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Maiklas Nilsenas savo knygoje „Atradimai kitaip“ imasi tyrinėti interneto, kaip mokslo atradimų variklio, reikšmę žmonijai. Šis šiuolaikinių technologijų produktas leidžia plėtoti Atvirojo mokslo idėją, suteikiant mokslininkams galimybę bendradarbiauti per atstumą ir operatyviai keistis turimais duomenimis. Tai neabejotinai keičia mūsų kolektyvinio intelekto prigimtį ir atveria kelius naujiems moksliniams pasiekimams.
268 pages, Hardcover
First published January 1, 2011
76 people are currently reading
2198 people want to read
About the author
Michael Nielsen
12 books1,631 followersMany of the books below listed as by me are not, in fact, by me. Goodreads makes it timeconsuming and difficult to change this, so I don't bother. It's a real pain though.
On friend requests: I use the "updates" list a lot to find new books. So I will only accept friend requests if either (a) we're actually friends; or (b) your list of updates is mostly books that look really interesting to me, and like the kind of thing I might otherwise not see. It doesn't mean I don't like you if I don't accept the request!
On technical books: I struggle with how to shelve technical books. Serious reading of such a book may take an hour or more per page; I will rarely seriously read the entire book for that reason. On the other hand, I would like to put those books on my Goodreads profile.
Up until October 2023 I mostly simply didn't put them on my profile, with a few exceptions. Going forward I will add more, but tend to shelve them under "serious_use". Roughly speaking, that means I will have seriously used a fair bit of the book - many hours (or, sometimes, far more) of use.
On friend requests: I use the "updates" list a lot to find new books. So I will only accept friend requests if either (a) we're actually friends; or (b) your list of updates is mostly books that look really interesting to me, and like the kind of thing I might otherwise not see. It doesn't mean I don't like you if I don't accept the request!
On technical books: I struggle with how to shelve technical books. Serious reading of such a book may take an hour or more per page; I will rarely seriously read the entire book for that reason. On the other hand, I would like to put those books on my Goodreads profile.
Up until October 2023 I mostly simply didn't put them on my profile, with a few exceptions. Going forward I will add more, but tend to shelve them under "serious_use". Roughly speaking, that means I will have seriously used a fair bit of the book - many hours (or, sometimes, far more) of use.
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Displaying 1 - 30 of 66 reviews
March 2, 2012
An important book, which lost its (tenuous from the start) grip on me on page 78.
Nielsen is an advocate for open science, and in this book he draws a picture of science standing at the threshold of its most important advance since the establishment of the Royal Society and the first norms of scientific publishing and data-sharing. The amplifying power of internet, he argues offers new opportunities for collaboration and sharing. The challenge is to move the bulk of the scientific community away from their closed and guarded approaches in order to take advantage of these opportunities.
Nielsen is clearly fired up:
The picture Nielsen draws is in many ways the opposite of that of Crick and Watson alternating between fervid bouts of creativity and languid cups of tea in the company of university popsies, stealing away with Franklin's x-rays and covering up their work as they rush for publication. He describes a number of projects that are models for the new kind of science he proposes, such as the Polymath Project (distributed mathematical problem-solving), open source software (the success of Linux, made possible through its modular nature, which allows a multitude of people to make innumerable small contributions), the Firefox bugtracker (which allows any user of this open source browser to identify issues and submit enhancements), and Kasparov vs The World (the grandmaster takes on the international chess community, who use online tools to suggest, evaluate and select their moves, drawing on their distributed specialised expertise).
I agree with Nielsen's argument, but I'm not inspired by his rhetoric. His book is largely pragmatic, which is really a rather wonderful thing - polemic will only get you so far, and I can see this as a book that one scientist might press upon another as the open side tries to win over the closed.
But as a pure reading experience, the book is like a rather stodgy and dull boiled pudding, studded with the odd tasty bit of crystallised fruit. Overal, it is delivered more like a lengthy lecture to an undergrad class than a book. The pages are peppered with phrases like these:
Those are all from a single page (admittedly, from the finl chapter, which I flipped to this morning just to see whether I should be slogging through the intervening pages rather than kicking it in). They're harmless in themselves, but they accrete in this puddingish texture, where over all the book feels earnest rather than inspiring. And that's not a note I want to end on, as I genuinely think the book is important, Nielsen is very very smart and persuasive, and the possible future he outlines is one that makes moral and intellectual sense. I wish I could have enjoyed the book more.
Nielsen is an advocate for open science, and in this book he draws a picture of science standing at the threshold of its most important advance since the establishment of the Royal Society and the first norms of scientific publishing and data-sharing. The amplifying power of internet, he argues offers new opportunities for collaboration and sharing. The challenge is to move the bulk of the scientific community away from their closed and guarded approaches in order to take advantage of these opportunities.
Nielsen is clearly fired up:
These tools are cognitive tools, actively amplifying our collective intelligence, making us smarter and so better able to solve the toughest scientific problems. To understand why all this matters, think back to the seventeenth century and the early days of modern science, the time of great discoveries, such as Galileo's observation of the moons of Jupiter, and Newton's formulation of the laws of gravitation. The greatest legacy of Galileo, Newton, and their contemporaries wasn't those one-off breakthroughs. It was the method of scientific discovery itself, a way of understanding how nature works. At the beginning of the seventeenth century extraordinary genius was required to make even the tiniest of scientific advances. By developing the method of scientific discovery, early scientists ensured that by the end of the seventeenth century such scientific advances were run-of-the-mill, the likely outcome of any competent scientific investigation. What previously required genius became routine, and science exploded.
Such improvements to the way discoveries are made are more important than any single discovery. They extend the reach of the human mind into new realms of nature. Today, online tools offer us a fresh opportunity to improve the ways discoveries are made, an opportunity on a scale not seen since the early days of modern science. I believe that the process of science - how discoveries are made - will change more in the next twenty years than it has in the past 300 years.
The picture Nielsen draws is in many ways the opposite of that of Crick and Watson alternating between fervid bouts of creativity and languid cups of tea in the company of university popsies, stealing away with Franklin's x-rays and covering up their work as they rush for publication. He describes a number of projects that are models for the new kind of science he proposes, such as the Polymath Project (distributed mathematical problem-solving), open source software (the success of Linux, made possible through its modular nature, which allows a multitude of people to make innumerable small contributions), the Firefox bugtracker (which allows any user of this open source browser to identify issues and submit enhancements), and Kasparov vs The World (the grandmaster takes on the international chess community, who use online tools to suggest, evaluate and select their moves, drawing on their distributed specialised expertise).
