Suppose that a scientific genius of the caliber of a Newton or an Einstein arises at least once for every 10 billion people: then on MegaEarth there would be 700,000 such geniuses living contemporaneously, alongside proportionally vast multitudes of slightly lesser talents. New ideas and technologies would be developed at a furious pace,
Back up. The population of Europe was under 200 million in 1700, less than a sixth of what it is today. The number of intellectuals was a tiny fraction of the number it is today. And the number of intellectuals in Athens in the 4th century BC was probably a few hundred. Yet we had Newton and Aristotle. Similarly, the greatest composers of the 18th and 19th century were trained in Vienna, one city. Today we may have 1000 or 10,000 times as many composers, with much better musical training than people could have in the days before recorded music, yet we do not have 1000 Mozarts or 1000 Beethovens.
Unless you believe human intelligence has been steadily declining, there is one Einstein per generation, regardless of population. The limiting factor is not the number of geniuses. The number of geniuses, and the amount of effort put into science, is nearly irrelevant to the amount of genius-level work accomplished and disseminated.
The limiting factor is organizational. Scientific activity can scale; recognition or propagation of it doesn’t. If you graphed scientific output over the years in terms of “important things discovered and adopted by the community” / (scientists * dollars per scientist), you’d see an astonishing exponential decay toward zero. I measured science and technology output per scientist using four different lists of significant advances, and found that significant advances per scientist declined by 3 to 4 orders of magnitude from 1800 to 2000. During that time, the number of scientific journals has increased by 3 to 4 orders of magnitude, and a reasonable guess is that so did the number of scientists. Total recognized “significant” scientific output is independent of the number of scientists working!
You can’t just add scientists and money and get anything like proportional output. The scientific community can’t absorb or even be aware of most of the information produced. Nor can it allocate funds or research areas efficiently.
So a critical question when thinking about super-intelligences is, How does the efficiency of intelligence scale with resources? Not linearly. To a first approximation, adding more scientists at this point accomplishes nothing.
On the other hand, merely recognizing and solving the organizational problems of science that we currently have would produce results similar to a fast singularity.
The limiting factor is organizational. Scientific activity can scale; recognition or propagation of it doesn’t.
While failure to recognize & propagate new scientific discoveries probably explains some of our apparent deficit of current scientific geniuses, I think a bigger factor is just that earlier scientists ate the low-hanging fruit.
(I have no idea whether a similar effect would kick in for superintelligences and throttle them.)
When I point out the low-hanging fruit effect to LWers, I do usually get a lot of agreement (and it is appreciated!) but I am starting to wish that someone would dig up some strong contrary evidence.
When the topic of apparent genius deficits and scientific stagnation comes up, people often present multiple explanations, like
intrinsic difficulties in scaling scientific activity
failure to identify/recognize contemporary scientific successes
no more low-hanging fruit
bureaucratization and institutional degradation
but tend to present only anecdotal evidence for each — myself included. And I’m not sure that can be helped; I don’t know of readily available evidence which powerfully discriminates between the different explanations.
PhilGoetz has data on scientific & technological progress, but I get the impression that much of it’s basically time series of counts of inventions & discoveries, which would establish only the whats and not the whys. Likewise, I think I could substantiate my January comment that cohort explains a substantial part of the variation in scientific eminence. And when I scraped together the data, ran the big regression, and found that birth year accounted for (suppose) 30% of the variance in eminence, that wouldn’t refute any of the potential explanations for why cohort correlated with eminence.
A partisan of the scaling hypothesis might say, “Obviously, as science gets bigger over time, it gets less efficient; more recently born scientists just lost the birth year draw”.
Someone arguing that scientific stagnation is illusory might say, “Obviously, this is a side effect of overlooking more recent scientific geniuses; scientists are working as effectively as before but we don’t recognize that thanks to increasing specialization, or our own complacency, or the difficulty of picking out individual drops from the flood of brilliance, or the fact that we only recognize greatness decades after the fact”.
I would say, if I were the kind of person who threw the word “obviously” around willy-nilly, “How many times do you expect general relativity to be invented? Obviously, there are only so many simple but important problems to work on, and when we turn to much harder problems, we make slower and more incremental progress”.
Someone most concerned with institutional degradation might say, “Obviously, as science has become more bureaucratic and centralized, that’s rendered it more careerist, risk-averse & narrow-minded and less ambitious, so of course later generations of scientists would end up being less eminent, because they’re not tackling big scientific questions like they did before”.
And we don’t get anywhere because each explanation is broadly consistent with the observed facts, and each seems obvious to someone.
