Neural Networks Quotes

Relationships may become wrecked by a quirky syndrome: the “Ain't broke, don't fix”-syndrome. When there is no interaction in the neural network and no breakthrough into the mind but only a shallow skin experience, living together might be very torturous. If a heartfelt bond has not been molded, nothing can be broken and thus nothing needs to be fixed. (“I wonder what went wrong.”)

The butterfly's wing beat of love can engender a mystical attraction in the neural meanders of our mind and generate emotional torments with a single stroke. ("I seek you")

When we get business networks to function similarly to neural networks and mycelium networks, we'll have a better world.

For more than 2 million years, human neural networks kept growing and growing, but apart from some flint knives and pointed sticks, humans had precious little to show for it. What then drove forward the evolution of the massive human brain during those 2 million years? Frankly, we don’t know.

As advances in AI, machine learning, and neural networks evolve, incomprehensibility will reach even higher levels - exposing these complex systems to both human and machine errors.

Posthuman. It was a word that came up in the media every five or six years, and it meant different things every time. Neural regrowth hormone? Posthuman. Sex robots with inbuilt pseudo intelligence? Posthuman. Self-optimizing network routing? Posthuman. It was a word from advertising copy, breathless and empty, and all he’d ever thought it really meant was that the people using it had a limited imagination about what exactly humans were capable of.

All the experiences in your life- from single conversations to your broader culture- shape the microscopic details of your brain. Neurally speaking, who you are depends on where you've been. Your brain is a relentless shape-shifter, constantly rewriting its own circuitry- and because your experiences are unique, so are the vast detailed patterns in your neural networks. Because they continue to change your whole life, your identity is a moving target; it never reaches an endpoint.

Unrelenting criticism, especially when it is ground in with parental rage and scorn, is so injurious that it changes the structure of the child’s brain. Repeated messages of disdain are internalized and adopted by the child, who eventually repeats them over and over to himself. Incessant repetitions result in the construction of thick neural pathways of self-hate and self-disgust. Over time a self-hate response attaches to more and more of the child’s thoughts, feelings and behaviors. Eventually, any inclination toward authentic or vulnerable self-expression activates internal neural networks of self-loathing. The child is forced to exist in a crippling state of self-attack, which eventually becomes the equivalent of full-fledged self-abandonment. The ability to support himself or take his own side in any way is decimated. With ongoing parental reinforcement, these neural pathways expand into a large complex network that becomes an Inner Critic that dominates mental activity. The inner critic’s negative perspective creates many programs of self-rejecting perfectionism. At the same time, it obsesses about danger and catastrophizes incessantly.

Describing good relatedness to someone, no matter how precisely or how often, does not inscribe it into the neural networks that inspire love. Self-help books are like car repair manuals: you can read them all day, but doing so doesn't fix a thing. Working on a car means rolling up your sleeves and getting under the hood, and you have to be willing to get dirt on your hands and grease beneath your fingernails. Overhauling emotional knowledge is no spectator sport; it demands the messy experience of yanking and tinkering that comes from a limbic bond. If someone's relationship today bear a troubled imprint, they do so because an influential relationship left its mark on a child's mind. When a limbic connection has established a neural pattern, it takes a limbic connection to revise it.

We get smarter and more creative as we age, research shows. Our brain's anatomy, neural networks, and cognitive abilities can actually improve with age and increased life experiences. Contrary to the mythology of Silicon Valley, older employees may be even more productive, innovative, and collaborative than younger ones... Most people, in fact, have multiple cognitive peaks throughout their lives.

Genocide is only possible when dehumanization happens on a massive scale, and the perfect tool for this job is propaganda: it keys right into the neural networks that understand other people, and dials down the degree to which we empathize with them.

The current technological and scientific revolution implies not that authentic individuals and authentic realities can be manipulated by algorithms and TV cameras, but rather that authenticity is a myth. People are afraid of being trapped inside a box, but they don’t realise that they are already trapped inside a box – their brain – which is locked within a bigger box – human society with its myriad fictions. When you escape the matrix the only thing you discover is a bigger matrix. When the peasants and workers revolted against the tsar in 1917, they ended up with Stalin; and when you begin to explore the manifold ways the world manipulates you, in the end you realise that your core identity is a complex illusion created by neural networks.

Optimal sculpting of key neural networks through healthy early relationships allows us to think well of ourselves, trust others, regulate our emotions, maintain positive expectations, and utilize our intellectual and emotional intelligence in moment-to-moment

Through practice, repeated signals have been passed along neural networks, strengthening synapses and thereby burning the skill into the circuitry. In

Intelligence is not limited to neural networks, Merrill. Indeed, half of human intelligence resides in our bodies outside our skulls…The genius of the irrational…This is the body’s intelligence, not the mind’s. Every living cell possess it…[the] indomitable will to survive.

Is a mind a complicated kind of abstract pattern that develops in an underlying physical substrate, such as a vast network of nerve cells? If so, could something else be substituted for the nerve cells – something such as ants, giving rise to an ant colony that thinks as a whole and has an identity – that is to say, a self? Or could something else be substituted for the tiny nerve cells, such as millions of small computational units made of arrays of transistors, giving rise to an artificial neural network with a conscious mind? Or could software simulating such richly interconnected computational units be substituted, giving rise to a conventional computer (necessarily a far faster and more capacious one than we have ever seen) endowed with a mind and a soul and free will?

The beauty of neuroplasticity is that when you make changes to what you remember and how you remember it, what you do and how you do it, your brain overrides the old neural networks with new ones.

The intense effort to develop artificial intelligence has increased our understanding of neural networks because at its core, AI is but an attempt to improve artificially what the brain already does effortlessly.

Scientists are cautiously beginning to question the view that the brain is the sole and absolute ruler over the body. The gut not only possesses an unimaginable number of nerves, those nerves are also unimaginably different from those of the rest of the body. The gut commands an entire fleet of signaling substances, nerve-insulation materials, and ways of connecting. There is only one other organ in the body that can compete with the gut for diversity—the brain. The gut’s network of nerves is called the “gut brain” because it is just as large and chemically complex as the gray matter in our heads. Were the gut solely responsible for transporting food and producing the occasional burp, such a sophisticated nervous system would be an odd waste of energy. Nobody would create such a neural network just to enable us to break wind. There must be more to it than that.

