A Thousand Brains - by Jeff Hawkins

Published: February 18, 2022

A Thousand Brains - by Jeff Hawkins

Read: 2022-02-18

Recommend: 10/10

Many of the thought experiments were eye-opening for me. I can see my own struggle between the old brain and new brain when I face many temptations: social networking, over-training, workaholism, etc. I need a constant reminder to choose knowledge over genes.

Notes

Here are some text that I highlighted in the book:

“Why does pain have to be so damn painful?” What, after all, is pain for? Pain is a proxy for death. It is a warning to the brain
“As I see it, we have a profound choice to make. It is a choice between favoring the old brain or favoring the new brain. More specifically, do we want our future to be driven by the processes that got us here, namely, natural selection, competition, and the drive of selfish genes? Or, do we want our future to be driven by intelligence and its desire to understand the world?”
Scientists say that the brain learns a model of the world. The word “model” implies that what we know is not just stored as a pile of facts but is organized in a way that reflects the structure of the world and everything it contains.
Indeed, we now understand that most of the cells in your neocortex are dedicated to creating and manipulating reference frames, which the brain uses to plan and think.
There was no convenient time to leave, so I picked a date and walked away from the businesses I helped create.
The newest part of our brain is the neocortex, which means “new outer layer.”
Under one square millimeter of neocortex (about 2.5 cubic millimeters), there are roughly one hundred thousand neurons, five hundred million connections between neurons (called synapses), and several kilometers of axons and dendrites.
In every region they have examined, scientists have found cells that project to some part of the old brain related to movement.
The evidence we have indicates that the complex circuitry seen everywhere in the neocortex performs a sensory-motor task. There are no pure motor regions and no pure sensory regions.
Mountcastle argued that, although a human neocortex is much larger than a rat or dog neocortex, they are all made of the same element—we just have more copies of that element.
If you look at the neocortex from the outside, you can’t see the regions; there are no demarcations, just like a satellite image doesn’t reveal political borders between countries.
Mountcastle proposed that the reason the regions look similar is that they are all doing the same thing. What makes them different is not their intrinsic function but what they are connected to.
Darwin knew what the algorithm was: evolution is based on random variation and natural selection. However, Darwin didn’t know where the algorithm was in the body. This was not known until the discovery of DNA many years later. Mountcastle, by contrast, didn’t know what the cortical algorithm was; he didn’t know what the principles of intelligence were. But he did know where this algorithm resided in the brain.
By this definition, there are roughly 150,000 cortical columns stacked side by side in a human neocortex.
For example, in people with congenital blindness, the visual areas of the neocortex do not get useful information from the eyes. These areas may then assume new roles related to hearing or touch.
When the brain’s predictions are verified, that means the brain’s model of the world is accurate. A mis-prediction causes you to attend to the error and update the model.
The term for this is sensory-motor learning. In other words, the brain learns a model of the world by observing how our sensory inputs change as we move.
The dendrite branches are clustered near the cell and collect the inputs. The axon is the output.
The connection points are called synapses.
Our mental states and the activity of neurons are one and the same.
Discovery Number One: The Neocortex Learns a Predictive Model of the World
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 that the neuron has recognized a pattern of activity in some other neurons. When the pattern of activity is detected, it creates a dendrite spike, which raises the voltage at the cell body, putting the cell into what we call a predictive state. The neuron is then primed to spike. It is similar to how a runner who hears “Ready, set…” is primed to start running. If a neuron in a predictive state subsequently gets enough proximal input to create an action potential spike, then the cell spikes a little bit sooner than it would have if the neuron was not in a predictive state.
Up to that point, most neuroscientists, including me, thought that the neocortex primarily processed sensory input. What I realized that day is that we need to think of the neocortex as primarily processing reference frames.
In mammals, the old brain parts where these map-creating neurons exist are called the hippocampus and the entorhinal cortex. In humans, these organs are roughly the size of a finger. There is one set on each side of the brain, near the center.
Again, to learn a complete model of something you need both grid cells and place cells. Grid cells create a reference frame to specify locations and plan movements. But you also need sensed information, represented by place cells, to associate sensory input with locations in the reference frame.
It is as if nature stripped down the hippocampus and entorhinal cortex to a minimal form, made tens of thousands of copies, and arranged them side by side in cortical columns. That became the neocortex.
