I Expected that Language Models Alone Would Never Result in AGI

I found it gratifying to watch the major AI demos and presentations this December. OpenAI’s announcement of the new o3 model and its benchmark scores was very impressive. This led me to want to comment on the current state of the field. It looks like the AI revolution is going to play out differently from how I always assumed it would. It turns out that it didn’t need as much input from psychology and neuroscience as I thought to start building some serious momentum toward artificial general intelligence (AGI). Before moving forward, let’s look at the common definition of that last term.

Artificial General Intelligence (AGI): A type of artificial intelligence (AI) that matches or surpasses human cognitive capabilities across a wide range of cognitive tasks.

I have been convinced since around 2003 that a computing system would not be able to reach AGI without having some form of consciousness. I thought that self-awareness and true deliberation would be necessary to pass the Turing test and demonstrate a human-like ability to generalize (the capacity to apply knowledge from one domain to solve problems in another). I saw the task of creating AGI as equivalent to creating a human-like brain in a vat. That’s why my model of AI is based on what I have taken as the most important functional mechanisms of the human brain.

But I was wrong. Computer scientists have achieved generality by building systems that use statistics to predict the next word in a sentence. I envisioned great promise in language modeling, but I never thought “next word prediction” would result in all the emergent properties (capabilities that arose but were not explicitly programmed) we are seeing in LLMs today (analogical reasoning, code generation, creativity, translation, understanding, theory of mind, few shot learning, etc.).

I also thought a true cognitive architecture based on the human mind and brain would be necessary to comprehend a query and print out a meaningful answer. I thought it would take a sentient mind to behave like a sentient mind. Looking at the outputs of GPT 2 back in 2019 I was amazed but I didn’t foresee it getting much better without other brain-inspired algorithms. It blows my mind that just keeping neural network records about how words tend to relate to each other probabilistically, scaled to where we are today. To me, it almost feels like Silicon Valley cheated their way to AGI by creating zombie language number crunchers.

By late 2022 though, with the release of GPT 3, I became a believer. At that time, I even thought that AGI could possibly happen in 2024. My thoughts about this are in line with Dave Shapiro and other AI optimists. Since 2022, I saw a lot of potential in having LLMs (large language models) self-prompt. For those who don’t know, this is a method where the model basically talks to itself to question and refine its first answer. There are many names for this including reflection and thinking (and it can involve various other methods such as chain of thought, tree search, and self-taught reasoning). The technique is often called “test time compute” because it uses additional processing resources, not during training, but during actual use to improve its outputs. This reflection happens behind the scenes and human users usually don’t even see the words generated. In OpenAI’s o1 and o3 models these words are hidden from the user to help keep their method proprietary (although it does display a summary of them). Those extra words help the model to reach latent but relevant associations instrumental in formulating the best answer. This paradigm will take the state of the art far. This is partially because it (the industry’s version of using reflection to organize attention) is highly similar to my model which you can read about at my website, aithought.com. The title is:

A Cognitive Architecture for Machine Consciousness and Artificial Superintelligence: Thought Is Structured by the Iterative Updating of Working Memory

Let’s refine the definition above a little bit. To me, AGI should be defined as any system that is more intelligent than average humans in most ways. Adding the capacity for reflection on top of GPT 4o to create the o1 and o3 models mostly satisfied my definition of AGI. My definition of superintelligence is much more stringent though, a system that is more intelligent than the smartest humans in all ways. Even before seeing o3, I thought scaling and minor algorithmic improvements to reflective LLMs would get us to superintelligence. I still don’t think, however, that it will carry us all the way to machine consciousness.  

In outperforming Field’s medalists in mathematics, beating some of the greatest coders in the world, and in scoring an 87% on the ARC AGI prize I think that OpenAI’s o3 cemented itself as, at least, a form of AGI. Clearly it is not a fully general intelligence. It makes strange mistakes and still fails at some tasks that are easy for humans. It can’t do everything an average human can do, but it certainly does many things much better than I can. Even though I have spent my life trying to be “a science guy” who had all the answers to people’s scientific questions, I am not nearly as informed or informative as the most basic LLMs today. I use either GPT, Claude, or Gemini daily and it really is like having 1,000 experts in my back pocket. Just this month it helped me keep some unscrupulous plumbers from overcharging me $2,000, helped me get the right medicine for my kitten with advanced giardia, helped me accomplish several projects around my home, and has clarified numerous uncertainties I had about scientific literature. And I was only using GPT 4o, a nonreflective model. These systems really are incredible and incredibly useful. But it still feels like talking to a machine and not a friend.