Let me use a second metaphor. Imagine that you found a tangle of seaweed on the edge of the shore and lifted it. The heaviest parts rest on the sand in a mesh, but some skeins extend vertically. This neural network is shaped like that: it looks like a tangled skein of a hundred thousand golden threads that has been drawn upward. The mass of it gathers in the pelvis, but strands from the same network extend upward to the spinal cord and brain.

The human brain is incredible in its capacity to heal and rewire itself. The human brain can be shaped and trained to be more resilient, calm, compassionate and alert—we can condition ourselves to be successful. Through mindfulness meditation, we can literally re-wire our brains through new experiences, which modify our neural network and our neural chemistry. Mindfulness also enhances gamma synchrony and improves the function of the human brain.

In our everyday lives, if we intentionally set out to learn new things or do familiar things in new ways (such as commuting to work via a new route or taking the bus instead of a car), we effectively rewire our brains and improve them. A physical workout builds muscle; a mental workout creates new synapses to strengthen the neural network.

We do not have time to develop a complicated neural network. This is a strictly procedural algorithm. Very complex, but not AI at all. We have to be able to test it in thousands of ways and know exactly how it responds and why. We can’t do that with a neural network.

Despite the feeling that we’re directly experiencing the world out there, our reality is ultimately built in the dark, in a foreign language of electrochemical signals. The activity churning across vast neural networks gets turned into your story of this, your private experience of the world: the feeling of this book in your hands, the light in the room, the smell of roses, the sound of others speaking.

We have more computing power in the drop ships’ tactical integrated neural network computers than existed on the entire planet fifty years ago, and somehow mission planning goes better when you’re outside in the fresh air and drawing diagrams and maps into the dirt with a pointy stick. “Two

The brain that we think of as a necessity for intelligence is only one possible form a neural network can take and that is determined by ecological function and species shape; it is not essential to intelligence. As neurologist Antonio Damasio puts it, “the mind is embodied, not just embrained.

In this way, each of us has unique neural networks, which are formed, reinforced, and constantly updated by the eclectic circumstances of our lives. Once circuits are formed, that increases the chances the same circuits will fire in the future. The neural networks embody our experiences and in turn guide future action. They contain the unique way each of us carries himself in the world, the way we walk, talk, and react. They are the grooves down which our behavior flows. A brain is the record of a life. The networks of neural connections are the physical manifestation of your habits, personality, and predilections. You are the spiritual entity that emerges out of the material networks in your head.

The neural networks responsible for sensory gating begin to process data as soon as the child is born .

convinced they should go all in on AI by using the neural network path planner being developed by Dhaval Shroff and

Your social network will eventually reflect your neural network.

the world manipulates you, in the end you realize that your core identity is a complex illusion created by neural networks.

I thought of my own self fifteen years ago, and how much I’ve changed in the same period. The me who exists today and the me who existed then, if put side by side, would look more than vaguely similar. But we are a completely different collection of molecules, with different hairlines and waistlines, and, it sometimes seems, little in common besides our names. What binds that me to this me, and allows me to maintain the illusion that there is continuity from moment to moment and year to year, is some relatively stable but gradually evolving thing at the nucleus of my being. Call it a soul, or a self, or an emergent by-product of a neural network, but whatever you want to call it, that element of continuity is entirely dependent on memory.

This wiring is manifest in well-worn neural network pathways, which are stimulated by triggers that remind us, implicitly, of childhood experience—our wounds, triumphs and longed-for experiences.

In a 2004 study, Angelo Maravita and Atsushi Iriki discovered that when monkeys and humans consistently use a tool to extend their reach, such as using a rake to reach an object, certain neural networks in the brain change their “map” of the body to include the new tool. This fascinating finding reinforces the idea that external tools can and often do become a natural extension of our minds.

In 1958 a Cornell professor, Frank Rosenblatt, attempted to do this by devising a mathematical approach for creating an artificial neural network like that of the brain, which he called a Perceptron.

life is an embedded system designed by god and r brains r the neural networks surrounded by neurons each neuron has its individual function but they r controlled by microcontroller ic's called r minds :)

The “losers” in memory competitions, this research suggests, stumble not because they remember too little. They have studied tens, perhaps hundreds of thousands of words, and often they are familiar with the word they ultimately misspell. In many cases, they stumble because they remember too much. If recollecting is just that—a re-collection of perceptions, facts, and ideas scattered in intertwining neural networks in the dark storm of the brain—then forgetting acts to block the background noise, the static, so that the right signals stand out. The sharpness of the one depends on the strength of the other.

I was cut off by the azan sounding... I imagined... a neural network spread throughout the entire country and to the Iranian diaspora across the whole planet. I felt very Persian just then, even though I didn't understand the chanting. Even though I wasn't Muslim. I was one tiny pulsar in a swirling, luminous galaxy of years of culture and heritage. There was nothing like it back home. Maybe the Super Bowl.

The vulva, clitoris, and vagina are actually best understood as the surface of an ocean that is shot through with vibrant networks of underwater lightning—intricate and fragile, individually varied neural pathways.

Converging empirical data show that when we observe other human beings expressing emotions, we simulate them with the help of the same neural networks that are active when we feel or express these emotions ourselves.

For their neural networks to function, plants use virtually the same neurotransmitters we do, including the two most important: glutamate and GABA (gamma aminobutyric acid). They also utilize, as do we, acetylcholine, dopamine, serotonin, melatonin, epinephrine, norepinephrine, levodopa, indole-3-acetic acid, 5-hydroxyindole acetic acid, testosterone (and other androgens), estradiol (and other estrogens), nicotine, and a number of other neuroactive compounds. They also make use of their plant-specific neurotransmitter, auxin, which, like serotonin, for example, is synthesized from tryptophan. These transmitters are used, as they are in us, for communication within the organism and to enhance brain function.

neural mechanisms for filtering sensory data inflows exist in the neural networks for every type of sensory input that we experience, including our nonkinesthetic feeling sense (what I have called heart perception in The Secret Teachings of Plants,

... [O]ne of the most influential approaches to thinking about memory in recent years, known as connectionism, has abandoned the idea that a memory is an activated picture of a past event. Connectionist or neural network models are based on the principle that the brain stores engrams by increasing the strength of connections between different neurons that participate in encoding an experience. When we encode an experience, connections between active neurons become stronger, and this specific pattern of brain activity constitutes the engram. Later, as we try to remember the experience, a retrieval cue will induce another pattern of activity in the brain. If this pattern is similar enough to a previously encoded pattern, remembering will occur. The "memory" in a neural network model is not simply an activated engram, however. It is a unique pattern that emerges from the pooled contributions of the cue and the engram. A neural network combines information in the present environment with patterns that have been stored in the past, and the resulting mixture of the two is what the network remembers... When we remember, we complete a pattern with the best match available in memory; we do not shine a spotlight on a stored picture.

