Book Report: Deep Thinking

kasparov-deep-thinking-cover“Our attitude matters, and not because we can stop the march of technological prowess even if we wanted to, but because our perspective on disruption affects how well prepared for it we will be. There is plenty of room between the utopian and dystopian visions of the fully automated and artificially intelligent future we are heading into at rapidly increasing speed. Each of us has a choice to make: to embrace these new challenges, or to resist them. Will we help shape the future and set the terms of our relationship with new technology or will we let others force the terms on us?” (Deep Thinking)

Title: Deep Thinking – Where Machine Intelligence Ends and Human Creativity Begins

Author: Garry Kasparov

Publisher: PublicAffairs

Publication Date: 2017

Origin: I’m old enough to have taken great interest in Kasparov’s matches against IBM’s Deep Blue chess computer; plus, I have a (small) academic background in machine learning and artificial intelligence. So Deep Thinking checked a couple of ‘interest’ boxes for me.

Moreover, at the time I read Deep Thinking, I was considering making a significant career change, so by reading the book I was – at least partly – hoping to gain some new information about modern approaches to AI, as presented by a very insightful thinker, in Kasparov.

Summary: In Deep Thinking, Kasparov uses his battles against Deep Blue as the story arc, a narrative backdrop of a ‘plot’ device through which he can explore the relentless advancement of machine capabilities and what that advancement means for humans, our societies, and our relationship with machines.

Early on, Kasparov introduces Moravec’s Paradox, which basically says that machines are bad at what humans are intuitively good at, and vice versa. For instance, machines are good at math and cold calculations, while humans are good at moving around nimbly without falling over.

Kasparov recounts his battles against Deep Blue, from the first match, in 1996 – which Kasparov easily won – to the second, in 1997, which heralded the end of human dominance of chess. In this retelling, Kasparov provides the ultimate insider’s story and shows a depth of reflection and of appreciation that may catch some people off-guard (after all, Kasparov is known to be an intense competitor and said some things in his earlier days that he now regrets).

Thousands of years of status quo human dominance, a few decades of weak competition, a few years of struggle for supremacy. Then, game over.

Kasparov’s matches against Deep Blue are used to illustrate a general pattern of human vs machine competition (emphasis added by me):

Thousands of years of status quo human dominance, a few decades of weak competition, a few years of struggle for supremacy. Then, game over. For the rest of human history, as the timeline draws into infinity, machines will be better than humans at chess. The competition period is a tiny dot on the historical timeline. This is the unavoidable one-way street of technological progress in everything from the cotton gin to manufacturing robots to intelligent agents.

Should we fear and fight the inevitable? Kasparov doesn’t think so, and much of Deep Thinking is deeply philosophical.

He advises that we keep our composure and take a pragmatic approach to dealing with the inevitable. Kasparov reminds us that almost all new things are scary at first, and quickly become accepted:

Despite the rapid pace of technological change that has been the norm for our entire lives, we are briefly amazed, or horrified, or both, by anything new, only to get used to it in just a few years. It’s important to keep our heads on straight during that exciting cusp period between shock and acceptance so that we may look ahead clearly and prepare the best we can.

But why do we feel so threatened? In many cases, it’s because we are focusing on the wrong things – the threats to the status quo that definitely exist. Instead, we should focus on the positives – what we gain from ever-smarter and more capable machines:

We haven’t lost free will; we have gained time that we don’t yet know what to do with. We have gained incredible powers, virtual omniscience, but still lack the sense of purpose to apply them in ways that satisfy us. We have taken more steps in the advance of civilization, toward reducing the level of randomness and inefficiency in our lives. It’s different, yes, and different can be disconcerting when it happens quickly, but that doesn’t make it harmful.

So what does it all mean for the meat-sacks who’ve enjoyed sitting comfortably at the top of the heap for the last few hundred thousand years? Should we fight the descent into a dystopian future plagued by machine overlords, or work with our machines – complementing each other’s weaknesses – to achieve heights that extend beyond our dreams and imagination?

Kasparov believes that the best path is clear:

This is not a choice between utopia or dystopia. It is not a matter of us versus anything else. We will need every bit of our ambition in order to stay ahead of our technology. We are fantastic at teaching our machines how to do our tasks, and we will only get better at it. The only solution is to keep creating new tasks, new missions, new industries that even we don’t know how to do ourselves. We need new frontiers and the will to explore them. Our technology excels at removing the difficulty and uncertainty from our lives, and so we must seek out ever more difficult and uncertain challenges.

My Take:

I was a little off-the-mark in my expectations for Deep Thinking, although that’s entirely on me – I made some assumptions, and they were misplaced.

