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Вакансія: людина. Як не залишитися без роботи в добу штучного інтелекту
Страх, що технології залишать людей без роботи дуже давній. Та жоден із етапів прогресу поки не спричинив цього. Машини перебрали на себе частину завдань, однак постійно зявлялися нові ніші, а людям залишалися найбільш складні й інтелектуальні задачі. Однак що станеться коли, штучний інтелект глибше проникне в наше життя? Адже тепер технології захоплюють одну з ключових сфер людської діяльності - прийняття рішень. Автори цієї книги переконують: не варто бути песимістом. Зрештою, тільки люди можуть надихати інших людей, здатні проявляти чуйність, наповнювати справи пристрастю, настроєм та веселощами.
336 pages, Hardcover
First published May 24, 2016
95 people are currently reading
1102 people want to read
About the author
Thomas H. Davenport
87 books134 followersTom Davenport holds the President's Chair in Information Technology and Management at Babson College. His books and articles on business process reengineering, knowledge management, attention management, knowledge worker productivity, and analytical competition helped to establish each of those business ideas. Over many years he's authored or co-authored nine books for Harvard Business Press, most recently Competing on Analytics: The New Science of Winning (2007) and Analytics at Work: Smarter Decisions, Better Results (2010). His byline has also appeared for publications such as Sloan Management Review, California Management Review, Financial Times, Information Week, CIO, and many others.
Davenport has an extensive background in research and has led research centers at Ernst & Young, McKinsey & Company, CSC Index, and the Accenture Institute of Strategic Change. Davenport holds a B.A. in sociology from Trinity University and M.A. and Ph.D. in sociology from Harvard University. For more from Tom Davenport, visit his website and follow his regular HBR blog.
Davenport has an extensive background in research and has led research centers at Ernst & Young, McKinsey & Company, CSC Index, and the Accenture Institute of Strategic Change. Davenport holds a B.A. in sociology from Trinity University and M.A. and Ph.D. in sociology from Harvard University. For more from Tom Davenport, visit his website and follow his regular HBR blog.
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Displaying 1 - 30 of 36 reviews
July 7, 2018
This is an odd sort of book. It starts by perhaps overstating the job loss catastrophe that is about to hit us all – I’m really not sure how bad this will be, as I’ve read just about every possible scenario told in detail from ‘experts’ of all shades. The options lie on a spectrum that runs from there will be no jobs for humans in the future, to there will be too few humans to do all of the jobs of the future. And the types of jobs of the future also run on a spectrum that starts at jobs being so mindlessly boring to make you prefer death over doing them, right up to work being about to become truly human for the first time in human history. While it would be nice to think this was a kind of game of ‘choose your own future’, I’ve a horrible feeling that some of these predictions are likely to be proven embarrassingly wrong fairly soon. My advice would be to prepare for the worst, since if the best happens you will be no worse off, whereas, if you hope for the best and the worst happens life really will be pretty horrible – a world, as Bauman says, of waste humans who are ‘failed consumers’.
This book starts by saying lots of jobs we currently do are about to disappear, and then goes on to say what most people who talk about the jobs of the future generally say – that if you want a job in the future you had better not be doing what the sort of things automation, computers and robots will be able to do better than you will. What you need to do is to augment what computers can do with your distinctly human characteristics. As the author says somewhere here, think about what a computer would hire you to do that it can’t do.
Now, this is all well and good. The example nearly always given to explain augmentation is chess, curiously enough. When the best human player in the world was first beaten by the best computer program a few decades ago this caused a psychological shift. At the time the computer was one of the world’s most powerful computers – but Moore’s Law has moved on apace and I guess now most computers can beat any world champion. The point is, however, and this is the augmentation argument, that if you give a relatively good player access to a computer, the relatively good player using this tool will beat the best computer program. This is then used as proof that the future belongs to those with skills that augment automated systems.
The problem here is that what those skills are doesn’t exactly stand still. It wasn’t that long ago, for instance, that truck drivers (which another book I read recently pointed out is the number one job in virtually every state in the US) were considered to be doing a task that was too complicated for computerised systems to cope with – and yet now some people predict that governments will soon be forced to legislate that humans driving any kind of vehicle is simply too dangers to allow to continue happening. In fact, the most likely outcome, these people suspect, will be that it will be so expensive to get insurance to drive a vehicle that this will mean the end of human drivers – and given we humans (you know, us drunk, texting, easily distracted humans) kill a million people per year on the roads, this might well be a good thing.
So, the book says we are about to see a major disruption in the types of jobs that exist and that the jobs you want to get if you want to still have a job in the near to medium term futre are augmentation jobs – which then begs the question what these jobs are likely to be. And the authors group these into five categories that all use the metaphor of ‘stepping’. The rest of the book (well, other than the last chapter, which looks at policy responses that might facilitate this new future) looks at each of those five options in turn. I’m just going to quote the authors on these:
“Stepping Up
Moving up above automated systems to develop more big-picture insights and decisions that are too unstructured and sweeping for computers or robots to be able to make.
Stepping Aside
Moving to a type of non-decision-oriented work that computers aren’t good at, such as selling, motivating people, or describing in straightforward terms the decisions that computers have made.
Stepping In
Engaging with the computer system’s automated decisions to understand, monitor, and improve them. This is the option at the heart of what we are calling augmentation, although each of these five steps can be described as augmenting.
Stepping Narrowly
Finding a specialty area within your profession that is so narrow that no one is attempting to automate it—and it might never be economical to do so.
Stepping Forward
Developing the new systems and technology that support intelligent decisions and actions in a particular domain.”
My problem with a lot of what follows, then, is that I just can’t see how any of the jobs that are discussed after this point in the book could really be ‘mass’ jobs. I get that it is likely we are going to prefer to be told we have six months left to live by a human, rather than a machine, even if it was the machine that worked out our prognosis in the first place. All the same, it is not clear to me that telling people they had better get their affairs in order is ever going to be an employment growth industry. And that is my problem with the last half or so of this book. I can see that virtually all of the jobs discussed aren’t exactly jobs machines are going to take any time soon, but I also can’t see them as jobs millions of humans are going to get to do either. Basically, this is the ‘someone has to be a supermodel’ argument – it could well be anybody, and it’s a pretty damn good career if it is you, but the chances of it actually being you are pretty close to bugger all.
It feels like the second half of this book is sustained by the two myths of our post-modern world – that the world is made up solely of individuals, and that success for these individuals is purely a question of merit. As a community of believers in these particular myths any ‘lottery job’ (supermodel, actor, insurance underwriting systems expert) seems equally available to everyone with the right levels of natural talent and perseverance. That this ought to be self-evident nonsense seems to stand in inverse relation to how strongly we, as a community of believers, continue to believe these myths.
All that said, this isn’t a terrible book. I just think that I think that the arguments raised here are made more simply by most of the other books and articles I’ve read on this subject lately, which say there are essentially four types of jobs: made up of combinations of routine and non-routine with these being either physical and conceptual. If you are in either of the routine jobs categories, physical or conceptual, your job is likely to disappear. If you can get into non-routine work, you should do that as soon as possible. Bad shit is likely to be about to happen, and people on the wrong side of the line are likely to feel the consequences fairly soon. Oh, and by the way, that line keeps moving and moving in ways that make the non-routine suddenly routine and then just as suddenly gone. Run as fast as you can – but remember, there’s probably no escape anyway.
Maybe I should have ended with a joke?
This book starts by saying lots of jobs we currently do are about to disappear, and then goes on to say what most people who talk about the jobs of the future generally say – that if you want a job in the future you had better not be doing what the sort of things automation, computers and robots will be able to do better than you will. What you need to do is to augment what computers can do with your distinctly human characteristics. As the author says somewhere here, think about what a computer would hire you to do that it can’t do.
Now, this is all well and good. The example nearly always given to explain augmentation is chess, curiously enough. When the best human player in the world was first beaten by the best computer program a few decades ago this caused a psychological shift. At the time the computer was one of the world’s most powerful computers – but Moore’s Law has moved on apace and I guess now most computers can beat any world champion. The point is, however, and this is the augmentation argument, that if you give a relatively good player access to a computer, the relatively good player using this tool will beat the best computer program. This is then used as proof that the future belongs to those with skills that augment automated systems.
The problem here is that what those skills are doesn’t exactly stand still. It wasn’t that long ago, for instance, that truck drivers (which another book I read recently pointed out is the number one job in virtually every state in the US) were considered to be doing a task that was too complicated for computerised systems to cope with – and yet now some people predict that governments will soon be forced to legislate that humans driving any kind of vehicle is simply too dangers to allow to continue happening. In fact, the most likely outcome, these people suspect, will be that it will be so expensive to get insurance to drive a vehicle that this will mean the end of human drivers – and given we humans (you know, us drunk, texting, easily distracted humans) kill a million people per year on the roads, this might well be a good thing.
So, the book says we are about to see a major disruption in the types of jobs that exist and that the jobs you want to get if you want to still have a job in the near to medium term futre are augmentation jobs – which then begs the question what these jobs are likely to be. And the authors group these into five categories that all use the metaphor of ‘stepping’. The rest of the book (well, other than the last chapter, which looks at policy responses that might facilitate this new future) looks at each of those five options in turn. I’m just going to quote the authors on these:
“Stepping Up
Moving up above automated systems to develop more big-picture insights and decisions that are too unstructured and sweeping for computers or robots to be able to make.
Stepping Aside
Moving to a type of non-decision-oriented work that computers aren’t good at, such as selling, motivating people, or describing in straightforward terms the decisions that computers have made.
Stepping In
Engaging with the computer system’s automated decisions to understand, monitor, and improve them. This is the option at the heart of what we are calling augmentation, although each of these five steps can be described as augmenting.
Stepping Narrowly
Finding a specialty area within your profession that is so narrow that no one is attempting to automate it—and it might never be economical to do so.
Stepping Forward
Developing the new systems and technology that support intelligent decisions and actions in a particular domain.”
My problem with a lot of what follows, then, is that I just can’t see how any of the jobs that are discussed after this point in the book could really be ‘mass’ jobs. I get that it is likely we are going to prefer to be told we have six months left to live by a human, rather than a machine, even if it was the machine that worked out our prognosis in the first place. All the same, it is not clear to me that telling people they had better get their affairs in order is ever going to be an employment growth industry. And that is my problem with the last half or so of this book. I can see that virtually all of the jobs discussed aren’t exactly jobs machines are going to take any time soon, but I also can’t see them as jobs millions of humans are going to get to do either. Basically, this is the ‘someone has to be a supermodel’ argument – it could well be anybody, and it’s a pretty damn good career if it is you, but the chances of it actually being you are pretty close to bugger all.