I agree with Nielsen's argument, but I'm not inspired by his rhetoric. His book is largely pragmatic, which is really a rather wonderful thing - polemic will only get you so far, and I can see this as a book that one scientist might press upon another as the open side tries to win over the closed.
But as a pure reading experience, the book is like a rather stodgy and dull boiled pudding, studded with the odd tasty bit of crystallised fruit. Overal, it is delivered more like a lengthy lecture to an undergrad class than a book. The pages are peppered with phrases like these:
"Earlier in the book we discussed the open access policies that some of the scientific grant agencies are introducing...."
"We'll now look at two strategies tan can be used to shift the culture of science...."
"There are lots of ways of this is happening; so let me describe just a few snapshots..."
"I won't make all the connections explicitly, since this isn't a textbook on political economy. If you're interested in exploring the connections further, please see 'Selected Sources and Suggestions for Further Reading', beginning on page 217."
Those are all from a single page (admittedly, from the finl chapter, which I flipped to this morning just to see whether I should be slogging through the intervening pages rather than kicking it in). They're harmless in themselves, but they accrete in this puddingish texture, where over all the book feels earnest rather than inspiring. And that's not a note I want to end on, as I genuinely think the book is important, Nielsen is very very smart and persuasive, and the possible future he outlines is one that makes moral and intellectual sense. I wish I could have enjoyed the book more.
June 8, 2019
I read Nielsen's new book cover to cover on my flights to / from an Open Access Week event in Tucson this week and I give it my strongest recommendation for a pleasurable read about a crucial topic. I am a scientist and my students and I practice open science as much as possible--open notebook science, open protocols, open data, open proposals, etc. I have also seen the author, Michael Nielsen speak a couple times, and I have read many of his blog posts. So, before reading this book I didn't necessarily expect to learn much or certainly to be further convinced of the possibility of transforming science in this new era. From the moment I started reading, though, I was captivated. Many of the stories were not new to me (such as Galaxy Zoo or the polymath project), but I hadn't heard them in such detail before and I enjoyed learning a lot more about those successful crowd- or citizen-science projects. There were also many success and failure stories in open or collaborative science that I hadn't known about, such as the Microsoft-sponsored "Kasparov versus the world" chess event, or the research into how small groups can make bad decisions if the collaborative conditions aren't set up correctly. I learned a lot from these new stories, and remained captivated throughout.
January 17, 2012
This book is about how we can/should do science from now on. It promotes Open Science approach, which is based on the ideas of sharing data in an open source fashion, using the network to focus the attention of experts, benefiting from intelligent amplification tools, and etc.
I think it is a must-read book for professional scientists and a good book for science enthusiasts. It is generally written very well. The downside is that at points it becomes repetitive and loses its fast pace. Even though the book is already very short (the main body is less than 200), it could still be compressed a bit more. In general, I am happy that I read it and I will probably go back to it in the future.
I think it is a must-read book for professional scientists and a good book for science enthusiasts. It is generally written very well. The downside is that at points it becomes repetitive and loses its fast pace. Even though the book is already very short (the main body is less than 200), it could still be compressed a bit more. In general, I am happy that I read it and I will probably go back to it in the future.
February 20, 2016
If you were one of the many people who excitedly picked up a copy of "wisdom of crowds" only to be disappointed when you realized that the passion with which the author wrote was matched only by the confirmation bias that accompanied it, then you will be extremely happy about this book. It's too looks at the role of collaboration in generating a finished product, but unlike wisdom of crowds, it is a solidly researched contribution to the field of network research.
The author looks at both the value and challenges of sharing data in the scientific community.
Great arguments, solid writing. I really enjoyed this book quite a bit and highly recommend it.
The author looks at both the value and challenges of sharing data in the scientific community.
Great arguments, solid writing. I really enjoyed this book quite a bit and highly recommend it.
Want to read
April 6, 2012After reading the review in Science, I'd like to take a peek at this book myself. http://www.sciencemag.org/content/336...
July 24, 2023
Reinventing Discovery – the new era of networked science
This book is essentially a plea for open access science. The idea is that more information in the open enables faster, more efficient, and more accurate science to be conducted.
Here are some notes I made – they don’t really represent my own views but more what Nielsen actually says.
Nielsen talks about the chess match – Kasparov v the World. Essentially, Gary Kaspaov (a chess master) was very nearly beaten by the ‘world team’ – a team of people collaborating on Chess moves via the internet. Even though Kasparov was the best player, whose chess knowledge matched everyone else on an individual basis, the World Team had a number of people who were specialists in particular moves in Chess. This is referred to as ‘latent micro expertise’ which meant that their cumulative efforts were impressive.
Similarly, in a large organisation you want to encourage micro expertise. You want to encourage people with a specific ability to solve a specific problem to assist the person in charge of solving that problem. This means getting people out of their silos and at least spending some time collaborating with others in the organisation, ideally over some sort of platform.
He also uses the metaphor of uranium. Uranium is unstable, and neutrons fly out occasionally. Below a certain mass, neutrons fly out of uranium atoms. Below a certain mass, these neutons don’t tend to collide with other atoms. However, above a certain mass, they DO collide with other uranium atoms, which cause more neutrons to fly out of those atoms. Those neutrons in turn collide with other atoms, which causes more neutrons to fly out, and eventually the whole thing blows up. This is called critical mass. Nielsen posits that intelligence is similar – the more information that is publicly available, the more people will latch on to it, and the faster ‘explosion’ (ie the creation of new ideas) can occur.
‘In most cases, what makes a creative insight important is precisely the fact that it combines ideas that were previously thought to be unrelated.’
Why is online collaboration different to committee work? Well, committees work at the pace of their slowest members and can be stifled by vetos from the most obstructive or annoying people. In online collaborations, you can just ignore these people. Also, committees tend to consist of people who were forced to sit on them, whereas online collaboration, when done well, will encourage people who are already passionate about a particular subject. Online is better than offline collaboration because of scale and diversity of ideas and skills.
However, if an online collaboration is large scale, you want to construct a system that will direct people to the sub-section of the collaboration where their knowledge is most useful.
Open source software is a good example of where this has arisen. Linux is a piece of opens source software which has reached incredible extents. They focus relentlessly on modularity – Linux is broken down into individual modules which people can contribute to depending on their specialism.
