The Inner Path of the Genius of Language (Lecture 6 of 6)

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He famously feuded with German scientist Gottfried Leibnitz, mainly over who invented calculus first, creating a schism in European mathematics that lasted over a century. How fitting that the unit of force is named after stubborn, persistent, amazing Newton, himself a force of nature. As a young man, his main interests were collecting beetles and studying geology in the countryside, occasionally skipping out on his classes at the University of Edinburgh Medical School to do so.


It was a chance invitation in to join a journey around the world that would make Darwin, who had once studied to become a country parson, the father of evolutionary biology. Aboard the HMS Beagle , between bouts of seasickness, Darwin spent his five-year trip studying and documenting geological formations and myriad habitats throughout much of the Southern Hemisphere, as well as the flora and fauna they contained. He noticed small differences between members of the same species that seemed to depend upon where they lived.

The finches of the Galapagos are the best-known example: From island to island, finches of the same species possessed differently shaped beaks, each adapted to the unique sources of food available on each island. This suggested not only that species could change — already a divisive concept back then — but also that the changes were driven purely by environmental factors, instead of divine intervention. Today, we call this natural selection. When Darwin returned, he was hesitant to publish his nascent ideas and open them up to criticism, as he felt that his theory of evolution was still insubstantial.

Instead, he threw himself into studying the samples from his voyage and writing an account of his travels. Through his industrious efforts, Darwin built a reputation as a capable scientist, publishing works on geology as well as studies of coral reefs and barnacles still considered definitive today. Darwin also married his first cousin, Emma Wedgwood, during this time. This was a level of attention uncommon among fathers at that time — to say nothing of eminent scientists. Through it all, the theory of evolution was never far from his mind, and the various areas of research he pursued only strengthened his convictions.

Darwin slowly amassed overwhelming evidence in favor of evolution in the 20 years after his voyage. All of his observations and musings eventually coalesced into the tour de force that was On the Origin of Species , published in when Darwin was 50 years old.

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The page book sold out immediately, and Darwin would go on to produce six editions, each time adding to and refining his arguments. It was based on two ideas: that species can change gradually over time, and that all species face difficulties brought on by their surroundings. From these basic observations, it stands to reason that those species best adapted to their environments will survive and those that fall short will die out. Nikola Tesla grips his hat in his hand. He points his cane toward Niagara Falls and beckons bystanders to turn their gaze to the future.

This bronze Tesla — a statue on the Canadian side — stands atop an induction motor, the type of engine that drove the first hydroelectric power plant. His designs advanced alternating current at the start of the electric age and allowed utilities to send current over vast distances, powering American homes across the country. He developed the Tesla coil — a high-voltage transformer — and techniques to transmit power wirelessly. Cellphone makers and others are just now utilizing the potential of this idea.

Tesla is perhaps best known for his eccentric genius. He once proposed a system of towers that he believed could pull energy from the environment and transmit signals and electricity around the world, wirelessly. But his theories were unsound, and the project was never completed. San Diego Comic-Con attendees dress in Tesla costumes. The American Physical Society even has a Tesla comic book where, as in real life, he faces off against the dastardly Thomas Edison.

While his work was truly genius, much of his wizardly reputation was of his own making. It was around for decades. But his ceaseless theories, inventions and patents made Tesla a household name, rare for scientists a century ago. And even today, his legacy still turns the lights on. Around Dec. But his conclusions changed history. And his law of inertia allowed for Earth itself to rotate. The church declared the sun-centered model heretical, and an inquisition in ordered Galileo to stop promoting these views. They placed him under house arrest until his death in , the same year Isaac Newton was born.

To say she was ahead of her time would be an understatement. Their collaboration started in the early s, when Lovelace was just 17 and still known by her maiden name of Byron. She was the only legitimate child of poet Lord Byron. Babbage had drawn up plans for an elaborate machine he called the Difference Engine — essentially, a giant mechanical calculator. In the middle of his work on it, the teenage Lovelace met Babbage at a party. There, he showed off an incomplete prototype of his machine.

Miss Byron, young as she was, understood its working, and saw the great beauty of the invention.

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It was mathematical obsession at first sight. The Analytical Engine was more than a calculator — its intricate mechanisms and the fact that the user fed it commands via a punch card meant the engine could perform nearly any mathematical task ordered. Lovelace even wrote instructions for solving a complex math problem, should the machine ever see the light of day. Many historians would later deem those instructions the first computer program, and Lovelace the first programmer.