It feels like the second half of this book is sustained by the two myths of our post-modern world – that the world is made up solely of individuals, and that success for these individuals is purely a question of merit. As a community of believers in these particular myths any ‘lottery job’ (supermodel, actor, insurance underwriting systems expert) seems equally available to everyone with the right levels of natural talent and perseverance. That this ought to be self-evident nonsense seems to stand in inverse relation to how strongly we, as a community of believers, continue to believe these myths.
All that said, this isn’t a terrible book. I just think that I think that the arguments raised here are made more simply by most of the other books and articles I’ve read on this subject lately, which say there are essentially four types of jobs: made up of combinations of routine and non-routine with these being either physical and conceptual. If you are in either of the routine jobs categories, physical or conceptual, your job is likely to disappear. If you can get into non-routine work, you should do that as soon as possible. Bad shit is likely to be about to happen, and people on the wrong side of the line are likely to feel the consequences fairly soon. Oh, and by the way, that line keeps moving and moving in ways that make the non-routine suddenly routine and then just as suddenly gone. Run as fast as you can – but remember, there’s probably no escape anyway.
Maybe I should have ended with a joke?
October 6, 2016
If you are a student at Babson College, or an executive concerned about automation, then this book is for you. It is written by two Boston business professors to explain to their students and consulting clients how to best upgrade their skills to benefit from automation. It is an eminently practical volume for MBAs or large corporation knowledge workers.
Authors are completely right on one essential point: augmentation, not automation, is inevitable because it creates the most profit and scales to an ever more capable workforce. (Ex: Facebook IT)
The five tracks (stepping up, stepping aside, stepping in, stepping narrowly, and stepping forward) are well structured with current examples to demonstrate ways that highly educated and broadly EQ talented humans will continue to be rewarded huge value. Especially via augmentation with advanced technology.
The authors claimed to aim for MECE categories (mutually exclusive collectively exhaustive) but in no way is that a complete list of how people can augment with technology. Feels like a partly ossified big business approach, while entrepreneurial thinking would use any available tech as a tool, creatively combined, to solve problems while sidestepping and disrupting existing organizations. Also, finding completely new areas of human need, applying empathy or caring to non-economically viable needs, finding new sensors or domains of data, asking questions to apply existing tools to new problems, or combining problems across domains... all of these and more are uncovered.
Yet, the authors deeply know big business, so it is interesting to observe ways technology is internally already used for productivity gains.
At authors' explicitly stated ethical presumption that increased productivity of automation should go towards increasing consumption and wealth, rather than to produce leisure of a 15hr week like Keynes predicted, permeates the work. They are after all building optimized analysts and executives at those elite universities!
This book offers nothing for the 80-90% of humans who do not fall into that elite executive category. It also says very little to inform social or economic policy in this changing world, and while they denigrate universal basic income, they offer no viable counter (socially guarantee of jobs, hahahaha).
Certainly proves that the industrial economy is undergoing massive tech changes, and constant learning is essential, as is welcoming and proactively adapting to all change.
Authors are completely right on one essential point: augmentation, not automation, is inevitable because it creates the most profit and scales to an ever more capable workforce. (Ex: Facebook IT)
The five tracks (stepping up, stepping aside, stepping in, stepping narrowly, and stepping forward) are well structured with current examples to demonstrate ways that highly educated and broadly EQ talented humans will continue to be rewarded huge value. Especially via augmentation with advanced technology.
The authors claimed to aim for MECE categories (mutually exclusive collectively exhaustive) but in no way is that a complete list of how people can augment with technology. Feels like a partly ossified big business approach, while entrepreneurial thinking would use any available tech as a tool, creatively combined, to solve problems while sidestepping and disrupting existing organizations. Also, finding completely new areas of human need, applying empathy or caring to non-economically viable needs, finding new sensors or domains of data, asking questions to apply existing tools to new problems, or combining problems across domains... all of these and more are uncovered.
Yet, the authors deeply know big business, so it is interesting to observe ways technology is internally already used for productivity gains.
At authors' explicitly stated ethical presumption that increased productivity of automation should go towards increasing consumption and wealth, rather than to produce leisure of a 15hr week like Keynes predicted, permeates the work. They are after all building optimized analysts and executives at those elite universities!
This book offers nothing for the 80-90% of humans who do not fall into that elite executive category. It also says very little to inform social or economic policy in this changing world, and while they denigrate universal basic income, they offer no viable counter (socially guarantee of jobs, hahahaha).
Certainly proves that the industrial economy is undergoing massive tech changes, and constant learning is essential, as is welcoming and proactively adapting to all change.
December 22, 2018
На відміну від багатьох футуристичних книжок, у цій можна знайти кілька вагомих стратегій співпраці людини і розумної технології.
August 29, 2018
1/5
Booooo. This book took me nearly two years to read because of how fucking awful it is. Super boring. I was expecting them to tell me at the end that the book was written by a robot to justify how meandering and pointless it was, but it turns out they're just lousy writers.
Over 250 pages of exposition that could have been summed up in a short article or bulleted list.
Booooo. This book took me nearly two years to read because of how fucking awful it is. Super boring. I was expecting them to tell me at the end that the book was written by a robot to justify how meandering and pointless it was, but it turns out they're just lousy writers.
Over 250 pages of exposition that could have been summed up in a short article or bulleted list.
March 2, 2017
The book deals with an interesting topic and an emergent future that might threaten jobs that humans look forward to because smart machines are coming. Think Boston Dynamics and several Japanese companies; why there's even some Indian companies looking for a piece of the pie that will create 'thinking' machines that can replace human beings at several tasks in a variety of industrial scenarios. And if you think labour is cheap in Asia, remember that industries in Asia are fast robotizing.
What then are people to do? Will they turn into Luddites and smash the machines? Or will they find jobs that machines will be unable to do? Or will artificial intelligence power machines to do what humans think they machines cannot do? Will humans be reduced to doing robotic tasks alongside robots?
The writers cite the 'Luddite fallacy" to argue that new jobs that require humans will appear even as they have appeared whenever technological paradigms have shifted in the past. Then again, the age of smart machines might not yet be too close at hand. At worse, There are tech breakthroughs to be achieved, bureaucracies might slow robotization down or, at best, humans will discover new aspects of humanity thanks to automation and robotization.
Humans might stay one step ahead of AI and robots thanks to their unique capabilities of ideation, pattern recognition and communication, say some experts. There are "tacit knowledge and judgement calls that can't be specified in an algorithm - at least not yet", they say.
The book looks critically at how smart 'smart machines' really are and then outlines strategies for humans to cope with increasing levels of automation, robotization and machine intelligence and learning because smart machines are moving beyond human support and taking to autonomous task performance - driverless cars, for instance. They are discovering context awareness and these models get better and better as more streams of data are fed in and decisions are made based on multiple possibilities - choosing the best route out of many, for instance. But scientists are still wondering and figuring out how and when machines will become self-aware and integrate breadth (the ability to do multiple things like humans) alongside depth (excelling at one task).
Some technologists say that it might be better for machines to 'augment' than just 'automate'. The former would allow for both 'superpowers' (computing gives a doctor more options to make decisions) and 'leverage' (the exercise of intelligent decision-making). Or would it be humans that 'augment' smart machines? Then there is the option of using machines for 'stepping up' - understanding the potential of a technological tool and then extending its value all the way down stream changing culture, behaviors and infrastructure. This approach, used in automated journalism for example, can create new ecosystems of content and partners.
The dystopian aspect is 'stepping aside' - giving the field over to smart machines so that humans can follow some niche tasks. Would humans revert to artisanal jobs, for instance? Or become leisure people who can tell the stories that drive social change? Then, there are 'purple people' who can 'step in' at the intersections of business and technologies that create smart machines. They will be needed to create, monitor and modify automated smart systems. There will also be humans who are super-specialized and therefore can 'step narrowly' into spaces the smart machines cannot enter. Finally there will be those who are 'stepping forward' - who are creating new and more advanced creative technologies for the world to use and, possibly, throwing up new problems in their wake.
The authors sum up their survey of the age of automation that is around the corner and their strategies to cope with the changes to come by advocating 'workplaces that combine sophisticated machines and humans in partnerships of mutual augmentation". While that might be fine, one still wonders what a proliferation of smart machines might actually do to jobs in areas of human density like Asia, Africa or South America. What would the teeming millions of semi- or non-literate humans end up in - utopia or dystopia? Read the book and come to your own conclusion.
What then are people to do? Will they turn into Luddites and smash the machines? Or will they find jobs that machines will be unable to do? Or will artificial intelligence power machines to do what humans think they machines cannot do? Will humans be reduced to doing robotic tasks alongside robots?
The writers cite the 'Luddite fallacy" to argue that new jobs that require humans will appear even as they have appeared whenever technological paradigms have shifted in the past. Then again, the age of smart machines might not yet be too close at hand. At worse, There are tech breakthroughs to be achieved, bureaucracies might slow robotization down or, at best, humans will discover new aspects of humanity thanks to automation and robotization.
Humans might stay one step ahead of AI and robots thanks to their unique capabilities of ideation, pattern recognition and communication, say some experts. There are "tacit knowledge and judgement calls that can't be specified in an algorithm - at least not yet", they say.
The book looks critically at how smart 'smart machines' really are and then outlines strategies for humans to cope with increasing levels of automation, robotization and machine intelligence and learning because smart machines are moving beyond human support and taking to autonomous task performance - driverless cars, for instance. They are discovering context awareness and these models get better and better as more streams of data are fed in and decisions are made based on multiple possibilities - choosing the best route out of many, for instance. But scientists are still wondering and figuring out how and when machines will become self-aware and integrate breadth (the ability to do multiple things like humans) alongside depth (excelling at one task).
Some technologists say that it might be better for machines to 'augment' than just 'automate'. The former would allow for both 'superpowers' (computing gives a doctor more options to make decisions) and 'leverage' (the exercise of intelligent decision-making). Or would it be humans that 'augment' smart machines? Then there is the option of using machines for 'stepping up' - understanding the potential of a technological tool and then extending its value all the way down stream changing culture, behaviors and infrastructure. This approach, used in automated journalism for example, can create new ecosystems of content and partners.
The dystopian aspect is 'stepping aside' - giving the field over to smart machines so that humans can follow some niche tasks. Would humans revert to artisanal jobs, for instance? Or become leisure people who can tell the stories that drive social change? Then, there are 'purple people' who can 'step in' at the intersections of business and technologies that create smart machines. They will be needed to create, monitor and modify automated smart systems. There will also be humans who are super-specialized and therefore can 'step narrowly' into spaces the smart machines cannot enter. Finally there will be those who are 'stepping forward' - who are creating new and more advanced creative technologies for the world to use and, possibly, throwing up new problems in their wake.
