Anil Ananthaswamy > Quotes

“Memory, connecting inconceivable mystery to inconceivable mystery, performs the impossible by the strength of her divine arms; holds together past and present,—beholding both,—existing in both . . . and gives continuity and dignity to human life. It holds us to our family, to our friends. Hereby a home is possible. —Ralph Waldo Emerson”
― Anil Ananthaswamy, The Man Who Wasn't There: Investigations into the Strange New Science of the Self

“The subjective emotion we feel is the brain’s best predictive guess that explains the [incoming] interoceptive information at a whole bunch of hierarchical levels,” said Seth. “It’s not just cognition looking down at physiology and interpreting it.”
― Anil Ananthaswamy, The Man Who Wasn't There: Investigations into the Strange New Science of the Self

“What gives me the right to speak of an “I,” and even of an “I” as cause, and finally of an “I” as cause of thought? . . . A thought comes when “it” wants, not when “I” want. —Friedrich Nietzsche For”
― Anil Ananthaswamy, The Man Who Wasn't There: Investigations into the Strange New Science of the Self

“Forever I shall be a stranger to myself. —Albert Camus”
― Anil Ananthaswamy, The Man Who Wasn't There: Investigations into the Strange New Science of the Self

“We cannot leave decisions about how AI will be built and deployed solely to its practitioners. If we are to effectively regulate this extremely useful, but disruptive and potentially threatening, technology, another layer of society—educators, politicians, policymakers, science communicators, or even interested consumers of AI—must come to grips with the basics of the mathematics of machine learning.”
― Anil Ananthaswamy, Why Machines Learn: The Elegant Math Behind Modern AI

“The most practical thing in the world is a good theory,” Hart told me. “If you know the theoretical properties of a procedure, you can have confidence employing that without having the bother of conducting endless experiments to figure out what it does or when it works and when it doesn’t work.”
― Anil Ananthaswamy, Why Machines Learn: The Elegant Math Behind Modern AI

“Ponder this for a moment. Newborn ducklings, with the briefest of exposure to sensory stimuli, detect patterns in what they see, form abstract notions of similarity/dissimilarity, and then will recognize those abstractions in stimuli they see later and act upon them.”
― Anil Ananthaswamy, Why Machines Learn: The Elegant Math Behind Modern AI

“Bohr wanted to make wave-particle dualism—the idea that nature has two faces and only shows one or the other at any one time—a key component of any interpretation of reality; Heisenberg put his “trust in the newly developed mathematical formalism,” to see what meanings it suggested, rather than presupposing any particular view of reality.”
― Anil Ananthaswamy, Through Two Doors at Once: The Elegant Experiment That Captures the Enigma of Our Quantum Reality

“What you do is you take the single value of the error, square it, swallow hard, because you are going to tell a lie, [and] you say that’s the mean squared error,”
― Anil Ananthaswamy, Why Machines Learn: The Elegant Math Behind Modern AI

“Henrietta Swan Leavitt worked as a human "computer" in the late 1800s. Her job was to count stars at the Harvard College Observatory, which had taken on the ambitious task of cataloging every star in the sky. The work demanded painstaking manual inspection of photographic plates to pin down the stars' position, color, and brightness. Edward Pickering, the director of the observatory, recruited men to do the job, only to be frustrated by their "lack of concentration and failure to pay attention to detail." Convinced that women could do a better job, he fired the men and hired a team of women, who were nicknamed "Pickering's Harem." Not only did Pickering get a more diligent team of workers, he paid them only about half as much as he paid the men. And he did not have to worry about the women wanting to make their own observations, for (as at Mount Wilson) they were not allowed to use the telescopes. It was as part of this team of desk-bound computers that Leavitt discovered something extraordinary.”
― Anil Ananthaswamy, The Edge of Physics: A Journey to Earth's Extremes to Unlock the Secrets of the Universe

“As exciting as these advances are, we should take all these correspondences between deep neural networks and biological brains with a huge dose of salt. These are early days. The convergences in structure and performance between deep nets and brains do not necessarily mean the two work in the same way; there are ways in which they demonstrably do not. For example, biological neurons “spike,” meaning the signals travel along axons as voltage spikes.”
― Anil Ananthaswamy, Why Machines Learn: The Elegant Maths Behind Modern AI

“Laurie, too, is well aware that the voices in her head, her paranoia, the messages she thinks she’s receiving from outside, are all, in some sense, a product of her altered self. “But that insight is a paradox. Without the insight you fear the external; with the insight you fear yourself,”
― Anil Ananthaswamy, The Man Who Wasn't There: Investigations into the Strange New Science of the Self

“A cartoon captioned “At home with the Heisenbergs” was stuck on the bathroom door outside the apartment, with Mrs. Heisenberg saying, “I can’t find my car keys,” and Mr. Heisenberg replying, “You probably know too much about their momentum.”)”
― Anil Ananthaswamy, Through Two Doors at Once: The Elegant Experiment That Captures the Enigma of Our Quantum Reality