Epigenetics suggests that even though our DNA code never changes, thousands of combinations, sequences, and patterned variations in a single gene are possible (just as thousands of combinations, sequences, and patterns of neural networks are possible in the brain).

Brain scans show that compulsive gamers have hyperconnected neural network. One researcher stated that, “Hyperconnectivity between these brain networks could lead to a more robust ability to direct attention toward targets, and to recognize novel information in the environment.

One of the reasons for learning about type is to recognize that we are constantly motivated, simply by the way we’ve established our neural networks, to shape reality along particular functional lines. Another is to recognize the possibilities for growth and change that exist within—and apart from—the framework we have created for ourselves. Even small changes in our usual way of doing things can make big differences in the way our brain is operating. We develop the ability to think in new ways, and this stimulates creative change in all areas of our lives.

The parts are relied upon to use their own analyses and choices as to how to respond, in essence, giving Gaia a network of trillions upon trillions of neural networks all working in their own sphere to help maintain Gaian homeodynamis. and all utilizing their own inherent genius to do so

Growth of the Body and the Brain. The physical growth of the human body increases in a roughly linear manner from birth through adolescence. In contrast, the brain’s physical growth follows a different pattern. The most rapid rate of growth takes place in utero, and from birth to age four the brain grows explosively. The brain of the four-year-old is 90 percent adult size! A majority of the physical growth of the brain’s key neural networks takes place during this time. It is a time of great malleability and vulnerability as experiences are actively shaping the organizing brain. This is a time of great opportunity for the developing child: safe, predictable, nurturing and repetitive experiences can help express a full range of genetic potentials. Unfortunately, however, it is also when the organizing brain is most vulnerable to the destructive impact of threat, neglect and trauma.

When much of our lives has been committed to protecting ourselves from the God we believe has betrayed us, left us, or at the very least simply never shown up, it is not easy to create new brain-cell firing patterns—neural networks—that are durable enough to carry our experiential belief that we are loved.

Finally, some scientists concede that consciousness is real and may actually have great moral and political value, but that it fulfils no biological function whatsoever. Consciousness is the biologically useless by-product of certain brain processes. Jet engines roar loudly, but the noise doesn’t propel the aeroplane forward. Humans don’t need carbon dioxide, but each and every breath fills the air with more of the stuff. Similarly, consciousness may be a kind of mental pollution produced by the firing of complex neural networks. It doesn’t do anything. It is just there. If this is true, it implies that all the pain and pleasure experienced by billions of creatures for millions of years is just mental pollution. This is certainly a thought worth thinking, even if it isn’t true. But it is quite amazing to realise that as of 2016, this is the best theory of consciousness that contemporary science has to offer us. Maybe

There must be a trick to the train of thought, a recursive formula. A group of neurons starts working automatically, sometimes without external impulse. It is a kind of iterative process with a growing pattern. It wanders about in the brain, and the way it happens must depend on the memory of similar patterns.

Mind is just a word we use to describe neural activity in the brain. No brain, no mind. We know this because if a part of the brain is destroyed through stroke or cancer or injury or surgery, whatever that part of the brain was doing is now gone. If the damage occurs in early childhood when the brain is especially plastic, or in adulthood in certain parts of the brain that are conducive to rewiring, then that brain function—that “mind” part of the brain—may be rewired into another neural network in the brain. But this process just further reinforces the fact that without neural connections in the brain there is no mind.

The third SADE, Rayland, who had inhabited Libre as an Independent, was unknown to most Librans. He had been network isolated and studied by Méridien neural scientists ever since he’d been diagnosed as a psychopath after he had stranded his ship and suffocated the entire crew, asking them what it felt like to die.

Over time, unique invisibles, perceivable only because of the sensitivity and openness of the sensory gating in that neural network, are able to be heard and, as well, expressed through the activity of that part of the self. This is what Goethe was talking about when he said that Every new object, clearly seen, opens up a new organ of perception in us.

Both scientists and laypeople can find themselves seduced into the easy trap of wanting to assign each function of the brain to a specific location. Perhaps because of pressure for simple sound bites, a steady stream of reports in the media (and even in the scientific literature) has created the false impression that the brain area for such-and-such has just been discovered. Such reports feed popular expectation and hope for easy labeling, but the true situation is much more interesting: the continuous networks of neural circuitry accomplish their functions using multiple, independently discovered strategies. The brain lends itself well to the complexity of the world, but poorly to clear-cut cartography.

Information contains an almost mystical power of free flow and self replication, just as water seeks it's own level or sparks fly upward.

Machine learning is the science of getting computers to act without being explicitly programmed,

Gain is a parameter in neural network modeling, which influences the probability that a neuron fires at a given activation level. Single cell recordings in non-human primates have shown that the likelihood of a neuron firing, given a constant sensory input, is enhanced when the stimulus dimension that is preferentially processed by the neuron is attended to.11

Children who begin working at an early age with music have, as habit, much less pre-attentional or unconscious gating in the neural network that attends to sound. Gating, in general, develops over time and with exposure, the pre-attentional self learning to gate whatever is not important to the conscious mind. Children, by nature, have much less gating than adults—gating

To cover all the bases, I also signed up for a class on psycholinguistics, which had a prerequisite in neural networking. In addition to not having taken it, I also didn’t know what neural networking was. For some reason, this didn’t really bother me, or seem like a problem. The handsome Italian professor wore the most elegant suits I had ever seen, in the most subtle colors—gray with a hint of smoky blue so elusive that you had to keep looking to be sure you hadn’t imagined it. The class met on the tenth floor of the psychology building, most of which was devoted to an institute for bat study and smelled accordingly. It was total sensory discord to see the handsome professor in his elegant suits stepping out of the elevator into the hall of stinky bats.

the physical substrate for thinking, the neural system, but the very fact that it has the form of a network implies that thought also has graph structure. This in turn suggests that language and its grammars,

The process of putting neural networks into a computer is known as deep learning. As this technology continues to develop, it may revolutionize a number of industries. In the future, when you want to talk to a doctor or lawyer, you might talk to your intelligent wall or wristwatch and ask for Robo-Doc or Robo-Lawyer, software programs that will be able to scan the internet and provide sound medical or legal advice.