The authors sum up their survey of the age of automation that is around the corner and their strategies to cope with the changes to come by advocating 'workplaces that combine sophisticated machines and humans in partnerships of mutual augmentation". While that might be fine, one still wonders what a proliferation of smart machines might actually do to jobs in areas of human density like Asia, Africa or South America. What would the teeming millions of semi- or non-literate humans end up in - utopia or dystopia? Read the book and come to your own conclusion.
June 7, 2019
1. This was really interesting and thought-provoking. Could have been just an article though. Once they said what they needed to say I feel like they kept interviewing people to make the same point.
2. The mind-blowing part is that low skill jobs are not necessarily the ones first threatened by automation. High-skilled jobs like radiologists are also becoming obsolete. Anything that can be reduced to machine-understandable rules.
3. They are very proud of themselves with their “step up,” “step in,” “step out,” etc. paradigm but it’s not clever it’s just confusing.
2. The mind-blowing part is that low skill jobs are not necessarily the ones first threatened by automation. High-skilled jobs like radiologists are also becoming obsolete. Anything that can be reduced to machine-understandable rules.
3. They are very proud of themselves with their “step up,” “step in,” “step out,” etc. paradigm but it’s not clever it’s just confusing.
This entire review has been hidden because of spoilers.
August 4, 2020
Really enjoyed this, as I'm learning new programming languages lately to keep an edge.
4.0/5
4.0/5
September 18, 2024
«Сліпий набір». Найефективніший спосіб навчитися швидше друкувати. (promo)
Кращі нотатки з книги:
“чимало досліджень доводять, що там, де у людей немає роботи, стаються погані речі. (Напевне, найкраще це показано в опублікованій 2002 року праці Брюса Вайнберґа з колегами, які проаналізували рівні криміногенної ситуації у США за 18-річний період». Усі виявлені ними спалахи злочинності збігалися з ростом безробіття і зниженням рівня доходів у чоловіків без вищої освіти).”
“Нижче ми розповідаємо, які саме ознаки свідчать про те, що ваш вид діяльності неухильно прямує до автоматизації.”
“І. У вашій сфері діяльності вже створено автоматизовані системи, які перебрали на себе деякі загальні завдання.”
“2. Ваша робота майже не передбачає фізичного контакту або довільного впливу на матеріальні явища.”
“3. Суть вашої роботи — передавання даних.”
“4. Ваша робота повʼязана з первинним аналізом неструктурованої інформації.”
“5. Ваше основне заняття — знаходити відповіді на запитання, спираючись на аналіз даних.”
“6. Суттєвим компонентом вашої роботи є кількісний аналіз.”
“7. Ваша робота складається із завдань, які можна закласти в навчальний тренажер або виконувати в режимі віртуальної реальності.”
“8. У вашій роботі принципово важливу роль відіграє послідовність.”
“9. Ваша основна функція — створювати оповідь на підставі наявної інформації.”
“10. Виконання роботи підпорядковане чітко визначеним формальним правилам.”
“У світі, де поруч із нами зʼявляється дедалі більше розумних машин, не варто залишатися пасивним спостерігачем. Треба щось робити, щоб пристосуватися до нових реалій. Маєте навчитися робити те, що не до снаги компʼютерам, або в той чи інший спосіб удосконалювати продукт, основну частину якого створює машина. І вам буде легше знайти сферу для застосування своїх талантів, якщо ви зрозумієте, в чому саме полягає ваша відносна перевага над комп’ютером.”
“У людей не заберуть роботу, яка потребує сміливості й нестандартного мислення. Тільки люди здатні надихати інших людей на дії, й тільки людям властиві такі цінні риси, як емпатія, дипломатичність і цілеспрямованість. Тільки люди можуть наповнювати справи, яким вони посвячуються, пристрастю, настроєм, веселощами - чи, зрештою, добрим смаком і відчуттям стилю. А машини, які досі були мʼязовою масою на підтримку наших мізків, можуть стати мізками, що доповнюватимуть гнучкість, глибину і жвавість людини.”
“«Ознака справжнього інноватора — його здатність зазирнути в майбутне й накреслити курс розвитку від однієї точки до іншої.”
“Ден Аріелі, автор бестселера «Передбачувано ірраціональний», вважає, що раціональні мотиви визначають нашу поведінку лише на 10 відсотків».”
“публікація 1983 року книжки Говарда Ґарднера «Структура розуму: теорія множинного інтелекту» (Frames of Mind: The Theory of Multiple Intelligences). Автор доводить, що тест IQ вимірює лише певну частку розумових здібностей людини й показує, наскільки за цим параметром люди різняться між собою. Натомість Ґарднер називає вісім форм множинного інтелекту…”
“[1] лінгвістичні здібності (завдяки яким деякі люди більш схильні вивчати мови і краще володіють словом, ніж інші);”
“[2] логічно-математичні здібності (здатність легко оперувати цифрами і розуміти логіку речей);”
“[3] просторові здібності (вміння малювати, схильність до образотворчих мистецтв);”
“[4] фізично-кінетостатичні здібності (володіння тілом, схильність до занять спортом);”
“[5] музичні здібності (схильність до занять музикою);”
“[6] міжособистісні здібності (здатність продуктивно спілкуватися з іншими людьми);”
“[7] внутрішньоособистісні здібності (схильність до самоаналізу і розуміння своєї поведінки);”
“[8] природничі здібності (розуміння природи).”
“Інший важливий аспект — креативність. Якщо ви любите дивитися на ютьюбі TED Talks, то, напевне, бачили найпопулярніше відео цього каналу — лекцію Кена Робінсона «Як школа вбиває креативність». Як стверджує Робінсон, «ми не нарощуємо креативність, ми, навпаки, переростаємо її. Чи, точніше, освіта вихолощує її з нас».”
“У згадуваному вище Єльському центрі досліджень емоційного інтелекту цю схему окреслено абревіатурою RULER*: спершу людина має навчитися розпізнавати свої емоції й почуття, тоді усвідомлювати їх, називати, висловлювати чи проявляти в інший спосіб і керувати ними. [*Абревіатуру RULER утворено з англійських дієслів recognize, understand, label, express, and regulate.]”
“Вілсон використав аналогію з кольорами: «Це так, наче люди з IT розмовляють синьою мовою, а люди бізнесу — червоною. А нам потрібні ті, хто б розмовляв фіолетовою»… У цій книжці ми говоримо про «фіолетових людей» як про тих, хто робить «кроки всередину» і, працюючи в організації, допомагає створювати автоматичні системи, відстежувати їхню ефективність і вносити зміни для вдосконалення. Ці люди — в самому епіцентрі ауґментації, бо вони наводять мости між бізнесом і організаційними вимогами, з одного боку, й автоматичними система-ми, що спираються на можливості сучасних технологій, з іншого. Технологічні системи не викликають у них переляку, вони готові «стрибати в пащу» цього «дракона» і робити все потрібне, щоб підтримати належне функціонування цих систем. Вони розуміються на технології, та все ж у більшості випадків зосереджують зусилля на тому, щоб максимально пристосувати їх до потреб бізнесу чи організаційного контексту.”
“Часто найбільш цінні ніші для вузькоспеціалізованої діяльності перебувають на перетині двох ширших сфер знання, які зазвичай не розглядають в єдиному контексті.”
“Успіх видатних людей — це продукт відповідного навчання, виваженої практики і мотиваційного драйву. Такий висновок зробив Майкл Гоув, спеціаліст із когнітивної психології, який присвятив свою карʼєру дослідженню виняткового розвитку інтелекту. Це перегукується з відомою оцінкою Герберта Саймона, що на шляху до набуття кваліфікації експерта людина засвоює приблизно 50 тисяч фрагментів фахової інформації й зазвичай цей процес триває не менше ніж 10 років.”
“три аспекти використання технологій, які можуть підсилювати вашу діяльність. 1)• Технології допомагають вам опановувати більше знань швидшими темпами. 2) • Технології беруть на себе допоміжну роботу, даючи вам можливість зосереджуватися на основній діяльності й поглиблювати фахову майстерність. 3) • Технології допомагають приєднувати вашу роботу до масштабніших проектів.”
“Моя команда часто доходила висновку, що ауґментація — поєднання зусиль розумних людей і розумних машин — є більш прагматичним підходом, аніж автоматизація.”
“Аугментація — це цілком реальний шлях до суттєвого збільшення ефективності праці та продуктивності. Прикладом може бути компанія Facebook: величезні масштаби і швидкість зростання спонукали організацію зосередитися на автоматизації орієнтованих на IT завдань, як-от управління величезною кількістю серверів компанії. Джей Парик, віце-президент Facebook з інженерних питань, в інтервʼю нам чітко дав зрозуміти, що тут ідеться саме про аугментацію: «Сенс усієї цієї автоматизації в тому, що вона дає змогу забрати дуже прості, але часомісткі завдання з тарілок наших неймовірно розумних людей. Нехай вони думають про наші наступні два роки, а не про те, що ми вже створили за два минулі роки»”
“ще один набір соціальних навичок, що його, на нашу думку, слід розвивати у школі — це схильність до постійного навчання.”
“Навчитися створювати партнерські відносини між людиною і машиною, вміти приймати розумні рішення й бути підприємливими в здобуванні знань — такими мають бути основні цілі освіти для формування «соціальних навичок», потрібних працівникові в добу аугментації.”
“А як щодо припущення, що люди без роботи активно проводитимуть вільний час і присвячуватимуть його творчості? На жаль, наявні дані геть не підтверджують цього. Як зазначає Дерек Томпсон у дещо провокативній статті «Світ без роботи», опублікованій в Atlantic, дослідження показують, що люди, які не працюють, більше сплять, більше дивляться телевізор і гортають інтернет. І не квапляться братися до живопису.”
Тренування мозку (promo)
Кращі нотатки з книги:
“чимало досліджень доводять, що там, де у людей немає роботи, стаються погані речі. (Напевне, найкраще це показано в опублікованій 2002 року праці Брюса Вайнберґа з колегами, які проаналізували рівні криміногенної ситуації у США за 18-річний період». Усі виявлені ними спалахи злочинності збігалися з ростом безробіття і зниженням рівня доходів у чоловіків без вищої освіти).”
“Нижче ми розповідаємо, які саме ознаки свідчать про те, що ваш вид діяльності неухильно прямує до автоматизації.”
“І. У вашій сфері діяльності вже створено автоматизовані системи, які перебрали на себе деякі загальні завдання.”
“2. Ваша робота майже не передбачає фізичного контакту або довільного впливу на матеріальні явища.”
“3. Суть вашої роботи — передавання даних.”
“4. Ваша робота повʼязана з первинним аналізом неструктурованої інформації.”
“5. Ваше основне заняття — знаходити відповіді на запитання, спираючись на аналіз даних.”
“6. Суттєвим компонентом вашої роботи є кількісний аналіз.”