I would put forth three lines of argument that might help.
First, what we consider a significant development is put in relation to its context. So, we naturally end up picking out the top-level entities and not the second-layer entities, let alone the third, fourth, fifth… modern science may have the same number of top-level discoveries, but these are underpinned by many more layers of discovery than earlier discoveries.
Second, let’s stop thinking about the jump from Aristotle to modern science for a minute. Let’s think about the jump from Novoselov and Geim’s discovery of Graphene to today.
In their first paper, they made graphene, put it on a substrate, hooked a few wires up to it, and did low-temperature transport measurements. Worth a nobel prize. Outside of the insight that led to it, pretty simple. Not everyone could do it, but many could.
In the following years, a bunch of progressively trickier experiments were performed.
As of two years ago, our clearest path to a publishable research paper in this area was to make an enormous pristine sheet of graphene, position a layer of boron nitride on top of it, position another layer of graphene on top of that in such a way that it didn’t short to the first piece, place a bunch of wires in very specific locations on this sandwich, then destroy the substrate that was all sitting on, all done so cleanly that it was smooth on every surface. This was insanely hard. This is also only a little trickier than normal for experiments in the field these days.
The low-hanging fruit has been taken, here. And it’s not simply that other people took it and we’re looking at sour grapes. I took some of that low-hanging fruit. There was a simple experiment to do, I did it, published it, and now I cannot do an experiment that simple again in this sub-field. My next experiment was substantially more complicated. The next experiment after that was far more complicated still.
Yes, we stand on the shoulders of giants, but we are in a progressively rarer atmosphere.
Third, and most critically, let’s look at the predictions of the ‘organizational inefficiency’ theory. if we were to scale back our scientific establishment to 1700-s levels, do you think we’d maintain our current level of scientific progress? That seems to be the implication, here, and it seems VERY dubious to me.
Funding agencies these days fund people who get PhDs.
To get a PhD, 90% of the time you need to generate a meaningful result of some kind within a limited time horizon. Scientists who go big and create nothing do not graduate and do not get funded later.
What some of them do learn to do is to manage several projects at the same time. They diversify, working on some big ideas which may fail, but insuring a steady stream of results by also working through some lesser issues with a higher probability of success.
It’s true that you can have a career stringing together nothing but small wins, but contrary to popular belief, the funding agencies (who rely on PhD scientific peer review committees) do fund many “high-risk, high-reward” projects.
In the private sector, for example, drug discovery projects have a vast failure rate but are funded nonetheless. Science funders do understand the biases toward career safety and are trying (imperfectly) to adjust for them.
when I scraped together the data, ran the big regression, and found that birth year accounted for (suppose) 30% of the variance in eminence, that wouldn’t refute any of the potential explanations for why cohort correlated with eminence
I’d love to see that data & analysis! Did you post it somewhere? Can you email it to me at gmail?
I think there was a LW post years ago saying that the word “obviously” is only used to cover up the fact that something isn’t obvious, and I agree with that more every year.
The evidence against the low-hanging fruit idea is that it explains only fame distribution across time, while the “attention and accretion model”, which says that people gain fame in proportion to the fame they already have, and total fame in a field is constant, explains fame distribution at any given moment as well as across time. If you use “attention and accretion” to explain fame distribution in the present, you will end up also explaining its distribution across time, not leaving very much for low-hanging fruit to explain.
Of course it is possible that low-hanging fruit is a strong factor, being cancelled out by some opposing strong factor such as better knowledge and tools. In fact, I think an economic-style argument might say that people work on the highest-return problems until productivity drops below C, then work on tools until it rises just above C, then work on problems, etc. So we should expect rate of return on worked-on problems to be fairly constant over time.
when I scraped together the data, ran the big regression, and found that birth year accounted for (suppose) 30% of the variance in eminence, that wouldn’t refute any of the potential explanations for why cohort correlated with eminence
I’d love to see that data & analysis! Did you post it somewhere? Can you email it to me at gmail?
I’m talking about a hypothetical analysis there. I haven’t actually collected the data and put it through the grinder (at least not yet)!
I think there was a LW post years ago saying that the word “obviously” is only used to cover up the fact that something isn’t obvious, and I agree with that more every year.
Yeah, I’m trying to install mental klaxons that go off when I unreflectively write (or read) “obvious” or “obviously”.
