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Plato and the Nerd: The Creative Partnership of Humans and Technology
How humans and technology evolve together in a creative partnership.
In this book, Edward Ashford Lee makes a bold claim: that the creators of digital technology have an unsurpassed medium for creativity. Technology has advanced to the point where progress seems limited not by physical constraints but the human imagination. Writing for both literate technologists and numerate humanists, Lee makes a case for engineering--creating technology--as a deeply intellectual and fundamentally creative process. Explaining why digital technology has been so transformative and so liberating, Lee argues that the real power of technology stems from its partnership with humans.
Lee explores the ways that engineers use models and abstraction to build inventive artificial worlds and to give us things that we never dreamed of--for example, the ability to carry in our pockets everything humans have ever published. But he also attempts to counter the runaway enthusiasm of some technology boosters who claim everything in the physical world is a computation--that even such complex phenomena as human cognition are software operating on digital data. Lee argues that the evidence for this is weak, and the likelihood that nature has limited itself to processes that conform to today's notion of digital computation is remote.
Lee goes on to argue that artificial intelligence's goal of reproducing human cognitive functions in computers vastly underestimates the potential of computers. In his view, technology is coevolving with humans. It augments our cognitive and physical capabilities while we nurture, develop, and propagate the technology itself. Complementarity is more likely than competition.
In this book, Edward Ashford Lee makes a bold claim: that the creators of digital technology have an unsurpassed medium for creativity. Technology has advanced to the point where progress seems limited not by physical constraints but the human imagination. Writing for both literate technologists and numerate humanists, Lee makes a case for engineering--creating technology--as a deeply intellectual and fundamentally creative process. Explaining why digital technology has been so transformative and so liberating, Lee argues that the real power of technology stems from its partnership with humans.
Lee explores the ways that engineers use models and abstraction to build inventive artificial worlds and to give us things that we never dreamed of--for example, the ability to carry in our pockets everything humans have ever published. But he also attempts to counter the runaway enthusiasm of some technology boosters who claim everything in the physical world is a computation--that even such complex phenomena as human cognition are software operating on digital data. Lee argues that the evidence for this is weak, and the likelihood that nature has limited itself to processes that conform to today's notion of digital computation is remote.
Lee goes on to argue that artificial intelligence's goal of reproducing human cognitive functions in computers vastly underestimates the potential of computers. In his view, technology is coevolving with humans. It augments our cognitive and physical capabilities while we nurture, develop, and propagate the technology itself. Complementarity is more likely than competition.
288 pages, Hardcover
Published August 25, 2017
38 people are currently reading
262 people want to read
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Displaying 1 - 8 of 8 reviews
March 10, 2019
I found myself somewhat ill-prepared to take on this detailed look at the state of computer engineering, though I have a(n aged) degree in it. Half of the book was very high level, explaining, for instance, the behavior of logic circuits. These basic descriptions were often followed by detailed college level math to tie the practical back to the theory. Note to self – avoid audiobooks with college level math formulas, these need to be seen to be understood. I got a few things out of this long book. In particular, the main point of the author, based on my occasional flashes of recognition, was that the digital world was not a perfect replicator of the physical world, because the digital word was discrete, with measurements taken at intervals, whereas the physical world was continuous, with changes that could occur between those digital measurements. Given this obvious weakness, the digital world can’t be counted on to perfectly model the real, physical world. BTW, I understood this before I picked this book up. The book references other books, including “Goedel, Escher, Bach” by Hofstadter, which I believe informed its style as well as some of the content. This might be one of those books that, if you want to listen to the audiobook, you have to follow along in a physical book in order to see formulas and highlight.
January 11, 2018
Edward Ashford Lee’s Plato and the Nerd became a technical narrative respecting computational ability. Lee’s narrative started with a differentiation between science and engineering and then delved into science for computer engineering. The narrative format captured Lee’s technical acumen as well as conversational persona for this investigative look into computational power and limitations. One of Lee’s favorite books inspired the title, Plato and the Nerd, and this title fit Lee’s discourse on digital creationism. A majority of Lee’s insight stemmed from a successful academic career. Plato and the Nerd included the fundamentals required for an appreciation of basic computational constraints. Bounds to computation had always been hardware as Lee illustrated during Plato and the Nerd. Hardware required immense resources for just 4 bit logic before today’s 64 bit devices flourished. Cyberphysical systems blossomed because micro-fabrication of ALU’s enabled the implementation of embedded software and portable hardware. Complex cyberphysical systems introduced uncertainty into computations, so Lee deduced the relevance of probabilistic models with simplified examples. Limits on probabilistic models appeared as their finiteness approached infinite, and Lee described the finitude for computation per Shannon’s Law and the Bekenstein bound. Engineering’s future thus departed independent and deterministic disciplines for hybridization and stochastics.