“7. Ваша робота складається із завдань, які можна закласти в навчальний тренажер або виконувати в режимі віртуальної реальності.”
“8. У вашій роботі принципово важливу роль відіграє послідовність.”
“9. Ваша основна функція — створювати оповідь на підставі наявної інформації.”
“10. Виконання роботи підпорядковане чітко визначеним формальним правилам.”
“У світі, де поруч із нами зʼявляється дедалі більше розумних машин, не варто залишатися пасивним спостерігачем. Треба щось робити, щоб пристосуватися до нових реалій. Маєте навчитися робити те, що не до снаги компʼютерам, або в той чи інший спосіб удосконалювати продукт, основну частину якого створює машина. І вам буде легше знайти сферу для застосування своїх талантів, якщо ви зрозумієте, в чому саме полягає ваша відносна перевага над комп’ютером.”
“У людей не заберуть роботу, яка потребує сміливості й нестандартного мислення. Тільки люди здатні надихати інших людей на дії, й тільки людям властиві такі цінні риси, як емпатія, дипломатичність і цілеспрямованість. Тільки люди можуть наповнювати справи, яким вони посвячуються, пристрастю, настроєм, веселощами - чи, зрештою, добрим смаком і відчуттям стилю. А машини, які досі були мʼязовою масою на підтримку наших мізків, можуть стати мізками, що доповнюватимуть гнучкість, глибину і жвавість людини.”
“«Ознака справжнього інноватора — його здатність зазирнути в майбутне й накреслити курс розвитку від однієї точки до іншої.”
“Ден Аріелі, автор бестселера «Передбачувано ірраціональний», вважає, що раціональні мотиви визначають нашу поведінку лише на 10 відсотків».”
“публікація 1983 року книжки Говарда Ґарднера «Структура розуму: теорія множинного інтелекту» (Frames of Mind: The Theory of Multiple Intelligences). Автор доводить, що тест IQ вимірює лише певну частку розумових здібностей людини й показує, наскільки за цим параметром люди різняться між собою. Натомість Ґарднер називає вісім форм множинного інтелекту…”
“[1] лінгвістичні здібності (завдяки яким деякі люди більш схильні вивчати мови і краще володіють словом, ніж інші);”
“[2] логічно-математичні здібності (здатність легко оперувати цифрами і розуміти логіку речей);”
“[3] просторові здібності (вміння малювати, схильність до образотворчих мистецтв);”
“[4] фізично-кінетостатичні здібності (володіння тілом, схильність до занять спортом);”
“[5] музичні здібності (схильність до занять музикою);”
“[6] міжособистісні здібності (здатність продуктивно спілкуватися з іншими людьми);”
“[7] внутрішньоособистісні здібності (схильність до самоаналізу і розуміння своєї поведінки);”
“[8] природничі здібності (розуміння природи).”
“Інший важливий аспект — креативність. Якщо ви любите дивитися на ютьюбі TED Talks, то, напевне, бачили найпопулярніше відео цього каналу — лекцію Кена Робінсона «Як школа вбиває креативність». Як стверджує Робінсон, «ми не нарощуємо креативність, ми, навпаки, переростаємо її. Чи, точніше, освіта вихолощує її з нас».”
“У згадуваному вище Єльському центрі досліджень емоційного інтелекту цю схему окреслено абревіатурою RULER*: спершу людина має навчитися розпізнавати свої емоції й почуття, тоді усвідомлювати їх, називати, висловлювати чи проявляти в інший спосіб і керувати ними. [*Абревіатуру RULER утворено з англійських дієслів recognize, understand, label, express, and regulate.]”
“Вілсон використав аналогію з кольорами: «Це так, наче люди з IT розмовляють синьою мовою, а люди бізнесу — червоною. А нам потрібні ті, хто б розмовляв фіолетовою»… У цій книжці ми говоримо про «фіолетових людей» як про тих, хто робить «кроки всередину» і, працюючи в організації, допомагає створювати автоматичні системи, відстежувати їхню ефективність і вносити зміни для вдосконалення. Ці люди — в самому епіцентрі ауґментації, бо вони наводять мости між бізнесом і організаційними вимогами, з одного боку, й автоматичними система-ми, що спираються на можливості сучасних технологій, з іншого. Технологічні системи не викликають у них переляку, вони готові «стрибати в пащу» цього «дракона» і робити все потрібне, щоб підтримати належне функціонування цих систем. Вони розуміються на технології, та все ж у більшості випадків зосереджують зусилля на тому, щоб максимально пристосувати їх до потреб бізнесу чи організаційного контексту.”
“Часто найбільш цінні ніші для вузькоспеціалізованої діяльності перебувають на перетині двох ширших сфер знання, які зазвичай не розглядають в єдиному контексті.”
“Успіх видатних людей — це продукт відповідного навчання, виваженої практики і мотиваційного драйву. Такий висновок зробив Майкл Гоув, спеціаліст із когнітивної психології, який присвятив свою карʼєру дослідженню виняткового розвитку інтелекту. Це перегукується з відомою оцінкою Герберта Саймона, що на шляху до набуття кваліфікації експерта людина засвоює приблизно 50 тисяч фрагментів фахової інформації й зазвичай цей процес триває не менше ніж 10 років.”
“три аспекти використання технологій, які можуть підсилювати вашу діяльність. 1)• Технології допомагають вам опановувати більше знань швидшими темпами. 2) • Технології беруть на себе допоміжну роботу, даючи вам можливість зосереджуватися на основній діяльності й поглиблювати фахову майстерність. 3) • Технології допомагають приєднувати вашу роботу до масштабніших проектів.”
“Моя команда часто доходила висновку, що ауґментація — поєднання зусиль розумних людей і розумних машин — є більш прагматичним підходом, аніж автоматизація.”
“Аугментація — це цілком реальний шлях до суттєвого збільшення ефективності праці та продуктивності. Прикладом може бути компанія Facebook: величезні масштаби і швидкість зростання спонукали організацію зосередитися на автоматизації орієнтованих на IT завдань, як-от управління величезною кількістю серверів компанії. Джей Парик, віце-президент Facebook з інженерних питань, в інтервʼю нам чітко дав зрозуміти, що тут ідеться саме про аугментацію: «Сенс усієї цієї автоматизації в тому, що вона дає змогу забрати дуже прості, але часомісткі завдання з тарілок наших неймовірно розумних людей. Нехай вони думають про наші наступні два роки, а не про те, що ми вже створили за два минулі роки»”
“ще один набір соціальних навичок, що його, на нашу думку, слід розвивати у школі — це схильність до постійного навчання.”
“Навчитися створювати партнерські відносини між людиною і машиною, вміти приймати розумні рішення й бути підприємливими в здобуванні знань — такими мають бути основні цілі освіти для формування «соціальних навичок», потрібних працівникові в добу аугментації.”
“А як щодо припущення, що люди без роботи активно проводитимуть вільний час і присвячуватимуть його творчості? На жаль, наявні дані геть не підтверджують цього. Як зазначає Дерек Томпсон у дещо провокативній статті «Світ без роботи», опублікованій в Atlantic, дослідження показують, що люди, які не працюють, більше сплять, більше дивляться телевізор і гортають інтернет. І не квапляться братися до живопису.”
Тренування мозку (promo)
May 4, 2025
I want to hate this book but I can’t. I wanted to write it off as hopelessly naive techno-optimist drivel. The first 25% of it is just that. But I stuck with it and, despite its innumerable flaws, which Ive meticulously outlined, it won me over. I’ve concluded that this book is: mid.
Let’s start with the bad:
• Way out of date references to existing automation tools and no reference to ChatGPT (since it’s 9 years old and that didn’t exist at the time) but lots of references to Watson, which has lost all relevance in the zeitgeist.
• pro-surveillance capitalism, as explained in the far better book "The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power" (2019).
• ignores or brushes off the inherent biases and flaws of big data as explained in the much better book "Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy" (2016). The phrase “garbage-in = garbage out” isn’t mentioned once.
But there actually was a lot of interesting stuff and provided a lot of the language and terminology about automation and augmentation of labor tasks with various tools. I think the strategies of adapting to the continually evolving labor automation, use the tools to augment labor and don’t worry too much about how many people might become unemployed, is interesting and is similar to where I stand. I want the robots to take my job and have worked hard over the years to eliminate drudgery and automate my tasks.
But then the authors hit you with this:
“The other widely discussed option is to somehow convince a cash-strapped government to guarantee you an income if you lose your job to automation. We don’t deny that it’s important for governments at every level to address this pressing issue. But government bureaucracies have always been slow to notice problems and address them with serious interventions, and some (particularly the U.S. government) are seeming particularly slow and ineffective right now.“
Gimme fucking a break. What an asinine and tone deaf paragraph. ‘Don’t try to fight for a better world! Just accept the robots taking over and do not dare try to threaten the powers that be to surrender an iota of their profits to keep your fellow worker from starving! You get yours and fuck the rest.’
This is why I like the idea of FALC, as outlined in the thorough, yet dull book “Fully automated luxury communism” (2019). That book at least offers an optimistic future that understands and explains the power dynamic of capitalism vs labor, instead of either ignoring it or trying to convince you that you shouldn’t try to want a better life for all, like this book.
The book spasms back and forth regarding the acceptability of governmental intervention in response to lower job availability thanks to automation and it ends with explaining how universal jobs programs are the ideal welfare programs, as opposed to universal basic income. No discussion about reducing weekly hours, of course. Absurd. Tone deaf. Annoying.
Anyway back to the good:
The crux of the book is that automation is only going to become more prevalent and there are 5 camps of how people will respond:
“• Stepping Up: Moving up above automated systems to develop more big-picture insights and decisions that are too unstructured and sweeping for computers or robots to be able to make.
Stepping Aside: Moving to a type of non-decision-oriented work that computers aren’t good at, such as selling, motivating people, or describing in straightforward terms the decisions that computers have made.
• Stepping In: Engaging with the computer system’s automated decisions to understand, monitor, and improve them. This is the option at the heart of what we are calling augmentation, although each of these five steps can be described as augmenting.
• Stepping Narrowly: Finding a specialty area within your profession that is so narrow that no one is attempting to automate it—and it might never be economical to do so.
• Stepping Forward: Developing the new systems and technology that support intelligent decisions and actions in a particular domain.”
My professional career has primarily been in the “Stepping In” category. I don’t shy away from these tools and want to augment workflows to improve efficiency. Or, at least that’s what I’ve strived to do. But currently it seems I’m more on the “Stepping Up & Narrowly” tracks. I genuinely would love for the robots to do what I do but I don’t think it’s gonna happen. So I just augment as best I can to eliminate needles drudgery.
