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Everyday Chaos: Technology, Complexity, and How We’re Thriving in a New World of Possibility
Artificial intelligence, big data, modern science, and the internet are all revealing a fundamental truth: The world is vastly more complex and unpredictable than we've allowed ourselves to see.
Now that technology is enabling us to take advantage of all the chaos it's revealing, our understanding of how things happen is changing--and with it our deepest strategies for predicting, preparing for, and managing our world. This affects everything, from how we approach our everyday lives to how we make moral decisions and how we run our businesses.
Take machine learning, which makes better predictions about weather, medical diagnoses, and product performance than we do--but often does so at the expense of our understanding of how it arrived at those predictions. While this can be dangerous, accepting it is also liberating, for it enables us to harness the complexity of an immense amount of data around us. We are also turning to strategies that avoid anticipating the future altogether, such as A/B testing, Minimum Viable Products, open platforms, and user-modifiable video games. We even take for granted that a simple hashtag can organize unplanned, leaderless movements such as #MeToo.
Through stories from history, business, and technology, philosopher and technologist David Weinberger finds the unifying truths lying below the surface of the tools we take for granted--and a future in which our best strategy often requires holding back from anticipating and instead creating as many possibilities as we can. The book’s imperative for business and beyond is simple: Make. More. Future.
The result is a world no longer focused on limitations but optimized for possibilities.
256 pages, Hardcover
Published May 14, 2019
65 people are currently reading
1566 people want to read
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Displaying 1 - 26 of 26 reviews
February 9, 2019
Everyday Chaos is an instantly excellent book. Right in the introduction, David Weinberger provokes you with: “The true complexity of the world far outstrips the laws and models we devise to explain it.” Enlarging on this, he digs the hole deeper: “At least since the ancient Hebrews, we have thought ourselves to be the creatures uniquely made by God with the capacity to receive his Revelation of the truth. Since the ancient Greeks, we’ve defined ourselves as the rational animals who are able to see the logic and order beneath the apparent chaos of the world.” We put ourselves on a pedestal, and worshipped.
Basically, we not only don’t know what we don’t know, we don’t understand what we think we know. The inescapable conclusion is - you must read on.
Every so often, our neatly ordered world is buffeted by some scientist/philosopher who says it’s not what you think. And it’s not why you think and not how you think it. It operates differently, for different reasons, different causes, with different relationships and outcomes. Newton, Einstein and Galileo performed these roles, changing the course of thought and action forever. Now, it seems Weinberger looks to artificial intelligence (AI) to take on that role.
His first and best example is a healthcare learning monster called Deep Patient. The Deep Patient project fed a computer with hundreds of data points for every one of 700,000 patients and let it figure out what it could, without restrictions. This is called deep learning, and results in findings humans never considered or could even imagine. It made diagnoses and predictions that were way outside the box for the medical fraternity, and were reliable. The lesson from Deep Patient is that deep learning systems do not have to simplify the world to what humans can understand, he says.
The book is very tight. Weinberger has it highly organized, as the ancient Greeks would have appreciated. He is easy to follow, entertaining along the way and provides an intriguingly different perspective on the way of the world. Can’t ask for much more.
Backing off to basics, Weinberger shows what Man does is anticipate. He loves to understand all the possibilities in advance, and prepare for them. Machines don’t have those blinders on. They operate in unanticipation, where anything is possible given the data that shows it. They let the data instruct them. No hypotheses are necessary. It goes against everything we’ve built to date.
This is the kind of analysis and exploration that pervades the book. It is a pleasure of discovery. It is a revelation to watch as he puts the puzzle pieces together. That he does it with humor is a delightful bonus.
In a discussion of programming AI, he hits on the ugly problem of decisions in an emergency. What is fair? What is appropriate? What will minimize the negative outcome? Who get to decide? The machine will do what we program it to do. He says: “Machine learning systems are profoundly nonmoral. They are just machines, not Just machines.”
We show our insecurity and ignorance by insisting that machines explain themselves to us. We insist on knowing how they came to their result. We want accountability for self-learning we cannot understand. We hold the machines to higher standards than we do for humans. Years ago, Google said it could not alter the objectionable results its search engine produced, because they didn’t understand how it got them. Algorithms are black boxes, and just because we couldn’t have come to the same answers, doesn’t mean we need to be able to parse every move to get to them. It wouldn’t help us.