Engineering, from Lee’s perspective, becomes the application of sciences for the solution of a valuable problem. Typical solutions of value pertain to business or social problems. Science, on the other hand, addresses any observable phenomena. An example of the difference appears where science includes Newton’s Laws of Motion while engineering produces actuators such as motors, solenoids, pneumatics, and hydraulics. Science provides the laws and theories. Engineering represents the application process of scientific laws and theories to product development and lifecycle tasks. Lee’s primary focus discusses the role of computational models in engineering. Physical prototypes arise from computational models known as analytical prototypes, and the quality of a computational model depends on programmatic sophistication as well as the computer hardware. Cukier and Schonberger adduce another aspect of computational modelling, the input data, yet Lee’s discussion does not declare any number of inputs and deliberates instead on programs. A preference for functional languages has emerged as engineers provide programmable descriptions of systems not stepwise instructions, and the computers infer their consequent computations. The tools for computation have changed the acceptable applied sciences and engineering methods, and the outcome produces the engineering of models for a vast array of problem and solution variations.
Cyberphysical systems have become the union of several engineering fields: computer, mechanical, electrical, and software. Development of Lee’s observations arise from the required computation involved in manifestation of physical objectives given specific mechanical, electrical, and computer hardware. Computer engineering empowers the nervous system of automated products for example servers, PLC’s, communication networks, and microprocessors. A system’s responsiveness to operator or environmental input is often the responsibility of computer components. Mechanical components of cyberphysical systems generate a product’s interaction with the tangible or physical world based on computed instructions. Physicists have named these interactions work and heat transfer per the First Law of Thermodynamics, and mechanical engineers design mechanisms based on the processes of work and heat transfer. Turbines, refrigerators, and cars represent mechanisms where increased automation has required the involvement of on-board electronics and controls. Electrical engineers create nano scale SoC’s for software engineers who embed intelligence into non Turing machines, so integration platforms such as IoT orchestrate multiple tasks. Proper integration of these mechanisms means hybridization of the traditional engineering schools into modernized fields: mechatronics, automation, and robotics exemplify new fields of engineering. Each new field requires subjects from the traditional mechanical, electrical, computer, and software schools for success.
Uncertainty appears inevitable for future computational models. Lee discusses deterministic examples of simple physical problems and then graduates the complexity of these examples. A simple example of an elastic collision between two pool balls becomes complicated once Lee introduces a third pool ball. Now, pool is a game with 15 balls plus 1 cue ball, and the first move in pool requires a collision amongst all 16 balls. If Lee observes inconsistencies with deterministic models for a collision amongst just three balls, then the deterministic computation of a sixteen ball collision seems fruitless and cumbersome. Bayesian probabilistic models resolve the most variability from deterministic models, and Lee prefers Bayesian models to frequentist approaches because Bayesian models do not require time. Time and any other continuous infinite set present too large of a data set for computation. Discussion of the Bekenstein bound grounds Lee’s requirement for discrete finite sets because these sets permit a finite number of events for computation. Continuous datasets of genuine interest require discretization techniques such as a Fourier Transform. Discrete events with discrete and finite likelihood for a finite universe comprise set of computable events per Lee’s rationale for a computational model of any desired cyberphysical system.
Edward Ashford Lee’s Plato and the Nerd shall remain known for Lee’s accessible explanations respecting computer engineering theory. Lee’s differentiation of science and engineering will become important as the demand for scientific theory vs engineered solutions fluctuates with the available technologies. Scientific breakthroughs should precede engineering innovations because Lee defines engineering as applied science. Different fields of engineers might unite as proliferated cyberphysical developments require knowledge of complimentary disciplines. The limits of cyberphysics would enable accurate computation for solutions of finite Bayesian probabilistic models. Cyberphysics should increase the prevalence of automated or programmable non-Turing machines. Automated material handling systems as well as IoT devices may provide good examples of such non-Turing machines because their intended function will include more than just computation. Finite Bayesian probabilistic models can describe cyberphysical events to a maximum Bekenstein bound, and any beyond the Bekenstein bound would exceed the current scope of Lee’s book. A relationship between entropy and digital information shall appear critical for investigation as cyberphysical system complexity increases, so increased quantum level experiments could yield results. Quantum computing may represent the norm for engineers as the boundaries of science and engineering expand because cubits will offer increased resolution and speed during computation.