Is a mind a complicated kind of abstract pattern that develops in an underlying physical substrate, such as a vast network of nerve cells? If so, could something else be substituted for the nerve cells – something such as ants, giving rise to an ant colony that thinks as a whole and has an identity – that is to say, a self? Or could something else be substituted for the tiny nerve cells, such as millions of small computational units made of arrays of transistors, giving rise to an artificial neural network with a conscious mind? Or could software simulating such richly interconnected computational units be substituted, giving rise to a conventional computer (necessarily a far faster and more capacious one than we have ever seen) endowed with a mind and a soul and free will? In short, can thinking and feeling emerge from patterns

The world consists of nations – a nation consists of people - a people consists of individuals -  an individual consists of psychological elements, collectively called "the mind" - and a mind is a product of a hundred billion nerve cells working relentlessly in proper harmony. Thus, a little change in the neural network inside one human brain, has the potential to essentially influence a whole nation, and even a whole world.

In addition to localized neural networks, hallucinogenic drugs have been documented to trigger such preternatural experiences, such as the sense of floating and flying stimulated by atropine and other belladonna alkaloids. These can be found in mandrake and jimsonweed and were used by European witches and American Indian shamans, probably for this very purpose.32 Dissociative anesthetics such as the ketamines are also known to induce out-of-body experiences. Ingestion of methylenedioxyamphetamine (MDA) may bring back long-forgotten memories and produce the feeling of age regression, while dimethyltryptamine (DMT)—also known as “the spirit molecule”—causes the dissociation of the mind from the body and is the hallucinogenic substance in ayahuasca, a drug taken by South American shamans. People who have taken DMT report “I no longer have a body,” and “I am falling,” “flying,” or “lifting up.

Our brain is therefore not simply passively subjected to sensory inputs. From the get-go, it already possesses a set of abstract hypotheses, an accumulated wisdom that emerged through the sift of Darwinian evolution and which it now projects onto the outside world. Not all scientists agree with this idea, but I consider it a central point: the naive empiricist philosophy underlying many of today's artificial neural networks is wrong. It is simply not true that we are born with completely disorganized circuits devoid of any knowledge, which later receive the imprint of their environment. Learning, in man and machine, always starts from a set of a priori hypotheses, which are projected onto the incoming data, and from which the system selects those that are best suited to the current environment. As Jean-Pierre Changeux stated in his best-selling book Neuronal Man (1985), “To learn is to eliminate.

All the experiences in your life – from single conversations to your broader culture – shape the microscopic details of your brain. Neurally speaking, who you are depends on where you’ve been. Your brain is a relentless shape-shifter, constantly rewriting its own circuitry – and because your experiences are unique, so are the vast, detailed patterns in your neural networks. Because they continue to change your whole life, your identity is a moving target; it never reaches an endpoint.

Deepfakes are built on a technology called generative adversarial networks (GAN). As the name suggests, a GAN is a pair of “adversarial” deep learning neural networks. The first network, the forger network, tries to generate something that looks real, let’s say a synthesized picture of a dog, based on millions of pictures of dogs. The other network, the detective network, compares the forger’s synthesized dog picture with genuine dog pictures, and determines if the forger’s output is real or fake.

They all have mechanisms for taking in and processing sensory data—and they all have mechanisms for reducing the amount of sensory inflows. They possess what are called sensory gating channels—or as William Blake and Aldous Huxley more comprehensively described the phenomenon, we all have within us the doors of perception. Sensory gating channels can be thought of as tiny apertures or gates or doors in specific sections of the nervous system’s neural network. They are similar to the lens in our eyes

Despite the feeling that we’re directly experiencing the world out there, our reality is ultimately built in the dark, in a foreign language of electrochemical signals. The activity churning across vast neural networks gets turned into your story of this, your private experience of the world: the feeling of this book in your hands, the light in the room, the smell of roses, the sound of others speaking. Even more strangely, it’s likely that every brain tells a slightly different narrative. For every situation with multiple witnesses, different brains are having different private subjective experiences. With seven billion human brains wandering the planet (and trillions of animal brains), there’s no single version of reality. Each brain carries its own truth. So what is reality? It’s like a television show that only you can see, and you can’t turn it off. The good news is that it happens to be broadcasting the most interesting show you could ask for: edited, personalized, and presented just for you.

Yann LeCun's strategy provides a good example of a much more general notion: the exploitation of innate knowledge. Convolutional neural networks learn better and faster than other types of neural networks because they do not learn everything. They incorporate, in their very architecture, a strong hypothesis: what I learn in one place can be generalized everywhere else. The main problem with image recognition is invariance: I have to recognize an object, whatever its position and size, even if it moves to the right or left, farther or closer. It is a challenge, but it is also a very strong constraint: I can expect the very same clues to help me recognize a face anywhere in space. By replicating the same algorithm everywhere, convolutional networks effectively exploit this constraint: they integrate it into their very structure. Innately, prior to any learning, the system already “knows” this key property of the visual world. It does not learn invariance, but assumes it a priori and uses it to reduce the learning space-clever indeed!

For simplicity’s sake, they assumed that the brain as a whole could be modeled as a vast, interconnected electrical circuit, with neurons serving as both the wires and the switches. That is, each neuron would receive electrical input from dozens or hundreds of other neurons. And if the total stimulation passed a certain threshold, that neuron would then “fire” and send an output pulse to dozens or hundreds more. The result—today it would be known as a “neural network” model—was admittedly a gross oversimplification of reality.

