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Why Greatness Cannot Be Planned: The Myth of the Objective
Why does modern life revolve around objectives? From how science is funded, to improving how children are educated -- and nearly everything in-between -- our society has become obsessed with a seductive that greatness results from doggedly measuring improvement in the relentless pursuit of an ambitious goal. In Why Greatness Cannot Be Planned, Stanley and Lehman begin with a surprising scientific discovery in artificial intelligence that leads ultimately to the conclusion that the objective obsession has gone too far. They make the case that great achievement can't be bottled up into mechanical metrics; that innovation is not driven by narrowly focused heroic effort; and that we would be wiser (and the outcomes better) if instead we whole-heartedly embraced serendipitous discovery and playful creativity.
Controversial at its heart, yet refreshingly provocative, this book challenges readers to consider life without a destination and discovery without a compass.
Controversial at its heart, yet refreshingly provocative, this book challenges readers to consider life without a destination and discovery without a compass.
154 pages, Kindle Edition
First published May 5, 2015
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8,170 people want to read
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December 7, 2019
At the AI conference I attended in Prague last summer, this author's talk stood out as the most interesting to me. Although the author is an AI researcher, this book is written for the lay reader. His point is a very simple one: if you always try to move towards an end goal, so much of the space of possibilities will go unexplored that the best solutions won't be found, except in the most straightforward of cases. Instead of heading toward the objective we should explore the space of possibilities by following novelty or interestingness wherever it leads us, collecting treasures along the way. After exploring the simplest solutions, the only way to go that is novel is towards the more complex, so this kind of exploration moves in the direction of increasing complexity.
He applies the idea to scientific research, to evolution, to art, and to education, and brings insight to how each of these fields could be reformed to be more creative and in doing so, paradoxically progress faster.
Evolution, for example: "survival of the fittest" implies there is one form which is the fittest form, and evolution is moving ever towards that goal. But if success at reproduction is all that matters to evolution (speaking anthropomorphically) it has never done better than bacteria. Clearly something else is going on here.
business: this is the difference between innovative new products and commoditization.
research: everyone knows that just getting 2% better on the metrics isn't the best way to decide which papers to publish, but we keep going back to it because if it does better it must be publishable.
The book has changed one of my long held beliefs, that something like Common Core and lots of testing are needed if education is to improve. If what Stanley is saying is right, this will only lead to small gains that then plateau. Instead, what education needs is diversity and freedom, and lots of cross-pollenization.
When I think about how to implement his ideas for artificial creativity, though, I keep running into the question of how to make decisions without an objective. It's all well and good to say "try everything!" but most things you try are bad ideas that won't lead anywhere. How can you build a fully autonomous system that can recognize "this has potential" without defining an objective?
He gives a few examples of how his systems actually outperform traditional search methods that move towards an objective. But when I followed up on researchers who cite his work, the picture gets muddier: it turns out that hybrids (which include an objective as well as exploration) often perform better overall. And in looking for what performs best, aren't we abandoning the principle behind all this anyway?
I've still got to put a lot of effort into considering his ideas, but they are intriguing. I also feel like they validate my own approach towards my work. My only complaint was that parts got repetitive, but I just skimmed those.
EDIT: One year later, these hybrid methods have a lot better empirical results showing that on certain hard problems, looking for a diverse set of good solutions does a better job at finding the best solution than just trying to find the best solution. Some high-profile game-playing programs like AlphaStar and Go-Explore made use of this technique. The term people are using is Quality Diversity (QD).
He applies the idea to scientific research, to evolution, to art, and to education, and brings insight to how each of these fields could be reformed to be more creative and in doing so, paradoxically progress faster.
Evolution, for example: "survival of the fittest" implies there is one form which is the fittest form, and evolution is moving ever towards that goal. But if success at reproduction is all that matters to evolution (speaking anthropomorphically) it has never done better than bacteria. Clearly something else is going on here.
business: this is the difference between innovative new products and commoditization.
research: everyone knows that just getting 2% better on the metrics isn't the best way to decide which papers to publish, but we keep going back to it because if it does better it must be publishable.
The book has changed one of my long held beliefs, that something like Common Core and lots of testing are needed if education is to improve. If what Stanley is saying is right, this will only lead to small gains that then plateau. Instead, what education needs is diversity and freedom, and lots of cross-pollenization.
When I think about how to implement his ideas for artificial creativity, though, I keep running into the question of how to make decisions without an objective. It's all well and good to say "try everything!" but most things you try are bad ideas that won't lead anywhere. How can you build a fully autonomous system that can recognize "this has potential" without defining an objective?
He gives a few examples of how his systems actually outperform traditional search methods that move towards an objective. But when I followed up on researchers who cite his work, the picture gets muddier: it turns out that hybrids (which include an objective as well as exploration) often perform better overall. And in looking for what performs best, aren't we abandoning the principle behind all this anyway?
I've still got to put a lot of effort into considering his ideas, but they are intriguing. I also feel like they validate my own approach towards my work. My only complaint was that parts got repetitive, but I just skimmed those.
EDIT: One year later, these hybrid methods have a lot better empirical results showing that on certain hard problems, looking for a diverse set of good solutions does a better job at finding the best solution than just trying to find the best solution. Some high-profile game-playing programs like AlphaStar and Go-Explore made use of this technique. The term people are using is Quality Diversity (QD).
August 7, 2022
The authors of this book performed an interesting experiment in 2006 where they created a web-based application called Picbreeder,which is still available for anyone totry. The program generates an array of small images withvery little details, and each very similar to the rest except for some small random variations. The human user is supposed to pick one of these images to trigger the next generation of images, which are small variations of the selected image. The same user, or someone else, can continue this selective "breeding" as long as desired. The strange finding was that ultimately it ends up generating surprising images that resemble recognizable objects such as a human face, a car, an insect, or a candle. The important observation here is that the users did not aim to create these images from the start, and it can be shown that if they aimed at something, most likely they would never get there. That is, the most surprising results are obtained when that was not the objective, and it reaches thisstate via a series of unrelatedinteresting images, which the authors call the "stepping stones".
In this book, the authors try to generalizethis observation into a much larger principle. They claim that while small improvementscan be achievedthrough a goal-directed approach, really ambitiousthings can only be achieved when we allow ourselves to wander around without specific objectives. For example, the first computer, Eniac, used vacuum tubes, but if someone wanted to create the first computer, say a hundred years ago, they would not have discovered the vacuumtube in the process. The discovery of the vacuum tube happened for a very different reason and acted as a stepping stone toward designing the first computer.
Their main attack is on the present culture of objective-driven processes in the world of science and technology. Starting from how research is funded and evaluated to how we are trained to think, the focus is always on specific objectives, and according to the authors, that is a very short-sighted approach and is a hindrance to spectacular discoveries.
They also try to view the process of natural evolution through this prism and present an alternative interpretation of the evolutionary process. The group also tried to apply these principles in an AI search algorithm that only looks for "novelty" in the search space, rather than maximizing some objective function, and show that in some cases, this approach produces better results than an objective-based algorithm.