Neurons take the same amount of time to search through a thousand maps as to search through one.
There are neurons in the old brain called head direction cells. As their name suggests, these cells represent the direction an animal’s head is facing.
Therefore, we proposed that each cortical column has a set of cells equivalent to grid cells, another set equivalent to place cells, and another set equivalent to head direction cells, all of which were first discovered in parts of the old brain.
The succession of thoughts that 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.
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 your body.
The method of loci supports two of the above premises: information is stored in reference frames and retrieval of information is a form of movement.
My point is that becoming an expert in a field of study requires discovering a good framework to represent the associated data and facts.
However, if you are not trained in mathematics, then equations and other mathematical notations will appear as meaningless scribbles. You may even recognize an equation as one you have seen before, but without a reference frame, you will have no idea how to manipulate it to solve a problem. You can be lost in math space, in the same way you can be lost in the woods without a map.
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 is reference frames all the way down.
Albert Einstein started with the same facts as his contemporaries. However, he found a better way to arrange them, a better reference frame, that permitted him to see analogies and make predictions that were surprising.
Thousand Brains Theory: knowledge of any particular item is distributed among thousands of complementary models.
I believe the future of AI will be based on brain principles. Truly intelligent machines, AGI, will learn models of the world using maplike reference frames just like the neocortex.
The extreme flexibility of human intelligence requires the attributes I described in this chapter: continuous learning, learning through movement, learning many models, and using general-purpose reference frames for storing knowledge and generating goal-oriented behaviors.
These thought experiments prove that our awareness, our sense of presence—which is the central part of consciousness—is dependent on continuously forming memories of our recent thoughts and experiences and playing them back as we go about our day.
This tells us that the qualia of color is not purely a property of the physical world. If it were, we would all say the dress has the same color. The color of the dress is a property of our brain’s model of the world. If two people perceive the same input differently, that tells us their model is different.
Fear of death and sorrow for loss are not required ingredients for a machine to be conscious or intelligent. Unless we go out of our way to give machines equivalent fears and emotions, they will not care at all if they are shut down, disassembled, or scrapped.
At some point in the future, we will accept that any system that learns a model of the world, continuously remembers the states of that model, and recalls the remembered states will be conscious.
Similarly, the neocortex learns a model of the world, which by itself has no goals or values. The emotions that direct our behaviors are determined by the old brain. If one human’s old brain is aggressive, then it will use the model in the neocortex to better execute aggressive behavior. If another person’s old brain is benevolent, then it will use the model in the neocortex to better achieve its benevolent goals. As with maps, one person’s model of the world might be better suited for a particular set of aims, but the neocortex does not create the goals.
The requirement is that an intelligent system can perform actions that change the locations of its sensors, but actions and locations don’t have to be physical.
The goals could be fixed, like our genetically determined desire to eat, or they could be learned, like our societally determined goals for how to live a good life. Of course, any goals must be built on top of safety measures such as Asimov’s first two laws.
Acquiring new knowledge and skills takes time regardless of how fast or big a brain might be. In some domains, such as mathematics, an intelligent machine could learn much faster than a human. In most fields, however, the speed of learning is limited by the need to physically interact with the world. Therefore, there can’t be an explosion of intelligence where machines suddenly know much more than we do.
The point is that anything capable of self-replication, especially viruses and bacteria, is a potential existential threat. Intelligence, on its own, is not.
Your brain is in a box, the skull. There are no sensors in the brain itself, so the neurons that make up your brain are sitting in the dark, isolated from the world outside. The only way your brain knows anything about reality is through the sensory nerve fibers that enter the skull. The nerve fibers coming from the eyes, ears, and skin look the same, and the spikes that travel along them are identical. There is no light or sound entering the skull, only electrical spikes.
Notice again that there is no light, touch, or sound entering the brain. None of the perceptions that make up our mental experiences—from the fuzziness of a pet, to the sigh of a friend, to the colors of fall leaves—come through the sensory nerves. The nerves only send spikes. And since we do not perceive spikes, everything we do perceive must be fabricated in the brain.