Engineering, from Lee’s perspective, becomes the application of sciences for the solution of a valuable problem. Typical solutions of value pertain to business or social problems. Science, on the other hand, addresses any observable phenomena. An example of the difference appears where science includes Newton’s Laws of Motion while engineering produces actuators such as motors, solenoids, pneumatics, and hydraulics. Science provides the laws and theories. Engineering represents the application process of scientific laws and theories to product development and lifecycle tasks. Lee’s primary focus discusses the role of computational models in engineering. Physical prototypes arise from computational models known as analytical prototypes, and the quality of a computational model depends on programmatic sophistication as well as the computer hardware. Cukier and Schonberger adduce another aspect of computational modelling, the input data, yet Lee’s discussion does not declare any number of inputs and deliberates instead on programs. A preference for functional languages has emerged as engineers provide programmable descriptions of systems not stepwise instructions, and the computers infer their consequent computations. The tools for computation have changed the acceptable applied sciences and engineering methods, and the outcome produces the engineering of models for a vast array of problem and solution variations.
Cyberphysical systems have become the union of several engineering fields: computer, mechanical, electrical, and software. Development of Lee’s observations arise from the required computation involved in manifestation of physical objectives given specific mechanical, electrical, and computer hardware. Computer engineering empowers the nervous system of automated products for example servers, PLC’s, communication networks, and microprocessors. A system’s responsiveness to operator or environmental input is often the responsibility of computer components. Mechanical components of cyberphysical systems generate a product’s interaction with the tangible or physical world based on computed instructions. Physicists have named these interactions work and heat transfer per the First Law of Thermodynamics, and mechanical engineers design mechanisms based on the processes of work and heat transfer. Turbines, refrigerators, and cars represent mechanisms where increased automation has required the involvement of on-board electronics and controls. Electrical engineers create nano scale SoC’s for software engineers who embed intelligence into non Turing machines, so integration platforms such as IoT orchestrate multiple tasks. Proper integration of these mechanisms means hybridization of the traditional engineering schools into modernized fields: mechatronics, automation, and robotics exemplify new fields of engineering. Each new field requires subjects from the traditional mechanical, electrical, computer, and software schools for success.
Uncertainty appears inevitable for future computational models. Lee discusses deterministic examples of simple physical problems and then graduates the complexity of these examples. A simple example of an elastic collision between two pool balls becomes complicated once Lee introduces a third pool ball. Now, pool is a game with 15 balls plus 1 cue ball, and the first move in pool requires a collision amongst all 16 balls. If Lee observes inconsistencies with deterministic models for a collision amongst just three balls, then the deterministic computation of a sixteen ball collision seems fruitless and cumbersome. Bayesian probabilistic models resolve the most variability from deterministic models, and Lee prefers Bayesian models to frequentist approaches because Bayesian models do not require time. Time and any other continuous infinite set present too large of a data set for computation. Discussion of the Bekenstein bound grounds Lee’s requirement for discrete finite sets because these sets permit a finite number of events for computation. Continuous datasets of genuine interest require discretization techniques such as a Fourier Transform. Discrete events with discrete and finite likelihood for a finite universe comprise set of computable events per Lee’s rationale for a computational model of any desired cyberphysical system.
Edward Ashford Lee’s Plato and the Nerd shall remain known for Lee’s accessible explanations respecting computer engineering theory. Lee’s differentiation of science and engineering will become important as the demand for scientific theory vs engineered solutions fluctuates with the available technologies. Scientific breakthroughs should precede engineering innovations because Lee defines engineering as applied science. Different fields of engineers might unite as proliferated cyberphysical developments require knowledge of complimentary disciplines. The limits of cyberphysics would enable accurate computation for solutions of finite Bayesian probabilistic models. Cyberphysics should increase the prevalence of automated or programmable non-Turing machines. Automated material handling systems as well as IoT devices may provide good examples of such non-Turing machines because their intended function will include more than just computation. Finite Bayesian probabilistic models can describe cyberphysical events to a maximum Bekenstein bound, and any beyond the Bekenstein bound would exceed the current scope of Lee’s book. A relationship between entropy and digital information shall appear critical for investigation as cyberphysical system complexity increases, so increased quantum level experiments could yield results. Quantum computing may represent the norm for engineers as the boundaries of science and engineering expand because cubits will offer increased resolution and speed during computation.
April 8, 2021
A diverse book that touches several building blocks of a digital entity going as far and wide as programming language tools like Javascript to theoretical notions of computation like Gödel's incompleteness theorems to Shannon entropy to switching circuit theory and the list goes on...
The bane and boon of this book is its diversity. On the plus side, it succinctly manages to summarise entire topics concerning each layer of the tower of abstractions in modern computers. However, given its meagre length of 250 pages, the book is dense and most of its light-touch approach leaves the readers demanding for more.