It is certainly a powerful and novel idea that offersa new way of looking at many things. I certainly felt intellectuallyenriched after reading the book. However, I also felt that the writers fell into a common trap many others fall into after they come up witha powerful idea -- that of over-generalizingthe scope of the idea. They start believing that this one idea can be applied everywhere. In thiscase, the authors do talk about this mistake but still fall into it.
First of all, they never discuss real-life cases where their ideas may not apply. For example, they talk of extremely successful individuals who did not aim at what ultimately made them famous but never mention other successful people who set their ultimate target very early on in their lives and doggedly pursued it till they reached their celebrity. They criticize the current process of objective-based policies and explain why greatness cannot be achieved that way but do not try to look at thousands of spectacular innovations that happened in spite of these restrictions.
Finally, they fail to offer a pragmatic alternative to objective-based thinking. In a world where resources are always limited, how else can we allocate those precious resourceswithout some sort of practical constraints? In reality, most of the scientificand technological improvements that happen are due to unambitioussmall steps done by mediocre people. The system asks us to predict an outcome and then go for it. However, in this sea of mediocrity, there are a few brilliant individuals who can see a little further or are just more creative, and they discover something that is away from the original objective. It is these leaps that form what theauthors are calling the stepping stones. That is, spectacularprogress still happens, despite our uninspiring processes, and the objective-basedparadigms ensure that the majority of mediocre innovators make the best use of the resources.
I am not suggesting that the process that exists today is ideal or optimum. Thinking like this can certainly drive us towards a better system. However, I don't believe the removal of the objective-based process altogether can be very efficient.
A great, thought-provoking book that I'd highly recommend. However, I think the book could have been much shorter without losing much of its communicative strength.
In this book, the authors try to generalizethis observation into a much larger principle. They claim that while small improvementscan be achievedthrough a goal-directed approach, really ambitiousthings can only be achieved when we allow ourselves to wander around without specific objectives. For example, the first computer, Eniac, used vacuum tubes, but if someone wanted to create the first computer, say a hundred years ago, they would not have discovered the vacuumtube in the process. The discovery of the vacuum tube happened for a very different reason and acted as a stepping stone toward designing the first computer.
Their main attack is on the present culture of objective-driven processes in the world of science and technology. Starting from how research is funded and evaluated to how we are trained to think, the focus is always on specific objectives, and according to the authors, that is a very short-sighted approach and is a hindrance to spectacular discoveries.
They also try to view the process of natural evolution through this prism and present an alternative interpretation of the evolutionary process. The group also tried to apply these principles in an AI search algorithm that only looks for "novelty" in the search space, rather than maximizing some objective function, and show that in some cases, this approach produces better results than an objective-based algorithm.
It is certainly a powerful and novel idea that offersa new way of looking at many things. I certainly felt intellectuallyenriched after reading the book. However, I also felt that the writers fell into a common trap many others fall into after they come up witha powerful idea -- that of over-generalizingthe scope of the idea. They start believing that this one idea can be applied everywhere. In thiscase, the authors do talk about this mistake but still fall into it.
First of all, they never discuss real-life cases where their ideas may not apply. For example, they talk of extremely successful individuals who did not aim at what ultimately made them famous but never mention other successful people who set their ultimate target very early on in their lives and doggedly pursued it till they reached their celebrity. They criticize the current process of objective-based policies and explain why greatness cannot be achieved that way but do not try to look at thousands of spectacular innovations that happened in spite of these restrictions.
Finally, they fail to offer a pragmatic alternative to objective-based thinking. In a world where resources are always limited, how else can we allocate those precious resourceswithout some sort of practical constraints? In reality, most of the scientificand technological improvements that happen are due to unambitioussmall steps done by mediocre people. The system asks us to predict an outcome and then go for it. However, in this sea of mediocrity, there are a few brilliant individuals who can see a little further or are just more creative, and they discover something that is away from the original objective. It is these leaps that form what theauthors are calling the stepping stones. That is, spectacularprogress still happens, despite our uninspiring processes, and the objective-basedparadigms ensure that the majority of mediocre innovators make the best use of the resources.
I am not suggesting that the process that exists today is ideal or optimum. Thinking like this can certainly drive us towards a better system. However, I don't believe the removal of the objective-based process altogether can be very efficient.
A great, thought-provoking book that I'd highly recommend. However, I think the book could have been much shorter without losing much of its communicative strength.
February 17, 2021
when the business + eastern philosophy book sections get disintermediated by nerdy AI researchers
"When all is said and done, when even visionaries grow weary of stale visions, when the ash of unrequited expectation settles on the cloak of the impenetrable future, there is but one principle that may yet pierce the darkness: To achieve our highest goals, we must be willing to abandon them."
"When all is said and done, when even visionaries grow weary of stale visions, when the ash of unrequited expectation settles on the cloak of the impenetrable future, there is but one principle that may yet pierce the darkness: To achieve our highest goals, we must be willing to abandon them."
January 16, 2018
Conflates objectives (goals) with objective functions (measurement of progress). The book is filled with good examples of bad objective functions, but then makes wild claims about the usefulness of objectives. In the end this whole book can be viewed as another good example of programmers overabstracting things to the point of absurdity.
December 30, 2024
I desperately wanted to like this book. I expected to enjoy it. I had pre-ordered the conclusion, had already bought the arguments. All I needed was the mechanical process of executing on a non-fiction book that defends a framework with narrative evidence. My good( )readers, how I was disappointed.
This is one of the most frustrating books I've ever read. It is short, and yet too long for its substance. It wants to make valid, empirically supported arguments, yet it falls into endless, vacuous repetition. And hell is this book vacuous. It is frustrating because it gets close to exploring questions you ask, but doesn't. It should have a rich background of examples, but it relies on poor analogies that aggrandize the authors' work - work that has not aged well.
And the book is imprecise. Vocabulary goes undefined, outcomes go unexplained. Different problems with how research is conducted are conflated. It becomes hard to take the authors seriously. And I already agreed with several of their conclusions! They make good points, seemingly by accident.
When I reached the halfway mark, I realized it wasn't going to improve, but I decided to finish it to confirm my suspicion and justify a review. Unfortunately, the book thinks it is much smarter than it actually is, and that its readers are dumber than they are.
I. Repetition and imprecision
There are three examples the authors invoke for analogy all the time: evolution, Picbreeder, and human innovation. What is Picbreeder? That's a research project the authors conducted, and which they proceed to mention every other page. They also use the figures of "treasure hunters" and "stepping stones" all over the place, denoting the ideas of opportunistic exploration and reusable discoveries.
Some of the repeated examples just don't support their thesis. The authors conflate invention with discovery when talking about innovation - "vacuum tubes enabled computation, but that wasn't their purpose, so if you wanted to build a computer you wouldn't have gone for vacuum tubes." So what? If your conclusion is that objectives don't work, that's a terrible example. Babbage built and designed mechanical computing machines before vacuum tubes - he had a goal! And he built designs that achieved that goal! The issue is that "computers" as the authors imagine were not a "goal" anyone elaborated. I cannot take "unimagined inventions" as "ambitious goals" that a search process is failing to meet.