Again as during fetal development, synapses that underlie cognitive and other abilities stick around if they’re used but wither if they’re not. The systematic elimination of unused synapses, and thus unused circuits, presumably results in greater efficiency for the neural networks that are stimulated—the networks that support, in other words, behaviors in which the adolescent is actively engaged. Just as early childhood seems to be a time of exquisite sensitivity to the environment (remember the babies who dedicate auditory circuits only to the sounds of their native language, eliminating those for phonemes that they do not hear), so may adolescence. The teen years are, then, a second chance to consolidate circuits that are used and prune back those that are not—to hard-wire an ability to hit a curve ball, juggle numbers mentally, or turn musical notation into finger movements almost unconsciously. Says Giedd, “Teens have the power to determine their own brain development, to determine which connections survive and which don’t, [by] whether they do art, or music, or sports, or videogames.

It is only natural for the human mind to follow verbal and conceptual triggers. One "hook word" associates with author Jean Leclercq described words in monastic reading that link passages or ideas-and the understanding of a passage builds not through logical analysis but rather through a not-entirely-random accumulation of passages, ideas and experiences surrounding the passage at hand. Psychologists who study the human brain and nervous system speak about the development and spread of neural networks. Our mind naturally tends to follow associations that are strongly connected.

Music is exquisite and requisite. It is the creative juice that greases the machinery for insight and serendipity. It unlocks the right brain, opening a door to novel domains. It constructs neural networks and fosters pattern recognition. It trains willpower and discipline. Playing music builds a bigger and better highway between the two sides of the brain. Its myelinated interconnectivity nurtures out-of-the-box thinking and simulates meditation and flow. Like dreaming, it allows our powerful unconscious mind to sift and filter all the accumulated detritus into a meaningful story.

This highly developed mycelial/plant root system connects all the plants in a particular ecorange into one self-organized whole that, itself, possesses capacities not perceivable in any of the parts. In essence, a large, self-organized neural network develops. This leads to the emergence of a unique identity in every identifiable ecorange on Earth. It is possible then, if you reclaim your capacity to feel, to make intelligent contact with the intelligence of any ecorange in which you are embedded to establish rapport and deep friendship and to learn from that relationship, to, in fact, learn to “think like a mountain” from the mountain itself

He postulated that many neurons can combine into a coalition, becoming a single processing unit. The connection patterns of these units, which can change, make up the algorithms (which can also change with the changing connection patterns) that determine the brain’s response to a stimulus. From this idea came the mantra “Cells that fire together wire together.” According to this theory, learning has a biological basis in the “wiring” patterns of neurons. Hebb noted that the brain is active all the time, not just when stimulated; inputs from the outside can only modify that ongoing activity. Hebb’s proposal made sense to those designing artificial neural networks, and it was put to use in computer programs.

One of the most studied organisms in this context is the tiny polyp Hydra, which possesses only a hundred thousand cells. Its neural network is concentrated in its head and foot: a first evolutionary step toward developing a brain and spinal cord. Hydra’s nervous system contains a chemical messenger—a minuscule protein—that resembles two of our own: vasopressin and oxytocin. A protein of this kind is called a neuropeptide. In vertebrates, the gene for this particular neuropeptide first doubled and then mutated in two places, creating the two closely related but specialized neuropeptides vasopressin and oxytocin, which have recently become the focus of interest, partly because of their important role as messengers in our social brains (see chapter 9).

It’s All about Scaling Most of the current learning algorithms were discovered more than twenty-five years ago, so why did it take so long for them to have an impact on the real world? With the computers and labeled data that were available to researchers in the 1980s, it was only possible to demonstrate proof of principle on toy problems. Despite some promising results, we did not know how well network learning and performance would scale as the number of units and connections increased to match the complexity of real-world problems. Most algorithms in AI scale badly and never went beyond solving toy problems. We now know that neural network learning scales well and that performance continues to increase with the size of the network and the number of layers. Backprop, in particular, scales extremely well. Should we be surprised? The cerebral cortex is a mammalian invention that mushroomed in primates and especially in humans. And as it expanded, more capacity became available and more layers were added in association areas for higher-order representations. There are few complex systems that scale this well. The Internet is one of the few engineered systems whose size has also been scaled up by a million times. The Internet evolved once the protocols were established for communicating packets, much like the genetic code for DNA made it possible for cells to evolve. Training many deep learning networks with the same set of data results in a large number of different networks that have roughly the same average level of performance.

In real brains neural networks do not exist in isolation. They communicate with other networks by way of synaptic transmission. For example, in order to see an apple, instead of a roundish, reddish blob, the various features of the stimulus, each processed by different visual subsystems, have to be integrated. As we saw in Chapter 7, the problem of understanding the manner in which this occurs is called the binding problem. One popular solution to this problem is based on the notion of neuronal synchrony. Synchronous (simultaneous) firing, and thus binding, has been proposed as an explanation of consciousness (chap. 7), but our interest here is more in the ability of synchronous firing between cells in different interconnected regions to coordinate plasticity across the regions.

Our conscious memory is full of gaps, of course, which is actually a good thing. Our brains filter out the ordinary and expected, which is utterly necessary to allow us to function. When you drive, for example, you rely automatically on your previous experiences with cars and roads; if you had to focus on every aspect of what your senses are taking in, you’d be overwhelmed and would probably crash. As you learn anything, in fact, your brain is constantly checking current experience against stored templates—essentially memory—of previous, similar situations and sensations, asking “Is this new?” and “Is this something I need to attend to?” So as you move down the road, your brain’s motor vestibular system is telling you that you are in a certain position. But your brain is probably not making new memories about that. Your brain has stored in it previous sitting experiences in cars, and the pattern of neural activity associated with that doesn’t need to change. There’s nothing new. You’ve been there, done that, it’s familiar. This is also why you can drive over large stretches of familiar highways without remembering almost anything at all that you did during the drive. This is important because all of that previously stored experience has laid down the neural networks, the memory “template,” that you now use to make sense out of any new incoming information. These templates are formed throughout the brain at many different levels, and because information comes in first to the lower, more primitive areas, many are not even accessible to conscious awareness.