Animal scientist Frans de Waal criticizes his peers for conducting biased tests on animals, somehow always proving than Man is a superior animal. David Weinberger is doing much the same thing between AI and Man. Until and unless we stop trying to instill the values of Man into machines, the machines will never achieve their potential. They will usher in dystopia instead of providing the answers we would never have considered on our own.
Weinberger says we have come to the point where we see “the world as further beyond our understanding than we’ve let ourselves believe.” It’s an update to Herbert Stein’s famous “The more I seem to know, the less I seem to know.” Everyday Chaos is the right book for this moment.
David Wineberg
Basically, we not only don’t know what we don’t know, we don’t understand what we think we know. The inescapable conclusion is - you must read on.
Every so often, our neatly ordered world is buffeted by some scientist/philosopher who says it’s not what you think. And it’s not why you think and not how you think it. It operates differently, for different reasons, different causes, with different relationships and outcomes. Newton, Einstein and Galileo performed these roles, changing the course of thought and action forever. Now, it seems Weinberger looks to artificial intelligence (AI) to take on that role.
His first and best example is a healthcare learning monster called Deep Patient. The Deep Patient project fed a computer with hundreds of data points for every one of 700,000 patients and let it figure out what it could, without restrictions. This is called deep learning, and results in findings humans never considered or could even imagine. It made diagnoses and predictions that were way outside the box for the medical fraternity, and were reliable. The lesson from Deep Patient is that deep learning systems do not have to simplify the world to what humans can understand, he says.
The book is very tight. Weinberger has it highly organized, as the ancient Greeks would have appreciated. He is easy to follow, entertaining along the way and provides an intriguingly different perspective on the way of the world. Can’t ask for much more.
Backing off to basics, Weinberger shows what Man does is anticipate. He loves to understand all the possibilities in advance, and prepare for them. Machines don’t have those blinders on. They operate in unanticipation, where anything is possible given the data that shows it. They let the data instruct them. No hypotheses are necessary. It goes against everything we’ve built to date.
This is the kind of analysis and exploration that pervades the book. It is a pleasure of discovery. It is a revelation to watch as he puts the puzzle pieces together. That he does it with humor is a delightful bonus.
In a discussion of programming AI, he hits on the ugly problem of decisions in an emergency. What is fair? What is appropriate? What will minimize the negative outcome? Who get to decide? The machine will do what we program it to do. He says: “Machine learning systems are profoundly nonmoral. They are just machines, not Just machines.”
We show our insecurity and ignorance by insisting that machines explain themselves to us. We insist on knowing how they came to their result. We want accountability for self-learning we cannot understand. We hold the machines to higher standards than we do for humans. Years ago, Google said it could not alter the objectionable results its search engine produced, because they didn’t understand how it got them. Algorithms are black boxes, and just because we couldn’t have come to the same answers, doesn’t mean we need to be able to parse every move to get to them. It wouldn’t help us.
Animal scientist Frans de Waal criticizes his peers for conducting biased tests on animals, somehow always proving than Man is a superior animal. David Weinberger is doing much the same thing between AI and Man. Until and unless we stop trying to instill the values of Man into machines, the machines will never achieve their potential. They will usher in dystopia instead of providing the answers we would never have considered on our own.
Weinberger says we have come to the point where we see “the world as further beyond our understanding than we’ve let ourselves believe.” It’s an update to Herbert Stein’s famous “The more I seem to know, the less I seem to know.” Everyday Chaos is the right book for this moment.
David Wineberg
June 30, 2019
First, my confession is I know little about technology. Every once in a while, however, I push myself to leave my favourite Victorian novelists and read something that will stretch my brain. This book did. Wow!
I knew nothing about “deep learning” or unanticipation or machine learning, or basically most of the words and concepts in this book. Now, I know next to nothing, but I do know that Weinberger’s analysis, commentary, explanations and arguments made sense to me. I could follow his more esoteric arguments (at least to me they were esoteric) because his prose style, examples and insights were based on my world. Ultimately, that is what this book does. It takes its focus on technology and shows how technology is our friend, not our enemy. The book explains how while the world “may be dangerous, accepting it is also liberating, for it enables us to harness the complexity of an immense amount of data around us.” (quotation taken from the dust jacket)
This book is a valuable tool for the layman, like myself, and a deeply fascinating book for our times.