The evidence against the low-hanging fruit idea is that it explains only fame distribution across time, while the “attention and accretion model”, which says that people gain fame in proportion to the fame they already have, and total fame in a field is constant, explains fame distribution at any given moment as well as across time. If you use “attention and accretion” to explain fame distribution in the present, you will end up also explaining its distribution across time, not leaving very much for low-hanging fruit to explain.
That’s a fascinating result (although I’d wait for more details about the data & models involved before allocating the bulk of my probability mass to it). Does that mean our perception of fewer geniuses nowadays is merely because older geniuses grabbed most of the fame and left less of it for later geniuses? That’s how it sounds to me but I may be over-interpreting.
Does that mean our perception of fewer geniuses nowadays is merely because older geniuses grabbed most of the fame and left less of it for later geniuses?
Do we perceive there are fewer geniuses nowadays? I think we tend to pick the one thing somebody did in each generation or decade that seems most impressive, and call whoever did it an Einstein, with no idea how hard or easy it really was.
For instance, some people called Watson and Crick the great geniuses of the generation after Einstein, for figuring out the structure of DNA. Yet Watson and Crick were racing people all over the world to find the structure, because they knew anybody with the right tools would be able to figure it out within a few months. It required only basic competence.
(What’s especially interesting about that case is that Watson and Crick both did things that showed genius after they were hailed as geniuses, given genius-level funding and freedom, and expected to do genius things. Were they geniuses all along (a low prior), did they develop genius in response to more-challenging conditions, or is funding and freedom more important than genius?)
Today, we’ve got genuine genius entrepreneurs like Sergei & Larry, Peter Thiel, and Elon Musk, yet the public thinks the great genius of that generation was Steve Jobs. Possibly because Apple spent many (dozen? hundred?) millions of dollars advertising Steve Jobs. Peter Thiel was never on a billboard.
My gut says there are fewer geniuses nowadays, although I don’t really trust it on this one.
As for guts that aren’t mine...Bruce G. Charlton. Gideon Rachman. Dean Keith Simonton, although he simultaneously argues that modern first-rate scientists, “[i]f anything”, need “more raw brains”. Cosma Shalizi, who I think is being serious there, not just florid.
I think we tend to pick the one thing somebody did in each generation or decade that seems most impressive, and call whoever did it an Einstein, with no idea how hard or easy it really was.
I think there are certainly people who do that. There are people (not sure I can name any, but I’m sure they exist...Ray Kurzweil, maybe?) who are relentlessly upbeat about the march of scientific genius & progress, and people who just like jumping on hype bandwagons. There are also people with gloomier outlooks.
Today, we’ve got genuine genius entrepreneurs like Sergei & Larry, Peter Thiel, and Elon Musk, yet the public thinks the great genius of that generation was Steve Jobs. Possibly because Apple spent many (dozen? hundred?) millions of dollars advertising Steve Jobs. Peter Thiel was never on a billboard.
I don’t intuitively think of “genius entrepreneurs” as a natural category...
That advertising (and similar hype) influences whom people think of as geniuses is a good point.
Back up. The population of Europe was under 200 million in 1700, less than a sixth of what it is today. The number of intellectuals was a tiny fraction of the number it is today. And the number of intellectuals in Athens in the 4th century BC was probably a few hundred. Yet we had Newton and Aristotle.
Those places were selected for having Newton and Aristotle though.
The limiting factor is organizational. Scientific activity can scale; recognition or propagation of it doesn’t.
What leads you to be confident that these are the bottlenecks?
I measured science and technology output per scientist using four different lists of significant advances, and found that significant advances per scientist declined by 3 to 4 orders of magnitude from 1800 to 2000.
Interesting. Is your research up online?
On the other hand, merely recognizing and solving the organizational problems of science that we currently have would produce results similar to a fast singularity.
You mean, we would have a lot more effective research, quickly? Or something more specific?
What leads you to be confident that these are the bottlenecks?
One important piece of data is the distribution of citations within fields. There have been many studies of this. What you find, generally, is that a field of study has a finite amount of attention available—if it has N researchers, they collectively perform cN paper-readings per year. The distribution of these paper-readings is a power law (a Zipf distribution), so that the number of researchers whose papers get read much grows much more slowly than N. No model based on the expected distribution of the merits of the papers or the scientists makes sense, particularly given how the Zipf distribution changes with the size of the field. The models that make sense are models that say that the odds of somebody reading a paper by person X are proportional to the odds that someone else cited X. That is, if you break down your model of citation distribution into a component to model randomly trawling the literature for citations, and a component to model quality of the papers, you find the random model explains nearly 100% of the data.
Interesting. Is your research up online?
No, but check your email.