I want to hate this book because it says some outlandish, and even factually incorrect, things (e.g.: incorrectly conflate’s Microsoft Clippy with Microsoft’s Bob). But then it hits you with a passage like this…
“Automation-oriented approaches create all these problems because they focus primarily or exclusively on cost reduction. Thus, even when the cost savings materialize, they may come at the long-term expense of revenues and profit margins. Augmentation approaches tend to be more likely to achieve value and innovation. We don’t think that any competent organization will be able to ignore the advantages of the intelligent machines that we describe in this book. But coupling them with intelligent people is, we believe, a better bet for the long run.”
If that were in the first chapter instead of in the back 3rd, I probably wouldn’t have badmouthed the book as much and wouldn’t have almost given up on it.
But then it hits you with this…
“Codelco was also interested in the productivity benefits of automation, of course. But the primary focus was worker safety. Chile has had a socialist government for much of the last decade, and Codelco is a 100 percent state-owned company. Automation initiatives designed primarily to eliminate labor in a state-owned company would not have been politically viable.”
Hmmm. Speak to that, author. Do you have any interesting information about the ideological & strategic differences between state-owned vs private-owned companies when it comes to automation? No? That’s all you’re gonna say about it and move on? Harumph.
I want to find and/or write an automation and labor saving book that merges the concepts from this book, the others referenced in this review, “Bullshit jobs” by David Graeber, and “The Checklist Manifesto” by Atul Gawande. We’ll see where things go.
As you can tell with all the “but then’s”, the book was a whiplash experience. It’s interesting if you’re into the philosophy of labor automation and the future of work. Otherwise skip it.
Let’s start with the bad:
• Way out of date references to existing automation tools and no reference to ChatGPT (since it’s 9 years old and that didn’t exist at the time) but lots of references to Watson, which has lost all relevance in the zeitgeist.
• pro-surveillance capitalism, as explained in the far better book "The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power" (2019).
• ignores or brushes off the inherent biases and flaws of big data as explained in the much better book "Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy" (2016). The phrase “garbage-in = garbage out” isn’t mentioned once.
But there actually was a lot of interesting stuff and provided a lot of the language and terminology about automation and augmentation of labor tasks with various tools. I think the strategies of adapting to the continually evolving labor automation, use the tools to augment labor and don’t worry too much about how many people might become unemployed, is interesting and is similar to where I stand. I want the robots to take my job and have worked hard over the years to eliminate drudgery and automate my tasks.
But then the authors hit you with this:
“The other widely discussed option is to somehow convince a cash-strapped government to guarantee you an income if you lose your job to automation. We don’t deny that it’s important for governments at every level to address this pressing issue. But government bureaucracies have always been slow to notice problems and address them with serious interventions, and some (particularly the U.S. government) are seeming particularly slow and ineffective right now.“
Gimme fucking a break. What an asinine and tone deaf paragraph. ‘Don’t try to fight for a better world! Just accept the robots taking over and do not dare try to threaten the powers that be to surrender an iota of their profits to keep your fellow worker from starving! You get yours and fuck the rest.’
This is why I like the idea of FALC, as outlined in the thorough, yet dull book “Fully automated luxury communism” (2019). That book at least offers an optimistic future that understands and explains the power dynamic of capitalism vs labor, instead of either ignoring it or trying to convince you that you shouldn’t try to want a better life for all, like this book.
The book spasms back and forth regarding the acceptability of governmental intervention in response to lower job availability thanks to automation and it ends with explaining how universal jobs programs are the ideal welfare programs, as opposed to universal basic income. No discussion about reducing weekly hours, of course. Absurd. Tone deaf. Annoying.
Anyway back to the good:
The crux of the book is that automation is only going to become more prevalent and there are 5 camps of how people will respond:
“• Stepping Up: Moving up above automated systems to develop more big-picture insights and decisions that are too unstructured and sweeping for computers or robots to be able to make.
Stepping Aside: Moving to a type of non-decision-oriented work that computers aren’t good at, such as selling, motivating people, or describing in straightforward terms the decisions that computers have made.
• Stepping In: Engaging with the computer system’s automated decisions to understand, monitor, and improve them. This is the option at the heart of what we are calling augmentation, although each of these five steps can be described as augmenting.
• Stepping Narrowly: Finding a specialty area within your profession that is so narrow that no one is attempting to automate it—and it might never be economical to do so.
• Stepping Forward: Developing the new systems and technology that support intelligent decisions and actions in a particular domain.”
My professional career has primarily been in the “Stepping In” category. I don’t shy away from these tools and want to augment workflows to improve efficiency. Or, at least that’s what I’ve strived to do. But currently it seems I’m more on the “Stepping Up & Narrowly” tracks. I genuinely would love for the robots to do what I do but I don’t think it’s gonna happen. So I just augment as best I can to eliminate needles drudgery.
I want to hate this book because it says some outlandish, and even factually incorrect, things (e.g.: incorrectly conflate’s Microsoft Clippy with Microsoft’s Bob). But then it hits you with a passage like this…
“Automation-oriented approaches create all these problems because they focus primarily or exclusively on cost reduction. Thus, even when the cost savings materialize, they may come at the long-term expense of revenues and profit margins. Augmentation approaches tend to be more likely to achieve value and innovation. We don’t think that any competent organization will be able to ignore the advantages of the intelligent machines that we describe in this book. But coupling them with intelligent people is, we believe, a better bet for the long run.”
If that were in the first chapter instead of in the back 3rd, I probably wouldn’t have badmouthed the book as much and wouldn’t have almost given up on it.
But then it hits you with this…
“Codelco was also interested in the productivity benefits of automation, of course. But the primary focus was worker safety. Chile has had a socialist government for much of the last decade, and Codelco is a 100 percent state-owned company. Automation initiatives designed primarily to eliminate labor in a state-owned company would not have been politically viable.”
Hmmm. Speak to that, author. Do you have any interesting information about the ideological & strategic differences between state-owned vs private-owned companies when it comes to automation? No? That’s all you’re gonna say about it and move on? Harumph.
I want to find and/or write an automation and labor saving book that merges the concepts from this book, the others referenced in this review, “Bullshit jobs” by David Graeber, and “The Checklist Manifesto” by Atul Gawande. We’ll see where things go.
As you can tell with all the “but then’s”, the book was a whiplash experience. It’s interesting if you’re into the philosophy of labor automation and the future of work. Otherwise skip it.
August 5, 2017
The central points that 'Only Humans Need Apply' advocates are important: That workplaces should combine sophisticated machines and humans in partnership of mutual augmentation; that knowledge workers should embrace and employers should pursue this augmentation for competitive reasons; and that this human/machine augmentation is something societies should encourage and enable.
However, I think this could have been accomplished in a 20 to 30 page article as opposed to a 250 page book.
However, I think this could have been accomplished in a 20 to 30 page article as opposed to a 250 page book.
May 22, 2018
It's easy to be a pessimist when it comes to robots taking over our jobs. To say that there is a threat to workforces around the world from automation, robotics, AI, etc., is an understatement. Smart people seem to be converging on the idea that now, more than ever before, humanity's ability to find meaningful and economically rewarding work is under grave threat. Of course, often, smart people are wrong. We've been worried about machines taking our jobs for at least the last 200 years. At every stage, however, humans have found new ways to derive meaning from work, and new ways to contribute to growing the economic pie.
This time is different, the smart people say. Whereas in previous eras, machines were taking physical labor from people - from agricultural work to factory work to advanced machinery - today's machines are encroaching on knowledge work of the highest order. Machines can now beat us in tasks we thought were fundamentally human: chess, writing, music composition. Humanity's very identity is under increasing attack by ever-improving machines.
Thomas Davenport and Julia Kirby acknowledge that many of these trends may scare people, for good reason. They are no Luddites. But while recognizing that it's a brave new world for jobs, they provide a useful framework for thinking about how humans can continue to thrive in an economy increasingly undergirded by machines. Rather than worrying about machine automation taking their jobs, people should be thinking about how machine augmentation can protect and improve their jobs. The authors have a core belief that there will always be a role for humans in the economy, no matter how far the machines advance. (A fair criticism of the book is that while the authors show how individual people can stay relevant, they fail to address how governments might ensure that the millions of people who for whatever reason do not or cannot take one of their strategies stay employed with meaningful work.)
How can you stay relevant? The authors provide a very easy-to-follow framework around "stepping":
1. Step up: look beyond the horizon and understand where the market is heading, and where you should leverage machines vs. people
2. Step aside: find non-machine replaceable skills or tasks that you can drive (e.g., jobs requiring empathy, humor, etc.)
3. Step in: work directly with the machines - coding them, structuring the inputs and outputs, tweaking them, etc
4. Step narrowly: find a job or expertise so narrow in scope as to likely not be economically feasible to automate (one example given is an expert on a rat colony that comes every 48 years to a part of India)
5. Step forward: design the next generation of machines, robots, AI software, etc.
For each of the five areas, the authors provide towards the end of the chapter some key summary points: "You're a candidate for stepping... if"; "You can build your skills for stepping...by," and "You're likely to be found in..."
The authors clearly did a ton of research to pull this work together, talking to dozens of companies on the leading edge of augmentation and automation. I enjoyed reading about many of these - such as Digital Reasoning (detects fraud), IPsoft (customer agent), Blue Prism (software robots), FaceFirst (shoplifter identification), Automated Insights (corporate earnings and sports reporting).
I have to admit that I enjoy the authors' "dad" humor (unclear if the source of said humor is Davenport or Kirby - either way, it's totally "dad" humor). In talking about a guy who'd spent time working at Circle and Square, they note that he "looks forward to a job at Triangle." They keep things interesting, which is always appreciated when you're reading about how computers are coming for all of our jobs...
This is a book that I think should be required reading for college students and others looking for jobs in today's knowledge economy. The structure of our job preparation pipeline hasn't yet changed to match the changing needs of the workforce, but this book provides an excellent way of thinking about how people can stay relevant in a fast-changing, unpredictable world.
This time is different, the smart people say. Whereas in previous eras, machines were taking physical labor from people - from agricultural work to factory work to advanced machinery - today's machines are encroaching on knowledge work of the highest order. Machines can now beat us in tasks we thought were fundamentally human: chess, writing, music composition. Humanity's very identity is under increasing attack by ever-improving machines.
Thomas Davenport and Julia Kirby acknowledge that many of these trends may scare people, for good reason. They are no Luddites. But while recognizing that it's a brave new world for jobs, they provide a useful framework for thinking about how humans can continue to thrive in an economy increasingly undergirded by machines. Rather than worrying about machine automation taking their jobs, people should be thinking about how machine augmentation can protect and improve their jobs. The authors have a core belief that there will always be a role for humans in the economy, no matter how far the machines advance. (A fair criticism of the book is that while the authors show how individual people can stay relevant, they fail to address how governments might ensure that the millions of people who for whatever reason do not or cannot take one of their strategies stay employed with meaningful work.)