I knew nothing about “deep learning” or unanticipation or machine learning, or basically most of the words and concepts in this book. Now, I know next to nothing, but I do know that Weinberger’s analysis, commentary, explanations and arguments made sense to me. I could follow his more esoteric arguments (at least to me they were esoteric) because his prose style, examples and insights were based on my world. Ultimately, that is what this book does. It takes its focus on technology and shows how technology is our friend, not our enemy. The book explains how while the world “may be dangerous, accepting it is also liberating, for it enables us to harness the complexity of an immense amount of data around us.” (quotation taken from the dust jacket)
This book is a valuable tool for the layman, like myself, and a deeply fascinating book for our times.
May 21, 2019
Machine learning means that organizations can now make predictions that humans can't entirely explain. Is this a bad thing? And what are the implications of such a world?
This is a fascinating book based on the key premise and questions above. Rife with interesting examples and cited research, Weinberger's relatively short book belies its remarkably ambitious scope: Another fifty pages of so would have paid off in spades.
This is a fascinating book based on the key premise and questions above. Rife with interesting examples and cited research, Weinberger's relatively short book belies its remarkably ambitious scope: Another fifty pages of so would have paid off in spades.
May 9, 2019
Everyday Chaos is a breathe of fresh air in a world of increasing complexity. Written in a philosophical point of view, this book tackles a myriad of topics like machine learning, justice, history, how different systems work with each other (interoperability), creativity, and meaning. This book does a remarkable job of marshalling all these ideas to create a compelling picture of the world we are living in.
This book is deep and practical at the same times. There are many occasions that it is funny as well. David Weinberger writing skills will take you to a fun, intellectual, and eye-opening journey in our complex world.
This book will appeal to those who are looking to read something intellectually enriching about the topic that touches everyone today.
This book is deep and practical at the same times. There are many occasions that it is funny as well. David Weinberger writing skills will take you to a fun, intellectual, and eye-opening journey in our complex world.
This book will appeal to those who are looking to read something intellectually enriching about the topic that touches everyone today.
August 27, 2023
I was expecting to enjoy this book a lot more than I did. The first half was good, but the second quite slow and repetitive. I wish it had gone deeper, and quite honestly I skimmed the last 50 pages before deciding I was finished with one chapter to go.
November 15, 2019
A good book that puts complexity into perspective. It shatters the illusion that we are in control and that we can really anticipate the future, at the same time introducing a view of the world where human endeavor earns its place.
The book is at its strongest in the beginning, looses some force after the first three chapters and has a good finish at the end of the last chapter. Certainly worth reading if your new to complexity or as a refresher if you're already somewhat acquainted with the subject.
The book is at its strongest in the beginning, looses some force after the first three chapters and has a good finish at the end of the last chapter. Certainly worth reading if your new to complexity or as a refresher if you're already somewhat acquainted with the subject.
January 2, 2023
The books starts well but the author loses me in second half.
Read the book again in 2023...and while there were some good references, the book did not fail to disappoint yet again!
Read the book again in 2023...and while there were some good references, the book did not fail to disappoint yet again!
December 12, 2020
This book tells stories "from history, business, and technology" and also philosophy to describe how we "think out."
I cannot remember what prompted me to seek this book -- a review that made me think it could help me grow the mental models I use in the creative parts of my job? Weinberger fixates on "progress" as the goal; I don't know that I agree with his definition of "progress."
That said, I read this with distractions, but even so synthesized a few ideas to help me with some work projects.
"[Daniel] Hillis calls cause and effect an 'illusion' and a 'convenient creation of our minds.' Causal explanation 'do not exist in nature' and are 'just our feeble attempts to force a storytelling framework onto systems that do no work like stories.'" [114]
Dries Buytaert (Drupal): "I try to get out of the way of the community." ... "That facilitates independent developers opening possibilities made real by Drupal's architecture and that serve the real needs of users. These are...real possibilities." [135]
"In an interoperable world in which everything affects everything else, the strategic path forward ma be to open as many paths as possible and enable everyone to charge down them all at once, together and apart." [142]
"While interoperability refers to the degree to which elements from different systems can work together, generatively is the ability of a tool or systems to be used in unanticipated ways. Interoperable systems are generative. Interoperable systems that connect generative systems are especially generative, creative, and unpredictable. Generatively is the degree to which interoperability enables unanticipation." [156]
"What drives generative progress is not a final destination...but rather the lowering of the barriers to invention--by interoperability, generatively, and an open network of collaborators--so that human ingenuity can be applied to the needs, desires, and whims that otherwise would have gone unnoticed and unaddressed." [158]
"If we think out in the world with tools, and if our use of those tools shows us what sort of place the world is, and if our new tools are substantially different from the old ones, then perhaps we are beginning to understand our world differently." [166]
I cannot remember what prompted me to seek this book -- a review that made me think it could help me grow the mental models I use in the creative parts of my job? Weinberger fixates on "progress" as the goal; I don't know that I agree with his definition of "progress."