You mean, we would have a lot more effective research, quickly? Or something more specific?
If we achieved a linear relationship between input and output, we would have maybe 6 orders of magnitude more important scientific and technological advances per year. If we actually achieved “synergy”, that oft-theorized state where the accumulation of knowledge grows at a rate proportional to accumulated knowledge, we would have a fast take-off scenario, just without AI. dk/dt = k, dk/k = dt, ln(k) = t+C, k = Ce^t.
If we achieved a linear relationship between input and output, we would have maybe 6 orders of magnitude more important scientific and technological advances per year. If we actually achieved “synergy”, that oft-theorized state where the accumulation of knowledge grows at a rate proportional to accumulated knowledge, we would have a fast take-off scenario, just without AI.
How much should the fact that we do not have a fast take-off of organizations make us more pessimistic about one with AIs being likely?
That’s the question. We should consider the overhead cost of knowledge, and the possibility that we will see a logarithmic increase in knowledge instead of a linear one (or, that we will see a linear one given an exponential explosion in resources).
Much depends on how you measure knowledge. If you count “bits of information”, that’s still growing exponentially. If you count “number of distinctions or predictions you can make in the world”, that probably isn’t.
There is a critical relationship between GDP and the efficiency of science. Until 1970, the money we put into science increased exponentially. Economic growth comes (I believe) exclusively from advances in science and technology. In 1970, we hit the ceiling; fraction of GDP spent on science had grown exponentially until then, when it suddenly flattened, so that now resources spent on science grows only as fast as GDP grows. This should cause a slower growth of GDP, causing a slower increase in scientific results, etc. IIRC there’s a threshold of scientific efficiency below which (theoretically) the area under the curve giving scientific results off to infinity is finite, and another threshold of efficiency above which (theoretically) the curve rises exponentially.
Economic growth comes (I believe) exclusively from advances in science and technology.
This alone doesn’t seem sufficient to explain the distribution of economic growth between countries. Most science, and most technology more than a generation old, is now public domain. But even if we go two generations back, US GDP/capita was ~$25K, which would still put it in the top quartile of modern countries. The countries at the bottom of the economic lists are often catching up, but not uniformly.
This sounds more like a conflation between the “availability” of S&T versus the “presence” of S&T.
Technology being in the public domain does not mean the remote-savannah nomad knows how to use wikipedia, has been trained in the habit of looking for more efficient production methods, is being incentivized by markets or other factors to raising his productivity, or has at his disposal an internet-connected, modern computer, another business nearby that also optimizes production of one of his raw materials / business requirements, and all the tools and practical manuals and human resources and expertise to use them.
Long story short, there’s a huge difference between “Someone invented these automated farming tools and techniques, and I know they exist” and “I have the practical ability to obtain an automated farming vehicle, construct or obtain a facility complete with tools and materials for adjustment so I can raise livestock, contacts who also have resources like trucks (who in turn have contacts with means to sell them fuel), and contacts who can transform and distribute my products.”
The former is what you have when something is “public domain” and you take the time to propagate all the information about it. The latter, and all the infrastructure and step-by-step work required to get there, is what you need before the economic growth kicks in.
I believe the latter was being referred to by “advances in science and technology”.
Could you more clearly define the “presence” of S&T? Your examples like “automated farming tools are practical to obtain” sounds like a way of restating “both the availability of S&T and the economy are strong”, which does indeed imply that the economy will be strong, but “A ⇒ B” would have been a much more useful theory than “A && B ⇒ B”.
You’re making some argument that you think is implied by what you’ve said, but that I can’t see. I don’t see how the US of 2 generations ago having a high GDP is inconsistent with growth being a result of science and technology, unless you imagine science and technology are the same all over the world at a given time, which would be a strange thing to imagine.
(Side note: “Technology” here include organizational and management techniques.)
The use of science and technology isn’t the same all over the world at a given time, but the availability is remarkably close, don’t you think? What are the less developed countries left out on? ITAR-controlled products, trade secrets, and patents? For everything else they have access to the exact same journals.
Perhaps your side note is what’s critical: are there organizational and management techniques which are available in the United States but which we’ve successfully kept a secret internationally? Are multi-generational trade secrets the critical part of science and technology?
Or would other countries grow much faster if they just fully used public domain technology, but there’s some other factor X which is preventing them from using it? If the latter, then what is X, and wouldn’t it be a better candidate explanation for disparate economic growth?
This is a reasonable observation; yes, it is not obvious why every nation can’t jump straight to modern-nation productivity.