The quote above comes in very late in the book, but I was saying it all along!
What is an ambitious goal, for the authors? It sounds like making world-historic discoveries would fit the bill. That's hard to argue against: research driven not by the potential for discovery but by measurable, incremental progress is not going to be revolutionary most of the time.
The authors sounded like they have a much bigger issue with quantification than with objectives. But even if the issue were objectives, then you still have plenty to work with! The healthcare industry has tons of examples of failed goal-setting. Perhaps it was a bit too far from the authors' field. But in judging the longevity of this work, consider that they published it the year OpenAI was founded. Looks like their "objective-driven" approach to AI is winning.
II. Evidence and arguments
If you're going to write a book that depends on a key premise, should you try to defend that premise?
The authors make one key statement early on and proceed to use it throughout the book, which I will paraphrase as "objective-driven search cannot reach end states that exploratory search could." Is this statement wrong? Not necessarily. But the authors do a terrible job supporting it.
Quotes like the above are typical. But why exactly couldn't someone reach an end state if they wanted to? Again, the authors are missing the problem of simply formulating objectives. You can't reach objectives you don't specify. That's not the fault of objective thinking.
A better argument, supported in sections about education and research funding, is that objective thinking can't dominate decision-making. This is especially true for research. But that is a different argument than saying objectives don't work. They work all too well! Goodhart's law is a thing.
Another key claim is that novelty search can outperform objective-driven search. This claim is very poorly explained. There are no measures for comparison, no description of how the relevant tasks were even set up. But they rely on these results for their argument all the way through.
III. Nitpicks
The book is very poorly written. Perhaps there was no outline, in the spirit of "exploration." The authors spend so many words clarifying things and explaining themselves that they clearly should have spent more time revising their draft. Let's look at a couple of examples where they indicate their writing wasn't clear:
This is a template for a drinking game.
I have many issues with ideas the authors discussed in their case studies on evolution and AI research, but this review is already long enough as it is.
Final nitpick, which decreases the perceived reliability of the authors: they misspelled John Stuart Mill's name as "Stewart." I wouldn't care, but this is the kind of thing that shows they can't be that familiar with his work.
IV. The good
This book did make me think quite a bit. Much of that is the realization that it is a bad book, and so the reader should tread carefully. But there were actually interesting thought experiments in there.
"What if evolution didn't impose reproduction and survival as a constraint?"
"What if there was a journal that didn't let reviewers see the results of experiments in their reviews?"
Those are interesting propositions. I don't like their answers, but it does force you to contend with what exactly you want out of your understanding of these systems.
This is an important observation to keep in mind. It's trivial, but the trivial still needs repeating.
V. erdict
Rarely have I been so disappointed with something I agree with so much. It's a shallow, "extended shower thought" as another reviewer put it. It makes you think, but only because you wanted more out of it.
There is a market for the book this one could have been. I hope someone writes it one day.
This is one of the most frustrating books I've ever read. It is short, and yet too long for its substance. It wants to make valid, empirically supported arguments, yet it falls into endless, vacuous repetition. And hell is this book vacuous. It is frustrating because it gets close to exploring questions you ask, but doesn't. It should have a rich background of examples, but it relies on poor analogies that aggrandize the authors' work - work that has not aged well.
And the book is imprecise. Vocabulary goes undefined, outcomes go unexplained. Different problems with how research is conducted are conflated. It becomes hard to take the authors seriously. And I already agreed with several of their conclusions! They make good points, seemingly by accident.
When I reached the halfway mark, I realized it wasn't going to improve, but I decided to finish it to confirm my suspicion and justify a review. Unfortunately, the book thinks it is much smarter than it actually is, and that its readers are dumber than they are.
I. Repetition and imprecision
There are three examples the authors invoke for analogy all the time: evolution, Picbreeder, and human innovation. What is Picbreeder? That's a research project the authors conducted, and which they proceed to mention every other page. They also use the figures of "treasure hunters" and "stepping stones" all over the place, denoting the ideas of opportunistic exploration and reusable discoveries.
Some of the repeated examples just don't support their thesis. The authors conflate invention with discovery when talking about innovation - "vacuum tubes enabled computation, but that wasn't their purpose, so if you wanted to build a computer you wouldn't have gone for vacuum tubes." So what? If your conclusion is that objectives don't work, that's a terrible example. Babbage built and designed mechanical computing machines before vacuum tubes - he had a goal! And he built designs that achieved that goal! The issue is that "computers" as the authors imagine were not a "goal" anyone elaborated. I cannot take "unimagined inventions" as "ambitious goals" that a search process is failing to meet.
“False analogy,� you might say.
The quote above comes in very late in the book, but I was saying it all along!
What is an ambitious goal, for the authors? It sounds like making world-historic discoveries would fit the bill. That's hard to argue against: research driven not by the potential for discovery but by measurable, incremental progress is not going to be revolutionary most of the time.
The authors sounded like they have a much bigger issue with quantification than with objectives. But even if the issue were objectives, then you still have plenty to work with! The healthcare industry has tons of examples of failed goal-setting. Perhaps it was a bit too far from the authors' field. But in judging the longevity of this work, consider that they published it the year OpenAI was founded. Looks like their "objective-driven" approach to AI is winning.
II. Evidence and arguments
If you're going to write a book that depends on a key premise, should you try to defend that premise?
The authors make one key statement early on and proceed to use it throughout the book, which I will paraphrase as "objective-driven search cannot reach end states that exploratory search could." Is this statement wrong? Not necessarily. But the authors do a terrible job supporting it.
Just as Ken could only evolve the Picbreeder Car by not trying to evolve a car, so could nature only produce [humans] by not trying to evolve [humans].
Quotes like the above are typical. But why exactly couldn't someone reach an end state if they wanted to? Again, the authors are missing the problem of simply formulating objectives. You can't reach objectives you don't specify. That's not the fault of objective thinking.
Our actual evolutionary ancestors, such as flatworms, don’t resemble us. So evolution couldn’t have been actively searching for us—otherwise we’d never have been found!
A better argument, supported in sections about education and research funding, is that objective thinking can't dominate decision-making. This is especially true for research. But that is a different argument than saying objectives don't work. They work all too well! Goodhart's law is a thing.
Another key claim is that novelty search can outperform objective-driven search. This claim is very poorly explained. There are no measures for comparison, no description of how the relevant tasks were even set up. But they rely on these results for their argument all the way through.
III. Nitpicks
The book is very poorly written. Perhaps there was no outline, in the spirit of "exploration." The authors spend so many words clarifying things and explaining themselves that they clearly should have spent more time revising their draft. Let's look at a couple of examples where they indicate their writing wasn't clear:
Note that we’re not suggesting that all-around poor proposals should be funded.
But we’re not suggesting that all scientists should work in isolation.
No reasonable person would suggest that all objectives should be wiped entirely from the Earth.