That said, I read this with distractions, but even so synthesized a few ideas to help me with some work projects.
"[Daniel] Hillis calls cause and effect an 'illusion' and a 'convenient creation of our minds.' Causal explanation 'do not exist in nature' and are 'just our feeble attempts to force a storytelling framework onto systems that do no work like stories.'" [114]
Dries Buytaert (Drupal): "I try to get out of the way of the community." ... "That facilitates independent developers opening possibilities made real by Drupal's architecture and that serve the real needs of users. These are...real possibilities." [135]
"In an interoperable world in which everything affects everything else, the strategic path forward ma be to open as many paths as possible and enable everyone to charge down them all at once, together and apart." [142]
"While interoperability refers to the degree to which elements from different systems can work together, generatively is the ability of a tool or systems to be used in unanticipated ways. Interoperable systems are generative. Interoperable systems that connect generative systems are especially generative, creative, and unpredictable. Generatively is the degree to which interoperability enables unanticipation." [156]
"What drives generative progress is not a final destination...but rather the lowering of the barriers to invention--by interoperability, generatively, and an open network of collaborators--so that human ingenuity can be applied to the needs, desires, and whims that otherwise would have gone unnoticed and unaddressed." [158]
"If we think out in the world with tools, and if our use of those tools shows us what sort of place the world is, and if our new tools are substantially different from the old ones, then perhaps we are beginning to understand our world differently." [166]
January 29, 2025
My conclusions:
1) Really it didn't catch my attention, I finished because I don't like to leave unfinished books.
2) The book does not give what the title promises. Does not deal with chaos and complexity. While it talks about Technology, it only does in the context of the author's narrow knowledge, but he doesn't relate to thriving either with a world of possibilities.
3) I gave it two stars because the last chapter improves a little bit by dealing with how technology affects other aspects of our lives, not very deep but at least it does.
Much of the author's ideas contradict what Stephen Wolfram thinks, that the world is made of simple rules in complex systems, which I tend to agree with. Is not that we don't understand how AI works, that is a work in progress and we don’t have all the tools yet. Also, Wolfram's description of the world is a description, not how the subjacent reality is.
Tesla patents are "open source" meaning that anyone can use them for "free". However, Tesla cars don't belong to the buyers, but to Tesla, what a paradox, right?
1) Really it didn't catch my attention, I finished because I don't like to leave unfinished books.
2) The book does not give what the title promises. Does not deal with chaos and complexity. While it talks about Technology, it only does in the context of the author's narrow knowledge, but he doesn't relate to thriving either with a world of possibilities.
3) I gave it two stars because the last chapter improves a little bit by dealing with how technology affects other aspects of our lives, not very deep but at least it does.
Much of the author's ideas contradict what Stephen Wolfram thinks, that the world is made of simple rules in complex systems, which I tend to agree with. Is not that we don't understand how AI works, that is a work in progress and we don’t have all the tools yet. Also, Wolfram's description of the world is a description, not how the subjacent reality is.
Tesla patents are "open source" meaning that anyone can use them for "free". However, Tesla cars don't belong to the buyers, but to Tesla, what a paradox, right?
March 31, 2020
This David Weinberger book brings us up to date on his thinking. Weinberger has been a prophet for the value of the Internet for decades, and in this book he confesses that it isn't all good--that the voices of the 'Net haven't changed the world in the way he imagined they might. Commerce got the jump on us.
Still, his explorations of machine learning suggest a new, realistic kind of optimism. It forces us to face uncertainty, to accept that we know very little, and to feel awe. Bravo for his honesty and continued reading of the future and thinking about our present. He offers a whole world view, about getting older as well as confronting the vastness of computer generated learning and the use of vast conglomerations of data.
