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What Algorithms Want: Imagination in the Age of Computing
The gap between theoretical ideas and messy reality, as seen in Neal Stephenson, Adam Smith, and Star Trek.
We depend on—we believe in—algorithms to help us get a ride, choose which book to buy, execute a mathematical proof. It's as if we think of code as a magic spell, an incantation to reveal what we need to know and even what we want. Humans have always believed that certain invocations—the marriage vow, the shaman's curse—do not merely describe the world but make it. Computation casts a cultural shadow that is shaped by this long tradition of magical thinking. In this book, Ed Finn considers how the algorithm—in practical terms, “a method for solving a problem”—has its roots not only in mathematical logic but also in cybernetics, philosophy, and magical thinking.
Finn argues that the algorithm deploys concepts from the idealized space of computation in a messy reality, with unpredictable and sometimes fascinating results. Drawing on sources that range from Neal Stephenson's Snow Crash to Diderot's Encyclopédie, from Adam Smith to the Star Trek computer, Finn explores the gap between theoretical ideas and pragmatic instructions. He examines the development of intelligent assistants like Siri, the rise of algorithmic aesthetics at Netflix, Ian Bogost's satiric Facebook game Cow Clicker, and the revolutionary economics of Bitcoin. He describes Google's goal of anticipating our questions, Uber's cartoon maps and black box accounting, and what Facebook tells us about programmable value, among other things.
If we want to understand the gap between abstraction and messy reality, Finn argues, we need to build a model of “algorithmic reading” and scholarship that attends to process, spearheading a new experimental humanities.
We depend on—we believe in—algorithms to help us get a ride, choose which book to buy, execute a mathematical proof. It's as if we think of code as a magic spell, an incantation to reveal what we need to know and even what we want. Humans have always believed that certain invocations—the marriage vow, the shaman's curse—do not merely describe the world but make it. Computation casts a cultural shadow that is shaped by this long tradition of magical thinking. In this book, Ed Finn considers how the algorithm—in practical terms, “a method for solving a problem”—has its roots not only in mathematical logic but also in cybernetics, philosophy, and magical thinking.
Finn argues that the algorithm deploys concepts from the idealized space of computation in a messy reality, with unpredictable and sometimes fascinating results. Drawing on sources that range from Neal Stephenson's Snow Crash to Diderot's Encyclopédie, from Adam Smith to the Star Trek computer, Finn explores the gap between theoretical ideas and pragmatic instructions. He examines the development of intelligent assistants like Siri, the rise of algorithmic aesthetics at Netflix, Ian Bogost's satiric Facebook game Cow Clicker, and the revolutionary economics of Bitcoin. He describes Google's goal of anticipating our questions, Uber's cartoon maps and black box accounting, and what Facebook tells us about programmable value, among other things.
If we want to understand the gap between abstraction and messy reality, Finn argues, we need to build a model of “algorithmic reading” and scholarship that attends to process, spearheading a new experimental humanities.
272 pages, Hardcover
First published February 17, 2017
80 people are currently reading
921 people want to read
About the author
Ed Finn
51 books11 followersEd Finn is Founding Director of the Center for Science and the Imagination at Arizona State University, where he is also Assistant Professor with a joint appointment in the School of Arts, Media, and Engineering and the Department of English.
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Displaying 1 - 24 of 45 reviews
June 16, 2017
I honestly don't know what to think of this book. It is erudite, fascinating, flawed and sloppy. It brings up many important issues, but seems unable to resist a clever line in the place of a precise one. It is a mile wide, and an inch deep, usually moving on instead diving deep. It is careless of facts (relativity is deterministic and does not support indeterminacy -- that's quantum mechanics , every Bitcoin client does not have to download the entire blockchain, and most don't (which is an interesting issue in its own right), and warehouse workers have been hidden from customers long before there was an Amazon) and sloppy in its language (using Skinner box for everything that even hints of behaviorism or operant conditioning, and using algorithm to mean any of: algorithm, program, user interface, software system or website, and using the mathematical term "effective computability" in so many ways it is impossible to keep track!). But it all hangs together and makes a crazy kind of sense. Well most of the time. It is also comes across as vaguely condescending towards regular computer users at times. But despite that all, it is worth reading. But the way it is written will likely prevent it from having much, if any impact outside of the humanities, unlike, say Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy which shared some concerns with this book, and presents them with infinitely more clarity, which is important when making an argument.