That isn’t to say that ambitious objectives can never be achieved.
Perhaps it isn’t obvious that there are problems with the way the AI community conducts research at all.
On the other hand, we don’t mean to suggest that no one should ever investigate whether OldReliable outperforms Weird.
This is a template for a drinking game.
I have many issues with ideas the authors discussed in their case studies on evolution and AI research, but this review is already long enough as it is.
Final nitpick, which decreases the perceived reliability of the authors: they misspelled John Stuart Mill's name as "Stewart." I wouldn't care, but this is the kind of thing that shows they can't be that familiar with his work.
IV. The good
This book did make me think quite a bit. Much of that is the realization that it is a bad book, and so the reader should tread carefully. But there were actually interesting thought experiments in there.
"What if evolution didn't impose reproduction and survival as a constraint?"
"What if there was a journal that didn't let reviewers see the results of experiments in their reviews?"
Those are interesting propositions. I don't like their answers, but it does force you to contend with what exactly you want out of your understanding of these systems.
The problem is that the stepping stone does not resemble the final product.
This is an important observation to keep in mind. It's trivial, but the trivial still needs repeating.
V. erdict
Rarely have I been so disappointed with something I agree with so much. It's a shallow, "extended shower thought" as another reviewer put it. It makes you think, but only because you wanted more out of it.
There is a market for the book this one could have been. I hope someone writes it one day.
April 3, 2022
Saw this book as a recommendation on Twitter by Patrick O’Shaughnessey. Was very inclined to agree with the book’s worldview, but would’ve preferred this is as 3-part blog post. The authors are AI researchers who used algorithmic search techniques to show how beginning with an end-state in mind can often lead to worse results than simply seeking novelty.
Main idea: when doing ambitious things, setting explicit objectives (end goals) can be counter-productive. Instead, seeking novelty at each step and exploring without an end-goal in mind can be a better approach.
“Think of the process of creation as a process of searching through the space of the room. As you can imagine, the kind of image you are most likely to paint depends on what parts of the room you’ve already visited .. the more you’ve explored the room yourself, the more you understand where you might be able to go next .. the places we have visited are stepping stones to new ideas�
The authors posit that simply following one’s curiosity and following a trail of stepping stones can lead to better outcomes than deciding what the end goal should be and the figuring out how to get there.
They also posit that “we don’t face a false choice between slavishly following objectives and aimless wandering�. Novelty seeking can be quantified, by observing how much what we’re doing is different from that in the past, and following a gradient of novelty instead of the gradient of “how close are we from our given objective�. Setting constraints on behaviour and following a novelty-seeking approach leads to a higher probability of achieving spectacular outcomes, in their opinion.
I would’ve loved for the book to be more rigorous, and to address the shortcomes of this approach better. Definitely has some interesting food for thought, though!
Main idea: when doing ambitious things, setting explicit objectives (end goals) can be counter-productive. Instead, seeking novelty at each step and exploring without an end-goal in mind can be a better approach.
“Think of the process of creation as a process of searching through the space of the room. As you can imagine, the kind of image you are most likely to paint depends on what parts of the room you’ve already visited .. the more you’ve explored the room yourself, the more you understand where you might be able to go next .. the places we have visited are stepping stones to new ideas�
The authors posit that simply following one’s curiosity and following a trail of stepping stones can lead to better outcomes than deciding what the end goal should be and the figuring out how to get there.
They also posit that “we don’t face a false choice between slavishly following objectives and aimless wandering�. Novelty seeking can be quantified, by observing how much what we’re doing is different from that in the past, and following a gradient of novelty instead of the gradient of “how close are we from our given objective�. Setting constraints on behaviour and following a novelty-seeking approach leads to a higher probability of achieving spectacular outcomes, in their opinion.
I would’ve loved for the book to be more rigorous, and to address the shortcomes of this approach better. Definitely has some interesting food for thought, though!
August 17, 2015
It is interesting to see discussion about the value of exploration, instead of focused march towards ambitious goals based on clear measures of “progress� coming from western researchers. It's quite interesting that this comes as insight from artificial intelligence research, complete with examples how learning algorithms based on exploration rather than set goals and metrics can achieve various results, like learning to navigate a maze, much faster than traditional approaches. I also greatly enjoyed reframing things like evolution as non-objective search with minimum criterion (survivability) instead of traditional talk about optimising fitness. It's quite a fascinating intellectual exercise.
It is worth noting that the book does not attempt to tell you that there is no value in objective-driven approach. The authors make a point of emphasising that the problem is with “ambitious� goals, which in this case usually means those without clear path towards them, i.e., those we do not really know how to achieve.
Having said all that, the premise, repeated over and over again throughout substantial part of the book, reads a lot like “beware of local maxima!� The thing is that the way some people would have us devise, say, cure to cancer, or improve educational situation in US, is by setting a clear numerical goal, and then measuring progress. The idea is that we will be going steadily towards the desired goal, and as long as the metric goes closer towards target values, we're all good. That's essentially gradient-based optimisation, and it is indeed prone to getting stuck in local extrema, should they exist.
Bringing that idea to the attention of potential decision makers is a worthy goal, as they might not have taken courses in optimisation methods, but I feel that authors insist too much on how in case of ambitious goals we *will* necessarily get stuck, because the breakthroughs required to achieve them will not look anything like the final goal. The necessity of such state of the affairs was not motivated sufficiently. I agree that it was shown that this may be the case: we may not know how to get to the final goal, and required “stepping stones� may be unexpected with respect to our current state of knowledge. It's a possibility that should be taken into account, and as authors show on various examples, currently rarely is. But I did not end up convinced that it is necessary to get stuck in local extremum, as authors would have you believe.
That's the only weak point of the book in my opinion, and it's still well worth a read.
It is worth noting that the book does not attempt to tell you that there is no value in objective-driven approach. The authors make a point of emphasising that the problem is with “ambitious� goals, which in this case usually means those without clear path towards them, i.e., those we do not really know how to achieve.
Having said all that, the premise, repeated over and over again throughout substantial part of the book, reads a lot like “beware of local maxima!� The thing is that the way some people would have us devise, say, cure to cancer, or improve educational situation in US, is by setting a clear numerical goal, and then measuring progress. The idea is that we will be going steadily towards the desired goal, and as long as the metric goes closer towards target values, we're all good. That's essentially gradient-based optimisation, and it is indeed prone to getting stuck in local extrema, should they exist.
Bringing that idea to the attention of potential decision makers is a worthy goal, as they might not have taken courses in optimisation methods, but I feel that authors insist too much on how in case of ambitious goals we *will* necessarily get stuck, because the breakthroughs required to achieve them will not look anything like the final goal. The necessity of such state of the affairs was not motivated sufficiently. I agree that it was shown that this may be the case: we may not know how to get to the final goal, and required “stepping stones� may be unexpected with respect to our current state of knowledge. It's a possibility that should be taken into account, and as authors show on various examples, currently rarely is. But I did not end up convinced that it is necessary to get stuck in local extremum, as authors would have you believe.