Still, his explorations of machine learning suggest a new, realistic kind of optimism. It forces us to face uncertainty, to accept that we know very little, and to feel awe. Bravo for his honesty and continued reading of the future and thinking about our present. He offers a whole world view, about getting older as well as confronting the vastness of computer generated learning and the use of vast conglomerations of data.
April 17, 2020
David Weinberger touches upon an array of topics starting with unanticipation, interoperability, strategy, progress , creativity and ends with making more meaning ...Out of what? You may ask. But there are no answers.
There are just pretty interesting facts(very old and new) and ideas unthought of...
What makes it a hooked reading is the segway from one thing to another. It may feel like you do not get anything concrete...but that is the point unanticipate and read on.
There are just pretty interesting facts(very old and new) and ideas unthought of...
What makes it a hooked reading is the segway from one thing to another. It may feel like you do not get anything concrete...but that is the point unanticipate and read on.
August 5, 2019
Minuteman. Read more later after no longer a new book? Concept that machines can learn and do what humans can't and hence solve problems beyond human comprehension, e.g. the new patient data systems that can predict diseases humans can't. And this is a fact of life, that much isn't subject to rational human analysis.
This entire review has been hidden because of spoilers.
February 1, 2020
Chaos is where we are
Weinberger gives us eight chapters of thinking about machine learning and he “chaos” of the evolving ways we operate in the world. Weinberger’s writing is engaging, even as it wanders around topics that don’t necessarily have a linear through-line. And, given who he is, it comes with oodles of references and notes for further and deeper reading.
Weinberger gives us eight chapters of thinking about machine learning and he “chaos” of the evolving ways we operate in the world. Weinberger’s writing is engaging, even as it wanders around topics that don’t necessarily have a linear through-line. And, given who he is, it comes with oodles of references and notes for further and deeper reading.
January 13, 2021
Incredibly deep discussion of what the world used to be and what it is today
It is difficult to review a work as diverse as this, yet deep in its discussion. It is not an overstatement to say that I highlighted sections within almost every page and went back to consider these sections as new perspectives unfolded.
It is difficult to review a work as diverse as this, yet deep in its discussion. It is not an overstatement to say that I highlighted sections within almost every page and went back to consider these sections as new perspectives unfolded.
February 7, 2024
It was ok, most of this is pretty old hat and he sweeps a lot of "openess" into "interoperability." He makes an interesting point about machine learning being inherently inexplicable because the world is inexplicable if we consider it honestly. Not sure it's true, but certainly interesting to ponder.
December 14, 2019
Absolutely brilliant book. The stories and anecdotes throughout help make the some of the complex and philosophical points being made so accessible. Epiphanies in every chapter and never gets slow or overly dense. One of the best I've read in a while, of any genre.
March 28, 2020
If I could give it more stars I would; incredible book. Thought provoking, timely, speaks to questions and arguments that we will be having for decades to come. Thank you, David Weinberger for writing this book.
June 26, 2019
Realise what is coming
The book puts you in a position where you will see how complex it will be to do the next steps. Not the steps just ahead of us, but further away.
The book puts you in a position where you will see how complex it will be to do the next steps. Not the steps just ahead of us, but further away.
August 28, 2019
Very good book
December 25, 2019
Heel interessante voorbeelden ter illustratie van de chaos. Vaktermen worden concreet uitgelegd.
Boek onder "handen" genomen in de Meetup Leesgroep.
Boek onder "handen" genomen in de Meetup Leesgroep.
September 11, 2023
The structure is quite chaotic and it often doesn't seem to follow a coherent thought or thread. Some points and stories are interesting but it feels too cobbled together.
January 7, 2024
Somewhat mundane book highlighting many examples of how complex our world is and how certain technologies like AI and algorithms are trying to make sense of it.
July 4, 2022
The author struggled to make cohesive points in this book. A lot of rambling. We understand the world is “complex”… but he didn’t offer much more than that.
April 9, 2022
It’s good to read something out of your wheelhouse once in a while. Especially given AI in healthcare. Something I took away was: It feels good to have an explanation but what if there is none? Machine Learning algorithms don’t use human-constructed models so the process for getting to an outcome might not make sense to use but computers can notice patterns we cannot see so we trust the output to be closer to the real prediction than our limited understanding of the problem at hand.