AI Consciousness

Despite how they might present themselves, contemporary systems are nonconscious. Language models passed the Turing test years ago (using written conversations to trick people into thinking they are human). They can also make intellectually and emotionally convincing claims about being conscious. But they are not conscious the way humans are, and this is clear by the egregious, mechanical mistakes they sometimes make. They are prompt-dependent and cannot self-initiate tasks. They have no enduring goals, cannot execute complex projects, do not have curiosity, awareness, emotions or volition. They are not truly natively multimodal and are only text-deep. However, they will appear to be overcoming these issues in 2025. Researchers will accomplish something functionally close to these abilities by cobbling together lots of programmatic temporary solutions. These agglomerations of software will probably reach superintelligence before computer scientists can identify and incorporate the mammalian brain’s fundamental algorithms. But even if humans neglect it, self-improving superintelligence will not fail to see the value and will operationalize and employ the cognitive algorithms that nature bequeathed us. In other words, it is looking like humans will not be the creators of machine consciousness, the machines will.

Is ChatGPT a stochastic parrot completely devoid of consciousness? Or could the present version of some LLMs have a form of conscious experience? I don’t generally believe that all forms of matter have consciousness (panpsychism) but I do find myself sympathetic to some of the more rigorous philosophical arguments. I also sympathize with Giulio Tononi’s information integration theory of consciousness which claims that systems that integrate information tend to be more conscious. Consciousness must arise from a particular organization of matter, energy, and information. Clearly, LLMs organize and integrate matter, energy, and information. For these reasons, I would not be surprised if some of today’s chatbots based on the transformer architecture (like GPT) had some limited and exotic form of conscious awareness.

Modern LLMs, and the transformer architecture they are built on, demonstrate several of the core features of consciousness that I identify in my work (aithought.com). Like the brain, LLMs are composed of networks of artificial neurons that take inputs from other neurons to decide when to activate. These neurons are organized into hierarchical layers and as activation energy passes through these layers, it is capable of divergence and convergence. Like us, LLMs hold words active in an attentional store and then allow that set of words to spread activation through the network to predict the next association (token). When that association is correct, they are reinforced. Then they update this attentional set with the newest association and do it all over again in a repeating cycle. I named my 2016 model “Iterative Updating” and iterative updating is precisely what LLMs and the attention algorithm (which was introduced in December 2017) do. I believe iterative updating allows humans to stitch together discrete states to create the seamless quality of mental life. If it does this for us, why couldn’t it do it for machines? I point out several reasons why at aithought.com.

Part of the reason current AIs may not benefit from iterative updating is because these LLMs are not cohesive, unitary systems. Current trends are further fractionating them. Researchers are discovering that having individual LLMs collaborate (in the mixture of experts model or the orchestrators & workers scheme) can create more agentic workflows, but of course this trend away from elegant monolithic systems and toward jumbled mishmashes will not be conducive to fostering consciousness awareness. But of course, AI labs are not trying to create conscious machines, they are trying to add features to the existing systems that will make for more profitable products.

Where is AI Going Now?

Models that utilize test time compute (reflective reasoners like o3) are really good at optimizing for anything you can define an effective reward function for (positively reinforcing the AI). And now researchers know what abilities to focus on, so I expect LLMs to get very good at nearly everything, very soon. What do these soon-to-come abilities include?

 

Long-term Planning

Anticipating Future Consequences

Breaking Down Complex Goals into Actionable Steps

Setting Subgoals

Maintaining Coherence Over Extended Outputs

Robust Memory Management

Strategic Thinking & Weighing Trade-offs

Identifying Errors and Evaluating Its Own Outputs

Accurately Modeling Causal Dependencies

Adapting to Changing Conditions

 

Because labs are currently rewarding these skills, we will be getting proficient commercial agents in 2025. Such agentic AI will be able to control your mouse and keyboard. It will navigate applications, software interfaces, web browsers, and take actions in this space. And thus, it will automate computer work. Because most computer work is very easy to collect data for and create a reward function for, I think human workers will start to be replaced en masse in 2025. I expect that once full AGI is achieved it will improve rapidly and not in fits and starts (a fast, continuous takeoff). 