How can you stay relevant? The authors provide a very easy-to-follow framework around "stepping":
1. Step up: look beyond the horizon and understand where the market is heading, and where you should leverage machines vs. people
2. Step aside: find non-machine replaceable skills or tasks that you can drive (e.g., jobs requiring empathy, humor, etc.)
3. Step in: work directly with the machines - coding them, structuring the inputs and outputs, tweaking them, etc
4. Step narrowly: find a job or expertise so narrow in scope as to likely not be economically feasible to automate (one example given is an expert on a rat colony that comes every 48 years to a part of India)
5. Step forward: design the next generation of machines, robots, AI software, etc.
For each of the five areas, the authors provide towards the end of the chapter some key summary points: "You're a candidate for stepping... if"; "You can build your skills for stepping...by," and "You're likely to be found in..."
The authors clearly did a ton of research to pull this work together, talking to dozens of companies on the leading edge of augmentation and automation. I enjoyed reading about many of these - such as Digital Reasoning (detects fraud), IPsoft (customer agent), Blue Prism (software robots), FaceFirst (shoplifter identification), Automated Insights (corporate earnings and sports reporting).
I have to admit that I enjoy the authors' "dad" humor (unclear if the source of said humor is Davenport or Kirby - either way, it's totally "dad" humor). In talking about a guy who'd spent time working at Circle and Square, they note that he "looks forward to a job at Triangle." They keep things interesting, which is always appreciated when you're reading about how computers are coming for all of our jobs...
This is a book that I think should be required reading for college students and others looking for jobs in today's knowledge economy. The structure of our job preparation pipeline hasn't yet changed to match the changing needs of the workforce, but this book provides an excellent way of thinking about how people can stay relevant in a fast-changing, unpredictable world.
September 8, 2017
This book deals with the concern of losing jobs because of artificial intelligence. They discuss the three eras of machines replacing humans:
• First, machines relived humans of work that was manually exhausting and mentally enervating.
• The second era of automation followed workers to the higher ground they’d headed for when machines took the grunt work. For the most part, this wasn’t the realm of the dirty and dangerous anymore. It was the domain of the dull.
• And this brings us to Era Three, with automation gaining in intelligence and now breathing down our necks. Now computers are proving in various setting that they are capable of making better decisions than humans.
The authors give you 10 reasons to be concerned that a knowledge worker’s job might be automated:
• There are automated systems available today to do some of its core tasks.
• It involves little physical contact or manipulation of things.
• It involves simple content transmission.
• It involves straightforward content analysis
• It involves answering data-dependent questions.
• It involves doing quantitative analysis.
• It involves tasks that can be simulated or performed virtually.
• Consistency of performance is critical in it.
• It involves the creation of data-based narratives.
• There are well-defined formal rules for performing the work.
The authors come to the philosophy of augmentation – starting with what minds and machines do individually today and figuring out how that work could be deepened rather than diminished between the two. The intent is never to have less work for those expensive, high-maintenance humans. It is always to allow them to do more valuable work.
The bulk of the book is spent around the five options for augmentation:
• Stepping up: Moving up the automated system to develop more big-picture insights and decisions that are too unstructured and sweeping for computers or robots to be able to make.
• Stepping aside: Moving to a non-decision-oriented work that computers aren’t good at, such as selling, motivating people, or describing in straightforward terms the decisions that computers have made.
• Stepping in: Engaging with the computer system’s automated decisions to understand, monitor, and improve them. This is the option at the heart of augmentation.
• Stepping narrowly: finding a specialty area within your profession that is so narrow that no one is attempting to automate it – and it might never be economical to do so.
• Stepping forward: developing the new systems and technology that support intelligent decisions and actions in a particular domain.
The book does make several references to IBM’s Watson. If you are interested, you can pick the book up and check them out through the index.
• First, machines relived humans of work that was manually exhausting and mentally enervating.
• The second era of automation followed workers to the higher ground they’d headed for when machines took the grunt work. For the most part, this wasn’t the realm of the dirty and dangerous anymore. It was the domain of the dull.
• And this brings us to Era Three, with automation gaining in intelligence and now breathing down our necks. Now computers are proving in various setting that they are capable of making better decisions than humans.
The authors give you 10 reasons to be concerned that a knowledge worker’s job might be automated:
• There are automated systems available today to do some of its core tasks.
• It involves little physical contact or manipulation of things.
• It involves simple content transmission.
• It involves straightforward content analysis
• It involves answering data-dependent questions.
• It involves doing quantitative analysis.
• It involves tasks that can be simulated or performed virtually.
• Consistency of performance is critical in it.
• It involves the creation of data-based narratives.
• There are well-defined formal rules for performing the work.
The authors come to the philosophy of augmentation – starting with what minds and machines do individually today and figuring out how that work could be deepened rather than diminished between the two. The intent is never to have less work for those expensive, high-maintenance humans. It is always to allow them to do more valuable work.
The bulk of the book is spent around the five options for augmentation:
• Stepping up: Moving up the automated system to develop more big-picture insights and decisions that are too unstructured and sweeping for computers or robots to be able to make.
• Stepping aside: Moving to a non-decision-oriented work that computers aren’t good at, such as selling, motivating people, or describing in straightforward terms the decisions that computers have made.
• Stepping in: Engaging with the computer system’s automated decisions to understand, monitor, and improve them. This is the option at the heart of augmentation.
• Stepping narrowly: finding a specialty area within your profession that is so narrow that no one is attempting to automate it – and it might never be economical to do so.
• Stepping forward: developing the new systems and technology that support intelligent decisions and actions in a particular domain.
The book does make several references to IBM’s Watson. If you are interested, you can pick the book up and check them out through the index.
January 18, 2019
This is a book with a wide scope -- the rise of cognitive computing and artificial intelligence, the impact on blue-collar and educated 'knowledge' workers, the larger themes of automation and it's impact on society, how to augment human strength's in a work of increased productivity.
Really well-written, often humorous and witty, and articulates arguments that are both inspiring and I believe to be insightful about the future. It's optimism is a great compliment to most of the existing literature in the field which is really pessimistic.
One of it's biggest insights is in the career roles that will continue to have relevance in the future.
Stepping Up is problem-solving and decision-making that relies upon seeing the big picture and uncertainty. Think CEOs, Risk Managers, Investors, etc...
Stepping Aside is complementing superior machine intelligence with complementary skills like emotional intelligence. Think Financial Advisors, Psychologists, Teachers, etc...
Stepping In means being a purple person in a world of blue engineers and red business people, it also means to manage, tweak, and improve automation once implemented. Think Product Managers, etc...
Stepping Narrowly is to become a hyper-expert such that no robot can replace your work and knowledge. Think being THE expert on the rat swarm that erupts in North East India every 48 years.
Stepping Forward refers to the technical and business brilliance of building and implementing automation machines. Think programmers, entrepreneurs, marketers, internal automation leaders, consultants, data scientists / analysts, and researchers.
However, I take strong issue with his parting conclusion on the minimum basic income that "it is unnecessary to contemplate such a drastic redistribution unless you have given up on the possibility of widespread employment in well-paying jobs... human strengths will continue to enable humans to produce economic value and to be paid for that value, there is no reason to decouple work from income." This view is oddly naive considering that a single 'low-hanging fruit' of automation that of automating long-distance truck drivers could eliminate over 1 million jobs in the United States while very optimistically creating jobs for ten of thousands within only 10-20 years. This is unprecedented in the speed and scale of dislocation upon the economy and employment market. While it is great to hear a more optimistic view on the issue, I firmly disagree.
Really well-written, often humorous and witty, and articulates arguments that are both inspiring and I believe to be insightful about the future. It's optimism is a great compliment to most of the existing literature in the field which is really pessimistic.
One of it's biggest insights is in the career roles that will continue to have relevance in the future.
Stepping Up is problem-solving and decision-making that relies upon seeing the big picture and uncertainty. Think CEOs, Risk Managers, Investors, etc...
Stepping Aside is complementing superior machine intelligence with complementary skills like emotional intelligence. Think Financial Advisors, Psychologists, Teachers, etc...
Stepping In means being a purple person in a world of blue engineers and red business people, it also means to manage, tweak, and improve automation once implemented. Think Product Managers, etc...
Stepping Narrowly is to become a hyper-expert such that no robot can replace your work and knowledge. Think being THE expert on the rat swarm that erupts in North East India every 48 years.
Stepping Forward refers to the technical and business brilliance of building and implementing automation machines. Think programmers, entrepreneurs, marketers, internal automation leaders, consultants, data scientists / analysts, and researchers.
However, I take strong issue with his parting conclusion on the minimum basic income that "it is unnecessary to contemplate such a drastic redistribution unless you have given up on the possibility of widespread employment in well-paying jobs... human strengths will continue to enable humans to produce economic value and to be paid for that value, there is no reason to decouple work from income." This view is oddly naive considering that a single 'low-hanging fruit' of automation that of automating long-distance truck drivers could eliminate over 1 million jobs in the United States while very optimistically creating jobs for ten of thousands within only 10-20 years. This is unprecedented in the speed and scale of dislocation upon the economy and employment market. While it is great to hear a more optimistic view on the issue, I firmly disagree.
July 24, 2019
This has been an excellent roadmap for my personal career, and has given me lenses to consider how other peers around me can consider their contributions of value around us!
The main thrust is that automation is ultimately limiting, because it is static. Humans are dynamic, and are able to respond to the changing states of the world. Of course, because there are many tasks that are routine--and many computations that computers are highly qualified to perform--the recommended relationship of humans to machines is augmentation: leveraging machines to grant humans either a superpower, or to give leverage. This positions humans to do what they do best: see, and respond.
At that, the authors show five ways humans can relate to technology:
Stepping Up - “Moving up above automated systems to develop more big-picture insights and decisions that are too unstructured and sweeping for computers or robots to be able to make.”
Stepping Aside - “Moving to a type of non-decision-oriented work that computer’s aren’t good at, such as selling, motivating people, or describing in straightforward terms the decisions that computers have made.”
Stepping In - “Engaging with the computer system’s automated decisions to understand, monitor, and improve them. This is the option at the heart of what we are calling augmentation, although each of these five steps can be described as augmenting.”
Stepping Narrowly - “Finding a specialty area within your profession that is so narrow that no one is attempting to automate it—and it might never be economical to do so.”
Stepping Forward - “Developing the new systems and technology that support intelligent decisions and actions in a particular domain.”
I personally have been able to identify I relate to Stepping In! This has only enabled me to engage in my work with all the more gusto, and to see the work my peers are doing and make space for them to use their abilities in their most human capacity. I'm grateful for the hopeful, life-giving outlook these MIT professors have for humanity and society.