Memories of middle or high school geometry invariably include an instructor drawing right triangles on a blackboard to explain the Pythagorean theorem. The lesson was that the square of the hypotenuse, or longest side, is equal to the sum of the squares of the other sides. A proof followed, adding a level of certainty rare in other high school classes, like social studies and English. Pythagoras, a sixth-century B. Greek philosopher and mathematician, is credited with inventing his namesake theorem and various proofs.

But forget about the certainty. Babylonian and Egyptian mathematicians used the equation centuries before Pythagoras, says Karen Eva Carr, a retired historian at Portland State University, though many scholars leave open the possibility he developed the first proof. Even so, we know enough to suspect Pythagoras was one of the great mathematicians of antiquity. His influence was widespread and lasting. It started in Sweden: a functional, user-friendly innovation that took over the world, bringing order to chaos. No, not an Ikea closet organizer.

He lived at a time when formal scientific training was scant and there was no system for referring to living things. The 18th century was also a time when European explorers were fanning out across the globe, finding ever more plants and animals new to science. He intended the simple Latin two-word construction for each plant as a kind of shorthand, an easy way to remember what it was. The names moved quickly from the margins of a single book to the center of botany, and then all of biology.

Linnaeus started a revolution, but it was an unintentional one.

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Today we regard Linnaeus as the father of taxonomy, which is used to sort the entire living world into evolutionary hierarchies, or family trees. But the systematic Swede was mostly interested in naming things rather than ordering them, an emphasis that arrived the next century with Charles Darwin. But his naming system, so simple and adaptable, remains. Linnaeus gave us a system so we could talk about the natural world. But no one mentioned Rosalind Franklin — arguably the greatest snub of the 20th century.

The British-born Franklin was a firebrand, a perfectionist who worked in isolation. Franklin was also a brilliant chemist and a master of X-ray crystallography, an imaging technique that reveals the molecular structure of matter based on the pattern of scattered X-ray beams. Her early research into the microstructures of carbon and graphite are still cited, but her work with DNA was the most significant — and it may have won three men a Nobel.

But in , in the prime of her career, she developed ovarian cancer — perhaps due to her extensive X-ray work. Franklin continued working in the lab until her death in at age Isaac Asimov — Asimov was my gateway into science fiction, then science, then everything else. A trained biochemist, the Russian-born New Yorker wrote prolifically, producing over books, not all science-related: Of the 10 Dewey Decimal categories, he has books in nine.

Richard Feynman — Feynman played a part in most of the highlights of 20th-century physics. In , he joined the Manhattan Project. As part of the space shuttle Challenger disaster investigation, he explained the problems to the public in easily understandable terms, his trademark. Feynman was also famously irreverent, and his books pack lessons I live by. FitzRoy founded the U. But after losing his fortunes, suffering from depression and poor health, and facing fierce criticism of his forecasting system, he slit his throat in Jean-Baptiste Lamarck — Lamarck may be remembered as a failure today, but to me, he represents an important step forward for evolutionary thinking.

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  • Before he suggested that species could change over time in the early 19th century, no one took the concept of evolution seriously. Lucretius 99 B. My path to the first-century B. Instead, she married rich. She also fought to make her alma mater more accessible to women, leading to an all-female dormitory, allowing more women to enroll. A champion of the national parks enough right there to make him a hero to me! Rolf O. As the wolf population has nearly disappeared and moose numbers have climbed, patience and emotional investment like his are crucial in the quest to learn how nature works.

    Marie Tharp — I love maps. So did geologist and cartographer Tharp. In the midth century, before women were permitted aboard research vessels, Tharp explored the oceans from her desk at Columbia University. With the seafloor — then thought to be nearly flat — her canvas, and raw data her inks, she revealed a landscape of mountain ranges and deep trenches. Her keen eye also spotted the first hints of plate tectonics at work beneath the waves. Science needs to get out of the lab and into the public eye.

    Over the past hundred years or so, these scientists have made it their mission. Sean M. Carroll — : The physicist and one-time Discover blogger has developed a following among space enthusiasts through his lectures, television appearances and books, including The Particle at the End of the Universe, on the Higgs boson.

    At the same time, Friston is exceptionally lucid and forthcoming about what drives him as a scholar. He finds it incredibly soothing—not unlike disappearing for a smoke—to lose himself in a difficult problem that takes weeks to resolve. And he has written eloquently about his own obsession, dating back to childhood, with finding ways to integrate, unify, and make simple the apparent noise of the world.