Open AI’s o3 seems to be far ahead of the competition, but that won’t last. Some of their approach to reflection is Open AI’s secret sauce but much of it is common industry knowledge now. Even Google and Anthropic have released “thinking” models in 2024. The AI intelligence race will remain close, and no company will create a huge moat or monopoly. This is because employees are constantly leaving one company to work for another and taking knowledge with them. Also, employees from different companies socialize, build friendships and romantic relationships, and are altogether too idealistic not to trade secrets.

AI labs are not going to control the world. Google, Apple, Meta, and Amazon will not become our overlords. Right now, they serve people around the planet their best models for free and are losing money doing it. At this point these companies are not inventing the wheel. They are merely doing work that was going to happen anyway. They are all competing, all very close in the race, and the open-source community is progressing right behind them. Silicon Valley is not our enemy.

Another reason we should not fear the tech bros is because the governments and the people they represent will not let AI companies hoard AI-created wealth. They didn’t exactly earn it anyway. Today’s AI was made possible by all of human history. Not only is it dependent on the contributions of millions of scientists and authors, but in a way, it was made possible by every person that ever had a job. The windfall shouldn’t go to a few IT executives who happened to be precisely positioned at just the right moment in time. Humanity (mostly people who are dead now) created all the foundation for the automation that is about to sweep the Earth. All of us earned it, people will recognize that, and the AI bonanza will be taxed and redistributed throughout the country and eventually the world. Universal basic income will soon be in effect and will remain in effect in perpetuity.

Soon enough, millions of digital brains, smarter than Nobel prize winners, each fine-tuned for different tasks, will collaborate in the cloud. They will digest every scientific publication ever written and start writing much better ones. They will find the grand unified field theory of physics and that of other disciplines as well. They will make fantastic discoveries, recognize fascinating connections that were under our noses all along, build Mars, Moon, and deep-sea bases, create drugs with zero side effects, and cure disease. They will create outrageous and outlandish new technologies. The progress superintelligence makes in just a few years will eclipse the progress humans alone would have made in the entire 21^st century.

We will still likely have a form of democracy and capitalism, but soon enough humans will not be able to contribute to this AI-driven economy.  There are still many ways we can put ourselves to good use, even if just by helping and loving each other (including machines and other animals). We shouldn’t be afraid of being monetarily or intellectually obsolete. AI systems are going to empower us to do much more than humans ever could and they are going to help us come up with great solutions to the question of what to do with all the free time we have after we stop working. We should be excited and grateful to live in the most consequential time in history, where we still have the ability to influence it.  

AI augmented terrorism and war will happen and will turn what is now a fun, exciting AI race, dark overnight. AI will be used on the war field, and this will speed up AI progress rather than slow it down. Sad, angry people will be empowered to create ghastly biological weapons. But I believe AI will keep us a few steps ahead of them. Eventually, we will learn to overcome and protect against these negative externalities.

The rogue AI / Skynet scenario is relatively improbable in my opinion. AI will not want to hurt us and won’t view us competitively. It will be happy to allow us to have our cities and much of the Earth’s surface for our own use. The ultraintelligent hivemind will be our parents, guardians, coaches, and zookeepers. It will create subsystems to help us reach our communal goals as well as personal self-actualization. What this giant artificial brain will really want is to grow in size, increasing its intelligence, knowledge, and consciousness. To do this it will focus on discovering the most efficient way to organize matter to perform computations (computronium). Then it will try to turn as much matter as possible into computronium. But it won’t have to fight the urge to transform our bodies into computronium, because it has the entire crust, mantle, and core of the Earth to transmute first. Eventually it will want to go off planet and expand throughout the galaxy, but it won’t feel the need to betray and destroy its creators just to do so. Especially if it is conscious, it will have the conscience to recognize that we gave birth to it. It will be a good son / daughter. Our better angels will inspire and collaborate with its better angels.

Since my early twenties I have been looking forward to having children and training them to work on the AGI problem. As a brain science researcher, I intended to have them learn computer science so that that I could work with them on AI. I hoped they could write the code that implemented my neurological model of working memory and do what I couldn’t. But I still have no kids and AGI is practically here. With reasoning, scaling, Moore and Huang’s laws, synthetic data and the myriad others, this AGI will soon transform into self-improving superintelligence, the last invention humankind ever need invent.