The main thrust is that automation is ultimately limiting, because it is static. Humans are dynamic, and are able to respond to the changing states of the world. Of course, because there are many tasks that are routine--and many computations that computers are highly qualified to perform--the recommended relationship of humans to machines is augmentation: leveraging machines to grant humans either a superpower, or to give leverage. This positions humans to do what they do best: see, and respond.
At that, the authors show five ways humans can relate to technology:
Stepping Up - “Moving up above automated systems to develop more big-picture insights and decisions that are too unstructured and sweeping for computers or robots to be able to make.”
Stepping Aside - “Moving to a type of non-decision-oriented work that computer’s aren’t good at, such as selling, motivating people, or describing in straightforward terms the decisions that computers have made.”
Stepping In - “Engaging with the computer system’s automated decisions to understand, monitor, and improve them. This is the option at the heart of what we are calling augmentation, although each of these five steps can be described as augmenting.”
Stepping Narrowly - “Finding a specialty area within your profession that is so narrow that no one is attempting to automate it—and it might never be economical to do so.”
Stepping Forward - “Developing the new systems and technology that support intelligent decisions and actions in a particular domain.”
I personally have been able to identify I relate to Stepping In! This has only enabled me to engage in my work with all the more gusto, and to see the work my peers are doing and make space for them to use their abilities in their most human capacity. I'm grateful for the hopeful, life-giving outlook these MIT professors have for humanity and society.
November 5, 2017
Ever since John Maynard Keynes published his works 100 years ago following the great depression, people have been concerned about job losses due to automation. Davenport describes the current state of automation and data science in context with human employability.
This book is written for those impacted and managers who lead the change. It provides insights for parents who help their kids to make better choices for their education and for educators telling them what skills are important to teach.
A fast read that brings home a few great points. A strong 4 Stars, he just does not hit it out of the park for me.
This book is written for those impacted and managers who lead the change. It provides insights for parents who help their kids to make better choices for their education and for educators telling them what skills are important to teach.
A fast read that brings home a few great points. A strong 4 Stars, he just does not hit it out of the park for me.
October 8, 2018
This book allowed me to see artificial intelligence in the workplace in a more favorable light. They argue that smart machines can augment the value that humans bring to the workplace. Freeing them from mundane repetitive tasks that can be a drag on workplace morale. The authors do lay out how to best prepare for the ever advancing technology that is AI. The premise is well fleshed out. I did find some areas a little dry. This probably speaks to my non-technical background. Industry, government, education and the individual knowledge worker have a responsibility in the success of machine-human integration.
This entire review has been hidden because of spoilers.
December 8, 2021
This book, is an attempt to come to an understanding of a deep and fundamental change, in the nature of industry. Will we compete, more and more, with the machines, that perhaps will some day replace us? Or will they serve along side us, augmenting our abilities? This is at this point, unclear. The author describes his thoughts on the later, or second of these.
February 2, 2021
Book has way too many fillers. A lot of very short anecdotes/examples that don't do much to aid your understanding - you'll forget about them soon after reading.
Read introduction + chapter summaries and you'll get 90% of the info.
Read introduction + chapter summaries and you'll get 90% of the info.
July 16, 2024
GLOSE, e-book
I started reading this while a University Professor and found it interesting to ponder. Then RL, Covid, and retirement happened and I find I no longer wish to revisit this book or the topic.
Thus, shelved as a DNF title and life goes on!
I started reading this while a University Professor and found it interesting to ponder. Then RL, Covid, and retirement happened and I find I no longer wish to revisit this book or the topic.
Thus, shelved as a DNF title and life goes on!
July 26, 2025
I find the rise of AI and its integration into everyday life fascinating and I have read a lot of books on this topic. This book had some good points but it was very long and as a result I found it a bit of a hard slog in places.
June 2, 2017
Would have been better as a magazine article, but contains a thorough and interesting analysis of the ongoing impact of smart machines. Sober but not dystopian.
August 19, 2020
Narrative is that there will always be human augmation that will be needed , however the job loses because of automation is real
November 26, 2021
Explain the positive sides of the technology. Also, widens my knowledge about uncertainty and risk of losing jobs to technology.
May 22, 2016
TAKE ACTION, OR BE SWEPT ASIDE BY AUTOMATION
ONLY HUMANS NEED APPLY is a complicated book--and even a little bit scary--so plan on giving it some time. First of all, note that this book is not just a theoretical book exploring some things that might happen in the future. Rather, the author explores what is already happening around the world as automation displaces many workers. Fortunately for the reader, the author provides specific action plans that you can take to protect your job.
I admit I was not expecting much from this book--mostly because I was not familiar with this author. I learned that Dr. Davenport has EXTENSIVE experience in this area, and has taught at Harvard Business School, as well as at University of Chicago.
So after researching the author's credentials a little bit, I decided to give this book a lot more attention.
The theme of the book is that automation is changing (and often eliminating jobs). There is no stopping it, so you need to adapt. You need to face this realistically, or you will be left behind: "Our main mission in this book has been to give you a sense of agency and to help you begin to make decisions for yourself about how to deal with advancing automation."
The reality is that traditional jobs are in jeopardy. Most of all, it's those jobs that can be reduced to a set of specific steps. The author calls that "Codifying" the work. If your job can be reduced to some set of steps, you are in trouble: "If work can be codified, it can be automated. And there’s also the corollary: If it can be automated in an economical fashion, it will be."
Oftentimes, the impact of automation is slow--just at the margins. So, for example, a company might have employed 10 people to do certain jobs in the past, but now, they only need 9. So it doesn't happen all at once; instead, you are sort of "edge toward" the door.
As an example of how automation has changed the nature of professions, Dr. Davenport uses the profession of radiology. Traditionally, this has been among the most highly paid professions. In recent years, however, part of this profession has been "codified." The process of radiology--what the doctor actually does--has been explicitly defined. The result of that was outsourcing of some of the work. And other parts were computerized.
So what can anyone do? The author has specific recommendations to deal with the threat of automation. By recognizing what is happening, you can have a strategy to survive.
One strategy is called "Stepping Aside." By this, the author means changing your job to focus on things that aren't easily automated: "Moving to a type of non-decision-oriented work that computers aren’t good at, such as selling or motivating people, or describing in straightforward terms the decisions that computers have made.”
Another strategy is "Stepping In." This means working to try to improve the automation. (This sounds to me like "If you can't beat 'em, join 'em!)
The strategy I liked he calls "Stepping Narrowly." This means specializing so narrowly that there is no incentive to automate it. "Finding a specialty area within your profession that is so narrow that no one is attempting to automate it—and it might never be economical to do so.”
Another option, the idea of "Stepping Forward," is to focus on "developing the new systems and technology that support intelligent decisions and actions in a particular domain."
You can also decide to try "Stepping Up." This means changing your job to look at the "big picture." Unfortunately, there are typically only a few people--at the top of the company, who are employed to do this. This is work that "cannot be done by robots. Most organizations have only a few people in such roles—but it is important out of proportion to the numbers."
Dr. Davenport argues that the anemic job market is a short-time anomaly, but rather a result of automation. "The persistent unemployment could no longer be called a cyclical phenomenon. It reflected a structural change, in part due to the growing sophistication of automation."
So in other words, get used to it. Many jobs will continue to be eliminated as automation continues. "Most everyone agrees that the growing ability to automate knowledge work will cause (indeed, is causing) labor dislocations that are painful in the short run..."
So all in all, I thought ONLY HUMANS NEED APPLY was an outstanding book. It is clear to me that the author absolutely understands what is happening. I appreciate his offering specific steps that we can take to deal with this ongoing automation.
Advance Review Copy courtesy of Edelweiss Book Distributors.
ONLY HUMANS NEED APPLY is a complicated book--and even a little bit scary--so plan on giving it some time. First of all, note that this book is not just a theoretical book exploring some things that might happen in the future. Rather, the author explores what is already happening around the world as automation displaces many workers. Fortunately for the reader, the author provides specific action plans that you can take to protect your job.
I admit I was not expecting much from this book--mostly because I was not familiar with this author. I learned that Dr. Davenport has EXTENSIVE experience in this area, and has taught at Harvard Business School, as well as at University of Chicago.
So after researching the author's credentials a little bit, I decided to give this book a lot more attention.
The theme of the book is that automation is changing (and often eliminating jobs). There is no stopping it, so you need to adapt. You need to face this realistically, or you will be left behind: "Our main mission in this book has been to give you a sense of agency and to help you begin to make decisions for yourself about how to deal with advancing automation."
The reality is that traditional jobs are in jeopardy. Most of all, it's those jobs that can be reduced to a set of specific steps. The author calls that "Codifying" the work. If your job can be reduced to some set of steps, you are in trouble: "If work can be codified, it can be automated. And there’s also the corollary: If it can be automated in an economical fashion, it will be."
Oftentimes, the impact of automation is slow--just at the margins. So, for example, a company might have employed 10 people to do certain jobs in the past, but now, they only need 9. So it doesn't happen all at once; instead, you are sort of "edge toward" the door.
As an example of how automation has changed the nature of professions, Dr. Davenport uses the profession of radiology. Traditionally, this has been among the most highly paid professions. In recent years, however, part of this profession has been "codified." The process of radiology--what the doctor actually does--has been explicitly defined. The result of that was outsourcing of some of the work. And other parts were computerized.
So what can anyone do? The author has specific recommendations to deal with the threat of automation. By recognizing what is happening, you can have a strategy to survive.
One strategy is called "Stepping Aside." By this, the author means changing your job to focus on things that aren't easily automated: "Moving to a type of non-decision-oriented work that computers aren’t good at, such as selling or motivating people, or describing in straightforward terms the decisions that computers have made.”
Another strategy is "Stepping In." This means working to try to improve the automation. (This sounds to me like "If you can't beat 'em, join 'em!)
The strategy I liked he calls "Stepping Narrowly." This means specializing so narrowly that there is no incentive to automate it. "Finding a specialty area within your profession that is so narrow that no one is attempting to automate it—and it might never be economical to do so.”
Another option, the idea of "Stepping Forward," is to focus on "developing the new systems and technology that support intelligent decisions and actions in a particular domain."
You can also decide to try "Stepping Up." This means changing your job to look at the "big picture." Unfortunately, there are typically only a few people--at the top of the company, who are employed to do this. This is work that "cannot be done by robots. Most organizations have only a few people in such roles—but it is important out of proportion to the numbers."
Dr. Davenport argues that the anemic job market is a short-time anomaly, but rather a result of automation. "The persistent unemployment could no longer be called a cyclical phenomenon. It reflected a structural change, in part due to the growing sophistication of automation."
So in other words, get used to it. Many jobs will continue to be eliminated as automation continues. "Most everyone agrees that the growing ability to automate knowledge work will cause (indeed, is causing) labor dislocations that are painful in the short run..."