Firefox has found a good way to solve problems – via an issue tracker. People can report issues and if enough of them do so it rises up a sort of league table, and people are encouraged to contribute to solving it. You can also use the issue tracker to suggest new developments. This central area of discussion encourages people to target their expertise directly.
Although you still have the problem of directing expert attention – how can you be sure that people are contributing to something where their skills are most needed?
One way that this was done was in the Mathworks competition. Essentially, Mathworks creates a competition every year where people can build a program to solve a particular issue. The example Nielsen gives is from 1998 - competitors were asked to design a program/formula that would select from a list of songs the number of songs that could most closely be put together in order to fill up as much of the length of a 74 minute CD as possible.
Mathworks designed a process wheeby each entry could be immediately judged by a secret formula and awarded as a score. In addition, the code for successful entries was immediately published. So people could see which entries got the highest score, work out what had changed with thatentry, and then finesse that particular aspect. This meant that expert attention was directed in the ways intended by the designers of the competition.
‘ If a participant in the Mathworks competition was stuck for ideas, they only needed to wait for a few hours tofind new ideas to stimulate and challenge them.’
Re politics
‘Group discussion actually makes people’s political decisions worse than they would have been if they had made those decisions individually.’
Groups focus on the information they all hold in common, not the information that they individually hold. Also they focus on knowledge held by high status group members and ignore the knowledge held by low status group members.
Most people do not consider politicians by building up a complete picture of their positions – we judge on how key aspects of their politics affects our interests. This is because there is no ‘shared praxis’ – ie. consensus on what position evinced by a politician is ‘good’ or ‘bad.’ This differs from the Mathworks competition where everyone as agreed that a higher score was good. So in order to have a successful collaboration, we need a shared praxis.
Other
Due to a shared praxis ‘In science it is often the people with the best ideas who win out, not the people with the most power.’
It is worth attempting to propagate this idea. What if it were possible to ‘match’ tasks across an organisation to people in that organisation? An algorithm could select tasks and allocate them to the person who had the greatest success in solving such problems before.
Nielsen posits that there are many unrecognised connections in science. You can use machine learning to trawl through endless amounts of data and recognise the connections between two previously unrelated things – ie. what Visulytix did with scans of eye retinas and identifying retinas that were diseased.
Google search queries can be used to predict which songs will top the chart and which stocks will do well. This is an example of identifying links between two previously unstudied data sets. What about use in political campaigns??
Data commons
‘’An unaided human’s ability to process large data sets is comparable to a dog’s ability to do arithmetic, and not much more valuable.’
Most data should be made public, in order that such patterns can be identified by computers. However, this means rea data – the people publishing it should actually be attempting to explain the data to others, and acting out of willingness rather than mere compulsion.
‘Today – the data web is in its earl days. Most data is still locked up. To the extent data is shared, many different technologies are being used to do the sharing. The open data sets that are available remain mostly unconnected to each other, still living inside their separate silos.’ This is very important – perhaps the most important sentence in the book. What is such data could be combined into a single set and then read by computers??
‘Imagine having the genome of newborn children immediately sequenced, and then correlated with a giant database of public health records to determine not just what diseases they are susceptible to…but also what environmental factors might increase their susceptibility to disease. ‘Your son has an 80 per cent chance of developing heart disease in his 40s if he is sedentary in his 20s and 30s. But with 3 hours exercise per week that probability drops to 15%.’
Practical steps
Most science journals were behind paywalls (at least when Nielsen wrote the book). This is a problem, as described earlier. Scientific info should be free.
Public Library of Science is a good example of where info has been made free. ArXiv (pronounced archive) has done this for physics preprints (ie. first drafts of journal articles. This enables people to see the latest developments in physics for free. It would be great if this were expanded to other fields.
The problem with open science is that scientists themselves have no incentive to make their data public, and lots of incentives to keep it secret. They also don’t have the time to publish online because the scientific community places greater prestige on journal articles.
Here is how this can be fixed
1. Compulsion – make people publish data online and for free if they want scientific grants.
2. Incentives (otherwise people will just do the bare minimum to get the grants). Come up with a process in which you can easily measure online citations and store it centrally. If you publish something online, and get cited a lot, this should show up in some sort of database which people use when considering scientists for job applications etc.
3. Raise public awareness – the public needs to know the importance of open science and push scientists to abide by it.
This book is essentially a plea for open access science. The idea is that more information in the open enables faster, more efficient, and more accurate science to be conducted.
Here are some notes I made – they don’t really represent my own views but more what Nielsen actually says.
Nielsen talks about the chess match – Kasparov v the World. Essentially, Gary Kaspaov (a chess master) was very nearly beaten by the ‘world team’ – a team of people collaborating on Chess moves via the internet. Even though Kasparov was the best player, whose chess knowledge matched everyone else on an individual basis, the World Team had a number of people who were specialists in particular moves in Chess. This is referred to as ‘latent micro expertise’ which meant that their cumulative efforts were impressive.
Similarly, in a large organisation you want to encourage micro expertise. You want to encourage people with a specific ability to solve a specific problem to assist the person in charge of solving that problem. This means getting people out of their silos and at least spending some time collaborating with others in the organisation, ideally over some sort of platform.
He also uses the metaphor of uranium. Uranium is unstable, and neutrons fly out occasionally. Below a certain mass, neutrons fly out of uranium atoms. Below a certain mass, these neutons don’t tend to collide with other atoms. However, above a certain mass, they DO collide with other uranium atoms, which cause more neutrons to fly out of those atoms. Those neutrons in turn collide with other atoms, which causes more neutrons to fly out, and eventually the whole thing blows up. This is called critical mass. Nielsen posits that intelligence is similar – the more information that is publicly available, the more people will latch on to it, and the faster ‘explosion’ (ie the creation of new ideas) can occur.
‘In most cases, what makes a creative insight important is precisely the fact that it combines ideas that were previously thought to be unrelated.’
Why is online collaboration different to committee work? Well, committees work at the pace of their slowest members and can be stifled by vetos from the most obstructive or annoying people. In online collaborations, you can just ignore these people. Also, committees tend to consist of people who were forced to sit on them, whereas online collaboration, when done well, will encourage people who are already passionate about a particular subject. Online is better than offline collaboration because of scale and diversity of ideas and skills.
However, if an online collaboration is large scale, you want to construct a system that will direct people to the sub-section of the collaboration where their knowledge is most useful.