April 14, 2017
The science fiction author Neal Stephenson comments on the cover of this book that it is 'highly enjoyable'. I suspect this is because in the opening of the book, Ed Finn repeatedly refers to Stephenson's impressive novel Snow Crash. If Stephenson actually found reading What Algorithms Want to be fun, he needs to get out more. I would, instead, describe it is extremely hard work to read - but it is hard work that is rewarded with some impressive insights. What Algorithms Want is both clever and able to cut away the glamour (in the old sense of the word) of the internet and the cyber world to reveal what's really going on beneath - as long as you can cope with the way that the book is written.
A Finn points out, most of us rely on algorithms, from Google’s search to Facebook’s timeline, not consciously considering that these aren’t just tools to help us, but processes that have their own (or their makers) intentions embodied in them. What’s more, there's really important material here about the insidious way the cyber world is driving us towards processes where the effort is not not concerned with a final product so much as the continuation of the system. However, there is also a fair amount of pretentious content that can be reminiscent of Sokal’s famous hoax - all too often, the sources Finn quotes seem to be using words from the IT world without entirely understanding them.
In terms of readability, What Algorithms Want suffers from same problem as the output of many of the university students I help with writing - ask them what their essay means and what they say is much clearer that what they've written. Finn is overly fond, for instance, of ‘fungible’ and ‘imbricate’, which are not words I'd really like to see outside of specialist publications.
Some of the specifics don't quite ring true. Finn talks about a modern equivalent of Asimov's fictional psychohistory, without covering the way that chaos theory makes it clear that an algorithmic representation of such a complex system could never produce useful predictions (any more than we can ever forecast the weather more than a few days into the future). Sometimes, Finn seems to be complaining about something that isn't a match to reality. So, for instance, he says 'you listen to a streaming music station that almost gets it right, telling yourself that these songs, not quite the right ones, are perfect for this moment because a magic algorithm selected them.' He absolutely misses the point. You don't do it because you think a magic algorithm produces perfection. You do it because you haven't time to spend an hour assembling the perfect playlist for the moment. It's a convenient compromise - and a far better match than listening to a random playlist off the radio.
This reflects a tendency to read too much into an example. For example, despite admitting that Netflix gave the makers of the series House of Cards carte blanche on content, Finn still identifies algorithmic aspects to the script. This comes out particularly when he comments at length on Fincher's idea of having Underwood address the audience direct, apparently not aware that this was one of the standout features of the (very non-algorithmic) original BBC series. Elsewhere, when talking about Uber, Finn says 'The company's opacity about pricing and the percentage of revenue shared with drivers makes it even more Iike an arbitrary video game' - but what conventional business does share this kind of information with its customers? Do you know the markup on products in your corner store, or what a McDonald's franchisee earns?
However, don't let these detailed complaints put you off. There is a huge amount to appreciate here, especially when Finn gets onto individual aspects of the impact of algorithms on our lives in the likes of Siri, Google and Netflix. And throughout there is much to challenge the reader, encouraging thought about technology we tend to take for granted. I just wish that it could have been written in a less obscure fashion.
A Finn points out, most of us rely on algorithms, from Google’s search to Facebook’s timeline, not consciously considering that these aren’t just tools to help us, but processes that have their own (or their makers) intentions embodied in them. What’s more, there's really important material here about the insidious way the cyber world is driving us towards processes where the effort is not not concerned with a final product so much as the continuation of the system. However, there is also a fair amount of pretentious content that can be reminiscent of Sokal’s famous hoax - all too often, the sources Finn quotes seem to be using words from the IT world without entirely understanding them.
In terms of readability, What Algorithms Want suffers from same problem as the output of many of the university students I help with writing - ask them what their essay means and what they say is much clearer that what they've written. Finn is overly fond, for instance, of ‘fungible’ and ‘imbricate’, which are not words I'd really like to see outside of specialist publications.