Nevertheless, I enjoyed the book tremendously: the descriptions of Deep Blue’s hardware and software architecture, and its algorithms, were just technical enough to scratch that itch; the insights into elite chess were enjoyable; and the descriptions of the matches themselves were riveting. Plus, the philosophical aspects were informed, well-articulated, and didn’t venture into the fluffy stuff that often plagues such topics

Plus, there was a certain familiarity to Deep Thinking, as it wandered into all sorts of familiar territory for me – from the characteristics that lead to elite performance, to the dangers of unfettered technological advancement, to privacy concerns, to the pitfalls of human intuition and associated fallacies; in my notes below, you’ll see cross-references to a handful of other book reports.

As a final note: I read Deep Thinking several months ago, and it’s been sitting in my large pile of “books for which I need to write reports” ever since. I finally got around to writing the report because of the talk I attended earlier in the week – I realized there were many, many similarities between what Professor Smith was telling the audience and what Kasparov espouses in Deep Thinking.

As an even more final note: if you want to see an excellent documentary about a very similar human vs. machine competition, this time with Go as the challenge game instead of chess, then check out AlphaGo. While I was watching it a few weeks ago, I kept thinking back to Kasparov’s tales of his encounter with Deep Blue, as the parallels are uncanny.

Read This Book If: …You will appreciate a thorough philosophical and pragmatic examination of the evolving relationship between humans and machines, or just want to find out what was going through Kasparov’s head as he took on the challenge of Deep Blue.

Notes and Quotes:

  • p2 introduced me to Moravec’s Paradox, which Kasparov loosely summarizes as “what machines are good at is where humans are weak, and vice versa”

“The willingness to keep trying new things – different methods, uncomfortable tasks – when you are already an expert at something is what separates good from great. Focusing on your strengths is required for peak performance, but improving your weaknesses has the potential for the greatest gains.”

  • p15, with some sage wisdom: “When Der Speigel asked me what I thought separated me, the world champion, from other strong chess players, I answered, ‘The willingness to take on new challenges,’ the same answer I would give today. The willingness to keep trying new things – different methods, uncomfortable tasks – when you are already an expert at something is what separates good from great. Focusing on your strengths is required for peak performance, but improving your weaknesses has the potential for the greatest gains.”
  • p18, reminiscent of some of my own experiences: “In nearly any competitive endeavor, you have to be damned good before luck can be of any use to you at all.” It’s worth point out, perhaps, that this message is not at odds with the overall thesis of Success and Luck. Yes, luck is often a deciding factor; no, it’s not sufficient in and of itself.
  • p25, speaking in the past-tense about the dawn of the computer age: “Utopian dreams about the fully automated world just around the corner shared column space with dystopian nightmares of, well, pretty much the same thing.”
  • p25 goes on to make an important point: “Every disruptive new technology, any resulting change in the dynamics of society, will produce a range of positive and negative effects and side effects that shift over time, often suddenly.”

“Every disruptive new technology, any resulting change in the dynamics of society, will produce a range of positive and negative effects and side effects that shift over time, often suddenly.”

  • p26: “Our attitude matters, and not because we can stop the march of technological prowess even if we wanted to, but because our perspective on disruption affects how well prepared for it we will be. There is plenty of room between the utopian and dystopian visions of the fully automated and artificially intelligent future we are heading into at rapidly increasing speed. Each of us has a choice to make: to embrace these new challenges, or to resist them. Will we help shape the future and set the terms of our relationship with new technology or will we let others force the terms on us?”
  • p26, about a trap into which every generation has fallen when it comes to machine intelligence: “We confuse performance – the ability of a machine to replicate or surpass the results of a human – with method, how those results are achieved.”
  • p43, after briefly alluding to Donald Trump’s idiotic (my word) “America First” policy: “You can’t discard the downsides of globalization while keeping the benefits.”
  • It’s as if he’s in my head and is offering me personalized advice, p51: “Despite the rapid pace of technological change that has been the norm for our entire lives, we are briefly amazed, or horrified, or both, by anything new, only to get used to it in just a few years. It’s important to keep our heads on straight during that exciting cusp period between shock and acceptance so that we may look ahead clearly and prepare the best we can.”

“When you have had success, when the status quo favors you, it becomes very hard to voluntarily change your ways.”

  • p60, summarizing a phenomena that impacts people and organizations, and relates to the quote from p15: “When you have had success, when the status quo favors you, it becomes very hard to voluntarily change your ways.”
  • p65, quoting an axiom of Bill Gates: “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten.”
  • p72…more than one organization could learn from this point: “if you change your strategy all the time you don’t really have one”

“To become good at anything you have to know how to apply basic principles. To become great at it, you have to know when to violate those principles.”