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    Friston traces his path to the free energy principle back to a hot summer day when he was 8 years old. He and his family were living in the walled English city of Chester, near Liverpool, and his mother had told him to go play in the garden. He turned over an old log and spotted several wood lice—small bugs with armadillo-shaped exoskeletons—moving about, he initially assumed, in a frantic search for shelter and darkness. After staring at them for half an hour, he deduced that they were not actually seeking the shade. He realized that the movement of the wood lice had no larger purpose, at least not in the sense that a human has a purpose when getting in a car to run an errand.

    Many species of wood lice will dry out in direct sunlight, and some respond to a rise in temperature with kinesis, an increased level of random movement. In the sense that it could be no other way. In just his first decade, the young Friston attended six different schools. At age 10 he designed a self-righting robot that could, in theory, traverse uneven ground while carrying a glass of water, using self-correcting feedback actuators and mercury levels.

    At school, a psychologist was brought in to ask him how he came up with it. When Friston was in his mid-teens, he had another wood-lice moment. He had just come up to his bedroom from watching TV and noticed the cherry trees in bloom outside the window. He suddenly became possessed by a thought that has never let go of him since. They were asked a series of questions, and their answers were punched into cards and run through a machine to extrapolate the perfect career choice.

    Friston had described how he enjoyed electronics design and being alone in nature, so the computer suggested he become a television antenna installer. Both Friston and the counselor had confused psychiatry with psychology, which is what he probably ought to have pursued as a future researcher. But it turned out to be a fortunate error, as it put Friston on a path toward studying both the mind and body, 5 and toward one of the most formative experiences of his life—one that got Friston out of his own head.

    At age 19, he spent an entire school vacation trying to squeeze all of physics on one page. He failed but did manage to fit all of quantum mechanics. After completing his medical studies, Friston moved to Oxford and spent two years as a resident trainee at a Victorian-era hospital called Littlemore. Friston was assigned a group of 32 chronic schizophrenic patients, the worst-off residents of Littlemore, for whom treatment mostly meant containment.

    For Friston, who recalls his former patients with evident nostalgia, it was an introduction to the way that connections in the brain were easily broken. Twice a week he led minute group therapy sessions in which the patients explored their ailments together, reminiscent of the Ask Karl meetings today. There was Hillary, 6 who looked like she could play the senior cook on Downton Abbey but who, before coming to Littlemore, had decapitated her neighbor with a kitchen knife, convinced he had become an evil, human-sized crow. And then there was Robert, an articulate young man who might have been a university student had he not suffered severe schizophrenia.

    Robert ruminated obsessively about, of all things, angel shit; he pondered whether the stuff was a blessing or a curse and whether it was ever visible to the eye, and he seemed perplexed that these questions had not occurred to others.


    To Friston, the very concept of angel shit was a miracle. After Littlemore, Friston spent much of the early s using a relatively new technology—PET scans—to try to understand what was going on inside the brains of people with schizophrenia. He invented statistical parametric mapping along the way. Unusually for the time, Friston was adamant that the technique should be freely shared rather than patented and commercialized, which largely explains how it became so widespread.

    Friston would fly across the world—to the National Institutes of Health in Bethesda, Maryland, for example—to give it to other researchers. The Gatsby—where researchers study theories of perception and learning in both living and machine systems—was then run by its founder, the cognitive psychologist and computer scientist Geoffrey Hinton. While the FIL was establishing itself as one of the premier labs for neuroimaging, the Gatsby was becoming a training ground for neuroscientists interested in applying mathematical models to the nervous system.

    Over time, Hinton convinced Friston that the best way to think of the brain was as a Bayesian probability machine. The idea, which goes back to the 19th century and the work of Hermann von Helmholtz, is that brains compute and perceive in a probabilistic manner, constantly making predictions and adjusting beliefs based on what the senses contribute. ImageNet helped propel neural networks—and Hinton—to the forefront of AI. Before Hinton left, however, Friston visited his friend at the Gatsby one last time. Even Friston has a hard time deciding where to start when he describes the free energy principle.

    He often sends people to its Wikipedia page. Markov is the eponym of a concept called a Markov blanket, which in machine learning is essentially a shield that separates one set of variables from others in a layered, hierarchical system. Each of us has a Markov blanket that keeps us apart from what is not us. And within us are blankets separating organs, which contain blankets separating cells, which contain blankets separating their organelles.

    The blankets define how biological things exist over time and behave distinctly from one another. This is it. Markov blankets around a leaf and a tree and a mosquito. In London, I saw them around the postdocs at the FIL, around the black-clad protesters at an antifascist rally, and around the people living in boats in the canals.