Some of the specifics don't quite ring true. Finn talks about a modern equivalent of Asimov's fictional psychohistory, without covering the way that chaos theory makes it clear that an algorithmic representation of such a complex system could never produce useful predictions (any more than we can ever forecast the weather more than a few days into the future). Sometimes, Finn seems to be complaining about something that isn't a match to reality. So, for instance, he says 'you listen to a streaming music station that almost gets it right, telling yourself that these songs, not quite the right ones, are perfect for this moment because a magic algorithm selected them.' He absolutely misses the point. You don't do it because you think a magic algorithm produces perfection. You do it because you haven't time to spend an hour assembling the perfect playlist for the moment. It's a convenient compromise - and a far better match than listening to a random playlist off the radio.
This reflects a tendency to read too much into an example. For example, despite admitting that Netflix gave the makers of the series House of Cards carte blanche on content, Finn still identifies algorithmic aspects to the script. This comes out particularly when he comments at length on Fincher's idea of having Underwood address the audience direct, apparently not aware that this was one of the standout features of the (very non-algorithmic) original BBC series. Elsewhere, when talking about Uber, Finn says 'The company's opacity about pricing and the percentage of revenue shared with drivers makes it even more Iike an arbitrary video game' - but what conventional business does share this kind of information with its customers? Do you know the markup on products in your corner store, or what a McDonald's franchisee earns?
However, don't let these detailed complaints put you off. There is a huge amount to appreciate here, especially when Finn gets onto individual aspects of the impact of algorithms on our lives in the likes of Siri, Google and Netflix. And throughout there is much to challenge the reader, encouraging thought about technology we tend to take for granted. I just wish that it could have been written in a less obscure fashion.
June 8, 2017
Um livro denso e bastante –às vezes reflexivo e vago demais– sobre o papel crescente dos algoritmos em nossa vida. Como as empresas estão migrando para serem grandes algoritmos: de logística (Amazon, UPS), entendimento de dados (Google), conexão (Facebook), transporte e transação financeira (Uber), etc. Ed Finn discute de ponta a ponta a mudança que essa tecnologia está gerando, de como nós interpretamos o mundo para tornar os dados computáveis por algoritmos ao como nós mudamos por conta dos algoritmos e para interagir com eles.
O livro passa pela explicação de como vários algoritmos foram desenvolvidos, da recomendação de filmes pela Netflix ao desenvolvimento da Siri. Mesmo assim, nessa linha de tecnologias e implicações, ainda gostei mais de Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy e Rise of the Robots: Technology and the Threat of a Jobless Future.
O livro passa pela explicação de como vários algoritmos foram desenvolvidos, da recomendação de filmes pela Netflix ao desenvolvimento da Siri. Mesmo assim, nessa linha de tecnologias e implicações, ainda gostei mais de Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy e Rise of the Robots: Technology and the Threat of a Jobless Future.
September 15, 2017
Fair warning: this book contains words like 'hermeneutics', verbs like 'negotiate' (as in 'it constantly negotiates the tensions between computation and material reality', or 'the humans who operate in that contested space must constantly negotiate between computational and cultural regimes of meaning'), and sentences like 'We need an experimental humanities, a set of strategies for direct engagement with algorithmic production and scholarship, drawing on theories of improvisation and experimental investigation to argue that a culture of process, of algorithmic production, requires a processual criticism that is both reflective and playful.' (No, that's not a pisstake; see page 13.) And apparently there are about 17 different kinds of 'arbitrage', most of which bear no obvious relation to the usual sense of the word.
As you might have guessed, What Algorithms Want teeters dangerously on the brink of empty, insufferable bullshit. But Finn actually has some interesting things to say, and somehow his style is less annoying, and much easier to follow, than it has any right to be. His lapses into humanities jargon and barely-meaningful imprecision tend to seem more like acts of overenthusiasm than deliberate obfuscation.
