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A review by briangodsey
The Quark and the Jaguar: Adventures in the Simple and the Complex by Murray Gell-Mann
5.0
I got this book as a prize from the math department of my college when I was a freshman or a sophomore. Though I liked the idea of learning more about quarks, I had a habit of not reading anything that wasn't required of me. So, The Quark and the Jaguar sat on my shelf for almost a decade before I took it seriously, and I'm glad for that---both that I took it seriously and that I waited so long.
I'm glad that I [finally] took the book seriously because there's a ton of good information and ideas in there. I'm glad that I waited because I think that even a couple of years ago I would not have fully understood or appreciated most of it. Four years ago, I had only basic knowledge of physics, biology, genetics, evolution, machine learning, probability, and political science, all of which are discussed in this Nobel Prize winner's book. In these last four years, I've learned a lot about all of these topics, not that it's really necessary for understanding the book; it definitely helps appreciate its importance, though.
Strictly speaking, The Quark and the Jaguar is about learning, albeit three distinct types of learning: (1) humans learning about our world and universe, (2) our world and universe learning what laws, rules, and configurations can function in the long run, and (3) computers designed by humans learning from data. Learning type (1) is obviously what Gell-Mann has done for most of his lifetime as a theoretical physicist and general applied scientist. Learning type (2) is what Gell-Mann has discovered in his lifetime as a scientist: that every system in our universe---from quantum physics to genetic evolution to economics---is an example or result of that system having tried many possibilities and settling on the few that work. The theory and literature on learning type (3) provide the necessary framework and terminology with which we can discuss learning types (1) and (2), since, in essence, all three types are one and the same, but with different physical objects at the center. Gell-Mann calls these objects "complex adaptive systems" and demonstrates how a machine learning algorithm can be very much like the process of evolution, the training of a dog, or even the settling of our cosmos into the physical laws we know and accept today.
The breadth of this book is incredible---especially since it's less than 400 pages---and what's even more amazing is that my only complaint about this book is that it was sometimes redundant and written at a level below my current scientific knowledge. It's clearly a book written for people who are not experts in any of the aforementioned fields, but Gell-Mann manages to make it relevant also for them. The only scientifically difficult subject matter is on information theory or quantum physics, and these pages are by no means necessary to the rest of the book.
The best aspect of the book, though, which is referenced throughout but becomes clear near the end, is that Gell-Mann tells people in no uncertain terms to look at the big picture, an action that seems incredibly uncommon in the world at large. Not only does he stress this for science, but he tells us how to do it in our everyday lives---work, community, environment, and politics included. There's even a concise summary chapter at the end in case you missed the message in between the examples throughout the book.
What I'm left with in the end is a strong feeling that the world and universe are largely a product of learning and chance, the two aspects of every valuable complex adaptive system. I am a product of this universe, and I operate the same way. If I learn how to learn and convince others to do the same, I can be successful or change the world, or both, whichever I prefer.
I'm glad that I [finally] took the book seriously because there's a ton of good information and ideas in there. I'm glad that I waited because I think that even a couple of years ago I would not have fully understood or appreciated most of it. Four years ago, I had only basic knowledge of physics, biology, genetics, evolution, machine learning, probability, and political science, all of which are discussed in this Nobel Prize winner's book. In these last four years, I've learned a lot about all of these topics, not that it's really necessary for understanding the book; it definitely helps appreciate its importance, though.
Strictly speaking, The Quark and the Jaguar is about learning, albeit three distinct types of learning: (1) humans learning about our world and universe, (2) our world and universe learning what laws, rules, and configurations can function in the long run, and (3) computers designed by humans learning from data. Learning type (1) is obviously what Gell-Mann has done for most of his lifetime as a theoretical physicist and general applied scientist. Learning type (2) is what Gell-Mann has discovered in his lifetime as a scientist: that every system in our universe---from quantum physics to genetic evolution to economics---is an example or result of that system having tried many possibilities and settling on the few that work. The theory and literature on learning type (3) provide the necessary framework and terminology with which we can discuss learning types (1) and (2), since, in essence, all three types are one and the same, but with different physical objects at the center. Gell-Mann calls these objects "complex adaptive systems" and demonstrates how a machine learning algorithm can be very much like the process of evolution, the training of a dog, or even the settling of our cosmos into the physical laws we know and accept today.
The breadth of this book is incredible---especially since it's less than 400 pages---and what's even more amazing is that my only complaint about this book is that it was sometimes redundant and written at a level below my current scientific knowledge. It's clearly a book written for people who are not experts in any of the aforementioned fields, but Gell-Mann manages to make it relevant also for them. The only scientifically difficult subject matter is on information theory or quantum physics, and these pages are by no means necessary to the rest of the book.
The best aspect of the book, though, which is referenced throughout but becomes clear near the end, is that Gell-Mann tells people in no uncertain terms to look at the big picture, an action that seems incredibly uncommon in the world at large. Not only does he stress this for science, but he tells us how to do it in our everyday lives---work, community, environment, and politics included. There's even a concise summary chapter at the end in case you missed the message in between the examples throughout the book.
What I'm left with in the end is a strong feeling that the world and universe are largely a product of learning and chance, the two aspects of every valuable complex adaptive system. I am a product of this universe, and I operate the same way. If I learn how to learn and convince others to do the same, I can be successful or change the world, or both, whichever I prefer.