    Invisible cloaks around everyone, and underneath each one a different living system that minimizes its own free energy. Or, to put it another way, when you are minimizing free energy, you are minimizing surprise. He programmed it to obey both basic physics and the free energy principle. The model generated results that looked like organized life. The only difference is that, as self-organizing biological systems go, the human brain is inordinately complex: It soaks in information from billions of sense receptors, and it needs to organize that information efficiently into an accurate model of the world.

    In seeking to predict what the next wave of sensations is going to tell it—and the next, and the next—the brain is constantly making inferences and updating its beliefs based on what the senses relay back, and trying to minimize prediction-error signals. But the limitation of the Bayesian model, for Friston, is that it only accounts for the interaction between beliefs and perceptions; it has nothing to say about the body or action. And in fact, this is how the free energy principle accounts for everything we do : perception, action, planning, problem solving.

    When I get into the car to run an errand, I am minimizing free energy by confirming my hypothesis—my fantasy—through action. For Friston, folding action and movement into the equation is immensely important. And all of this fine motor movement exists on a continuum with bigger plans, explorations, 10 and actions.

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    So what happens when our prophecies are not self-fulfilling? What does it look like for a system to be overwhelmed by surprise? When the brain assigns too little or too much weight to evidence pouring in from the senses, trouble occurs. Someone with schizophrenia, for example, may fail to update their model of the world to account for sensory input from the eyes. Where one person might see a friendly neighbor, Hillary might see a giant, evil crow. So: The free energy principle offers a unifying explanation for how the mind works and a unifying explanation for how the mind malfunctions.

    It stands to reason, then, that it might also put us on a path toward building a mind from scratch. They loaded some letters written by the king into a machine-learning engine and laboriously trained the system to recognize various textual features: word repetition, sentence length, syntactical complexity, and the like. By the end of the training process, the system was able to predict whether a royal missive had been written during a period of mania or during a period of sanity. This kind of pattern-matching technology—which is roughly similar to the techniques that have taught machines to recognize faces, images of cats, and speech patterns—has driven huge advances in computing over the past several years.

    But it requires a lot of up-front data and human supervision, and it can be brittle. The neural network learns by playing the game over and over, optimizing for whatever moves might get it to the final screen, the way a dog might learn to perform certain tasks for a treat. But reinforcement learning, too, has pretty major limitations. In the real world, most situations are not organized around a single, narrowly defined goal. Sometimes you have to stop playing Breakout to go to the bathroom, put out a fire, or talk to your boss.

    To Friston and his enthusiasts, this failure makes complete sense. Clearly, neural networks ought to do the same. It helps that the Bayesian formulas behind the free energy principle—the ones that are so difficult to translate into English—are already written in the native language of machine learning. Julie Pitt, head of machine-learning infrastructure at Netflix, discovered Friston and the free energy principle in , and it transformed her thinking.

    But a free energy agent always generates its own intrinsic reward: the minimization of surprise. And that reward, Pitt says, includes an imperative to go out and explore. The goal was to compare an agent driven by active inference to one driven by reward-maximization. The Fristonian agent started off slowly. But eventually it started to behave as if it had a model of the game, seeming to realize, for instance, that when the agent moved left the monster tended to move to the right. The Fristonian agent started slowly, actively exploring options—epistemically foraging, Friston would say—before quickly attaining humanlike performance.

    The first time I asked Friston about the connection between the free energy principle and artificial intelligence, he predicted that within five to 10 years, most machine learning would incorporate free energy minimization. The second time, his response was droll. His straight, sparkly white teeth showed through his smile as he waited for me to follow his wordplay. While I was in London, Friston gave a talk at a quantitative trading firm.

    The Inner Path of the Genius of Language (Lecture 6 of 6) The Inner Path of the Genius of Language (Lecture 6 of 6)
    The Inner Path of the Genius of Language (Lecture 6 of 6) The Inner Path of the Genius of Language (Lecture 6 of 6)
    The Inner Path of the Genius of Language (Lecture 6 of 6) The Inner Path of the Genius of Language (Lecture 6 of 6)
    The Inner Path of the Genius of Language (Lecture 6 of 6) The Inner Path of the Genius of Language (Lecture 6 of 6)
    The Inner Path of the Genius of Language (Lecture 6 of 6) The Inner Path of the Genius of Language (Lecture 6 of 6)
    The Inner Path of the Genius of Language (Lecture 6 of 6) The Inner Path of the Genius of Language (Lecture 6 of 6)
    The Inner Path of the Genius of Language (Lecture 6 of 6) The Inner Path of the Genius of Language (Lecture 6 of 6)
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