So overall I quite enjoyed this. But I'm not convinced Finn knew exactly what he wanted to say, and I reckon what he did say could have been communicated much more clearly in half the space, were he so inclined. And the first chapter is probably the biggest pile of humanities-wankery that I've ever read voluntarily and without throwing the book against a wall. (The 'Coda' at the end might have given it a run for its money, if it weren't so brief.) So I guess the fact that I somehow found this worth reading is very high praise, of a sort.
(A sidenote: I found Neal Stephenson's blurb quite surprising, given what I thought he thought about this branch of academia. Admittedly that was mostly based on my hazy memory of a scene in Cryptonomicon, a novel I started to read years ago and never finished.)
As you might have guessed, What Algorithms Want teeters dangerously on the brink of empty, insufferable bullshit. But Finn actually has some interesting things to say, and somehow his style is less annoying, and much easier to follow, than it has any right to be. His lapses into humanities jargon and barely-meaningful imprecision tend to seem more like acts of overenthusiasm than deliberate obfuscation.
So overall I quite enjoyed this. But I'm not convinced Finn knew exactly what he wanted to say, and I reckon what he did say could have been communicated much more clearly in half the space, were he so inclined. And the first chapter is probably the biggest pile of humanities-wankery that I've ever read voluntarily and without throwing the book against a wall. (The 'Coda' at the end might have given it a run for its money, if it weren't so brief.) So I guess the fact that I somehow found this worth reading is very high praise, of a sort.
(A sidenote: I found Neal Stephenson's blurb quite surprising, given what I thought he thought about this branch of academia. Admittedly that was mostly based on my hazy memory of a scene in Cryptonomicon, a novel I started to read years ago and never finished.)
February 10, 2018
Twice I read (that is, listened) to this book, because I thought, the first time, that there must have been something more to it. Pretty much there wasn't.
The Bad:
* constant puddles and swamps of art-wank, intellectual-wank writing that often isn't even correct, only obscure.
* excessive reference to a few books (yes, I have read Snow Crash and nearly all the others mentioned) reduce the power of the thesis overall.
* just not well written, somewhat shocked but not really shocking.
* by "Algorithms" he means "large systems of computational workings" and hardly ever 'algorithms'.
* the constant attempts to connectalgorithms large computational systems to culture don't really work.
The last two are acceptable, the first a disaster.
The Good:
* nuggets of awesome insight into all kinds of systems, like about how High Frequency Trading is - really - bizarre. I would have *much* preferred a few articles with the good, hard, stuff, and dump the rest.
That's it. There's nothing else to say.
The Bad:
* constant puddles and swamps of art-wank, intellectual-wank writing that often isn't even correct, only obscure.
* excessive reference to a few books (yes, I have read Snow Crash and nearly all the others mentioned) reduce the power of the thesis overall.
* just not well written, somewhat shocked but not really shocking.
* by "Algorithms" he means "large systems of computational workings" and hardly ever 'algorithms'.
* the constant attempts to connect
The last two are acceptable, the first a disaster.
The Good:
* nuggets of awesome insight into all kinds of systems, like about how High Frequency Trading is - really - bizarre. I would have *much* preferred a few articles with the good, hard, stuff, and dump the rest.
That's it. There's nothing else to say.
October 11, 2020
A superb clarification not just of how and why algorithms came to play such a central role in the organization of our world, but also of the cultural, theoretical and philosophical transformations this new era has ushered in. Shows once again that the age of algorithms requires humanities perspectives if we really want to understand what is goin on.
December 31, 2023
No lo leí entero, porque era innecesariamente denso. El lenguaje académico intrincadísimo hacía las ideas incomprensibles. Sin embargo, me aportó citas y alguna que otra idea interesante. Los capítulos que me interesaron fueron el que iba sobre Siri y el de Netflix y el cine algoritmo.
May 9, 2018
Insufferably pretentious writing makes it tiresome to read, even though the content is interesting and at times insightful.