Our reality is similar to the brain-in-a-vat hypothesis; we live in a simulated world, but it is not in a computer—it is in our head.
If you rely only on your personal experiences, then it is possible to live a fairly normal life and believe that the Earth is flat, that the moon landings were faked, that human activity is not changing the global climate, that species don’t evolve, that vaccines cause diseases, and that mass shootings are faked.
For example, say we had a history book that contained many factual errors. The book begins with a set of instructions to the reader. The first instruction says, “Everything in this book is true. Ignore any evidence that contradicts this book.” The second instruction says, “If you encounter others who also believe this book is true, then you should assist them in anything they need, and they will do the same for you.” The third instruction says, “Tell everyone you can about the book. If they refuse to believe the book is true, then you should banish or kill them.”
The history book may be factually incorrect, but life is not about having a correct model of the world. Life is about replication.
Actively looking for evidence to disprove our beliefs is the scientific method. It is the only approach we know of that can get us closer to the truth.
Genes don’t understand anything. They don’t enjoy being genes, and they don’t suffer when they fail to replicate. They are simply complex molecules that are capable of replication.
We may leave the house in the morning resolved to eat only healthy food. Yet, when we see and smell a piece of cake, we often eat it anyway. The old brain is in control, and the old brain evolved in a time when calories were hard to come by. The old brain does not know about future consequences. In the battle between the old brain and the neocortex, the old brain usually wins.
man has the ability to control her own fertility and is empowered to exercise that option if she wants to. I call this a clever solution because, in the battle between the old brain and the neocortex, the old brain almost always wins. The invention of birth control shows how the neocortex can use its intelligence to get the upper hand.
The desire for sex is the mechanism that evolution came up with to serve the genes’ interests. Even if we don’t want more children, it is difficult to stop having sex. So, we used our intelligence to create birth-control methods that let the old brain have as much sex as it wants without creating more children.
Instead of fighting the old brain, the neocortex lets the old brain get what it wants but prevents the undesirable end result
In the previous chapter, I described how the brain’s model of the world can be inaccurate and why false beliefs can persist despite contrary evidence. For review, here are the three basic ingredients:
1. Cannot directly experience: False beliefs are almost always about things that we can’t directly experience. If we cannot observe something directly—if we can’t hear, touch, or see it ourselves—then we have to rely on what other people tell us. Who we listen to determines what we believe.
2. Ignore contrary evidence: To maintain a false belief, you have to dismiss evidence that contradicts it. Most false beliefs dictate behaviors and rationales for ignoring contrary evidence.
3. Viral spread: Viral false beliefs prescribe behaviors that encourage spreading the belief to other people.
Calling these “good” and “bad” is somewhat subjective. From a replicating gene’s point of view, they are all successful.
I see the current human situation as a battle between two powerful forces. In one corner, we have genes and evolution, which have dominated life for billions of years. Genes don’t care about the survival of individuals. They don’t care about the survival of our society. Most don’t even care if our species goes extinct, because genes typically exist in multiple species. Genes only care about making copies of themselves
we have created two existential threats: nuclear weapons and climate change.
It is human nature—aka old brain—to suspect everyone wants to steal your idea, where the reality is that you are lucky if anyone cares about your idea at all
As I see it, we have a profound choice to make. It is a choice between favoring the old brain or the new brain. More specifically, do we want our future to be driven by the processes that got us here, namely, natural selection, competition, and the drive of selfish genes? Or do we want our future to be driven by intelligence and the desire to understand the world? We have the opportunity to choose between a future where the primary driver is the creation and dissemination of knowledge and a future where the primary driver is the copying and dissemination of genes.
There is no absolute right or wrong; there are only choices that we get to make. If people say we should never allow DNA editing on principle, then, whether they realize it or not, they have chosen a future that is in the best interests of our existing genes or, as is often the case, of viral false beliefs.
Curiosity is one of our old-brain functions. It is hard to resist exploring, even when it would be safer not to. If humans could travel to the stars, it would just be an extension of what we have always done, spreading our genes to as many places as possible.
But I want to make the case for knowledge over genes. There is a fundamental difference between the two, a difference that makes preserving and spreading knowledge, in my opinion, a more worthy goal than preserving and spreading our genes.
Knowledge is different. Knowledge has both a direction and an end goal.