“If this sounds like the probabilities of matrix mechanics, you are not mistaken. Schrödinger himself, in another stroke of insight, showed that wave mechanics and matrix mechanics are mathematically equivalent (in hindsight, it was a mathematician called John von Neumann who would really prove the equivalence a few years later).”
― Anil Ananthaswamy, Through Two Doors at Once: The Elegant Experiment That Captures the Enigma of Our Quantum Reality

“many people with schizophrenia can tickle themselves.”
― Anil Ananthaswamy, The Man Who Wasn't There: Investigations into the Strange New Science of the Self

“What Born realized was that the symbols Heisenberg was manipulating in his equations were mathematical objects called matrices, and there was an entire field of mathematics devoted to them, called matrix algebra. For example, Heisenberg had found that there was something strange about his symbols: when entity A was multiplied by entity B, it was not the same as B multiplied by A; the order of multiplication mattered. Real numbers don’t behave this way. But matrices do. A matrix is an array of elements. The array can be a single row, a single column, or a combination of rows and columns. Heisenberg had brilliantly intuited a way of representing the quantum world and asking questions about it using such symbols, while being unaware of matrix algebra.”
― Anil Ananthaswamy, Through Two Doors at Once: The Elegant Experiment That Captures the Enigma of Our Quantum Reality

“The fact that we are now dealing in probabilities is not, presumably, because we do not know enough about the particle. Matrix mechanics says you have all the information you can possibly have. Yet, if you take a million identically prepared particles in the same state (the same combination of states A and B) and perform a million identical measurements, then, on average, x2 number of times you will find the particle in state A, y2 of the time you’ll find it in state B. But you can never predict the answer you’ll get for any single particle. You can only talk statistically. Nature, it seems, is not deterministic in the quantum realm.”
― Anil Ananthaswamy, Through Two Doors at Once: The Elegant Experiment That Captures the Enigma of Our Quantum Reality

“The advantage of wave mechanics, in Schrödinger’s opinion, was the idea that nature even at the smallest scales was continuous, not discrete. There were no quantum jumps.”
― Anil Ananthaswamy, Through Two Doors at Once: The Elegant Experiment That Captures the Enigma of Our Quantum Reality

“Decades later, Widrow, recalling Wiener’s personality in a book, painted a particularly evocative picture of a man whose head was often, literally and metaphorically, “in the clouds” as he walked the corridors of MIT buildings: “We’d see him there every day, and he always had a cigar. He’d be walking down the hallway, puffing on the cigar, and the cigar was at angle theta—45 degrees above the ground. And he never looked where he was walking…But he’d be puffing away, his head encompassed in a cloud of smoke, and he was just in oblivion. Of course, he was deriving equations.”
― Anil Ananthaswamy, Why Machines Learn: The Elegant Math Behind Modern AI

“In classical physics, solving a wave equation for, say, a sound wave can give you the pressure of the sound wave at a certain point in space and time. Solving Schrödinger’s wave equation gives you what’s called a wavefunction. This wavefunction, denoted by the Greek letter ψ (psi, pronounced “sigh”), is something quite strange. It represents the quantum state of the particle, but the quantum state is not a single number or quantity that reveals, for example, that the electron is at this position at this time and at that position at another time. Rather, ψ is itself an undulating wave that has, at any given moment in time, different values at different positions. Even more weirdly, these values are not real numbers; rather, they can be complex numbers with imaginary parts. So the wavefunction at any instant in time is not localized in a region of space; rather, it is spread out, it’s everywhere, and it has imaginary components. The Schrödinger equation, then, allows you to calculate how the state of the quantum system, ψ, changes with time. Schrödinger”
― Anil Ananthaswamy, Through Two Doors at Once: The Elegant Experiment That Captures the Enigma of Our Quantum Reality

“Once trained, the LLM is ready for inference. Now given some sequence of, say, 100 words, it predicts the most likely 101st word. (Note that the LLM doesn’t know or care about the meaning of those 100 words: To the LLM, they are just a sequence of text.) The predicted word is appended to the input, forming 101 input words, and the LLM then predicts the 102nd word. And so it goes, until the LLM outputs an end-of-text token, stopping the inference. That’s it!

An LLM is an example of generative AI. It has learned an extremely complex, ultra-high-dimensional probability distribution over words, and it is capable of sampling from this distribution, conditioned on the input sequence of words. There are other types of generative AI, but the basic idea behind them is the same: They learn the probability distribution over data and then sample from the distribution, either randomly or conditioned on some input, and produce an output that looks like the training data.”
― Anil Ananthaswamy, Why Machines Learn: The Elegant Math Behind Modern AI

“The fact that we are now dealing in probabilities is not, presumably, because we do not know enough about the particle. Matrix mechanics says you have all the information you can possibly have. Yet, if you take a million identically prepared particles in the same state (the same combination of states A and B) and perform a million identical measurements, then, on average, x2 number of times you will find the particle in state A, y2 of the time you’ll find it in state B. But you can never predict the answer you’ll get for any single particle. You can only talk statistically. Nature, it seems, is not deterministic in the quantum realm. Recall that something similar happens with the double slit. We cannot predict where exactly a single photon will land on the screen—we can only assign probabilities for where it might go.”
― Anil Ananthaswamy, Through Two Doors at Once: The Elegant Experiment That Captures the Enigma of Our Quantum Reality