May 27, 2022
Enjoyable book on cultural influences of algorithms (in the broad sense), covering examples from Netflix, DeepMind, “Thinking tools” like Memex and Star Trek’s computer, the movie “Her” and Bitcoin. It was well readable, particularly since many treatments of algorithms from the social sciences have almost no overlap with what algorithms mean for the people in the tech industry which can feel confusing. While Finn’s take on “algorithm” is broader than the computer scientist’s “Finite list of commands to solve an abstract problem” (or similar), it always deals with systems which solve abstract problems (or try to go beyond this), never with something that feels more like “modern computing in general”, “big tech companies” or “How society works since the internet”. It has a lot of references to other works, a lot of them new to me, though I was familiar with Hayles and some of the works in computing history (And even Phil agre was quoted!).
My only problem is that I hoped for a more in-depth treatment of the connection of the culutre of algorithms with a culture of magic, which the book’s introduction talked about. It was there, but not as extensive as I hoped for.
An enjoyable read for people in tech as well as in social sciences.
My only problem is that I hoped for a more in-depth treatment of the connection of the culutre of algorithms with a culture of magic, which the book’s introduction talked about. It was there, but not as extensive as I hoped for.
An enjoyable read for people in tech as well as in social sciences.
March 27, 2018
1.5/5.0
September 10, 2020
Masterful ...
May 12, 2018
This book looks at "algorithmic culture machines" - all the ways algorithms and computation steer our everyday, our knowledge systems, our culture. How they shape us and also make us shape ourselves in order to accommodate them. Drawing upon all the usual suspects, like Netflix, PageRank, Uber, Bitcoin, Gamification ... but looking at it more from a Media studies and humanities perspective.
The text is approachable yet still reads so very academic in its structure and in how it weaves in and then is glued to its references and metaphors. Which was mildly entertaining (hey, I remember texts like these) and then also mildly annoying (namedropping Bush's memex, sure, but do we really need to draw a long analogy between it and today's knowledge culture). Also, partially it felt the author was more concerned with making a clever metaphorical statement, than to get all the details right. But, still worth it though.
The text is approachable yet still reads so very academic in its structure and in how it weaves in and then is glued to its references and metaphors. Which was mildly entertaining (hey, I remember texts like these) and then also mildly annoying (namedropping Bush's memex, sure, but do we really need to draw a long analogy between it and today's knowledge culture). Also, partially it felt the author was more concerned with making a clever metaphorical statement, than to get all the details right. But, still worth it though.
June 28, 2018
What? No idea what the message, if any, is. Very little of substance and certainly not the answer to the question.
June 1, 2024
Tellekt edisyonunu tavsiye etmiyorum.
Çevirinin vasatlığı tahammül edilebilir gibi değil.
Çevirinin vasatlığı tahammül edilebilir gibi değil.
February 2, 2019
As far as I know, this may be one of the first, if not the first, non-academic book marrying the subject of applied algorithms with the technique of "critical reading" from liberal arts, literature, English etc., and the result is not only interesting but should be part of the standard toolkit and views taken into account for people who work with and develop algorithmically-rich products on a regular basis. To be sure, the author does not discuss the technical nature of how to design algorithms with this or that property, but instead paints a path from formal computation, highlighting key concepts in these domains, paramount of which, the notion of effective computability, and show how these concepts are applied and mutated to impact culture and society at large.
In the case of effective computability, which basically means computable in some finite number of steps, the author shows how the desire to construct abstract procedures that are effectively computable, slowly became a practical metric of quality in applied computation, the runtime, and how this key constraint and view to understanding algorithms has informed and motivated how these procedures have been crafted for various use-cases, from gaming to prediction models/engines. What information is left out, which information is preserved, these decisions have in recent times, become not only technical requirements, but in a real sense value-judgements from the literary use of the word. Further, one could possibly infer much from these algorithm writers, or assemblers of code, based on their decisions on what item is left in or removed, altered, what is fudged, how it is fudged, how something is imputed etc.
The critique of the algorithm writer in this way is not only valid, but highly relevant in our world, as unlike aerospace engineers, the technical detail impacts the culture and society of the world in far more ways than just as a critical component of a consumer product, hundreds of millions of decisions are being made and aggregated every day that in some nontrivial way is impacting an individual life. Although this may have always been true in a decentralized societal network way pre-digital era, the magnitude of decision making was nowhere near as large, and there was not a small clique of authors "tipping fate" one way or the other with their creations. I suspect this interdisciplinary domain is rich with gold and serves as a place of fruitful computing and liberal arts research for decades to come, as well as inform society on the augmented and automated cultural transformation we are experiencing. In a way, we can view crafters of algorithms the same as crafters of literary culture, except instead of using a natural language like English, they are using the formal language derived from some logic.