Open source software is a good example of where this has arisen. Linux is a piece of opens source software which has reached incredible extents. They focus relentlessly on modularity – Linux is broken down into individual modules which people can contribute to depending on their specialism.
Firefox has found a good way to solve problems – via an issue tracker. People can report issues and if enough of them do so it rises up a sort of league table, and people are encouraged to contribute to solving it. You can also use the issue tracker to suggest new developments. This central area of discussion encourages people to target their expertise directly.
Although you still have the problem of directing expert attention – how can you be sure that people are contributing to something where their skills are most needed?
One way that this was done was in the Mathworks competition. Essentially, Mathworks creates a competition every year where people can build a program to solve a particular issue. The example Nielsen gives is from 1998 - competitors were asked to design a program/formula that would select from a list of songs the number of songs that could most closely be put together in order to fill up as much of the length of a 74 minute CD as possible.
Mathworks designed a process wheeby each entry could be immediately judged by a secret formula and awarded as a score. In addition, the code for successful entries was immediately published. So people could see which entries got the highest score, work out what had changed with thatentry, and then finesse that particular aspect. This meant that expert attention was directed in the ways intended by the designers of the competition.
‘ If a participant in the Mathworks competition was stuck for ideas, they only needed to wait for a few hours tofind new ideas to stimulate and challenge them.’
Re politics
‘Group discussion actually makes people’s political decisions worse than they would have been if they had made those decisions individually.’
Groups focus on the information they all hold in common, not the information that they individually hold. Also they focus on knowledge held by high status group members and ignore the knowledge held by low status group members.
Most people do not consider politicians by building up a complete picture of their positions – we judge on how key aspects of their politics affects our interests. This is because there is no ‘shared praxis’ – ie. consensus on what position evinced by a politician is ‘good’ or ‘bad.’ This differs from the Mathworks competition where everyone as agreed that a higher score was good. So in order to have a successful collaboration, we need a shared praxis.
Other
Due to a shared praxis ‘In science it is often the people with the best ideas who win out, not the people with the most power.’
It is worth attempting to propagate this idea. What if it were possible to ‘match’ tasks across an organisation to people in that organisation? An algorithm could select tasks and allocate them to the person who had the greatest success in solving such problems before.
Nielsen posits that there are many unrecognised connections in science. You can use machine learning to trawl through endless amounts of data and recognise the connections between two previously unrelated things – ie. what Visulytix did with scans of eye retinas and identifying retinas that were diseased.
Google search queries can be used to predict which songs will top the chart and which stocks will do well. This is an example of identifying links between two previously unstudied data sets. What about use in political campaigns??
Data commons
‘’An unaided human’s ability to process large data sets is comparable to a dog’s ability to do arithmetic, and not much more valuable.’
Most data should be made public, in order that such patterns can be identified by computers. However, this means rea data – the people publishing it should actually be attempting to explain the data to others, and acting out of willingness rather than mere compulsion.
‘Today – the data web is in its earl days. Most data is still locked up. To the extent data is shared, many different technologies are being used to do the sharing. The open data sets that are available remain mostly unconnected to each other, still living inside their separate silos.’ This is very important – perhaps the most important sentence in the book. What is such data could be combined into a single set and then read by computers??
‘Imagine having the genome of newborn children immediately sequenced, and then correlated with a giant database of public health records to determine not just what diseases they are susceptible to…but also what environmental factors might increase their susceptibility to disease. ‘Your son has an 80 per cent chance of developing heart disease in his 40s if he is sedentary in his 20s and 30s. But with 3 hours exercise per week that probability drops to 15%.’
Practical steps
Most science journals were behind paywalls (at least when Nielsen wrote the book). This is a problem, as described earlier. Scientific info should be free.
Public Library of Science is a good example of where info has been made free. ArXiv (pronounced archive) has done this for physics preprints (ie. first drafts of journal articles. This enables people to see the latest developments in physics for free. It would be great if this were expanded to other fields.
The problem with open science is that scientists themselves have no incentive to make their data public, and lots of incentives to keep it secret. They also don’t have the time to publish online because the scientific community places greater prestige on journal articles.
Here is how this can be fixed
1. Compulsion – make people publish data online and for free if they want scientific grants.
2. Incentives (otherwise people will just do the bare minimum to get the grants). Come up with a process in which you can easily measure online citations and store it centrally. If you publish something online, and get cited a lot, this should show up in some sort of database which people use when considering scientists for job applications etc.
3. Raise public awareness – the public needs to know the importance of open science and push scientists to abide by it.
May 14, 2012
"In the most successful online collaborations this use of microexpertise approaches an ideal in which collaboration routinely locates ... people with just the right microexpertise for the occasion. In particular, as creative collaboration is scaled up, problems can be exposed to people with a greater and greater range of expertise... Instead of being an occasional fortuitous coincidence, serendipity becomes commonplace. The collaboration achieves a kind of designed serendipity..." (27)
"In this chapter we'll identify four powerful patterns that open source collaborations have used to scale. (1) a relentless commitment to working in a modular way, finding clever ways of splitting up the overall task into smaller subtasks; (2) encouraging small contributions, to reduce barriers to entry; (3) allowing easy reuse of earlier work by other people; and (4) using signaling mechanisms such as scores to help people decide where to direct their attention." (48-9)
"This points the way to a fundamental requirement that must be met if we're to amplify collective intelligence: participants must share a body of knowledge and techniques. It's that body of knowledge and techniques that they use to collaborate. When this shared body exists, we'll call it shared praxis..." (75)
"Citizen science can be a powerful way both to collect and also to analyze enormous data sets." (151)
"The problem today is that while it's now in the collective interest of scientists to adopt new technologies, their individual interest remain aligned with journal publication." (189)
"In this chapter we'll identify four powerful patterns that open source collaborations have used to scale. (1) a relentless commitment to working in a modular way, finding clever ways of splitting up the overall task into smaller subtasks; (2) encouraging small contributions, to reduce barriers to entry; (3) allowing easy reuse of earlier work by other people; and (4) using signaling mechanisms such as scores to help people decide where to direct their attention." (48-9)
"This points the way to a fundamental requirement that must be met if we're to amplify collective intelligence: participants must share a body of knowledge and techniques. It's that body of knowledge and techniques that they use to collaborate. When this shared body exists, we'll call it shared praxis..." (75)
"Citizen science can be a powerful way both to collect and also to analyze enormous data sets." (151)
"The problem today is that while it's now in the collective interest of scientists to adopt new technologies, their individual interest remain aligned with journal publication." (189)
September 21, 2012
Lots of fun to read. I started off really enthusiastic about this. Then it got to be a little bit repetitive by repeating its themes and same 3-4 examples too many times. Its still very interesting to learn about collective intelligence. The thesis of the book is to encourage science to take on a new paradigm of open source data collection and shared results. Seems to be a little idealistic but it makes a pretty good point of how fast innovation could advance if everybody is directed in a similar path and gives free time and energy to put into a universal goal. I believe this would work only if it is formatted in an interesting way that would capture the imagination of the volunteers and there would need to be some un-named incentive that hasn't been presented yet. It is fun to think of a world that can advance as quickly as a potential collective of scientist working for one goal. Its powerful what a group of people can do when properly motivated and directed. If any of this is interesting to you, then you may like this book.