  • p77…I remember reading something similar years ago about solo musicians and how they deviate from the composition; come to think of it, it’s similar to one of the major philosophies of Zlatan Ibrahimovic, too: “To become good at anything you have to know how to apply basic principles. To become great at it, you have to know when to violate those principles.”
  • p109 talks about the Polgár sisters, whom I first learned about in Bounce
  • p113: “To be the best in any competitive endeavor you have to hate losing more than you are afraid of it… no matter how much you love the game, you have to hate to lose if you want to stay on top. You have to care, and care deeply.”
  • p117, reminiscent of part of the message of Weapons of Math Destruction: “Anti-discrimination laws in the United States make it illegal to ask applicants about age, gender, race, and health, but algorithmic social media analysis can identify those in a split second, as well as make very accurate guesses about things like sexual preference, political leanings, and income level.”
  • p117, this time reminiscent of Data and Goliath: “History tells us that eventually the desire for services wins out over a vague desire for privacy.”

“Our lives are being converted into data. This trend will accelerate as the tools become vastly more powerful and it will happen both voluntarily in exchange for services and due to the increasing public and private demand for security. This cannot be stopped, so what matters more than ever is watching the watchers.”

  • p118, again very much inline with both Weapons of Math Destruction and Data and Goliath: “Our lives are being converted into data. This trend will accelerate as the tools become vastly more powerful and it will happen both voluntarily in exchange for services and due to the increasing public and private demand for security. This cannot be stopped, so what matters more than ever is watching the watchers. The amount of data we produce will continue to expand, and largely to our benefit, but we must monitor where it goes and how it is used. Privacy is dying, so transparency must increase.”
  • p138, after describing some high-pressure matches: “I took two lessons away from this discovery. The first is that we often do our best thinking under pressure… We often do not realize how powerful our intuitive abilities are until we have no choice but to rely on them.” (the second lesson was that, “everyone loves a good story”)
  • p139, wading into narrative fallacies: “Engines don’t care about the story. They expose the reality that the only story in a chess game is each individual move, weak or strong. This isn’t nearly as fun or interesting as the narrative method, but it’s the truth, and not just in chess. The human need to understand things as a story instead of as a series of discrete events can lead to many flawed conclusions.”
  • p151 reminds me of both UW’s Problem Lab and The Pentagon’s Brain: “As with the early days of ARPA, the concept at Bell Labs was to describe big problems and then work on creating the technology to solve them, instead of starting with a specific problem in mind.”
  • Also on p151, a point I’ve made many times, often while discussing marketing automation and its problem with local maxima; quoting Alan Perlis: “Optimization hinders evolution.”

“Evolution isn’t improvement; it’s change. Usually from simple to complex, but the key to it is increasing diversity, a shift in the nature of a thing. Optimization can make computer code faster but it won’t change its nature or create anything new.”

  • p152 continues: “But evolution isn’t improvement; it’s change. Usually from simple to complex, but the key to it is increasing diversity, a shift in the nature of a thing. Optimization can make computer code faster but it won’t change its nature or create anything new.”
  • p158, quoting Kasparov’s teacher, Mikhail Botvinnik, while discussing an overconfident Vasily Smyslov: “Conceit does not put one in the right frame of mind for work.”
  • p174, simple but true (and often overlooked): “There is no value in a theoretical weakness; you have to be able to exploit it.”
  • p225: “We haven’t lost free will; we have gained time that we don’t yet know what to do with. We have gained incredible powers, virtual omniscience, but still lack the sense of purpose to apply them in ways that satisfy us. We have taken more steps in the advance of civilization, toward reducing the level of randomness and inefficiency in our lives. It’s different, yes, and different can be disconcerting when it happens quickly, but that doesn’t make it harmful.”
  • p225: “The danger isn’t intellectual stagnation or an addiction to instant fact-finding missions. The real risk is substituting superficial knowledge for the type of understanding and insight that is required to create new things.”

“If we only rely on our machines to show us how to be good imitators, we will never take that next step to becoming creative innovators.”

  • p229, on innovation in general: “The earlier on in the development tree you look, the bigger the potential for disruption is, and the more work it will take to achieve. If we only rely on our machines to show us how to be good imitators, we will never take that next step to becoming creative innovators.”
  • p230 talks a bit about the dangers of “innovating at the margins”; that is, leaving big problems unchallenged while we instead focus on small optimizations and tweaks
  • p233, on the promise of humans working with machines to achieve expertise is cognitively intense tasks, and relating to the so-called 10,000 hour rule: “Practice has shown that technology can greatly reduce that time by making training far more efficient.”
  • p233, haha…I wouldn’t say I lecture people on this point, but I’ve certainly brought it up from time-to-time. I always challenge myself to remember. From Kasparov, “Compare that to what you do when you can’t remember something and reflexively reach for your phone. Do you at least pause for a minute to see if you can figure it out on your own? You may not be a world champion in training, and you might just be looking up some movie trivia or a friend’s email address, but it is still worth getting those cognitive muscles a little exercise on occasion.”
  • p240: wow, it took this long for Daniel Kahneman, Amos Tversky, and Dan Ariely to make an appearance! They appear in a section about how terrible humans are at thinking logically.