My only recommendation for those who want to read this book is it helps to be familiar with the works of Neal Stephenson, specifically his book, Snowcrash, which is liberally referenced in the first 1/3 of the text.
The author hints that at some future time, we will have thinkers studying how Sim City developers chose to model poverty in their game, and how this impacted a generation of policy thinkers. I agree, and I'm excited to learn more of these impact so that we can better craft societally beneficial computation in the future. Highly recommended, full stop!
In the case of effective computability, which basically means computable in some finite number of steps, the author shows how the desire to construct abstract procedures that are effectively computable, slowly became a practical metric of quality in applied computation, the runtime, and how this key constraint and view to understanding algorithms has informed and motivated how these procedures have been crafted for various use-cases, from gaming to prediction models/engines. What information is left out, which information is preserved, these decisions have in recent times, become not only technical requirements, but in a real sense value-judgements from the literary use of the word. Further, one could possibly infer much from these algorithm writers, or assemblers of code, based on their decisions on what item is left in or removed, altered, what is fudged, how it is fudged, how something is imputed etc.
The critique of the algorithm writer in this way is not only valid, but highly relevant in our world, as unlike aerospace engineers, the technical detail impacts the culture and society of the world in far more ways than just as a critical component of a consumer product, hundreds of millions of decisions are being made and aggregated every day that in some nontrivial way is impacting an individual life. Although this may have always been true in a decentralized societal network way pre-digital era, the magnitude of decision making was nowhere near as large, and there was not a small clique of authors "tipping fate" one way or the other with their creations. I suspect this interdisciplinary domain is rich with gold and serves as a place of fruitful computing and liberal arts research for decades to come, as well as inform society on the augmented and automated cultural transformation we are experiencing. In a way, we can view crafters of algorithms the same as crafters of literary culture, except instead of using a natural language like English, they are using the formal language derived from some logic.
My only recommendation for those who want to read this book is it helps to be familiar with the works of Neal Stephenson, specifically his book, Snowcrash, which is liberally referenced in the first 1/3 of the text.
The author hints that at some future time, we will have thinkers studying how Sim City developers chose to model poverty in their game, and how this impacted a generation of policy thinkers. I agree, and I'm excited to learn more of these impact so that we can better craft societally beneficial computation in the future. Highly recommended, full stop!
March 15, 2025
El presente libro de Ed Finn se interroga acerca de la influencia del algoritmo en nuestra cultura, nuestros hábitos y nuestra sociedad. El estilo "académico" del autor, ampuloso y abstracto, lleno de términos como "ontologías", "epistemologías", "retóricas de concreción inclusivas", etc., así como la ausencia de explicaciones sobre determinados conceptos del mundo de la computación, no facilitan precisamente la lectura. A pesar de todo, y una vez recorridos los primeros y más densos capítulos, se cuentan en esta obra muchas historias interesantes que ejemplifican el impacto de los algoritmos sobre nuestro mundo. El buscador de Google, el desarrollo del asistente Siri de Apple, el sistema híbrido de clasificación y recomendación de películas de Netflix (y su relación con la producción de la serie House of Cards), la historia de Cow Click (un juego creado como parodia que se volvió irónicamente viral), el submundo de pseudo esclavos digitales del mercado Mechanical Turk de Amazon, las transacciones de los algoritmos HFT, la aparición de Bitcoin o los efectos de los algoritmos de selección de noticias de Facebook sobre el periodismo tradicional son ejemplos de los efectos de esta interacción entre el ser humano y el código implementado. La adaptación de los algoritmos al servicio del usuario acaba por modificar el propio comportamiento humano, y a la vez la respuesta humana a los algoritmos conduce a la modificación y el aprendizaje de los mismos. El acceso a miles de servicios se realiza al coste de nuestra privacidad y del comercio de nuestros gustos, preferencias y datos personales, que son vendidos y almacenados por cientos de empresas encargadas de elaborar un perfil detallado de cada usuario para presentarle, en milésimas de segundo de ventaja sobre los competidores, anuncios