That's the only weak point of the book in my opinion, and it's still well worth a read.
June 16, 2023
The central idea of the book is reasonably compelling and might be stated as follows. A sufficiently complex and ambitious goal is rarely achieved with an explicit plan that charts a course from start to finish. Rather, greatness is more often than not the product of unfettered, interest- and novelty-driven exploration which creates ample surface area for serendipitous surprises. For instance, the invention of penicillin happened by accident, and vacuum tubes weren't invented with their utility for computing in mind. The author—who invented an obscure genetic algorithm to update neural network weights—argues that we ought to give up the notion of having explicit objectives, and instead adopt a more experimental approach to engaging the world, one which gathers "stepping stones" from which we can explore new, untraversed areas. Novelty and interest ought to trump linear, unidimensional objectives.
Sure, yeah, there's something to be said about the unpredictability of scientific advances (see Thomas Kuhn) or the idea of exposing yourself to positive shocks / tail risks / Black Swans (see Nassim Taleb). But the book is an extended shower thought and pedantic exercise in shitty analogical reasoning which, by the final few chapters, devolves into utter gibberish. Let's not forget that "neuro-evolution of augmenting topologies" lost ground to backprop and reinforcement learning.
Sure, yeah, there's something to be said about the unpredictability of scientific advances (see Thomas Kuhn) or the idea of exposing yourself to positive shocks / tail risks / Black Swans (see Nassim Taleb). But the book is an extended shower thought and pedantic exercise in shitty analogical reasoning which, by the final few chapters, devolves into utter gibberish. Let's not forget that "neuro-evolution of augmenting topologies" lost ground to backprop and reinforcement learning.
February 2, 2025
3.5 rounded off to 4. Very interesting premise but probably better as a long blog post rather than a book. However the book does a good job of showing examples on how curiosity and novelty and non objective or result based exploration of things can lead to greatness and outsized outcomes.
December 18, 2020
This is one of the most thought-provoking books I have read. And I could relate even better because I work in the area of AI like the authors and many of the examples and the final case-study is from this area. Also it's inspiring to see such deep insight coming from the tool they built called Picbreeder.
The core idea presented in the book is that "objectives" are unnecessary and rather a hinderance while solving "complex" problems. Objectives might be useful for simple problems where it is easy to chart out a path to the goal. However in case of more complex problems where the route to the goal is not known, objectives cause more harm than good.
Next, an alternate model is presented called the novelty search. It finds solutions based on how "interesting" the solution is rather than how close it is to an "objective". The authors connect this idea to the stepping stones, which is a great analogy. They argue that a complex goal lies somewhere in a hazy lake with a very low visibility. It is difficult or impossible to reach a specific goal in this lake and an objective in this case is like a "broken compass". However, it is much more relevant to explore the lake by finding new stepping stones based on novelty or interestingness and how many new stepping stones it can lead to. True success lies in exploring the space of the lake rather than trying to reach a specific imaginary point.
Finally, a variety of use-cases are discussed from education, innovation to evolution and AI where a novelty based approach makes more sense rather than an objective based approach.
This book presents an intriguing idea which can literally change the way we operate in life. Sometimes it gets a bit repetitive but overall it drives the point. I highly recommend this book!
The core idea presented in the book is that "objectives" are unnecessary and rather a hinderance while solving "complex" problems. Objectives might be useful for simple problems where it is easy to chart out a path to the goal. However in case of more complex problems where the route to the goal is not known, objectives cause more harm than good.
Next, an alternate model is presented called the novelty search. It finds solutions based on how "interesting" the solution is rather than how close it is to an "objective". The authors connect this idea to the stepping stones, which is a great analogy. They argue that a complex goal lies somewhere in a hazy lake with a very low visibility. It is difficult or impossible to reach a specific goal in this lake and an objective in this case is like a "broken compass". However, it is much more relevant to explore the lake by finding new stepping stones based on novelty or interestingness and how many new stepping stones it can lead to. True success lies in exploring the space of the lake rather than trying to reach a specific imaginary point.
Finally, a variety of use-cases are discussed from education, innovation to evolution and AI where a novelty based approach makes more sense rather than an objective based approach.
This book presents an intriguing idea which can literally change the way we operate in life. Sometimes it gets a bit repetitive but overall it drives the point. I highly recommend this book!
October 2, 2023
I've been aware of this book since its inception, but I often ponder why I didn't dive into it sooner. At first glance, it appears unassuming, a slim volume that doesn't seem to promise a substantial challenge. But how wrong would one be to underestimate its force field! This book not only lives up to the greatness of its title but stands as one of the rare works capable of giving voice to the myriad private thoughts I've collected over the years.
To be perfectly candid, I've never scribbled as fervently in the margins of a book as I have with this one � Mortimer Adler himself might commend my dedication to syntopic reading. Nearly every paragraph catapulted me into the stratosphere, providing a sweeping perspective on our humanity, only to pull me forcefully back to Earth, and then, in a stunning twist, throw me to the edge of the sun or solar system to grasp the larger picture in which the insignificance of our modern explorations lies.
I've yet to fully grasp the far-reaching impact this book will have on me, but I am reasonably confident that I've grasped its essence. Perhaps this certainty stems from my deep engagement with the landscape of artificial intelligence, my extensive readings on optimization, and my understanding of the challenges inherent in navigating the vast search space. Nevertheless, the ideas presented in this book have found a comfortable home within me.
Since delving into its pages, I find myself continually experiencing the Baader-Meinhof phenomenon. And even though I possess a solid grounding in probability theory, the qualia of this experience leaves a taste that can only be described as a superposition of both sour and sweet.
To be perfectly candid, I've never scribbled as fervently in the margins of a book as I have with this one � Mortimer Adler himself might commend my dedication to syntopic reading. Nearly every paragraph catapulted me into the stratosphere, providing a sweeping perspective on our humanity, only to pull me forcefully back to Earth, and then, in a stunning twist, throw me to the edge of the sun or solar system to grasp the larger picture in which the insignificance of our modern explorations lies.
I've yet to fully grasp the far-reaching impact this book will have on me, but I am reasonably confident that I've grasped its essence. Perhaps this certainty stems from my deep engagement with the landscape of artificial intelligence, my extensive readings on optimization, and my understanding of the challenges inherent in navigating the vast search space. Nevertheless, the ideas presented in this book have found a comfortable home within me.
Since delving into its pages, I find myself continually experiencing the Baader-Meinhof phenomenon. And even though I possess a solid grounding in probability theory, the qualia of this experience leaves a taste that can only be described as a superposition of both sour and sweet.
March 29, 2021
Stanley and Lehman explore the myth of the objective. When we focus on ambitious objectives—concrete, specific goals—we are setting ourselves up for failure. Objectives can be useful when we're looking at changes that are adjacent to where we already are. However, when our goal is major innovation, objectives actively hinder progress. The path between where we are today and the objective is often indirect and may require moving away from a seemingly direct path.