“You have to be brutally honest where it counts the most… If you are truthful and diligent with collecting data and making your evaluations, you will find you get better and better at making correct estimations.”

  • p244, Kasparov touches on how he can reconcile his “jump first, ask questions later” attitude with “the cool objectivity required to play elite chess”, and relating back to humans’ lousiness at intuitive statistical estimation: “You have to be brutally honest where it counts the most… If you are truthful and diligent with collecting data and making your evaluations, you will find you get better and better at making correct estimations.”
  • p249: “But now that machines are entering the decision-making space, how do we interact with them? Many jobs will continue to be lost to intelligent automation, but if you’re looking for a field that will be booming for many years, get into human-machine collaboration and process architecture and design. This isn’t just ‘UX,’ user experience, but entirely new ways of bringing human-machine coordination into diverse fields and creating the new tools we need in order to do so.”

“Intelligence is whatever machines haven’t done yet.” – Larry Tesler

  • p251, quoting from Larry Tesler’s “AI effect”, which seems to present a delightfully moving target: “intelligence is whatever machines haven’t done yet.”
  • p254 reminds me of a colleague I had who liked to project things “in the limit” (yes, like calculus); it always bugged me because most of the time we live in and have to deal with the now, not the limit: “From a distance, it’s a good example of how human time scales and human capabilities are rendered practically insignificant compared to accelerating technical progress.”

“Thousands of years of status quo human dominance, a few decades of weak competition, a few years of struggle for supremacy. Then, game over. For the rest of human history, as the timeline draws into infinity, machines will be better than humans at chess. The competition period is a tiny dot on the historical timeline. This is the unavoidable one-way street of technological progress in everything from the cotton gin to manufacturing robots to intelligent agents.”

  • p254 expands on the previous point, after summarizing the history of chess: “Draw that out as a timeline. Thousands of years of status quo human dominance, a few decades of weak competition, a few years of struggle for supremacy. Then, game over. For the rest of human history, as the timeline draws into infinity, machines will be better than humans at chess. The competition period is a tiny dot on the historical timeline. This is the unavoidable one-way street of technological progress in everything from the cotton gin to manufacturing robots to intelligent agents.”

“It is almost always better to start looking for alternatives and how to advance the change into something better instead of trying to fight it and hold on to the dying status quo.”

  • p255: “A corollary is that it is almost always better to start looking for alternatives and how to advance the change into something better instead of trying to fight it and hold on to the dying status quo.”
  • p258: “This is not a choice between utopia or dystopia. It is not a matter of us versus anything else. We will need every bit of our ambition in order to stay ahead of our technology. We are fantastic at teaching our machines how to do our tasks, and we will only get better at it. The only solution is to keep creating new tasks, new missions, new industries that even we don’t know how to do ourselves. We need new frontiers and the will to explore them. Our technology excels at removing the difficulty and uncertainty from our lives, and so we must seek out ever more difficult and uncertain challenges.”
  • p259: “If we stop dreaming big dreams, if we stop looking for a greater purpose, then we may as well be machines ourselves.”

“We need new frontiers and the will to explore them. Our technology excels at removing the difficulty and uncertainty from our lives, and so we must seek out ever more difficult and uncertain challenges… If we stop dreaming big dreams, if we stop looking for a greater purpose, then we may as well be machines ourselves.”

Lee Brooks is a freelance technology marketer based in the high-tech hub of Waterloo, Ontario, Canada.

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Posted in Books, Everything, Math and Science
4 comments on “Book Report: Deep Thinking
  1. […] via neural networks, and deep learning in general, was seen as a bit of a dead end. Even with some pretty impressive achievements, the computational and data needs for continued and general-purpose advancement just weren’t […]

  2. […] reminiscent of some of the philosophical messages within Deep Thinking: “The past is an anchor for most experts, leaving them trapped in knowledge and judgments […]

  3. Do you take suggestions?
    “The Woman Who Smashed Codes: A True Story of Love, Spies, and the Unlikely Heroine Who Outwitted America’s Enemies”

    https://www.harpercollins.com/9780062430489/the-woman-who-smashed-codes

    prob needs a thorough review 🙂

    and of course, its an old, but classic, and one of my favourites of all time, The Soul of a New Machine.

    • Lee Brooks says:

      My reading page makes it clear that I do take suggestions (thanks, I’ve added The Woman Who Smashed Codes to my cart)!

      I’ve only got the epub of The Soul of a New Machine, so it’s a huge pain to do the necessary mark-ups.

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