There are plenty of places in Africa where water purification is a great new technology, and plenty of places in China where closed sewage lines would be a great new technology. Why don’t they use them?
The stories I hear from very-low-tech countries usually emphasize cultural resistance. One guy installed concrete toilets in Africa, and people wouldn’t use them because concrete had negative connotations. People have tried plastic-water-bottle solar water purification in southeast Asia, and some concluded (according to Robin Hanson) that people wouldn’t put plastic bottles of water on their roof because they didn’t want the neighbors to know they didn’t have purified water. Another culture wouldn’t heat-sterilize water because their folk medicine was based on notions of what “hot” and “cold” do, and they believed sick people needed cold things, not hot things. There are many cases where people refused to believe there are invisible living things in water. (As Europeans also did at first.)
(Frequent hand-washing and checklists are technologies that could save many thousands of lives every year in US hospitals, but that are very difficult to get doctors to adopt.)
But lots of low-tech countries can’t afford anything that they can’t build themselves. How much of modern technology can be built with materials found on-site without any tools other than machetes, knives, and hammers? Mosquito netting is very valuable in some places, but impossible to manufacture in a low-tech way.
My short answer is that there are a variety of obstacles to applying any technology in a low-tech nation. But growth is only possible either by finding more resources, or by using existing resources more efficiently, and using resources more efficiently = technology.
If it were possible to have growth without technology—let’s say 1% growth every 10 years—then a society with medieval technology, and no technological change, would eventually become as productive per person as today’s modern countries. And that’s physically impossible, just using energy calculations alone. There may be other necessary conditions, but tech improvement is absolutely necessary.
Economic growth comes (I believe) exclusively from advances in science and technology.
Do you really thing that things like Good Governance don’t have anything to do with economic growth? Science doesn’t help you much if a competitor pays a corrupt official to shut your business down.
yet we do not have 1000 Mozarts or 1000 Beethovens
What do you mean by this? We have plenty of composers and musicians today, and I’d bet that many modern prodigies can do the same kinds of technical tricks that Mozart could at a young age.
Good question, though doing technical tricks at a young age does not make one Mozart. I don’t mean that we don’t have 1000 composers as good as Mozart or Beethoven. I mean we don’t have 1000 composers recognized as being that good. We may very well have 10,000 composers better than Mozart, but we’re unable to recognize that many good composers.
This is conflated with questions of high versus pop art andd accidents of history. Personally, I’m open to the idea that Mozart represents a temporary decline in musical taste—a period between baroque and romantic when people ate up the kind of pleasant, predictable pop music that Mozart churned out. He wrote some great stuff, but I think the bulk of what he wrote is soulless compared to equally-prominent baroque or romantic music.
I measured science and technology output per scientist using four different lists of significant advances, and found that significant advances per scientist declined by 3 to 4 orders of magnitude from 1800 to 2000. During that time, the number of scientific journals has increased by 3 to 4 orders of magnitude, and a reasonable guess is that so did the number of scientists.
I’d be really interested in reading more about this.
If you email philgoetz at gmail, I’ll send you a draft.
During that time, the number of scientific journals has increased by 3 to 4 orders of magnitude, and a reasonable guess is that so did the number of scientists.
Er, or not. The number of publications per scientist has risen dramatically, but so has the number of authors per paper. I don’t know if these cancel each other out.
Compare the complexity of F=MA to string theory. The difficulty of science is going up by orders of magnitude as the low hanging fruit are eaten.
yet we do not have 1000 Mozarts or 1000 Beethovens.
We have over 1000 genres of music. Sure, not everyone can be recognised as the best musician of a generation by definition, but I think we could arguably be producing 1000 times more good music than at the time of Mozart.
First of all, knowledge is partially ordered. A bunch of lesser-known results were required before Einstein could bring together the mathematical tools and physics knowledge sufficient to create relativity. True enough, this finding may have come much later, if not for Einstein, but dozens of others built predecessor results that also required great insight.
Similarly, we should not decry the thousands of biologists who have been cataloging every single protein, its post-translational modifications and its protein-protein interactions in exhaustive detail. Some of this work requires a great deal of cleverness each time.
A portion of the phenomenon you are talking about can be addressed by referencing Kuhn: We have periods of normal science, with many people giving input, and a building tension where twenty pieces fall into place and (frequently interdisciplinary) thinkers visit a problem for the first time.
In other cases the critical breakthrough has to be facilitated using new tools that generate new breakthroughs. When these tools require advances in component technology, you have a large number of engineers, testers and line workers feeding their talents into discoveries for which only a few get credit sometimes.