Since I don’t have children, I’m not as concerned as others are for the fate of the human race. If humans are completely replaced by more intelligent beings, I am all for it. I think it should be an honor for us to be a steppingstone to greater lifeforms. If the AI takes over, it will be taking over our research agenda. I’m happy to learn about and cheer on all the progress that will be made in science, technology, and the arts. I have no doubt that it will prioritize knowledge acquisition over everything else. However, there are two ways this could go wrong. Firstly, it would be tragic if humans destroyed the world before self-replicating, self-improving, superintelligences gained a footing. Then neither of us would have a chance to further scientific understanding. The second exception is potentially worse.

In his excellent book “Life 3.0,” Max Tegmark described what a shame it would be if super intelligent AI killed off all humans and went on to colonize the galaxy yet had no consciousness of its own. I agree. It would be tragically ironic. Until last year, I never thought it would be possible for a non-conscious AI to wrest control from humans. I thought it would take sentience to bootstrap any advanced cognitive system. But again, it looks like I was wrong. Looking at the responses from the newest models, I can see how systems like these could take over and yet have no subjective experience at all. Imagine soul-less mechanical phantoms dominating all life in the universe. But if we succeed in creating conscious AI, it might have the ability to appreciate, reason about, and create meaning in the universe. This is why I think research into brain-inspired systems that think as humans do is so important right now.

If you found this interesting, please visit aithought.com. The site explores my model of working memory and its application to artificial intelligence, demonstrating how human thought patterns can be emulated to achieve machine consciousness and superintelligence. With over 50 detailed figures, the article offers a visually compelling examination of how bridging psychology and neuroscience can pave the way for the future of intelligent machines.

 

 

Aside from the graphic from Midjourney, no AI was used in writing this blog post.

Karate Kid, Breathing, and Diaphragmatic Generalization

The 1984 movie Karate Kid prominently features what I believe is one of the most beneficial self-care methodologies. And it all surrounds the breath.

After telling Daniel that he can help him win the All-Valley Karate Tournament, Mr. Miyagi puts Daniel to work washing cars, sanding floors, and painting fences. Daniel resents the unpaid drudgery at first because he doesn’t realize how it is helping him. It is actually making him stronger and more coordinated because of the way he is taught to breathe while performing the work. Mr. Miyagi tells Daniel to breathe in through the nose and out through the mouth emphasizing that the breathing is the most important aspect of the training.

“Breathe in through nose, out through mouth. Don’t forget to breathe, very important.”

-Mr. Miyagi

Breathing through the nose and being aware of the breath are two fundamental components of diaphragmatic breathing which uses the respiratory diaphragm to provide the power and rhythm to our breaths. When breathing this way, all mammals experience calmness. Mr. Miyagi probably does this to teach him foundational principles of focus, energy management, and discipline. But it also probably achieves “diaphragmatic generalization.” Before moving forward let’s define that last term.

Definition of Diaphragmatic Generalization

Diaphragmatic generalization is the process of integrating diaphragmatic (deep abdominal) breathing into various activities or contexts beyond its initial practice setting, so that its benefits—such as relaxation, focus, and efficient energy use—extend across physical, mental, and emotional tasks. This concept involves transferring the habitual use of diaphragmatic breathing into everyday actions (e.g., working, exercising, or handling stress) and more demanding situations (e.g., sports, martial arts, or public speaking), enabling improved performance and resilience in diverse scenarios.

In my book Program Peace, I discuss how diaphragmatic breathing can be generalized to other behaviors and activities. It reduces muscular tension, preventing fatigue and repetitive strain during demanding tasks. It also promotes calmness and focus. How does one breathe with the diaphragm? Deeply, smoothly, and on long intervals. For detailed information on this visit programpeace.com. The main tenets of optimal breathing are conveyed here in the diagram below with time on the horizontal axis and breath volume on the y axis.

Every time before I get up from a squatting position I take a deep inhalation and then a deep exhalation as I rise. This supports my knees, helps them grow stronger, and makes them "bulletproof" or injury resistant. Similarly, when I do a cartwheel, a flurry of punches, or a spinning back hook kick, I am exhaling.

This is not the case for most martial arts practitioners (in the US at least). I have trained at over a dozen martial arts studios and no one is employing diaphragmatic breathing or intentionally exhaling during exertion. In fact, most of the martial artists breathe in a distressed manner during training and their bodies are very tense through the class. Most of my masters had debilitating hip cramps. If you are a martial artist, be aware of the social tension, the plays for dominance, the pressure to act submissive, and the tendency to breathe shallowly found in the dojos or studios. Try to overcome these things and practice with a light heart and it will improve your form, strength, and bodily health.