The authors refer to these intermediate discoveries as stepping stones. The myth of the objective assumes that the best way to pursue an objective is to follow the stepping stones that are nearest to the ultimate goal: the objective becomes the objective function. But this is like trying to go through a maze by always taking the path that points toward the exit. The true path is much more roundabout. The objective is a false compass.
This might not be so bad if we had good visibility of the possible paths between here and there. However, to continue with the stepping stone metaphor, these stepping stones are in a foggy landscape. The objective is tempting because we cannot see the full set of paths available to us. We want some way to navigate through the fog. However, as the authors emphasize, following a false compass is no better than not having one. Although I find their claim about objectives as a false compass compelling in the end, I did find the argument itself to be fairly weak. It was mostly argued from example and focused rather too much on the exact paths by which innovations were reached: sure, our path to computers involved vacuum tubes, but that doesn't mean that vacuum tubes were the only path to computers (especially given that we didn't stay at vacuum tubes).
If objectives provide a false compass, what are we to do? Do we just wander around randomly and hope for good luck? No. We can be more intentional than that. The authors encourage us to utilize non-objective search. Instead of measuring progress against a destination, exploration is driven by some measurement relative to the present.
This measurement has two parts. First, there are constraints or backstops. Some paths should be avoided because we cannot actually make our way through them. Constraints include physical laws (e.g., the speed of light), continued ability to participate (an organism that dies too quickly can't reproduce), and domain specific requirements (medicine should not do harm). Constraints tell you where not to go. Second, there should be something points us in a direction. The authors are particularly fond of novelty search, which follows the direction that is most interesting (for some domain relevant definition of interesting). The important part is that both constraints and guiding principles are relative to the present, not to some unrealized future state. From here, don't go a direction that will kill you. From here, go the direction which looks the most interesting.
This general idea is not completely incompatible with objectives. The authors argue convincingly that we shouldn't use objectives as objective functions, as the measure of which direction is best to go. That will cause us to miss necessary detours. However, can't we use objectives as part of a more nuanced guiding methodology? The authors encourage us to completely abandon objectives. Instead of trying to have any control over the direction we go, if we want to be innovative, we should become treasure hunters. We should always follow the path that is the most interesting. That path may lead us places that are incredibly innovative, but we won't know where we are going in advance.
I didn't find this part of the argument completely compelling.Overall, I still feel like objectives can be useful. I agree that treasure hunting is one way to innovate. It may be the only way to achieve truly groundbreaking innovations. However, there is a spectrum between objective-friendly adjacent inventions and major innovation. Even if we cannot achieve an objective in full, the interesting things we discovery on the way will likely be more aligned with the goal of the objective than if we just wander about following what is interesting or novel—even if we can't solve world hunger, trying is likely to at least reduce hunger. The problems come when we conflate objectives—places we want to go—with objective functions—measures of how close we are to being there.
However, I do agree that objectives make terrible fitness functions. If we measure progress only by measuring distance from the ideal, then we'll likely get stuck at a boring dead end. I agree with the fundamental assertion that when we are determining which next step to take, we should use an objective function that measure progress relative to where we are now, not relative to some particular place we want to be.
Maybe instead of objectives we can think of north stars. A north star is something which guides your direction. It is not a destination you can ever reach. A north star reduces the number of next steps to consider. Since we never expect to reach a north star, we don't measure success against the north star. Rather, we measure success by what we achieved while going that direction. Let's replace the objective, in the negative sense used in this book, with the north star. Let's allow ourselves to wander, and also allow ourselves a general direction to that wandering. Perhaps Stanley and Lehman might say that makes a north star more like a constraint, something which defines which directions not to go. However, I think that the positive framing of a north star is more inspiring than a constraint.
Despite the fact that this book was not as well argued as I would like, I believe that it was worth the read because it caused me to think more deeply about objectives rather than just blindly accepting them as the way things should be done.
The authors refer to these intermediate discoveries as stepping stones. The myth of the objective assumes that the best way to pursue an objective is to follow the stepping stones that are nearest to the ultimate goal: the objective becomes the objective function. But this is like trying to go through a maze by always taking the path that points toward the exit. The true path is much more roundabout. The objective is a false compass.
This might not be so bad if we had good visibility of the possible paths between here and there. However, to continue with the stepping stone metaphor, these stepping stones are in a foggy landscape. The objective is tempting because we cannot see the full set of paths available to us. We want some way to navigate through the fog. However, as the authors emphasize, following a false compass is no better than not having one. Although I find their claim about objectives as a false compass compelling in the end, I did find the argument itself to be fairly weak. It was mostly argued from example and focused rather too much on the exact paths by which innovations were reached: sure, our path to computers involved vacuum tubes, but that doesn't mean that vacuum tubes were the only path to computers (especially given that we didn't stay at vacuum tubes).
If objectives provide a false compass, what are we to do? Do we just wander around randomly and hope for good luck? No. We can be more intentional than that. The authors encourage us to utilize non-objective search. Instead of measuring progress against a destination, exploration is driven by some measurement relative to the present.
This measurement has two parts. First, there are constraints or backstops. Some paths should be avoided because we cannot actually make our way through them. Constraints include physical laws (e.g., the speed of light), continued ability to participate (an organism that dies too quickly can't reproduce), and domain specific requirements (medicine should not do harm). Constraints tell you where not to go. Second, there should be something points us in a direction. The authors are particularly fond of novelty search, which follows the direction that is most interesting (for some domain relevant definition of interesting). The important part is that both constraints and guiding principles are relative to the present, not to some unrealized future state. From here, don't go a direction that will kill you. From here, go the direction which looks the most interesting.
This general idea is not completely incompatible with objectives. The authors argue convincingly that we shouldn't use objectives as objective functions, as the measure of which direction is best to go. That will cause us to miss necessary detours. However, can't we use objectives as part of a more nuanced guiding methodology? The authors encourage us to completely abandon objectives. Instead of trying to have any control over the direction we go, if we want to be innovative, we should become treasure hunters. We should always follow the path that is the most interesting. That path may lead us places that are incredibly innovative, but we won't know where we are going in advance.
I didn't find this part of the argument completely compelling.Overall, I still feel like objectives can be useful. I agree that treasure hunting is one way to innovate. It may be the only way to achieve truly groundbreaking innovations. However, there is a spectrum between objective-friendly adjacent inventions and major innovation. Even if we cannot achieve an objective in full, the interesting things we discovery on the way will likely be more aligned with the goal of the objective than if we just wander about following what is interesting or novel—even if we can't solve world hunger, trying is likely to at least reduce hunger. The problems come when we conflate objectives—places we want to go—with objective functions—measures of how close we are to being there.
However, I do agree that objectives make terrible fitness functions. If we measure progress only by measuring distance from the ideal, then we'll likely get stuck at a boring dead end. I agree with the fundamental assertion that when we are determining which next step to take, we should use an objective function that measure progress relative to where we are now, not relative to some particular place we want to be.