March 18, 2012
Michael Nielsen presents how Internet enhances our knowledge and problem-solving skills. He provides examples how (and when) a well-done collective intelligence can outsmart the most capable minds and be used in the leading edge of science and technology.
Moreover, the book not only contains examples, but has a visionary part - it shows an already started path to science more open among scientists and also inviting amateurs for their meaningful contribution. If you are interested in the future of knowledge making and sharing (or at least, if you consider Wikipedia a great thing) - read it.
When it comes to drawbacks: the book seems to be written in a chatty style (call it 'light' if you like it), with some repetitions, unpolished parts and (IMHO) could be losslessly compressed to 1/2 of its volume. Moreover, it is kind of ironic that a book on open knowledge is only accessible as a copyrighted material. While I like having a paper book in my hands I am astonished there is not a pdf/wiki on the author's blog.
Moreover, the book not only contains examples, but has a visionary part - it shows an already started path to science more open among scientists and also inviting amateurs for their meaningful contribution. If you are interested in the future of knowledge making and sharing (or at least, if you consider Wikipedia a great thing) - read it.
When it comes to drawbacks: the book seems to be written in a chatty style (call it 'light' if you like it), with some repetitions, unpolished parts and (IMHO) could be losslessly compressed to 1/2 of its volume. Moreover, it is kind of ironic that a book on open knowledge is only accessible as a copyrighted material. While I like having a paper book in my hands I am astonished there is not a pdf/wiki on the author's blog.
November 8, 2011
Decent discussion about the open science and how to approach it given the constraints, for example, faced by the career demands of the scientists. In times I had the feeling that the writing was a bit sloppy and repetitive, but this was easy enough book to read and a useful way to start discussion for real.
June 16, 2012
a masterpiece from a master
February 13, 2019
Nielsen’s book echoes my frustration with academia. He argues that the current state of academic science is untenable (it is!). An absolute must-read for any scientist.
May 16, 2021
Reinventing Discovery feels like a clear manifesto for my ideas on open science. For context, I am a physicist currently working on the ATLAS experiment at CERN and as a software researcher for the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP), and I try to make as much of my work open by design. I found it interesting how many of the same points that Michael Nielsen brought up in the book (published in 2011) on the problems that plague science are still quite relevant today. I am biased towards Nielsen's writings though as we share common goals for open science and Nielsen's book on machine learning,
Neural Networks and Deep Learning
, was one of my first formal introductions to the subject and the arguments and presentation of machine learning "made sense" to me. Conditioned on that, and my my work angled at producing more open and reproducible science it is perhaps not difficult to understand why this section in the final few pages of the book in Chapter 9 resonated with me strongly:
If I am to critique the book, I would have enjoyed some additional examples beyond The Polymath Project, Galaxy Zoo, and Kasparov vs. "the World", which are used extensively throughout the book to hammer home the arguments. However, this is perhaps unfair as I can believe that these examples were carefully selected to both make points and to use as touchstones to tie together the narrative. Also, I am not a historian of science — even recent science — and as things like GitHub were still growing and Zenodo didn't even exist at the time of publication (Zenodo was launched in 2013 according to Wikipedia) there might not have been as many examples as there are today.
If you're a scientist who is also a programmer, you have a special role to play, an opportunity to build the new tools that redefine how science is done. Be bold in experimenting with new ideas: this is the golden age of scientific software. But also be bold in asserting the value of your work. Today, your work is likely to be undervalued by old-fashioned colleagues, not because of malice, but because of a lack of understanding. Explain to other scientists how they should cite your work. Work in cahoots with your scientist programmer friends to establish shared norms for citation, and for sharing of code. And then work together to gradually ratchet up the pressure on other scientists to follow those norms. Don't just promote your own work, but also insist more broadly on the value of code as a scientific contribution in its own right, every bit as valuable as more traditional forms.
If I am to critique the book, I would have enjoyed some additional examples beyond The Polymath Project, Galaxy Zoo, and Kasparov vs. "the World", which are used extensively throughout the book to hammer home the arguments. However, this is perhaps unfair as I can believe that these examples were carefully selected to both make points and to use as touchstones to tie together the narrative. Also, I am not a historian of science — even recent science — and as things like GitHub were still growing and Zenodo didn't even exist at the time of publication (Zenodo was launched in 2013 according to Wikipedia) there might not have been as many examples as there are today.
Overall, I would say that if you're someone who has a passing interest in open science this is a great book to read. It also does a great job of shining a light on the real institutional and sociological problems that face science today in terms of moving forward in its goals.
May 22, 2018
Really enjoyed this, it covers the groundwork of science being practiced in a different manner than in the past... some examples are the FoldIt and Zooniverse efforts where data is analyzed by citizen scientists and data used by scientists... but also engages non-scientists to discuss the work as well.
He covers some great ground where open data, involvement of the public, and a forum that encourages use and discussion amongst both highly studied scientists and laymen. Of course there are only a few of these projects primarily as - scientists aren’t rewarded to create these kinds of projects in general. So, the few that are there are unique in their area. Generally the data is shared via the internet and anyone can come and help, search and just peruse the information.
Nielsen also goes over the pitfalls of the use of information, the reality of how scientists work and of the influences to not share currently out weigh sharing.
But what is most interesting is that he talks about how this could be a new chapter in doing science... where data is openly shared, through a forum that allows discussion, helpful mentors and a focus on developing questions to discover new information.
He does seem to indicate that this type of work does better with sciences that don’t involve “social sciences” since opinions of what is right or wrong tend to get in the way of clearly looking at data- without pre-conceived answers.