As the language areas of the left hemisphere enter their sensitive period during the middle of the second year of life, grammatical language in the left integrates with the interpersonal and prosodic elements of communication already well developed in the right. As the cortical language centers mature, words are joined together to make sentences and can be used to express increasingly complex ideas flavored with emotion. As the frontal cortex continues to expand and connect with more neural networks, memory improves and a sense of time slowly emerges and autobiographical memory begins to connect the self with places and events, within and across time. The emerging narratives begin to organize the nascent sense of self and become the bedrock of our sense of self in interpersonal and physical space

While there are more neural connections within a half brain than between the two halves, there are still massive connections across the hemispheres. Even so, cutting those connections does little to one’s sense of conscious experience. That is to say, the left hemisphere keeps on talking and thinking as if nothing had happened even though it no longer has access to half of the human cortex. More important, disconnecting the two half brains instantly creates a second, also independent conscious system. The right brain now purrs along carefree from the left, with its own capacities, desires, goals, insights, and feelings. One network, split into two, becomes two conscious systems. How could one possibly think that consciousness arises from a particular specific network? We need a new idea to cope with this fact.

Two years later, DeepMind engineers used what they had learned from game playing to solve an economic problem of vital interest: How should Google optimize the management of its computer servers? The artificial neural network remained similar; the only things that changed were the inputs (date, time, weather, international events, search requests, number of people connected to each server, etc.), the outputs (turn on or off this or that server on various continents), and the reward function (consume less energy). The result was an instant drop in power consumption. Google reduced its energy bill by up to 40 percent and saved tens of millions of dollars—even after myriad specialized engineers had already tried to optimize those very servers. Artificial intelligence has truly reached levels of success that can turn whole industries upside down.

Plant biologist Peter Barlow adds that the tips of the roots “form a multiheaded advancing front. The complete set of tips endows the plant with a collective brain, diffused over a large area, gathering, as the root system grows and develops, information” crucial to the plant’s survival.50 And, as he continues: “One attribute of a brain, as the term is commonly understood, is that it is an organ with a definite structure and location which gathers or collects information, which was originally in the form of vibrations (heat, light, sound, chemical, mechanical, . . .) in the ambient environment and somehow transforms them into an output or response.”51 By this definition, plants do have brains just as we do, but given their capacity to live for millennia (in the case of some aspen root systems, over 100,000 years) their neural networks can, in many instances, far exceed our own.

Neuroimaging studies show the dramatic sensitivity of adolescents to peers. Ask adults to think about what they imagine others think of them, then about what they think of themselves. Two different, partially overlapping networks of frontal and limbic structures activate for the two tasks. But with adolescents the two profiles are the same. “What do you think about yourself?” is neurally answered with “Whatever everyone else thinks about me.”23

In the 1990s, a set of renegade researchers set aside many of the earlier era’s assumptions, shifting their focus to machine learning. While machine learning dated to the 1950s, new advances enabled practical applications. The methods that have worked best in practice extract patterns from large datasets using neural networks. In philosophical terms, AI’s pioneers had turned from the early Enlightenment’s focus on reducing the world to mechanistic rules to constructing approximations of reality. To identify an image of a cat, they realized, a machine had to “learn” a range of visual representations of cats by observing the animal in various contexts. To enable machine learning, what mattered was the overlap between various representations of a thing, not its ideal—in philosophical terms, Wittgenstein, not Plato. The modern field of machine learning—of programs that learn through experience—was born.

Here are some practical Dataist guidelines for you: ‘You want to know who you really are?’ asks Dataism. ‘Then forget about mountains and museums. Have you had your DNA sequenced? No?! What are you waiting for? Go and do it today. And convince your grandparents, parents and siblings to have their DNA sequenced too – their data is very valuable for you. And have you heard about these wearable biometric devices that measure your blood pressure and heart rate twenty-four hours a day? Good – so buy one of those, put it on and connect it to your smartphone. And while you are shopping, buy a mobile camera and microphone, record everything you do, and put in online. And allow Google and Facebook to read all your emails, monitor all your chats and messages, and keep a record of all your Likes and clicks. If you do all that, then the great algorithms of the Internet-of-All-Things will tell you whom to marry, which career to pursue and whether to start a war.’ But where do these great algorithms come from? This is the mystery of Dataism. Just as according to Christianity we humans cannot understand God and His plan, so Dataism declares that the human brain cannot fathom the new master algorithms. At present, of course, the algorithms are mostly written by human hackers. Yet the really important algorithms – such as the Google search algorithm – are developed by huge teams. Each member understands just one part of the puzzle, and nobody really understands the algorithm as a whole. Moreover, with the rise of machine learning and artificial neural networks, more and more algorithms evolve independently, improving themselves and learning from their own mistakes. They analyse astronomical amounts of data that no human can possibly encompass, and learn to recognise patterns and adopt strategies that escape the human mind. The seed algorithm may initially be developed by humans, but as it grows it follows its own path, going where no human has gone before – and where no human can follow.

The auditory cortex, as an example, processes the sound inputs that have not already been gated earlier in the stream. It specifically works with tone, pitch, harmony, loudness, and beat patterning or timing. In people that use auditory inputs as a primary or major area of sensory processing musicians for instance there is much less gating of sound in the deeper levels of the brain than in nonmusicians. In consequence, much more sound input reaches the auditory cortex. Because the auditory cortex is continually used to work with larger amounts of sound inflows (with more subtlety), it becomes highly developed and shows tremendous plasticity, that is, continuous new neuronal development. Frances Densmore, for example, the ethnomusicologist who recorded thousands of Native plant songs in the early twentieth century, could perceive pitch differentiations as tiny as 1/32 in deviation. (She had as well total recall and prefect pitch.) The more a sensory modality is consciously used to analyze incoming sensory inflows, the more sensitive it becomes, and the larger the neural network within it becomes.

