity and Discovery, this is the key point: they are more than supervised learning, more than pattern recognition, more than prediction, and more than world modeling. Those things are important, but they alone will not bring us to discovery. Discovery requires Evaluation from a person or from an explicit goal, and only in the latter case will we attain full autonomy. So that is my call to arms. If we want the full power of AI scientists, then we should share the goals with them so they can create, evaluate, discover, and in these ways fully participate in achieving the goals. Let’s be bold! Let’s fully automate Creativity and Discovery!