I find the ideas presented really interesting and could be a great way for humanity to solve some bigger problems that effect us all... but we collectively- humanity plus scientists must decide that we all need to move this way at the same time- in ways that benefit us all. Could be a great way to solve disease, but the money made by preventing knowledge of cures would certainly squash that idea.... sad but true.
Worth reading.
He covers some great ground where open data, involvement of the public, and a forum that encourages use and discussion amongst both highly studied scientists and laymen. Of course there are only a few of these projects primarily as - scientists aren’t rewarded to create these kinds of projects in general. So, the few that are there are unique in their area. Generally the data is shared via the internet and anyone can come and help, search and just peruse the information.
Nielsen also goes over the pitfalls of the use of information, the reality of how scientists work and of the influences to not share currently out weigh sharing.
But what is most interesting is that he talks about how this could be a new chapter in doing science... where data is openly shared, through a forum that allows discussion, helpful mentors and a focus on developing questions to discover new information.
He does seem to indicate that this type of work does better with sciences that don’t involve “social sciences” since opinions of what is right or wrong tend to get in the way of clearly looking at data- without pre-conceived answers.
I find the ideas presented really interesting and could be a great way for humanity to solve some bigger problems that effect us all... but we collectively- humanity plus scientists must decide that we all need to move this way at the same time- in ways that benefit us all. Could be a great way to solve disease, but the money made by preventing knowledge of cures would certainly squash that idea.... sad but true.
Worth reading.
October 20, 2023
Very occasionally I will discover authors about whom I can say, “I want to read everything this person has written.” I’m beginning to suspect that Nielsen might be one such person. I came to this book in a roundabout way, by first reading his excellent and more recent essay on Metascience, and then wanting to learn more on the subject.
This book itself spoke to me very deeply as a scientist. Many of Nielsen’s ideas are things that I had already been curious about (the Polymath project, open source code, and the “wiki” structure) but I had not synthesized them into such a cohesive and compelling vision. Focusing on the “network effect” is a great choice I think, as it illustrates the social aspect of scientific research and also the nonlinear way in which cooperative efforts scale.
I would love to read an updated version of this book for 2023. One glaring difference between today and a decade ago is the potential of deep learning and AI tools to be integrated into our scientific workflows. We are already seeing this with, for example, the use of GPT as an assistant for typesetting or programming. Still, I feel we have barely scratched the surface. Indeed I feel that, even with more than a decade of developments, scientists have barely scratched the surface of 2011 tools, let alone 2023 tools.
Other developments that may be worth surveying are the replicability crisis (Nielsen and Qiu focus heavily on this in their essay on Metascience), progress in automated theorem proving, growth of arXiv-like cousins like biorXiv, the use of OpenReview for many CS conferences, etc.
I can’t recommend this book enough to anyone interested in science. 5 stars.
This book itself spoke to me very deeply as a scientist. Many of Nielsen’s ideas are things that I had already been curious about (the Polymath project, open source code, and the “wiki” structure) but I had not synthesized them into such a cohesive and compelling vision. Focusing on the “network effect” is a great choice I think, as it illustrates the social aspect of scientific research and also the nonlinear way in which cooperative efforts scale.
I would love to read an updated version of this book for 2023. One glaring difference between today and a decade ago is the potential of deep learning and AI tools to be integrated into our scientific workflows. We are already seeing this with, for example, the use of GPT as an assistant for typesetting or programming. Still, I feel we have barely scratched the surface. Indeed I feel that, even with more than a decade of developments, scientists have barely scratched the surface of 2011 tools, let alone 2023 tools.
Other developments that may be worth surveying are the replicability crisis (Nielsen and Qiu focus heavily on this in their essay on Metascience), progress in automated theorem proving, growth of arXiv-like cousins like biorXiv, the use of OpenReview for many CS conferences, etc.
I can’t recommend this book enough to anyone interested in science. 5 stars.
January 26, 2023
Michael Nielsen has some absolutely fascinating views on human scientific development and the ideas from Reinventing Discovery are completely in line with what I'd love to see modern science become; a networked, well incentivized, decentralized system for knowledge generation that collaborates and engages the world. Let public be what public is, and let government-funded research be open to all.
With a close look into specific open tools, citizen science, democratization of research and data, open science projects, and blogs, Nielsen ends the book with the Open Science Imperative, calling upon researchers, universities, and specifically funders, to create wide agreement and decisive change on researcher incentives. It is a massive change from publication-based knowledge generation but he compares it to the lane change in Sweden 1967, where the whole of Sweden began driving on the right lane instead of the left. A drastic change *can* happen.
For Sweden, it took 10 years of discussion to reach their shift, while I see that since this book was released, many knowledge-generating processes are changing: OpenReview, ArXiv, codebase citations, gold standard open source, many research bloggers, a separate social incentive system, independently funded researchers, and so much else.
With a close look into specific open tools, citizen science, democratization of research and data, open science projects, and blogs, Nielsen ends the book with the Open Science Imperative, calling upon researchers, universities, and specifically funders, to create wide agreement and decisive change on researcher incentives. It is a massive change from publication-based knowledge generation but he compares it to the lane change in Sweden 1967, where the whole of Sweden began driving on the right lane instead of the left. A drastic change *can* happen.
For Sweden, it took 10 years of discussion to reach their shift, while I see that since this book was released, many knowledge-generating processes are changing: OpenReview, ArXiv, codebase citations, gold standard open source, many research bloggers, a separate social incentive system, independently funded researchers, and so much else.
April 21, 2020
I read this book expecting a thoughtful discussion of how we can use crowdsourcing and networking to improve life science. What I read was a very poorly edited book (so many typos that I stopped counting) that tries to apply analogies from mathematics to biological science that simply do not work. As many have said in science, ideas are a dime a dozen, its really a matter of doing the work to produce new science. The challenge in the empirical world of science are not so much the generation of new ideas, but the development and testing of hypotheses. The author is clearly well versed in the fields of math and physics, but fails to understand the challenges to doing empirical science in biology. The book could have used a thorough editing (I expected better from Princeton University Press) and the insights of scientists from biology and chemistry. I would love to see a rewrite of this book after having the author explore how empirical science is done... this world has been networking in various ways for hundreds of years. Technology is enabling new ways to do this, but they are simply not explored in this book.