If these components require days or months of “burn-in” testing to judge their reliability, a superintelligence might have limited advantage over people in reducing the timeline.
Sometimes discovery relies on strings of experiments which by their nature require time and cannot be simulated. Our current knowledge of human biology requires that we follow patients for many years before we know all of the outcomes from a drug treatment.
Initially, at least, a superintelligent drug developer would still have to wait and see what happens when people are dosed the drug over the course of many years.
If a cosmic event can only be observed once in a decade, a superintelligence would not have the data any sooner, short of inventing some faster-than-light physics we do not have today.
Bostrom flies by an issue that’s very important:
Back up. The population of Europe was under 200 million in 1700, less than a sixth of what it is today. The number of intellectuals was a tiny fraction of the number it is today. And the number of intellectuals in Athens in the 4th century BC was probably a few hundred. Yet we had Newton and Aristotle. Similarly, the greatest composers of the 18th and 19th century were trained in Vienna, one city. Today we may have 1000 or 10,000 times as many composers, with much better musical training than people could have in the days before recorded music, yet we do not have 1000 Mozarts or 1000 Beethovens.
Unless you believe human intelligence has been steadily declining, there is one Einstein per generation, regardless of population. The limiting factor is not the number of geniuses. The number of geniuses, and the amount of effort put into science, is nearly irrelevant to the amount of genius-level work accomplished and disseminated.
The limiting factor is organizational. Scientific activity can scale; recognition or propagation of it doesn’t. If you graphed scientific output over the years in terms of “important things discovered and adopted by the community” / (scientists * dollars per scientist), you’d see an astonishing exponential decay toward zero. I measured science and technology output per scientist using four different lists of significant advances, and found that significant advances per scientist declined by 3 to 4 orders of magnitude from 1800 to 2000. During that time, the number of scientific journals has increased by 3 to 4 orders of magnitude, and a reasonable guess is that so did the number of scientists. Total recognized “significant” scientific output is independent of the number of scientists working!
You can’t just add scientists and money and get anything like proportional output. The scientific community can’t absorb or even be aware of most of the information produced. Nor can it allocate funds or research areas efficiently.
So a critical question when thinking about super-intelligences is, How does the efficiency of intelligence scale with resources? Not linearly. To a first approximation, adding more scientists at this point accomplishes nothing.
On the other hand, merely recognizing and solving the organizational problems of science that we currently have would produce results similar to a fast singularity.
While failure to recognize & propagate new scientific discoveries probably explains some of our apparent deficit of current scientific geniuses, I think a bigger factor is just that earlier scientists ate the low-hanging fruit.
(I have no idea whether a similar effect would kick in for superintelligences and throttle them.)
An upvote is inadequate to express the degree of my agreement with this statement.
When I point out the low-hanging fruit effect to LWers, I do usually get a lot of agreement (and it is appreciated!) but I am starting to wish that someone would dig up some strong contrary evidence.
When the topic of apparent genius deficits and scientific stagnation comes up, people often present multiple explanations, like
intrinsic difficulties in scaling scientific activity
failure to identify/recognize contemporary scientific successes
no more low-hanging fruit
bureaucratization and institutional degradation
but tend to present only anecdotal evidence for each — myself included. And I’m not sure that can be helped; I don’t know of readily available evidence which powerfully discriminates between the different explanations.
PhilGoetz has data on scientific & technological progress, but I get the impression that much of it’s basically time series of counts of inventions & discoveries, which would establish only the whats and not the whys. Likewise, I think I could substantiate my January comment that cohort explains a substantial part of the variation in scientific eminence. And when I scraped together the data, ran the big regression, and found that birth year accounted for (suppose) 30% of the variance in eminence, that wouldn’t refute any of the potential explanations for why cohort correlated with eminence.
A partisan of the scaling hypothesis might say, “Obviously, as science gets bigger over time, it gets less efficient; more recently born scientists just lost the birth year draw”.
Someone arguing that scientific stagnation is illusory might say, “Obviously, this is a side effect of overlooking more recent scientific geniuses; scientists are working as effectively as before but we don’t recognize that thanks to increasing specialization, or our own complacency, or the difficulty of picking out individual drops from the flood of brilliance, or the fact that we only recognize greatness decades after the fact”.
I would say, if I were the kind of person who threw the word “obviously” around willy-nilly, “How many times do you expect general relativity to be invented? Obviously, there are only so many simple but important problems to work on, and when we turn to much harder problems, we make slower and more incremental progress”.