Edward Lee's writing style is very conversational. He takes frequent digressions which, for some readers, could be quite frustrating. However, he successfully manages to convey the powerful idea of "models" in computer science. It very regularly happens that we become so habituated to fundamental principles in a field that we start believing those principles to be universal truths. Lee crosses over various areas of CS(and physics) and successfully shows how those universal truths are simply a stack of models. Moreover, such models are driven by the idiosyncrasies of their creators and the culture of their surroundings!
The book treads dangerously on the line between popular science and pure technical content. Some of the proofs (like the incompleteness theorem) will definitely fall outside the comprehension of non-CS folks and for that matter even trained computer scientists. However, if you do manage to latch on through this very dense 250 pages, you would get a scenic rooftop view to the radiant world of Computer Science!
The bane and boon of this book is its diversity. On the plus side, it succinctly manages to summarise entire topics concerning each layer of the tower of abstractions in modern computers. However, given its meagre length of 250 pages, the book is dense and most of its light-touch approach leaves the readers demanding for more.
Edward Lee's writing style is very conversational. He takes frequent digressions which, for some readers, could be quite frustrating. However, he successfully manages to convey the powerful idea of "models" in computer science. It very regularly happens that we become so habituated to fundamental principles in a field that we start believing those principles to be universal truths. Lee crosses over various areas of CS(and physics) and successfully shows how those universal truths are simply a stack of models. Moreover, such models are driven by the idiosyncrasies of their creators and the culture of their surroundings!
The book treads dangerously on the line between popular science and pure technical content. Some of the proofs (like the incompleteness theorem) will definitely fall outside the comprehension of non-CS folks and for that matter even trained computer scientists. However, if you do manage to latch on through this very dense 250 pages, you would get a scenic rooftop view to the radiant world of Computer Science!
November 18, 2018
This book is made of digressions. The digressions are so deep you will forget the original point (the author does). Or maybe there isn't one. Then it just ends out of nowhere, having presented a random selection of concepts across the science and engineering fields. What was the point of presenting the OSI model? I already have access to Wikipedia. If there was a claim or an idea the author wanted to get across I completely failed to notice in between the bafflingly frequent mentions of his dishwasher.
October 4, 2022
As a phd candidate in computer engineer who is researching on cyber-physical systems this book is like a great inspiration for a general look in the evolution of digital systems. It is mainly a sociology book in my opinion. It starts with a comparison of platonic and aristotle point of view on technological changes with a cartesian doubt. Iterates both views thoroughly and places the computational science and engineering away from natural science approaches. In each steps it gives a bottom up approach from electrical constructions to digital logic followed by modelistic structures within computer science explaining networks and software. The approach systematically moves toward more abstract constructions explaining computational and number theoritical views. Later gives the best introductory explanation of turing machine (although there are hints indicating that there are major technicalities in the models left out in the book).
My favorite part was the explanation of universal turing machine to ideas of digital universe. The transition is so smooth followed by views on determinism/nondeterminism, quantum, chaos, probability, and improbability theories.
It was definitely a difficult read, but it was worth every second.
My favorite part was the explanation of universal turing machine to ideas of digital universe. The transition is so smooth followed by views on determinism/nondeterminism, quantum, chaos, probability, and improbability theories.
It was definitely a difficult read, but it was worth every second.
August 1, 2018
Is AI ‘our biggest existential threat’, as Elon Musk once claimed? Lee doesn’t think so. What is more likely to happen and what we should want to happen, he explains in this book, is that humans and machines complement each other. We are creative, they can crunch lots of data at mesmerising speeds. We may not be more than just neurons (Lee isn’t a dualist), but it is unlikely we’ll ever be able to reconstruct human brains and minds in machines. This book brilliantly explains machines from semiconductors to programming languages to
mathematical possibilities. It gets very technical and mathy at points. Lee shows how engineers are creative rather than technical: the most technical layere are abstracted away from them. He also talks about the relationship between tech and society: ‘I do not see how a true humanist today can understand society without understanding technology’, he says and I could not agree more.
mathematical possibilities. It gets very technical and mathy at points. Lee shows how engineers are creative rather than technical: the most technical layere are abstracted away from them. He also talks about the relationship between tech and society: ‘I do not see how a true humanist today can understand society without understanding technology’, he says and I could not agree more.
March 2, 2020
Some parts were too technical for me but I cannot deny that it is very well written. I am a philosophy graduate, and it was like reading something about both philosophy of science and new media/technology.
August 10, 2023
Too much nerd, not enough Plato.
Displaying 1 - 8 of 8 reviews