April 26, 2020
This is a nice introduction to the way relative recent technologies (basically the internet) afford a better way to collaborate and do science. Roughly, in today's research environment, the rewarded behavior is to publish frequently. The problem with this is, among other things, that time spent developing collective tools or time spent making discoveries and the associated work/data available, does not really advance personal publication. Think of it in terms of game theory: the people who selfishly devout all their time writing papers and doing research will do better in today's reward system than people who spend time sharing their results and tools, etc. This is a problem, because the community as a whole is better off if people share results and help each other. Michael Nielsen offers some potential solutions and lays out the problem much better than I just did; it is definitely worth a read if you're interested. The book is a few years old and my impression is that the problem is less prominent in the field of computer science, which is where I spend most of my time. Still, the problem very much persists today.
December 15, 2016
Me recomendaram muito este livro por conta de uma apresentação sobre ciência cidadã que fiz (ciência feita por não cientistas). Li e gostei bastante, mas ele falou exatamente o que eu já estava apresentando, não me acrescentou muito.
Michael Nielsen discute como colaboramos muito mais hoje em dia graças a internet e o tipo de produção científica que é possibilitada. A descrição do Galaxy Zoo e de como as pessoas estão fazendo descobertas astronômicas de casa, classificando as imagens, foi especialmente marcante.
Vale para cientistas se interessarem por acesso aberto e pela importância de comunicar melhor o que fazemos. Mas ele bate no ponto mais forte, enquanto o sistema de incentivos não mudar e não passarmos a valorizar esse tipo de atitude, nada vai mudar.
Ah, a descrição de Galileu contando o que havia descoberto por códigos foi especialmente marcante. A explicação fica para quem ler ;)
Michael Nielsen discute como colaboramos muito mais hoje em dia graças a internet e o tipo de produção científica que é possibilitada. A descrição do Galaxy Zoo e de como as pessoas estão fazendo descobertas astronômicas de casa, classificando as imagens, foi especialmente marcante.
Vale para cientistas se interessarem por acesso aberto e pela importância de comunicar melhor o que fazemos. Mas ele bate no ponto mais forte, enquanto o sistema de incentivos não mudar e não passarmos a valorizar esse tipo de atitude, nada vai mudar.
Ah, a descrição de Galileu contando o que havia descoberto por códigos foi especialmente marcante. A explicação fica para quem ler ;)
October 14, 2019
The book has an incredibly strong start – I was enthralled by its description of attention architecture, especially. The second third has a lot of detail about some interesting open science projects and then it moved to something of a polemic. An update would be very welcome as it is a few years old now and countless things have moved on in this field. If it were updated, I think the sections of the conclusion encouraging scientists to blog, share data etc might usefully be expanded to a section of protocol advice.
I’d like to know more about the ideas around attention architecture and collaboration. Four stars for the compelling and inspiring exploration of these. Highly recommended short read for non-scientists and scientists alike who are interested in how we can evolve new ways of working online.
I’d like to know more about the ideas around attention architecture and collaboration. Four stars for the compelling and inspiring exploration of these. Highly recommended short read for non-scientists and scientists alike who are interested in how we can evolve new ways of working online.
March 2, 2015
From browsing the clear and lively Reinventing Discovery you might not guess that Michael Nielsen is a physicist and well-known contributor to quantum computation theory. However, if you delve into the book, you'll soon detect the careful thinking patterns of a trained analyst or scientist. Nielsen put his scientific career on the back burner in 2008 to focus on bringing about a revolution in how science is carried out (https://en.wikipedia.org/wiki/Michael...). This book is one fruit of his new mission. It aims to sway scientists as well as to educate the public and appeals both to logic and emotion. Nielsen is pushing all of us toward what he hopes will be a leap in scientific progress built on an explosion of online collaboration and open data sharing among scientists and even the interested lay public.
The title expresses Nielsen's belief that we are poised on the cutting edge of a fundamental reshaping of how most science gets done. Using some fascinating case studies he illustrates how this new era has already crept onto the scene. He cites the Polymath Project as successfully demonstrating the power of online collaboration. In this case an important mathematical challenge was solved entirely in the public eye over the course of a few weeks in 2009. Over 20 mathematicians contributed jointly to a web blog devoted to the problem that was hosted by Tim Gowers, a highly renowned mathematician. It was a heady time: Gowers later said that the Polymath process was "to normal research as driving is to pushing a car" (p 2).
Nielsen reviews the legacy process of scientific discovery which, he notes, has worked well yet whose essence has changed little since the advent of the first scientific journal in 1665 in England. He goes on to show the power of new resources and mechanisms that can support shared discovery (as in Polymath) but whose adoption by the broad scientific community lags. He asserts that the solution of many important scientific questions now and in future would benefit by the deliberate, thoughtful harnessing of the collective mind, and by openly sharing data, both research results as well as tips and informal knowledge about how to organize and carry out research. Nielsen foresees the need for creative and relentless design and redesign of collaborative platforms, for careful structuring of data and query mechanisms to facilitate automated access, and for open sharing of scientific data on a scale that dwarfs even current efforts like the Human Genome project or the Sloan Digital Sky Survey (SDSS) (p 98).
Nielsen lucidly presents many samples of these new ways of doing science, from Polymath to the Hap(lotype) Map to Galaxy Zoo (a website where anyone can help classify celestial objects) to the astounding Kasparov versus the World chess match in 1999. He draws on appropriate arenas outside science, including the software Open Source movement, to discuss advanced practices for enabling rapid and efficient online collaboration. "Architecture of attention" (p 32) and "designed serendipity" (p 27) are two memorable phrases he employs to tag the essence of these new practices. He contrasts all too briefly the new to some established ways of collaborating: conventional (hierarchical) organizations; the marketplace; offline small groups. He launches the effort to distil some principles that could guide pioneers who wish to implement online collaboration in a new problem-solving arena; it's but a tantalizing glimpse into what must become a well-explored domain if the new collaboration style is to catch fire. He sketches a dream of one day having an open data web that can machines can query on their own. He notes that with such a large trove of accessible data, our notion of "explanation" may need to change: no longer will Occam's Razor be as sure a guide to what is a good explanation, since machines can develop explanations that no human can. Without digging deeply into it, he notes that inevitably new types of questions can be asked and answered when large troves of machine-accessible data begin to jostle shoulders in the public domain.