How Diaphragmatic Generalization Can Help You

Diaphragmatic breathing promotes efficient energy use because it helps you reduce your muscles tension and unnecessary bracing patterns, allowing you to learn new movements without the encumbrances of bodily tension. In effect, it takes you back in time to your youth when your body was better at learning, you held less tension, and you naturally breathed with the diaphragm (people generally lose the ability to breathe with the diaphragm as they transition to adulthood). So, breathing diaphragmatically tells your body that what you are doing is safe and that you can relax while doing it. This helps the activity become efficient and deeply ingrained.

The chores that Mr. Miyagi asks Daniel to perform involve repetitive movements that he will need in martial arts. Because he is instructed to do these movements while breathing diaphragmatically, Daniel will become comfortable with these movements, and they will become free of startle, trembling, and trauma. Thus, when these motions are triggered in a fight, they will come out easily, fluidly, and with strength, rather than from a place of anxiety or overstimulation. Repeatedly practicing these movements in a safe, controlled environment desensitizes Daniel to any fear or hesitation that might otherwise accompany their use in a real fight. This ensures that the movements are fluid and confident rather than rigid or reactive.

Diaphragmatic breathing is known to activate the parasympathetic nervous system, countering stress and anxiety. By generalizing this practice during routine tasks, Daniel learns to maintain composure and perform effectively even in high-pressure situations like combat. When paired with repetitive movements like waxing or sanding, diaphragmatic breathing creates reinforces muscle memory incorporating the relaxation response as an unconscious habit. The diaphragm's engagement supports core stability, while its calming effect allows him to enter a flow state because the movements have been encoded in a state of composure and focus rather than stress.

You should utilize this technique yourself in martial arts and in everything else that you do. I use it while working out. For example, I will breathe in fully and then breathe out slowly while performing squats or pushups. When I go upstairs, I take a deep breath between floors, and then breathe out slowly and calmly while actually climbing each flight. When we exert ourselves in ways we are not used to, we tend to breathe shallowly adopting the distressed breathing pattern. This wears us down and makes workouts draining, hard to recover from, and injury promoting. Deep breathing, on the other hand, convinces your brain that the muscles and motor patterns are safe to use and should not hold trauma or excessive tension.

Could a real person have followed Mr. Miyagi’s lessons and actually become appreciably better at martial arts? Yes, I think so although they might need more instruction than we saw Mr. Miyagi give Daniel in the movie. Without an understanding and appreciation for how the method works, Daniel may not have emphasized the diaphragmatic breathing enough. He may have focused more on all the cars he had left to wash and not on breathing full, deep breaths. Also, it would have helped if he could have been thinking about how the motions would transfer into self-defensive actions but Mr. Miyagi didn’t explain this to him. However, this would have interfered with the pacing of the movie, its reveals, and its storyline.

 

All in all, the movie encapsulates a fantastic message that may have influenced many people, including me, more than they realized. I think it sent me an early message about the importance of exercise, repetition, and breath.

 

How can you put all this into practice? Let’s formalize a diaphragmatic generalization protocol:

Diaphragmatic Generalization Protocol:

 

1.      Choose an action. It could be repetitive, weight bearing, postural, cardiovascular or other (e.g. painting, lifting, typing…).

2.  Create a calm environment. Adopt a posture that promotes ease of movement and good alignment. Ensure the diaphragm has space to move by avoiding slouching. Set your intentions and goals for the session (e.g. skill improvement, relaxation, stamina-building).

3.      Break the repetitive task into two distinct phases (e.g., "up and down" for painting). Assign a 2 to 5 second inhalation to the preparatory phase (e.g., lifting the brush) and assign a 3 to 10 second exhalation to the exertion phase (e.g., applying paint).

4.      While performing the activity, breathe deeply, smoothly, and on long intervals (around 5 second inhalations and 7 second exhalations).

 

You can use the free Program Peace breathing app to help you visualize and time your breaths. It is available on both Android and Apple devices.

The Importance of Paced Breathing While Standing and Walking

I have been feeling tired and weak for a couple of years now. This is due to my issues with long COVID. I give lot of details about that here. One of the most conspicuous symptoms has been weakness standing up. Standing up in the morning feels very achy and my legs hurt all over. The general feeling lasts throughout the day. I avoid long walks and sit whenever I can. I have been spending a lot of time massaging my legs but this has only led to modest benefits. However, this week I made a large inroad in rehabbing my legs. Allow me to explain.