Thiel, the PayPal cofounder who had invested in SpaceX, holds a conference each year with the leaders of companies financed by his Founders Fund. At the 2012 gathering, Musk met Demis Hassabis, a neuroscientist, video-game designer, and artificial intelligence researcher with a courteous manner that conceals a competitive mind. A chess prodigy at age four, he became the five-time champion of an international Mind Sports Olympiad that includes competition in chess, poker, Mastermind, and backgammon. In his modern London office is an original edition of Alan Turing’s seminal 1950 paper, “Computing Machinery and Intelligence,” which proposed an “imitation game” that would pit a human against a ChatGPT–like machine. If the responses of the two were indistinguishable, he wrote, then it would be reasonable to say that machines could “think.” Influenced by Turing’s argument, Hassabis cofounded a company called DeepMind that sought to design computer-based neural networks that could achieve artificial general intelligence. In other words, it sought to make machines that could learn how to think like humans.

most students reported a state of total involvement in what was being taught, he would rate the moment “inspired.” The inspired moments of learning shared the same active ingredients: a potent combination of full attention, enthusiastic interest, and positive emotional intensity. The joy in learning comes during these moments. Such joyous moments, says University of Southern California neuroscientist Antonio Damasio, signify “optimal physiological coordination and smooth running of the operations of life.” Damasio, one of the world’s leading neuroscientists, has long been a pioneer in linking findings in brain science to human experience. Damasio argues that more than merely letting us survive the daily grind, joyous states allow us to flourish, to live well, and to feel well-being. Such upbeat states, he notes, allow a “greater ease in the capacity to act,” a greater harmony in our functioning that enhances our power and freedom in whatever we do. The field of cognitive science, Damasio notes, in studying the neural networks that run mental operations, finds similar conditions and dubs them “maximal harmonious states.

A unique part of the plant root, the root apex (or apices, which are the pointed ends of the root system) is a combination sensitive finger, perceiving sensory organ, and brain neuron. Each root hair, rootlet, and root section contains an apex; every root mass millions, even billions, of them. For example, a single rye plant has more than 13 million rootlets with a combined length of 680 miles. Each of the rootlets are covered with root hairs, over 14 billion of them, with a combined length of 6,600 miles. Every rootlet, every root hair, has at its end a root apex. Every root apex acts as a neuronal organ in the root system. In contrast, the human brain has approximately 86 billion neurons, about 16 billion of which are in the cerebral cortex. Plants with larger root systems, and more root hairs, can have considerably more brain neurons than the 14 billion contained in rye plants; they can even rival the human brain in the number of neurons. And when you look at the interconnected network of plant roots and micorrhizal mycelia in any discrete ecosystem, you are looking at a neural network much larger than any individual human has ever possessed.

When General Genius built the first mentar [Artificial Intelligence] mind in the last half of the twenty-first century, it based its design on the only proven conscious material then known, namely, our brains. Specifically, the complex structure of our synaptic network. Scientists substituted an electrochemical substrate for our slower, messier biological one. Our brains are an evolutionary hodgepodge of newer structures built on top of more ancient ones, a jury-rigged system that has gotten us this far, despite its inefficiency, but was crying out for a top-to-bottom overhaul. Or so the General genius engineers presumed. One of their chief goals was to make minds as portable as possible, to be easily transferred, stored, and active in multiple media: electronic, chemical, photonic, you name it. Thus there didn't seem to be a need for a mentar body, only for interchangeable containers. They designed the mentar mind to be as fungible as a bank transfer. And so they eliminated our most ancient brain structures for regulating metabolic functions, and they adapted our sensory/motor networks to the control of peripherals. As it turns out, intelligence is not limited to neural networks, Merrill. Indeed, half of human intelligence resides in our bodies outside our skulls. This was intelligence the mentars never inherited from us. ... The genius of the irrational... ... We gave them only rational functions -- the ability to think and feel, but no irrational functions... Have you ever been in a tight situation where you relied on your 'gut instinct'? This is the body's intelligence, not the mind's. Every living cell possesses it. The mentar substrate has no indomitable will to survive, but ours does. Likewise, mentars have no 'fire in the belly,' but we do. They don't experience pure avarice or greed or pride. They're not very curious, or playful, or proud. They lack a sense of wonder and spirit of adventure. They have little initiative. Granted, their cognition is miraculous, but their personalities are rather pedantic. But probably their chief shortcoming is the lack of intuition. Of all the irrational faculties, intuition in the most powerful. Some say intuition transcends space-time. Have you ever heard of a mentar having a lucky hunch? They can bring incredible amounts of cognitive and computational power to bear on a seemingly intractable problem, only to see a dumb human with a lucky hunch walk away with the prize every time. Then there's luck itself. Some people have it, most don't, and no mentar does. So this makes them want our bodies... Our bodies, ape bodies, dog bodies, jellyfish bodies. They've tried them all. Every cell knows some neat tricks or survival, but the problem with cellular knowledge is that it's not at all fungible; nor are our memories. We're pretty much trapped in our containers.

One of the most remarkable properties of our brain is its capacity to change and adapt to our individual world. Neurons and neural networks actually make physical changes when stimulated; this is called neuroplasticity. The way they become stimulated is through our particular experiences: The brain changes in a “use dependent” way. The neural networks involved in piano playing, for example, will make changes when activated by a child practicing her piano. These experience-dependent changes translate into better piano playing. This aspect of neuroplasticity—repetition leads to change—is well known and is why practice in sports, arts, and academics can lead to improvement. A key principle of neuroplasticity is specificity. In order to change any part of the brain, that specific part of the brain must be activated. If you want to learn to play the piano, you can’t simply read about piano playing, or watch and listen to YouTube clips of other people playing piano. You must put your hands on the keys and play; you have to stimulate the parts of the brain involved in piano playing in order to change them. This principle of “specificity” applies to all brain-mediated functions, including the capacity to love. If you have never been loved, the neural networks that allow humans to love will be undeveloped, as in Gloria’s case. The good news is that with use, with practice, these capabilities can emerge. Given love, the unloved can become loving.

Can a reasonable man ever truly question the nobility of the heat engine he calls his body? What option does he have but to heap praise on his form, to self-adore, to admire, and to hold it up as the greatest statement of beauty in a beautiful garden? What, though, is to be admired in such a frighteningly fragile machine; a perilously needy contraption laced with kilometres of liquid and electrical conduits prone to leaks, rot, clogs, and short-circuits? What is there to be proud of in a machine that has an eight hour battery life and is predetermined to spend half its existence in a defenceless, catatonic coma? What is to be revered in a mechanism let loose in a sealed off room where almost everything—including its single source of light and warmth—makes it sick, but whose immune system functions by late entry crisis-response imitation? Where is the awe in a contrivance that freezes and dies if placed a little over here, or overheats and dies if placed a little over there? Where is the wonder in an instrument that is crushed to a pulp if dropped a little down there, or boiled away to nothing if lifted a little up there? Where is the marvel in an appliance where three-quarters of the planet’s surface will drown it, and three-quarters of the atmosphere will asphyxiate it? What is there to be cherished in a machine born innately greedy and so utterly useless that it has to wait three years for its neural networks to hook-up and come online before it even begins to get a hint of who or even what it is, and only then can it start to relearn absolutely everything its forebears had already bothered to learn? Where is the artistry in a thinking engine whose sweetest fuel can only be embezzled from other thinking engines?