The book, then, is a survey of the landscape and some possible ways forward. It feels like just an opening manifesto in a campaign that is in tune with large societal shifts such as social networking and putting online just about everything. It is supported by sections of notes, references, suggestions for further reading and an index. As noted, it is studded with compelling real-world examples. Scientific culture change is no minor matter and Nielsen deals with some obstacles. More inspiration than howto, despite the many examples, it does not address details on how to implement solutions in particular disciplines. Also, I suspect that much more fine-grained attention than Nielsen has given will be needed to pick apart and tackle the significant personal and money-related resistances that hamper adoption of his proposals. The large amount of for-profit science is hardly mentioned here; as an outside observer, I wonder if that is so trivial an omission in this age dominated by large, rich corporations.
No book can do it all; Reinventing Discovery makes a good initial exploration of possibilities to enhance the process of discovery. This book should appeal to anyone interested in how science "happens" (or doesn't happen!), in how to potentially accelerate progress without huge financial investments, in novel ways to tackle many classes of problems.
The title expresses Nielsen's belief that we are poised on the cutting edge of a fundamental reshaping of how most science gets done. Using some fascinating case studies he illustrates how this new era has already crept onto the scene. He cites the Polymath Project as successfully demonstrating the power of online collaboration. In this case an important mathematical challenge was solved entirely in the public eye over the course of a few weeks in 2009. Over 20 mathematicians contributed jointly to a web blog devoted to the problem that was hosted by Tim Gowers, a highly renowned mathematician. It was a heady time: Gowers later said that the Polymath process was "to normal research as driving is to pushing a car" (p 2).
Nielsen reviews the legacy process of scientific discovery which, he notes, has worked well yet whose essence has changed little since the advent of the first scientific journal in 1665 in England. He goes on to show the power of new resources and mechanisms that can support shared discovery (as in Polymath) but whose adoption by the broad scientific community lags. He asserts that the solution of many important scientific questions now and in future would benefit by the deliberate, thoughtful harnessing of the collective mind, and by openly sharing data, both research results as well as tips and informal knowledge about how to organize and carry out research. Nielsen foresees the need for creative and relentless design and redesign of collaborative platforms, for careful structuring of data and query mechanisms to facilitate automated access, and for open sharing of scientific data on a scale that dwarfs even current efforts like the Human Genome project or the Sloan Digital Sky Survey (SDSS) (p 98).
Nielsen lucidly presents many samples of these new ways of doing science, from Polymath to the Hap(lotype) Map to Galaxy Zoo (a website where anyone can help classify celestial objects) to the astounding Kasparov versus the World chess match in 1999. He draws on appropriate arenas outside science, including the software Open Source movement, to discuss advanced practices for enabling rapid and efficient online collaboration. "Architecture of attention" (p 32) and "designed serendipity" (p 27) are two memorable phrases he employs to tag the essence of these new practices. He contrasts all too briefly the new to some established ways of collaborating: conventional (hierarchical) organizations; the marketplace; offline small groups. He launches the effort to distil some principles that could guide pioneers who wish to implement online collaboration in a new problem-solving arena; it's but a tantalizing glimpse into what must become a well-explored domain if the new collaboration style is to catch fire. He sketches a dream of one day having an open data web that can machines can query on their own. He notes that with such a large trove of accessible data, our notion of "explanation" may need to change: no longer will Occam's Razor be as sure a guide to what is a good explanation, since machines can develop explanations that no human can. Without digging deeply into it, he notes that inevitably new types of questions can be asked and answered when large troves of machine-accessible data begin to jostle shoulders in the public domain.
The book, then, is a survey of the landscape and some possible ways forward. It feels like just an opening manifesto in a campaign that is in tune with large societal shifts such as social networking and putting online just about everything. It is supported by sections of notes, references, suggestions for further reading and an index. As noted, it is studded with compelling real-world examples. Scientific culture change is no minor matter and Nielsen deals with some obstacles. More inspiration than howto, despite the many examples, it does not address details on how to implement solutions in particular disciplines. Also, I suspect that much more fine-grained attention than Nielsen has given will be needed to pick apart and tackle the significant personal and money-related resistances that hamper adoption of his proposals. The large amount of for-profit science is hardly mentioned here; as an outside observer, I wonder if that is so trivial an omission in this age dominated by large, rich corporations.
No book can do it all; Reinventing Discovery makes a good initial exploration of possibilities to enhance the process of discovery. This book should appeal to anyone interested in how science "happens" (or doesn't happen!), in how to potentially accelerate progress without huge financial investments, in novel ways to tackle many classes of problems.
February 13, 2021
Mostly focused on Open Science and Citizen Science, gave me better idea of what the vision of Open Science looks like and what was its history and perhaps also what to prioritise in open science (e.g. open data much more important then open access to papers; need to acknowledging alternative scientific outputs aside from papers as outputs worth recongnition).
Interesting but not that full of new ideas and concepts as I hoped, also the title is a bit misleading I think
Interesting but not that full of new ideas and concepts as I hoped, also the title is a bit misleading I think
January 21, 2024
Clear-eyed, provocative look at the state of culture of scientific research and how it can be reformed with the aid of collaborative technology and the willingness on the part of scientists to embrace a new mindset of openness and transparency in scientific work. As a technologist, and a science-fanboy, it brings together two of my greatest aspirations and kindles a wildfire in my mind. Absolutely loved it!
August 29, 2017
This is a great book - he talks about science and gives many excellent cases for the open science initiative.
However, sometimes the author details too much about some things - he uses a whole page for some things that could be said in 2 sentences.
Anyway, would recommend!
However, sometimes the author details too much about some things - he uses a whole page for some things that could be said in 2 sentences.
Anyway, would recommend!
April 11, 2019
everyone should read this book, which is not a book, it is an injection of power that opens your mind. I am not joking, if the meaning of this reading was grasped and applied in all sciences and in the social, in politics and in the economy, in school and in medicine, we had a better world
December 21, 2021
The most important ideas are covered early on but this is a short book and the anecdotes and arguments that follow are as entertaining and inspiring as they are thought-provoking. Possibly one of the most annotated books I own and surely one that defined my path and thinking going forward.
December 11, 2023
FINALLY finished this one and i think it's really really good. great introduction to the issue of open science. super interesting presentation of knowledge. super fun examples. and so much convincing optimism!!!! great book
January 3, 2018
quite re-iterative and doesn't really offer anything beyond repeating mass media news
March 8, 2018
Simply wonderful. If you care at all about the progress of science, technology, or humanity, then read this book ASAP.
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