Maybe instead of objectives we can think of north stars. A north star is something which guides your direction. It is not a destination you can ever reach. A north star reduces the number of next steps to consider. Since we never expect to reach a north star, we don't measure success against the north star. Rather, we measure success by what we achieved while going that direction. Let's replace the objective, in the negative sense used in this book, with the north star. Let's allow ourselves to wander, and also allow ourselves a general direction to that wandering. Perhaps Stanley and Lehman might say that makes a north star more like a constraint, something which defines which directions not to go. However, I think that the positive framing of a north star is more inspiring than a constraint.
Despite the fact that this book was not as well argued as I would like, I believe that it was worth the read because it caused me to think more deeply about objectives rather than just blindly accepting them as the way things should be done.
December 22, 2024
Somehow without the oomph I expected, it becomes tiring and repetitive, their research interesting but dated and not foundational. That doesn’t discount the validity and value of the point they’re trying to make but it does not make for a good book.
August 29, 2021
More like 3.5
Very verbose but fairly accessible to general audience. Made me rethink/question my goals and career plans.
Very verbose but fairly accessible to general audience. Made me rethink/question my goals and career plans.
August 31, 2021
One of the few books that had truly affected how I see the world.
The author took his findings from the AI research (which I follow, which is how I learned about the book in the first place) and applied it to everyday life. So, on the surface this book looks like yet another self-help book, but the ideas in it originate from the AI research. I'm not sure if it's valid to make this transfer between the fields, but I really liked it in this case.
The book's idea is that it makes no sense to track progress towards any sufficiently ambitious goal. If you want to become a billionaire, it makes no sense to track and to maximize your salary. If you want to land on the moon, you won't make progress by climbing higher and higher mountains, even though it does get you a little closer to the moon.
The important corollary is that it makes no sense to plan for anything truly ambitious. If you can't measure if your actions bring you closer to the goal, then why bother planning? And it also means that the really ambitious goal you have in mind will probably cannot be achieved.
This is terrifying and is in contrast to the belief that anyone can achieve anything, provided enough grit. But at the same time it's liberating, because once you stop pursuing your single goal, you'll start seeing many other possibilities around you. Collect useful ideas -- become a "stepping stones" collector -- and see where they lead you, without having a specific objective in mind. This could make life much more interesting and rewarding.
I didn't agree with the author everywhere. But I do agree with the general idea, and this book definitely generates many opportunities to think deeply. My personal book of the year so far :)
The author took his findings from the AI research (which I follow, which is how I learned about the book in the first place) and applied it to everyday life. So, on the surface this book looks like yet another self-help book, but the ideas in it originate from the AI research. I'm not sure if it's valid to make this transfer between the fields, but I really liked it in this case.
The book's idea is that it makes no sense to track progress towards any sufficiently ambitious goal. If you want to become a billionaire, it makes no sense to track and to maximize your salary. If you want to land on the moon, you won't make progress by climbing higher and higher mountains, even though it does get you a little closer to the moon.
The important corollary is that it makes no sense to plan for anything truly ambitious. If you can't measure if your actions bring you closer to the goal, then why bother planning? And it also means that the really ambitious goal you have in mind will probably cannot be achieved.
This is terrifying and is in contrast to the belief that anyone can achieve anything, provided enough grit. But at the same time it's liberating, because once you stop pursuing your single goal, you'll start seeing many other possibilities around you. Collect useful ideas -- become a "stepping stones" collector -- and see where they lead you, without having a specific objective in mind. This could make life much more interesting and rewarding.
I didn't agree with the author everywhere. But I do agree with the general idea, and this book definitely generates many opportunities to think deeply. My personal book of the year so far :)
August 2, 2021
Отличная книга про то, что амбициозные цели не работают. Авторы немного заезженно повторяют одни и те же примеры в качестве аргументов к своей точки зрения, но раскрытия текущих работающих систем в мире (образование, научная деятельность), действительно была пересмотрена под новым углом.
Список основных пунктов из книги:
- Цели работают в случае, если они скромные и обозримые. Не больше пары лет вперёд.
- Фундамент важен для обнаружения возможностей. Если мы отправим современного гения на пару сотен лет назад, то изобрести компьютер он не сможет.
- Амбициозные цели часто достигались в разрез плана.
- Инновации не идут намеченным путём, а скорее методом проб и ошибок.
- Ответом на замену целей является чуйка. В связи с чем отход от плана иногда может принести большую награду. Правда эта награда, не та что мы ожидаем.
- Stepping stones, not milestones.
Список основных пунктов из книги:
- Цели работают в случае, если они скромные и обозримые. Не больше пары лет вперёд.
- Фундамент важен для обнаружения возможностей. Если мы отправим современного гения на пару сотен лет назад, то изобрести компьютер он не сможет.
- Амбициозные цели часто достигались в разрез плана.
- Инновации не идут намеченным путём, а скорее методом проб и ошибок.
- Ответом на замену целей является чуйка. В связи с чем отход от плана иногда может принести большую награду. Правда эта награда, не та что мы ожидаем.
- Stepping stones, not milestones.
June 27, 2019
5 Stars. This is a must read if you are engaged in research and development--regardless of your field or specialty. The authors provide deep insight into some of the misconceptions and errors that have become accepted as fact when it comes to seeking ambitious objectives. The authors introduce the concept of search as a process of discovery. This leads to a related idea: creativity as a kind of search. They use the concept of stepping stones that lead to new developments and how the challenge is to discover the proper stepping stones within the search space. The implications of the authors' arguments are far reaching and persuasive. I give the book 5 stars because it has altered my perception of how to pursue ambitious objectives.
October 5, 2022
Goal setting, breaking it down to the steps, and methodical execution is the best way to achieve an objective that is within sight. But how do you shoot for the stars? How do you come up with extraordinary progress? You can't just plan and will your way to greatness. The only thing you could do is to be open-minded and curious, follow your guts and dig deep into what feels interesting. This is the best way to discover something truly new. It might be not at all what you were looking for, but it could be something great nonetheless.
This is the tl;dr of the book, and I would take a star off because the book could have been quite a bit shorter - it's the same argument over and over again. I also think authors did not do good enough job explaining why objectives and divide and conquer are so great.
With that caveat aside, I think the main idea of this book is profound: you can't plan greatness. The reason is rather simple: if you could see the next big thing (invention, idea, whatever) from where we as humanity stand - then the job is easy - set the objective and execute. But the thing is that of course we don't see all the possible big next things! We can only see so far. By focusing on objectives exclusively, we deprive ourselves of the opportunity to explore the unexplored. And time and time again completely accidental discoveries in one place lead to unprecedented improvements in others.
Authors demonstrate this effect more formally with the aid of a computer simulation, which is quite convincing, and is a great model for the problem.
I also really liked their application of this idea to education: standardized testing does not improve the learning; the accuracy of the tests does not improve the education; setting objectives to improve the education does not improve it. Most people understand this intuitively, yet society meeps moving towards more and more standardized tests, which is really counter-productive. Tests have their application of course, but it should not be the primary method, and it should not be the goal in and of itself.