So all in all, I thought ONLY HUMANS NEED APPLY was an outstanding book. It is clear to me that the author absolutely understands what is happening. I appreciate his offering specific steps that we can take to deal with this ongoing automation.
Advance Review Copy courtesy of Edelweiss Book Distributors.
September 21, 2022
If you want to position yourself on the winning side of the technology race in the Age of the Smart Machines, it is in your best interest to read this book.
The core premise here is to avoid automation and to embrace augmentation. Avoid jobs that are likely to become automated (think bank teller and check out clerk) and embrace the opportunity to automate those tasks in your current job that are routine and computers can do more efficiently. This will free you up to perform tasks where humans can add the most value.
Automation means being replaced by a machine. Augmentation means developing a synergistic relationship with smart machines.
The framework presented offers five options for augmentation:
~ stepping up
~ stepping aside
~ stepping in
~ stepping narrowly
~ stepping forward
Stepping up, in or forward requires vigilant training in upgrading STEM skills (science, technology, engineering and math). This career path is not for everyone, which makes stepping aside and stepping narrowly options for some.
Stepping Aside requires developing skills in work areas that computers are not particularly good at, such as selling and motivating people. Stepping Narrowly involves finding a specialty area within your profession that is so narrow that no one is likely to automate it because it is simply not economical to do so.
If you are already STEM-inclined, then embrace automation in one of three ways:
~ Stepping Up means positioning yourself above automated systems to develop more big-picture insights that will likely affect your business or industry.
~ Stepping In requires you to engage with the automated decision-making power of computer software to understand, monitor and improve their decision-making capability.
~ Stepping Forward is the most STEM-intensive option and requires developing new systems and technology that support automation opportunities within your industry.
Make no mistake, we all have aspects of our jobs that can be automated. If it can be automated, it will be automated. Allow computers and robots to do what they are highly capable of doing. After all, few us still do long division using paper and pencil. Computers took over the calculator function long ago.
By embracing the inevitability of automation you can free yourself of tediousness and drudgery and perform higher level tasks that will keep you engaged in your work and comfortably fed.
Only Humans Need Apply: Winners and Losers in the Age of Smart Machines offers worthy insights to help augment the views presented in Rise of the Robots: Technology and the Threat of a Jobless Future.
Access Gene Babon's reviews of books on Business Leadership and Business Strategy at Pinterest.
The core premise here is to avoid automation and to embrace augmentation. Avoid jobs that are likely to become automated (think bank teller and check out clerk) and embrace the opportunity to automate those tasks in your current job that are routine and computers can do more efficiently. This will free you up to perform tasks where humans can add the most value.
Automation means being replaced by a machine. Augmentation means developing a synergistic relationship with smart machines.
The framework presented offers five options for augmentation:
~ stepping up
~ stepping aside
~ stepping in
~ stepping narrowly
~ stepping forward
Stepping up, in or forward requires vigilant training in upgrading STEM skills (science, technology, engineering and math). This career path is not for everyone, which makes stepping aside and stepping narrowly options for some.
Stepping Aside requires developing skills in work areas that computers are not particularly good at, such as selling and motivating people. Stepping Narrowly involves finding a specialty area within your profession that is so narrow that no one is likely to automate it because it is simply not economical to do so.
If you are already STEM-inclined, then embrace automation in one of three ways:
~ Stepping Up means positioning yourself above automated systems to develop more big-picture insights that will likely affect your business or industry.
~ Stepping In requires you to engage with the automated decision-making power of computer software to understand, monitor and improve their decision-making capability.
~ Stepping Forward is the most STEM-intensive option and requires developing new systems and technology that support automation opportunities within your industry.
Make no mistake, we all have aspects of our jobs that can be automated. If it can be automated, it will be automated. Allow computers and robots to do what they are highly capable of doing. After all, few us still do long division using paper and pencil. Computers took over the calculator function long ago.
By embracing the inevitability of automation you can free yourself of tediousness and drudgery and perform higher level tasks that will keep you engaged in your work and comfortably fed.
Only Humans Need Apply: Winners and Losers in the Age of Smart Machines offers worthy insights to help augment the views presented in Rise of the Robots: Technology and the Threat of a Jobless Future.
Access Gene Babon's reviews of books on Business Leadership and Business Strategy at Pinterest.
December 6, 2025
Треба було прочитати цю книгу трохи раніше, як тільки придбала :)
Але ось опції, як втримати робоче місце в час поширення штучного інтелекту:
- Кроки вгору: делегуємо задачі ШІ, а самі робимо важливіші справи;
- Кроки вбік: вчимося професій, які НЕ можуть бути автоматизовані;
- Кроки всередину: вчимося розробляти ШІ рішення;
- Кроки вузькою стежкою: набуваємо вузького і глибокого досвіду, який буде невигідно автоматизувати;
- Кроки вперед: бігти у обійми ШІ і стаємо новаторами у сфері.
В принципі, грунтовно і зрозуміло.
І позиція авторів щодо ауґментації роботи за допомогою ШІ, а не заміни людини машинами, дуже імпонує, адже процес назад не повернеш, але і робити повні автоматизовані переходи ризиковано.
Але ось опції, як втримати робоче місце в час поширення штучного інтелекту:
- Кроки вгору: делегуємо задачі ШІ, а самі робимо важливіші справи;
- Кроки вбік: вчимося професій, які НЕ можуть бути автоматизовані;
- Кроки всередину: вчимося розробляти ШІ рішення;
- Кроки вузькою стежкою: набуваємо вузького і глибокого досвіду, який буде невигідно автоматизувати;
- Кроки вперед: бігти у обійми ШІ і стаємо новаторами у сфері.
В принципі, грунтовно і зрозуміло.
І позиція авторів щодо ауґментації роботи за допомогою ШІ, а не заміни людини машинами, дуже імпонує, адже процес назад не повернеш, але і робити повні автоматизовані переходи ризиковано.
July 17, 2016
I hope all elected government officials, public and private school administrators, teachers, and corporate executives read this book for clarity on augmentation vs. automation as we anticipate major advances in automation and AI. The last chapter is the most interesting in my view—it speaks to increasing need for awareness and action in public policy, social policy, and early education to prepare us for a future of working with automation, emphasizing the importance of a mindset and macro-approach toward augmentation. We need policies and practices to get in front of it. Read each chapter summary and then skip to the last two chapters if you don’t have time to read the entire book. While it is a worthwhile read, I found myself dozing off regularly given the academic tone often wrought with passive voice and lengthy strings of prepositional phrases (why I’m giving it 4 stars instead of 5). The main emphasis is to show how augmentation, not automation, is the wiser approach to implementing technology. In other words, having a macro goal to augment human beings’ abilities to do more advanced work leaving the machines and robots to do mundane tasks that knowledge workers don’t want to do and don’t have the time to do. Many examples are from the insurance industry (adding to the snooze factor). Still, the premise and framework are good and worth reading for more insight into how we can view the future of technology and how we, as humans, can better prepare ourselves to work with it. But those who work in management or systems consulting--or any sort of high-tech work--will be quite familiar with the scenarios described in the 5 “stepping” segments. It’s unfortunate this book wasn’t written in a more engaging tone for workers at risk or soon to be redundant in the workforce if they take no action—they are the ones that truly need to understand this stuff. It's more likely only geeks like me will read this book.
January 3, 2017
Great analysis of the rapidly emerging AI-augmented world
The book offers a great analysis of the rapidly emerging AI-augmented world and provides a well-grounded discussion of the issues. Most welcome is a comprehensive analysis of and suggestions for the ways forward--the essential part that some hopelessly alarmist and therefore significantly less useful books on the subject fail to provide.
The book offers a great analysis of the rapidly emerging AI-augmented world and provides a well-grounded discussion of the issues. Most welcome is a comprehensive analysis of and suggestions for the ways forward--the essential part that some hopelessly alarmist and therefore significantly less useful books on the subject fail to provide.
July 24, 2016
We will definitely need to work with robots and AI in future. Just as it's silly to expect our supermarket clerk not to use the cashier machine to sum up our grocery bill, it is also silly not for us to make use of robots and AI. They are simply tools. No doubt this time is different as they are going to replace knowledge workers, who were previously thought to be irreplaceable.
The author introduces 5 ways for us to have a job in future:
1. Step Up: become the person who decides where to use robots and AI
2. Step in: works with robots and AI
3. Step aside: do things that machines cannot do, like any artisanal work
4. Step narrowly: become an expert in a narrow area that it's not economical to deploy robots/AI
5. Step forward: write the code for robots/AI itself
The robots are here and it's futile to run. We just have to learn to use them wisely. Governments can no longer just let market forces work (since that will result in the rich taking all the gains), but must redistribute wealth more effectively.
The author introduces 5 ways for us to have a job in future:
1. Step Up: become the person who decides where to use robots and AI
2. Step in: works with robots and AI
3. Step aside: do things that machines cannot do, like any artisanal work
4. Step narrowly: become an expert in a narrow area that it's not economical to deploy robots/AI
5. Step forward: write the code for robots/AI itself
The robots are here and it's futile to run. We just have to learn to use them wisely. Governments can no longer just let market forces work (since that will result in the rich taking all the gains), but must redistribute wealth more effectively.
October 8, 2023
More relevant now than when first issued.
What seemed to be an obscure future is here now with Watson and Skynet. We saw it coming and should have been prepared.
The only drawback of the book is that it tries to make the point the hard way with repetition. Well, we need to flesh out the book.
So, we can get shocking headlines the news stations like to call what are “Expert Systems” that can handle or assist a particular skill, AI (Artificial intelligence) which is a whole different animal.
In any event, it is worth the read as it goes in-depth into the fun we face.
Today an AI algorithm can listen to you typing and interpret it.
An older concept of us “owning” the machines that work for us can be found in “A Piece of the Action” by Stuart M Speiser. Even though it is dated it can apply. ISBN-13: 978-0442270100
Or after reading this book if you are paranoid skip watching “The Terminator” (1984) and watch “The Creation of the Humanoids” (1962) Where they take more than our jobs.
What seemed to be an obscure future is here now with Watson and Skynet. We saw it coming and should have been prepared.
The only drawback of the book is that it tries to make the point the hard way with repetition. Well, we need to flesh out the book.
So, we can get shocking headlines the news stations like to call what are “Expert Systems” that can handle or assist a particular skill, AI (Artificial intelligence) which is a whole different animal.
In any event, it is worth the read as it goes in-depth into the fun we face.
Today an AI algorithm can listen to you typing and interpret it.
An older concept of us “owning” the machines that work for us can be found in “A Piece of the Action” by Stuart M Speiser. Even though it is dated it can apply. ISBN-13: 978-0442270100
Or after reading this book if you are paranoid skip watching “The Terminator” (1984) and watch “The Creation of the Humanoids” (1962) Where they take more than our jobs.
Displaying 1 - 30 of 36 reviews