February 12, 2024
Chaos Everywhere by David Weinberger
A super interesting read that challenges our assumptions on how the world works and the approaches we should alter with the increasingly available technical advancements.
It gets a bit technical at some points, but nothing that isn’t digestible by non tech readers. Chaos Everywhere hits on how machine learning is making accurate decisions in a way that we don’t fully understand by pulling millions of data points together.
I would definitely recommend this as it made me think about ways we make decisions and how an unanticipation/open source view may provide better results.
A super interesting read that challenges our assumptions on how the world works and the approaches we should alter with the increasingly available technical advancements.
It gets a bit technical at some points, but nothing that isn’t digestible by non tech readers. Chaos Everywhere hits on how machine learning is making accurate decisions in a way that we don’t fully understand by pulling millions of data points together.
I would definitely recommend this as it made me think about ways we make decisions and how an unanticipation/open source view may provide better results.
September 19, 2021
Troppo ottimismo, ma vale comunque la pena di leggerlo
Weinberger è uno dei nomi più noti per quanto riguarda l'evoluzione del nostro approcciarci con la tecnologia. Il fatto che sia filosofo di formazione lo aiuta sicuramente a evitare le trappole del tecnicismo troppo spinto e ad avere uno sguardo più ampio. Devo dire però che in questo libro mi pare manchi qualcosa. Weinberger tesse le magnifiche e progressive sorti del machine learning, anche saltando un po' di palo in frasca, e di come il modello di Laplace, o se volete di Newton, di causalità sia oramai stato buttato via per arrivare a un nuovo modello che dal nostro punto di vista è fondamentalmente casuale e si basa più sulla generatività, cioè la capacità di un sistema di far generare qualcosa che non era stato pensato a priori. Che questo succeda è indubbio; i dubbi sono sull'accettare acriticamente che se un test A-B dà un risultato questo sia la verità e non una fluttuazione statistica. (Sì, ho presente il concetto di correlazione e anche quello di campione statistico). Inutile poi aggiungere che il "progresso casuale", come e più del progresso causale, è toccato dal pregiudizio del sopravvissuto; mostrare solo gli esempi vincenti non dà il quadro completo. Infine non riesco a capire perché essere in una rete iperconnessa comporti che il progresso non sia lineare. I balzi possono esserci in ogni modello, e la connessione al più dovrebbe permettere maggiori migliorie marginali perché c'è più gente che ci lavora. Detto tutto questo, penso che sia comunque un ottimo testo per vedere come sta cambiando non tanto il mondo quanto la nostra percezione del mondo. La traduzione di Massimo Durante è infine scorrevole, ma in qualche punto qua e là mi ha lasciato una sensazione strana.
Weinberger è uno dei nomi più noti per quanto riguarda l'evoluzione del nostro approcciarci con la tecnologia. Il fatto che sia filosofo di formazione lo aiuta sicuramente a evitare le trappole del tecnicismo troppo spinto e ad avere uno sguardo più ampio. Devo dire però che in questo libro mi pare manchi qualcosa. Weinberger tesse le magnifiche e progressive sorti del machine learning, anche saltando un po' di palo in frasca, e di come il modello di Laplace, o se volete di Newton, di causalità sia oramai stato buttato via per arrivare a un nuovo modello che dal nostro punto di vista è fondamentalmente casuale e si basa più sulla generatività, cioè la capacità di un sistema di far generare qualcosa che non era stato pensato a priori. Che questo succeda è indubbio; i dubbi sono sull'accettare acriticamente che se un test A-B dà un risultato questo sia la verità e non una fluttuazione statistica. (Sì, ho presente il concetto di correlazione e anche quello di campione statistico). Inutile poi aggiungere che il "progresso casuale", come e più del progresso causale, è toccato dal pregiudizio del sopravvissuto; mostrare solo gli esempi vincenti non dà il quadro completo. Infine non riesco a capire perché essere in una rete iperconnessa comporti che il progresso non sia lineare. I balzi possono esserci in ogni modello, e la connessione al più dovrebbe permettere maggiori migliorie marginali perché c'è più gente che ci lavora. Detto tutto questo, penso che sia comunque un ottimo testo per vedere come sta cambiando non tanto il mondo quanto la nostra percezione del mondo. La traduzione di Massimo Durante è infine scorrevole, ma in qualche punto qua e là mi ha lasciato una sensazione strana.
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