Mendel Kaelen, a Dutch postdoc in the Imperial lab, proposes a more extended snow metaphor: “Think of the brain as a hill covered in snow, and thoughts as sleds gliding down that hill. As one sled after another goes down the hill, a small number of main trails will appear in the snow. And every time a new sled goes down, it will be drawn into the preexisting trails, almost like a magnet.” Those main trails represent the most well-traveled neural connections in your brain, many of them passing through the default mode network. “In time, it becomes more and more difficult to glide down the hill on any other path or in a different direction. “Think of psychedelics as temporarily flattening the snow. The deeply worn trails disappear, and suddenly the sled can go in other directions, exploring new landscapes and, literally, creating new pathways.” When the snow is freshest, the mind is most impressionable, and the slightest nudge—whether from a song or an intention or a therapist’s suggestion—can powerfully influence its future course. Robin Carhart-Harris’s theory of

It is certainly conceivable, as at least one well-known physicist has speculated (to hoots from most of his colleagues), that the human brain takes advantage of quantum mechanical effects. Yet there is no evidence whatsoever that this is the case. Certainly, the physics of a neuron depends on quantum mechanics, just as the physics of a transistor does, but there is no evidence that neural processing takes place at the quantum mechanical level as opposed to the classical level; that is, there is no evidence that quantum mechanics is necessary to explain human thought. As far as we know, all the relevant computational properties of a neuron can be simulated on a conventional computer. If this is indeed the case, then it is also possible to simulate a network of tens of billions of such neurons, which means, in turn, that the brain can be simulated on a universal machine. Even if it turns out that the brain takes advantage of quantum computation, we will probably learn how to build devices that take advantage of the same effects—in which case it will still be possible to simulate the human brain with a machine.

We pay a high price for this ingenious neural machinery, though, because the default mode network is responsible for mind-wandering. “Experience sampling”—which involves asking people about their mood and thoughts at random moments throughout the day—suggests that our minds wander from what we’re actually doing an amazing 30 percent to 50 percent of the time that we’re awake, and that this is often associated with feelings of unhappiness.6–8 According to Harvard psychologists Matthew Killingsworth and Daniel Gilbert, who created an iPhone app, Rate Your Happiness, to gather some of this data, fluctuations in happiness depend more on what we’re thinking than what we’re doing. Crucially, the results suggest that mind-wandering is the cause rather than the consequence of negative emotions. As the opening verse of the Dhammapada expresses it, “Our life is shaped by our mind; we become what we think. Suffering follows an evil thought as the wheels of a cart follow the oxen that draw it.”9 Less poetically, the psychologists concluded that “the ability to think about what is not happening is a cognitive achievement that comes at an emotional cost.” So, while

Brain imaging studies suggest that a couple brain areas in particular are involved in cognitive control: the anterior cingulate cortex (ACC) and the lateral prefrontal cortex (lateral PFC). We’ll be referring to these together as the “cognitive control regions” of the brain. There is still some debate about the precise role played by each of these regions, but one plausible characterization is that the ACC is a kind of smoke detector, and the lateral PFC is the fire response team. Like a smoke detector, the ACC is in constant monitoring mode, waiting to detect a whiff of danger, such as an instance of cognitive conflict. In the case of the Stroop task, we’ve got two automatic processes that are in conflict: the identification of a typeface or color versus the automatic processing of a simple word (assuming you’re literate and it’s your native language). This conflict alerts the ACC, which then sends out an alarm to the lateral PFC to come deal with the situation. The lateral PFC is responsible for many higher cognitive functions, such as the integration of conscious and unconscious knowledge, working memory (the small spotlight of consciousness that allows us to focus on explicit information), and conscious planning. Most relevantly, when it comes to the case of the Stroop task, the lateral PFC also exerts control over other areas of the brain by strengthening the activation of task-relevant networks at the expense of other networks. By weakening certain neural pathways, the lateral PFC essentially tells them to stop doing what they are doing, which is the neural equivalent of fire-retarding foam. In the Stroop task presented above,

The rats that Marian Diamond studied had either an enriched or an impoverished environment. That changed their brain state. If you’re surrounded by a nurturing physical, emotional, mental, and spiritual environment, you’re in one brain state. If you’re surrounded by danger, uncertainty, and hostility, you’re in a quite different brain state. Brain states, along with mental, emotional, and spiritual states, run the gamut. When the brain’s Enlightenment Circuit is turned on, you’re in a happy and positive state. When the Default Mode Network (DMN) of Chapter 2 predominates, you’re in a negative and stressed state. State Progression Cognitive psychologist Michael Hall has been fascinated by human potential for over 40 years. He has studied the most advanced methods, authored more than 30 books on the topic, and mapped the stages by which people change. Unpleasant experiences are what usually motivate us to change. These involve mental, emotional, or spiritual states. Examples of such states are despair, stagnation, anger, or resentment. Hall calls these “unresourceful” states. We can cultivate resourceful states, such as joy, empowerment, mastery, and contentment. To describe the movement of a person from an unresourceful to a resourceful state, Hall uses the term “state progression.” Hall’s “state progression” model has several steps: Identify the unresourceful state. Identify the desired state. Countercondition dysfunctional behavioral patterns that maintain the unresourceful state. Activate change toward the desired state. Experience the target state. Repeat the experience of the desired state. Condition new behaviors that reinforce the desired state. That’s the promise of directing your attention consciously rather than defaulting to the brain’s negativity bias. Attention sustained over time produces state progression and triggers neural plasticity. If you focus on positive beliefs and thoughts repeatedly, bringing your mind and focus back to the good, you then use attention in the service of positive neural plasticity. When we have practiced sufficiently to be able to maintain this focus, we achieve a condition that Hall calls positive state stability. Our minds become stable in that new state. Their default setting is no longer to focus on the negative. The brain’s negativity bias is no longer hijacking our attention and directing it toward the negative things that are happening, either in our own lives or in the world. We have moved through the stages of state progression to positive state stability.