Someone most concerned with institutional degradation might say, “Obviously, as science has become more bureaucratic and centralized, that’s rendered it more careerist, risk-averse & narrow-minded and less ambitious, so of course later generations of scientists would end up being less eminent, because they’re not tackling big scientific questions like they did before”.
And we don’t get anywhere because each explanation is broadly consistent with the observed facts, and each seems obvious to someone.
I would put forth three lines of argument that might help.
First, what we consider a significant development is put in relation to its context. So, we naturally end up picking out the top-level entities and not the second-layer entities, let alone the third, fourth, fifth… modern science may have the same number of top-level discoveries, but these are underpinned by many more layers of discovery than earlier discoveries.
Second, let’s stop thinking about the jump from Aristotle to modern science for a minute. Let’s think about the jump from Novoselov and Geim’s discovery of Graphene to today.
In their first paper, they made graphene, put it on a substrate, hooked a few wires up to it, and did low-temperature transport measurements. Worth a nobel prize. Outside of the insight that led to it, pretty simple. Not everyone could do it, but many could.
In the following years, a bunch of progressively trickier experiments were performed.
As of two years ago, our clearest path to a publishable research paper in this area was to make an enormous pristine sheet of graphene, position a layer of boron nitride on top of it, position another layer of graphene on top of that in such a way that it didn’t short to the first piece, place a bunch of wires in very specific locations on this sandwich, then destroy the substrate that was all sitting on, all done so cleanly that it was smooth on every surface. This was insanely hard. This is also only a little trickier than normal for experiments in the field these days.
The low-hanging fruit has been taken, here. And it’s not simply that other people took it and we’re looking at sour grapes. I took some of that low-hanging fruit. There was a simple experiment to do, I did it, published it, and now I cannot do an experiment that simple again in this sub-field. My next experiment was substantially more complicated. The next experiment after that was far more complicated still.
Yes, we stand on the shoulders of giants, but we are in a progressively rarer atmosphere.
Third, and most critically, let’s look at the predictions of the ‘organizational inefficiency’ theory. if we were to scale back our scientific establishment to 1700-s levels, do you think we’d maintain our current level of scientific progress? That seems to be the implication, here, and it seems VERY dubious to me.
Funding agencies these days fund people who get PhDs.
To get a PhD, 90% of the time you need to generate a meaningful result of some kind within a limited time horizon. Scientists who go big and create nothing do not graduate and do not get funded later.
What some of them do learn to do is to manage several projects at the same time. They diversify, working on some big ideas which may fail, but insuring a steady stream of results by also working through some lesser issues with a higher probability of success.
It’s true that you can have a career stringing together nothing but small wins, but contrary to popular belief, the funding agencies (who rely on PhD scientific peer review committees) do fund many “high-risk, high-reward” projects.
In the private sector, for example, drug discovery projects have a vast failure rate but are funded nonetheless. Science funders do understand the biases toward career safety and are trying (imperfectly) to adjust for them.
I’d love to see that data & analysis! Did you post it somewhere? Can you email it to me at gmail?
I think there was a LW post years ago saying that the word “obviously” is only used to cover up the fact that something isn’t obvious, and I agree with that more every year.
The evidence against the low-hanging fruit idea is that it explains only fame distribution across time, while the “attention and accretion model”, which says that people gain fame in proportion to the fame they already have, and total fame in a field is constant, explains fame distribution at any given moment as well as across time. If you use “attention and accretion” to explain fame distribution in the present, you will end up also explaining its distribution across time, not leaving very much for low-hanging fruit to explain.
Of course it is possible that low-hanging fruit is a strong factor, being cancelled out by some opposing strong factor such as better knowledge and tools. In fact, I think an economic-style argument might say that people work on the highest-return problems until productivity drops below C, then work on tools until it rises just above C, then work on problems, etc. So we should expect rate of return on worked-on problems to be fairly constant over time.
I’m talking about a hypothetical analysis there. I haven’t actually collected the data and put it through the grinder (at least not yet)!
Yeah, I’m trying to install mental klaxons that go off when I unreflectively write (or read) “obvious” or “obviously”.
That’s a fascinating result (although I’d wait for more details about the data & models involved before allocating the bulk of my probability mass to it). Does that mean our perception of fewer geniuses nowadays is merely because older geniuses grabbed most of the fame and left less of it for later geniuses? That’s how it sounds to me but I may be over-interpreting.
Do we perceive there are fewer geniuses nowadays? I think we tend to pick the one thing somebody did in each generation or decade that seems most impressive, and call whoever did it an Einstein, with no idea how hard or easy it really was.