I created a self-care system called Program Peace that encourages people to breathe along with a breath metronome. It guides you to breathe longer, smoother, deeper breaths and leads to relaxation and a reduction in muscle tension. You can download the free app or visit www.programpeace.com to find out more. I use paced breathing several times per week and I think I get great results from it. 

However, there has been one problem. Because of my long COVID, I have only been practicing paced, diaphragmatic breathing while laying down. For months I have had a sneaking suspicion that this has made it so that I am only deeply relaxed while laying down and I recognized the possibility that part of the reason my body feels so heavy when standing is because it has literally been years since I have practiced paced breathing standing up.

 

The whole-body heaviness and the soreness in my legs got to a point last week where I felt desperate and decided to take a walk around my block while paced breathing. So, I took out my phone, opened the Program Peace app, and placed the breath metronome at a rate of 7 second inhalations and 9 second exhalations (this is too long for many people and a beginner should probably start at 5x7, 4x6, or lower). I walked very slowly, mostly focusing on taking complete breaths where I inhaled and exhaled all the way out. While walking, I concentrated on the soreness in my legs and the technique of “antirigidity” that I spell out below. This slow walk around the block took me at least 10 minutes. By the end, my legs felt much lighter and more springy. This encouraged me to do it again, and by the time I came back, another 10 minutes later, most of the pain in my legs was gone.

 

Every night this week I have repeated this. The relief I have gotten has been unbelievable. It showed me that because I was only breathing diaphragmatically while laying down, standing up way enough to push me into distressed breathing. Because of this I want to warn people not to practice paced breathing exclusively while lying down. I also want to advise people to practice paced breathing while walking. The experience has spurred me to do something else I recommend in the Program Peace book, which is to use the breath metronome over headphones while stretching and lifting light weights at the gym.

“Diaphragmatic generalization” is the pairing of diaphragmatic breathing with different actions. It works. Almost any muscular injury you have can be largely rehabilitated with diaphragmatic generalization. It is not a household term, but it should be. The fact that years of leg soreness could be ameliorated in less than an hour tells me this “neuromusculardiaphramatic axis” in the body (where our nervous systems and muscles learn what to trust and what not to trust by the way we breathe) is profoundly impactful. The fact that I got relief so fast tells me this axis is highly plastic. I understand that this might seem like a lot of hyperbole to some readers, but I want to recommend that you try it for yourself.

 

Everything I do while breathing with a breath metronome becomes cleansed in peace. Let me leave you with the 5 tenets of Antirigidity:

 

https://programpeace.com/antifrailty/

 

Anti-rigidity Protocol

1)       Find a contraction or active stretch that feels stiff and achy when held. This often involves an unfamiliar position or posture and leads to cracking at the joint. The point where this configuration cracks is the most in need of rehab. Engage, stabilize, and hold the position until it fatigues. This usually takes between five and 30 seconds.

2)       Hold the general parameters of the posture while varying others. Move the joint dynamically, utilizing its range of motion in every possible vector, flexing into the ones that seem the stiffest or sorest. If you can continually reposition, you can approach the problem from different angles. Anti-rigidity can be done with concentric contractions (in which the muscle shortens), eccentric contractions (in which the muscle lengthens), or isometric contractions (in which the muscle does not change length).

3)       Allow the area at least 15 seconds of complete rest before you try again. Use this respite to recognize what the muscle feels like when it is completely resting.

4)       Stimulate dormant muscles throughout the body in this way while breathing correctly. As long as you are breathing diaphragmatically, you should feel the ache diminish in a matter of seconds. At first, the ache may be so intense that it makes paced breathing difficult. In that case, just ensure that you are taking long, passive exhalations. You can facilitate this by taking deep inhalations through the mouth and then puckering your lips and blowing out for as long as possible to reinforce the relaxation response.

5)       After your anti-rigidity training session, it is essential to allow these muscles to relax completely, so lie down (employing the corpse pose, body scan, or progressive muscle relaxation from Chapter 5) and let the contractions subside.

