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Why Greatness Cannot Be Planned: The Myth of the Objective

4.04  ·  Rating details ·  221 ratings  ·  26 reviews

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 illusion: that greatness results from doggedly measuring improvement in the relentless pursuit of an ambitious goal. In Why Greatness Cannot Be Planned, Stanley and Lehman

Kindle Edition, 154 pages
Published May 5th 2015 by Springer
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Douglas Summers-Stay
Dec 04, 2018 rated it it was amazing
Shelves: non-fiction
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 possibiliti ...more
Piotr Kalinowski
Jul 15, 2015 rated it really liked it
Shelves: philosophy
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 en ...more
John B.
Jun 26, 2019 rated it it was amazing  ·  review of another edition
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 ...more
Ravi Raman
Dec 28, 2018 rated it really liked it
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 simp ...more
Kaur Kuut
Jan 15, 2018 rated it it was ok
Shelves: non-fiction
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.
Matthew Trevithick
Jan 29, 2017 rated it liked it
Makes an interesting case saying - at the right point in your creative / creation journey - the pursuit of objectives becomes detrimental instead of helpful. The book advocates pursuit of the journey after base objectives have been achieved (and highly emphasizes the importance of all the foundational startup work required to do anything well) and how switching to learning and searching instead of trying to find will bring you closer to your final creative breakthrough.

Wasn't a huge fan of the
Jindřich Mynarz
Mar 31, 2016 rated it really liked it
The book's core lessons read like a self-help advice on the surface, but in fact they are backed by solid experimental results. Recommended read for anyone working creatively.
Feb 24, 2019 rated it it was amazing
Superb reading. The kind of research that I would probably pursue if I would've stayed in academia. Takes the insights from AI algorithms and makes a strong case about how setting objectives is not the way to achieve true discovery. It argues that "deception" happens in problems when the distant, complex and desirable objective (such as finding human-like intelligence in AI) don't usually look like the "stepping stones" that gets them there: a worm doesn't look at all like a human, but was a nec ...more
Mar 02, 2019 rated it really liked it
Read this on a friend's recommendation. The title is a bit clickbait-y, but the author is very careful to qualify his claims.

The main message is that, when faced with complicated problems, singlemindedly going after an objective can and will get you stuck in dead ends. He argues that since complicated problems don't have an obvious solution it's very likely that the next towards the solution is not actually directly closer to your objective. Makes sense to me - I think my main takeaway is to thi
Sam Hummel
Oct 21, 2018 rated it liked it
Really good insight. Unfortunately, very poorly written. The main point of paragraphs were often the *last* sentence in the paragraph, and sometimes preceded by a phrase like “The point is...” Sheesh. Also repeated themselves over and over. Needed to hire a talented editor to discipline their writing. It’s really too bad because I’m sure their great ideas would travel a lot further and help the world more if written better. Hopefully, someone will come along, build on their ideas, and relate the ...more
May 10, 2017 rated it really liked it
The main idea of the book is very interesting: for humans to achieve ambitious goals; they shouldn't set them as objectives. This may be counter intuitive, but the author presents arguments and also shows experiments that prove his point.

However, the book is extremely repetitive, and could have been summarized in less than half of the pages, or it could have been written as an extended article, or the authors could have backed their idea with more real life examples of ambitious goals being ach
Jun 26, 2018 rated it really liked it
In this book, AI researchers Ken Stanley and Joel Lehman introduce and extend their concept of "novelty search" in evolutionary algorithms. Basically, researchers – and society as a whole – should abandon objectives entirely. Why? Innovation comes from the unexpected. We should be seeking divergence rather than consensus; that's where all the progress and discoveries come from. They cover what this means for a range of areas, from science funding to education to life advice (pursue what's intere ...more
Ruben Paz
Nov 02, 2018 rated it really liked it
Interesting concepts, but quite repetitive. You can grasp where it is going by reading just the first chapter. It's worth a quick read for the core concept, but I felt the second half of the book is mainly filling to make it book instead of a paper.

Still, I believe this book gave me some ground to position my personal belief on life planning and some arguments to understand and explain modern chaotic processes.
Jul 26, 2017 rated it it was amazing
This review has been hidden because it contains spoilers. To view it, click here.
Kaelan Lupton
Sep 14, 2019 rated it liked it
this was an interesting read as it pertained directly to some neuroevolution computing work i was doing. it's an interesting book written at the intersection between applied computing and philosophy. found it was just sort of reiterating the same point over and over though. already a short book, could have been 50 pages and just as effective
Maria Graetsch
Jul 05, 2020 rated it it was amazing
This book provides are really interesting view on how greatness can eventuate by using what is novel and interesting to guide us. It challenges the very notion of needing objectives and demonstrates how having objectives can sometimes get in the way of achieving greatness. The authors use ideas of stepping stones and search paradigm in different contexts to explore their points. I found myself disagreeing with the author multiple times, and really enjoyed the read because each disagreement was a ...more
Mar 12, 2020 rated it liked it
Shelves: science
The core idea is fascinating, but this is meant for the uninitiated. There's a lot of repetition as a result. However, there are some very interesting bits sprinkled throughout the book which makes well worth the read, especially given that it is pretty short.
Plexiform Identity
Aug 04, 2020 rated it it was amazing
Short & inspirational

This book serves some beautiful computer science inspired philosophy. Idea of not just relying on objective oriented algorithms for problems/search spaces is seemingly simple but difficult to put into practice. Counterintuitive and powerful stuff!
Robb Seaton
Apr 13, 2019 rated it it was ok
Interesting idea but the presentation leaves a lot to be desired.
Jan 02, 2018 rated it really liked it
Interesting book with a great central idea. The only problem I had was that the book was slightly repetitive.
Nate Matthews
Oct 01, 2018 rated it really liked it
Not the best writing (a little repetitive), but fascinating subject matter!
Bojan Avramovic
Jul 22, 2020 rated it it was amazing
Prelepo se osećam kada pročitam knjigu koja opisuje potpuno drugačiji uvid od mainstream načina razmišljanja. Ova knjiga je upravo takva.
Aaron Prescott
Jun 05, 2018 rated it it was ok
The premise of the book is interesting. Many of the ideas conveyed by the author are worth pondering. My only critique is that it could have been accomplished with 1/3 of the writing.
Andrew Bauer
Apr 11, 2020 rated it it was amazing
Shelves: 4pt5-stars
It communicates a very important message with clear practical applications, but like much popular science -- and the unpopular Mr D Trump -- it suffers from endless repetition and myriad example.
Alex Telfar
May 18, 2017 rated it it was ok
i was excited for this book, but found it to be a total disappointment. I didn't really get what they are on about.

curiosity, play, exploration are still objective functions...

it just reminds me of regularizers. your loss function doesn't optimised exactly what you want, but you can add other (objective functions - regularizers) to help push things in the right direction.
Alex V
Dec 16, 2016 rated it it was amazing
Interesting read, a quite new perspective on a subject that has been hammered by many coming from the same angle. Finding refreshing new avenues is always worth the time.
Johan Sjöberg
rated it it was amazing
Jun 28, 2018
rated it really liked it
Dec 25, 2019
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May 19, 2018
Avinash Varma
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May 26, 2020
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