personalizados que mueven muchísimo dinero a través de Internet. Las grandes empresas emplean modelos en la creación de dichos algoritmos que, necesariamente, requieren de importantes simplificaciones de la realidad, simplificaciones invisibles para el usuario pero que en algunas ocasiones salen a la luz, como pequeños fallos en Matrix. La información periodística y las noticias no son buscadas por el lector, sino que son ellas las que nos buscan, presentándonos titulares que invitan a enlazar al contenido de la noticia para de esta forma conseguir su porción del pastel publicitario. Las recomendaciones de los algoritmos de películas y libros basadas en nuestros gustos acaban por moldear nuestras propias preferencias. Las indicaciones de Google Map para circular son aceptadas sin dudar por los conductores como venidas de un oráculo mágico, y las informaciones que nos vienen de las redes sociales son compartidas con nuestros contactos sin considerar su fiabilidad, en una demostración de hasta qué punto el algoritmo se ha convertido en un compañero de confianza. Adicciones, trabajadores mal pagados, tráfico de datos personales e información sesgada son el reverso oscuro de un cambio que ha modificado, nuestra vida en múltiples aspectos, y que lo seguirá haciendo.
August 14, 2018
Right from the start I must be clear that this book is not for everyone. If you are uncomfortable with concepts like emergence or unfamiliar with the history of cybernetics or expect to read about good implementation of algorithms then you might find it a struggle. On the hand, if you are interested in exploring the scaffolding of your future life by digital systems then this book will make you wonder about implementations of complex abstractions.
As well as being an assistant professor with a joint appointment in the School of Arts, Media and Engineering and the Department of English, the author is a founding director of the Center for Science and the Imagination at Arizona State University and the book is in the tradition of those cross-disciplinary books about User Interface design, like Brenda Laurel’s excellent 1991 book ‘Computers as Theatre’. It could be illustrated by the Joseph Wright of Derby painting, ‘An experiment on a Bird in an Airpump’ - enthusiastic amateurs marvel at science while freaking out. Finn explores our historical relationship with not understanding how things work - he believes that most people think of algorithms through the metaphor of magic.
Algorithms scaleably solve repeatable human computational problems. Finn observes how the use of complex algorithms has changed how we think about what problems are computational and what solution means. We humans have changed the way that we ask questions in order to get better answers from Google, creating a cybernetic feedback loop whereby humans now often use the same kind of language with each other. Hashtag irony. With Google Search and the Adsense marketplace working together “magically,” the time that is taken to algorithmically determine the ads that will be displayed is the tax we pay to search.
In a very interesting discussion about crypto currencies, Finn imagines a time when all purchases are made via a blockchain. The blockchain, controlled by anonymous (but probably foreign) miners who add their transaction fees to their own personal fortunes, is independent of any one fiat currency. If the treasuries of individual countries are no longer required to issue money and cannot claim tax, then how will we fund schools, roads and the NHS?
However, this is not a doom and gloom view of the future or a warning about the Singularity. It is instead an enquiry into the kinds of AIs that humans have imagined in films like ‘Her’ and how the reality may be different as the interrelationships of algorithms are not controlled by a director. Google are now aiming to move to telling us what to do next rather than asking us what we want to do. No doubt we will be more productive, fitter, healthier and happier, if creeped out.
This is not a book to read quickly, the ideas are too complex, but I believe the effort is very worthwhile. Score 10/10
Review by Gabrielle Liddy
Originally published: https://www.bcs.org/content/conWebDoc...
As well as being an assistant professor with a joint appointment in the School of Arts, Media and Engineering and the Department of English, the author is a founding director of the Center for Science and the Imagination at Arizona State University and the book is in the tradition of those cross-disciplinary books about User Interface design, like Brenda Laurel’s excellent 1991 book ‘Computers as Theatre’. It could be illustrated by the Joseph Wright of Derby painting, ‘An experiment on a Bird in an Airpump’ - enthusiastic amateurs marvel at science while freaking out. Finn explores our historical relationship with not understanding how things work - he believes that most people think of algorithms through the metaphor of magic.