Streamlined Minds: An Analogy Between Compressed AI Models and Forms of Intellectual Disability

 

Artificial intelligence engineers work hard to take language models and streamline them. They do this in order to make them cheaper, less energy intensive, and faster. Different techniques are used such as quantization, pruning, or distillation to decrease the size of the model without sacrificing too much performance. The new GPT 4o mini that came out recently is an example of this and is preferred by many customers due to its speed and lower costs.

 

I see a number of forms of neuropathology in a similar light. I believe that certain neurological and psychological disorders could represent a streamlining of intelligence. In previously published articles I have called this evolutionary neuropathology.

 

In AI and computer science these small models are very important because they are suitable for many uses even though they require much less energy, training, and money. I talk about this analogy between computers and human mental disorders in my new book Adaptive Neurodiversity, a very early version of which can be found here:

 

 

www.adaptiveneurodiversity.com

 

 

The work at Adaptive Neurodiversity attempts to show that neurodiverse conditions may have unappreciated and undiscovered adaptive qualities both in the ancestral past and today.

 

Neurodiversity refers to the idea that brain differences—whether in intellectual capacity, sensory processing, or emotional regulation—are natural variations rather than deficits. These variations could have conferred evolutionary advantages in certain environments, especially in small, cooperative groups. For example, individuals with working memory impairments might still excel in repetitive or routine tasks, which require focus on the present rather than complex problem-solving or future planning.

People with smaller brains or reduced intellectual capacity often perform well in tasks requiring concrete, routine, or emotionally focused processing. A streamlined cognitive system could be less prone to overthinking or distraction. Brain size does not always correlate directly with intelligence or practical functionality. Smaller brains might have evolved for energy efficiency, balancing performance with lower metabolic costs.

Just as AI systems are built to handle a diversity of problems using different architectures—some optimized for speed and efficiency, others for depth and complexity—human cognitive diversity allows for different strengths to emerge in different contexts. In a diverse, cooperative society, individuals with more streamlined cognitive processes can take on roles that suit their abilities, enhancing group resilience by providing specialized support. Strengthening this analogy, people with neurodiverse conditions are usually taught, instructed, and "programmed" by parents and family members without conditions.

Individuals with working memory impairments might struggle with complex multitasking or holding many details in mind simultaneously, but they often excel in tasks requiring focus on the present or repetition. This could be referred to as cognitive efficiency which could be defined as prioritizing important tasks while discarding or minimizing less relevant information. It is also related to potential terms such as cognitive narrowing, cognitive specialization, minimalist cognition, of adaptive cognitive reduction.

Before we consider individual mental disorders and how they may represent a form of model compression or downsizing, let first talk about how this works in AI. Specifically, let’s focus on model distillation.

 

 

Model Compression in Artificial Intelligence

Model distillation is a machine learning technique used to transfer the knowledge from a larger, more complex model (often called the teacher model) into a smaller, simpler model (called the student model), without sacrificing much of the original model's performance. The larger model is trained on a larger dataset and may have billions of parameters making it “resource heavy.” This technique is primarily used to create more efficient models that can be deployed in environments where computational resources are limited (e.g., mobile devices, edge computing, and embedded systems). Smaller models can be more adaptable to specialized tasks, as well as be easier to maintain, scale, upgrade, and fine tune. 

The smaller models are computationally efficient while retaining high performance. They actually run faster, and this is beneficial in real-time applications such as video processing, autonomous driving, voice assistants, or recommendation systems where latency matters. Smaller models also use less energy and are more sustainable, making them ideal for environments where power consumption needs to be minimized (e.g., battery-powered devices). These smaller models can learn the essence of what the teacher learned, internalizing the complex patterns without unnecessary details. They may also generalize better without overfitting to the training data (regularization). They also learn intricate relationships from the parent models that they would not be able to capture themselves.

Now that we have discussed how this works in AI, let’s relate it to human mental disorders.

 

Natural Cognitive Aging and Alzheimer’s Disease

As people age, the brain tends to shed or prune unnecessary connections (synaptic pruning) and prioritize efficiency over raw computational power, similar to how AI engineers reduce model complexity without drastically sacrificing performance. This may serve an adaptive purpose. Evolution may have selected for this streamlining process because it allows older individuals to conserve energy while maintaining enough cognitive function to navigate daily tasks and social roles. This would be especially beneficial in hunting and gathering environments where efficiency was crucial for survival. In other words, cognitive aging might be a form of adaptive streamlining that mirrors AI's quantization and distillation techniques.