The book does not really talk about this, but I see a direct confirmation of the central idea in all the major computer science discoveries of the last 80 years. All the great innovative things came from places that invested into basic research - without thinking about the future applications too much - scientists and engineers were exploring things that looked interesting, not what would be "good for business". A lot of big companies today lose the sight of this fact. If you really want to create something radically new, you have to let people explore on their own, without a particular plan.
Overall, highly recommend the book to anyone, especially you work as an engineer, scientist, or an educator. It's a bit tedious at times, but the idea and the framework is really good.
This is the tl;dr of the book, and I would take a star off because the book could have been quite a bit shorter - it's the same argument over and over again. I also think authors did not do good enough job explaining why objectives and divide and conquer are so great.
With that caveat aside, I think the main idea of this book is profound: you can't plan greatness. The reason is rather simple: if you could see the next big thing (invention, idea, whatever) from where we as humanity stand - then the job is easy - set the objective and execute. But the thing is that of course we don't see all the possible big next things! We can only see so far. By focusing on objectives exclusively, we deprive ourselves of the opportunity to explore the unexplored. And time and time again completely accidental discoveries in one place lead to unprecedented improvements in others.
Authors demonstrate this effect more formally with the aid of a computer simulation, which is quite convincing, and is a great model for the problem.
I also really liked their application of this idea to education: standardized testing does not improve the learning; the accuracy of the tests does not improve the education; setting objectives to improve the education does not improve it. Most people understand this intuitively, yet society meeps moving towards more and more standardized tests, which is really counter-productive. Tests have their application of course, but it should not be the primary method, and it should not be the goal in and of itself.
The book does not really talk about this, but I see a direct confirmation of the central idea in all the major computer science discoveries of the last 80 years. All the great innovative things came from places that invested into basic research - without thinking about the future applications too much - scientists and engineers were exploring things that looked interesting, not what would be "good for business". A lot of big companies today lose the sight of this fact. If you really want to create something radically new, you have to let people explore on their own, without a particular plan.
Overall, highly recommend the book to anyone, especially you work as an engineer, scientist, or an educator. It's a bit tedious at times, but the idea and the framework is really good.
February 19, 2025
- In the last section, the author extrapolates anti-objective ideas against innovation, which holds some truth when viewed through the lens of narrative fallacy.
- He then presents a solution: looking for interesting next steps and pursuing them if they prove worthwhile.
- The author argues that without an objective, there is no destination, and randomness appears to be the projected solution. However, he clarifies that this is not the case.
- Instead, he emphasizes serendipity—continuously exploring until something interesting emerges, then taking the next step.
- He illustrates this concept with Steve Jobs, who, according to the author, thrived without a rigid objective.
- However, this could also be an example of narrative fallacy, where hindsight shapes the interpretation of events.
Overall, while the idea is great, the execution falls short due to excessive extrapolation.
December 28, 2018
This book will challenge - to your core - any ideas you have about what it takes to achieve something truly great and innovative. Instead of optimizing, planning and continuously improving in a specific dimension (or set of dimensions) the book asserts that one must instead seek stepping stones based on interestingness - while ignoring a far off objective - if you have any hope of getting somewhere remarkable. Building on AI research about how algorithms perform when seeking a goal (vs when simply exploring), I'm surprised to just hear about this book. Parts are repetitive, but the "big idea" is profound and that alone makes it worth reading.
February 28, 2025
An inspiring creative and world opening masterpiece of the book for the first 90% last 10% is a bit of a slog but still well worth reading kudos to Kenneth and Joel on a wonderful wonderful book which will be a North Star for certain in my future. The only question is whether we can build a world for people are open to the interest beauty curiosity and abstract measures of value that actually lead to great new breakthroughs.
April 30, 2024
Is's quite encouraging along with reading this book, I've been given more confidence to explore the world. The greatnesss or happiness can be a very large goal, if we leave that alone and try to live out the most, it should turn out to be fantastic, who knows.
January 25, 2025
The core idea is interesting, but the book feels a bit drawn out. Skimmed through some parts. Makes a convincing argument that many great things cannot be planned. I like this because I enjoy aimless exploration more than following a planned route.
May 30, 2023
La idea del libro es de las más interesantes que he leído, pero es algo repetitivo y menos técnico de lo que pensaba. Mejor ver una conferencia o escuchar un podcast del tema
November 25, 2023
A great intro to novelty seeking algorithms and the fallacies of objective thinking.
January 14, 2025
tldr; chase novelty, not objectives. loved the idea but the text felt a bit too overwritten
January 18, 2025
Introduces non-objective novelty seeking search and establishes it as a legitimate method different from aimless wandering.
Though the book borrows from the field of search algorithms, it reframes "evolution" in a fundamentally different way, the philosophical implications of which are mind expanding.
Offers a fresh perspective on ambitious goals
Though the book borrows from the field of search algorithms, it reframes "evolution" in a fundamentally different way, the philosophical implications of which are mind expanding.
Offers a fresh perspective on ambitious goals
February 6, 2025
A mind bending book!
October 27, 2023
I began reading this book with the objective of finishing it to learn what it had to offer but realized that I was actually on a novelty search that would lead to unplanned stepping stones and my activities resembled a non-objective treasure hunt rather than what I set out to achieve in the beginning.
August 10, 2020
Interesting book! Key takeaways: while objectives are good in certain cases, more ambitious goals are less likely to be achieved by defining objectives, as many times their solutions require qualitatively different approaches. They introduce the idea of "Stepping Stones", which are ideas that lead to other ideas - the invention of the steam engine is a stepping stone to trains, the invention of computers was a stepping stone to the internet, Number Theory and Cryptography were stepping stones to Bitcoin, etc. Without the proper stepping stones, many ideas are out of reach, and defining explicit objectives towards these out-of-reach goals is likely to trick us into thinking we've made progress as we work towards them. For example, say your goal is to be a billionaire, and you treat money in your bank account as the objective. You'd believe that working harder in your current job is leading you closer to your goal, when in reality you need to do something qualitatively different to achieve your goal, likely resulting in a temporary drop in salary. So in this case, the objective is "Deceptive".
Another key point is that it's very hard to predict where stepping stones will lead us and which stepping stones are necessary for a particular goal, so instead of guiding our stepping-stone collection process towards a particular far-off goal, it's probably better to just try to find the most interesting stepping stones that are currently one hop away. They argue for the "Treasure hunter" - people not fixed on a particular far-off objective, but instead just searching for interesting stepping stones one hop away. The authors extend these ideas beyond necessarily technical topics and apply this way of thinking towards other aspects of life.
I found the ideas very interesting, and would highly recommend the book.
Another key point is that it's very hard to predict where stepping stones will lead us and which stepping stones are necessary for a particular goal, so instead of guiding our stepping-stone collection process towards a particular far-off goal, it's probably better to just try to find the most interesting stepping stones that are currently one hop away. They argue for the "Treasure hunter" - people not fixed on a particular far-off objective, but instead just searching for interesting stepping stones one hop away. The authors extend these ideas beyond necessarily technical topics and apply this way of thinking towards other aspects of life.
I found the ideas very interesting, and would highly recommend the book.
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