Algorithms scaleably solve repeatable human computational problems. Finn observes how the use of complex algorithms has changed how we think about what problems are computational and what solution means. We humans have changed the way that we ask questions in order to get better answers from Google, creating a cybernetic feedback loop whereby humans now often use the same kind of language with each other. Hashtag irony. With Google Search and the Adsense marketplace working together “magically,” the time that is taken to algorithmically determine the ads that will be displayed is the tax we pay to search.
In a very interesting discussion about crypto currencies, Finn imagines a time when all purchases are made via a blockchain. The blockchain, controlled by anonymous (but probably foreign) miners who add their transaction fees to their own personal fortunes, is independent of any one fiat currency. If the treasuries of individual countries are no longer required to issue money and cannot claim tax, then how will we fund schools, roads and the NHS?
However, this is not a doom and gloom view of the future or a warning about the Singularity. It is instead an enquiry into the kinds of AIs that humans have imagined in films like ‘Her’ and how the reality may be different as the interrelationships of algorithms are not controlled by a director. Google are now aiming to move to telling us what to do next rather than asking us what we want to do. No doubt we will be more productive, fitter, healthier and happier, if creeped out.
This is not a book to read quickly, the ideas are too complex, but I believe the effort is very worthwhile. Score 10/10
Review by Gabrielle Liddy
Originally published: https://www.bcs.org/content/conWebDoc...
September 25, 2017
Like a lot of books being written about what's happening to our cybernetic environment in 2017 due to the combination of big data, machine learning, social media and cyberwarfare, this book struggles not to date itself within months of publication, and to come to some conclusion that actually makes a difference to the reader. The closest to successful in this intellectual competition (which because it's occurring in books is probably completely marginal and irrelevant in any case) in my view have been Tung Hui-Hu's Prehistory of the Cloud and Benjamin Bratton's The Stack. But this book is a good effort, and has some really good analyses of particular phenomena and works of art (such as the movie Her, or Amazon's Mechanical Turks), so I put it on the recommended side of the stack, with the warning that there might be better books in the same vein around for you to waste your time on -- I'm still looking for them myself.
July 11, 2017
The title is a bit misleading. I expected more attention to the value-laden nature of technology and the philosophy of technology or at least more engagement with creativity as a construct. Instead, the book provided a detailed account of the rise and depth of algorithms in our culture and hinted at the philosophical implications. The book is more than historical though. I like the books emphasis on the necessity of abstraction for algorithmic thinking and the extensive detail about how particular algorithms have moved from serving to creating spaces (e.g., google).
May 31, 2018
A great book, by any computation
Ed’s book is an interesting examination into how the machines and systems we have created are now influencing our behaviors and society at large. I recommend to anyone looking to for insight on how the digital is effecting the analog, in both positive and negative ways.
(Great job, cousin Ed :-)
Ed’s book is an interesting examination into how the machines and systems we have created are now influencing our behaviors and society at large. I recommend to anyone looking to for insight on how the digital is effecting the analog, in both positive and negative ways.
(Great job, cousin Ed :-)
January 28, 2018
Great book. Not what I was expecting (though I'm not sure what I was precisely expecting).
The author traces the rise of algorithms in our culture and its philosophical implications.
This is not an easy read, but fascinating and rewarding.
The author traces the rise of algorithms in our culture and its philosophical implications.
This is not an easy read, but fascinating and rewarding.
September 28, 2018
While this is written in the style of a phd thesis it manages to be interesting, thought provoking and accessible to the layperson. It also manages to walk that line where not necessarily agreeing with the author at certain points doesn’t spoil the book or the enjoyment of the story as it unfolds.
March 9, 2017
Review to follow
May 12, 2017
The Netflix chapter is a gem among many. I keep reading the work in this field, though, hoping to find some that get into the weeds of the algorithms.
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