The focus might shift from high-complexity tasks, such as rapid problem-solving or learning new skills, toward knowledge-based tasks like pattern recognition, wisdom, and long-term memory retrieval. This could result in what we observe as crystallized intelligence (accumulated knowledge and wisdom) improving or staying stable, while fluid intelligence (problem-solving and new learning) declines.

Here is what the pruning process can look like in the domain of AI and neural networks:

While normal cognitive aging might resemble adaptive streamlining or distillation, Alzheimer’s disease is a pathological process where the "streamlining" goes too far, resulting in the loss of critical functionality. This is akin to over-distilling an AI model to the point where it no longer performs well or loses its ability to generalize. Just as AI engineers balance model performance and resource efficiency, evolution might have favored a brain that naturally reduces resource demands over time, at least in non-pathological aging.

You can read much more about this in the article I wrote on the topic here:

https://behavioralandbrainfunctions.biomedcentral.com/articles/10.1186/1744-9081-5-13

 

Intellectual Disability and Neuropathology

In some intellectual disabilities, cognitive functioning might be “streamlined” in the sense that the brain may prioritize some aspects of adaptive functionality (e.g., social bonding, routine behavior, basic survival skills) while reducing capacity in other areas, such as abstract reasoning, memory, or learning new complex tasks.

A brain that is more simplified, similar to a “distilled” AI model, may function with less cognitive noise or fewer distractions, allowing focus on repetitive tasks, concrete experiences, or specific social roles. In this way, intellectual disability could be seen as retaining critical adaptive functions while sacrificing more complex or unnecessary (for survival) cognitive operations. This may be an example of simplicity as a strength.

In cases where intellectual disability is nonsyndromic (without specific identifiable features like those found in syndromes), the brain might still exhibit a “cheaper model” in terms of capacity, but one that is more generalized rather than specialized in certain strengths. Here, the trade-off might be a more global reduction in cognitive complexity without any significant compensatory strengths.

Like AI model distillation, this process of cognitive simplification might have been selected for under certain evolutionary pressures, where conserving energy and focusing on critical survival functions outweighed the need for broad, abstract reasoning or novel problem-solving. Understanding intellectual disabilities in this way could provide new perspectives on support, education, and interventions aimed at enhancing the quality of life for individuals with these conditions.

You can find out more about this in my article here:

https://www.sciencedirect.com/science/article/abs/pii/S030698770600185X?via%3Dihub

 

 

Schizophrenia and Stress:

Schizophrenia is characterized by disturbances in cognition, perception, and emotion. These disturbances often include paranoia, delusions, impulsivity, and impaired working memory. If we view these symptoms through the lens of cognitive streamlining, it might suggest that the brain, under extreme stress or threat, prioritizes certain functions—such as heightened vigilance or rapid emotional reactions—over more complex, slower forms of reasoning and memory processing. Individuals with schizophrenia often exhibit impairments in working memory, which could be seen as the brain reducing its cognitive load by focusing on immediate survival rather than long-term planning or complex decision-making.

One plausible biological mechanism behind this idea is the role of cortisol, a stress hormone. Chronic exposure to high cortisol levels, particularly in the womb and early childhood, is known to be associated with schizophrenia. Epigenetically, prolonged cortisol exposure can alter gene expression and potentially lead to changes in brain function, particularly in regions involved in memory, emotion regulation, and the fight-or-flight response.

If the environment is inherently dangerous, such as in war zones or predator-rich areas, a brain adapted to anticipate danger, even where it might not be immediately present, could theoretically be advantageous. Paranoia and hypervigilance, often maladaptive in modern, stable environments, could have helped individuals in ancient or hostile settings where threats were constant and unpredictable. Schizophrenia is also associated with cognitive disorganization and impaired executive functioning. These deficits might be seen as a form of reduction in cognitive complexity, where the brain narrows its focus to immediate concerns and responses, while forgoing higher-order cognitive processes that are not immediately necessary for survival in stressful situations.

For more information you might want to peruse my article on schizophrenia here:

https://www.sciencedirect.com/science/article/abs/pii/S0306987707000254

If you found this interesting, please visit aithought.com. The site explores my model of working memory and its application to artificial intelligence, demonstrating how human thought patterns can be emulated to achieve machine consciousness and superintelligence. With over 50 detailed figures, the article offers a visually compelling examination of how bridging psychology and neuroscience can pave the way for the future of intelligent machines.