For instance, some people called Watson and Crick the great geniuses of the generation after Einstein, for figuring out the structure of DNA. Yet Watson and Crick were racing people all over the world to find the structure, because they knew anybody with the right tools would be able to figure it out within a few months. It required only basic competence.
(What’s especially interesting about that case is that Watson and Crick both did things that showed genius after they were hailed as geniuses, given genius-level funding and freedom, and expected to do genius things. Were they geniuses all along (a low prior), did they develop genius in response to more-challenging conditions, or is funding and freedom more important than genius?)
Today, we’ve got genuine genius entrepreneurs like Sergei & Larry, Peter Thiel, and Elon Musk, yet the public thinks the great genius of that generation was Steve Jobs. Possibly because Apple spent many (dozen? hundred?) millions of dollars advertising Steve Jobs. Peter Thiel was never on a billboard.
My gut says there are fewer geniuses nowadays, although I don’t really trust it on this one.
As for guts that aren’t mine...Bruce G. Charlton. Gideon Rachman. Dean Keith Simonton, although he simultaneously argues that modern first-rate scientists, “[i]f anything”, need “more raw brains”. Cosma Shalizi, who I think is being serious there, not just florid.
I think there are certainly people who do that. There are people (not sure I can name any, but I’m sure they exist...Ray Kurzweil, maybe?) who are relentlessly upbeat about the march of scientific genius & progress, and people who just like jumping on hype bandwagons. There are also people with gloomier outlooks.
I don’t intuitively think of “genius entrepreneurs” as a natural category...
That advertising (and similar hype) influences whom people think of as geniuses is a good point.
This seems an important issue to me.
Those places were selected for having Newton and Aristotle though.
What leads you to be confident that these are the bottlenecks?
Interesting. Is your research up online?
You mean, we would have a lot more effective research, quickly? Or something more specific?
One important piece of data is the distribution of citations within fields. There have been many studies of this. What you find, generally, is that a field of study has a finite amount of attention available—if it has N researchers, they collectively perform cN paper-readings per year. The distribution of these paper-readings is a power law (a Zipf distribution), so that the number of researchers whose papers get read much grows much more slowly than N. No model based on the expected distribution of the merits of the papers or the scientists makes sense, particularly given how the Zipf distribution changes with the size of the field. The models that make sense are models that say that the odds of somebody reading a paper by person X are proportional to the odds that someone else cited X. That is, if you break down your model of citation distribution into a component to model randomly trawling the literature for citations, and a component to model quality of the papers, you find the random model explains nearly 100% of the data.
No, but check your email.
If we achieved a linear relationship between input and output, we would have maybe 6 orders of magnitude more important scientific and technological advances per year. If we actually achieved “synergy”, that oft-theorized state where the accumulation of knowledge grows at a rate proportional to accumulated knowledge, we would have a fast take-off scenario, just without AI. dk/dt = k, dk/k = dt, ln(k) = t+C, k = Ce^t.
How much should the fact that we do not have a fast take-off of organizations make us more pessimistic about one with AIs being likely?
That’s the question. We should consider the overhead cost of knowledge, and the possibility that we will see a logarithmic increase in knowledge instead of a linear one (or, that we will see a linear one given an exponential explosion in resources).
Much depends on how you measure knowledge. If you count “bits of information”, that’s still growing exponentially. If you count “number of distinctions or predictions you can make in the world”, that probably isn’t.
There is a critical relationship between GDP and the efficiency of science. Until 1970, the money we put into science increased exponentially. Economic growth comes (I believe) exclusively from advances in science and technology. In 1970, we hit the ceiling; fraction of GDP spent on science had grown exponentially until then, when it suddenly flattened, so that now resources spent on science grows only as fast as GDP grows. This should cause a slower growth of GDP, causing a slower increase in scientific results, etc. IIRC there’s a threshold of scientific efficiency below which (theoretically) the area under the curve giving scientific results off to infinity is finite, and another threshold of efficiency above which (theoretically) the curve rises exponentially.
This alone doesn’t seem sufficient to explain the distribution of economic growth between countries. Most science, and most technology more than a generation old, is now public domain. But even if we go two generations back, US GDP/capita was ~$25K, which would still put it in the top quartile of modern countries. The countries at the bottom of the economic lists are often catching up, but not uniformly.
This sounds more like a conflation between the “availability” of S&T versus the “presence” of S&T.
Technology being in the public domain does not mean the remote-savannah nomad knows how to use wikipedia, has been trained in the habit of looking for more efficient production methods, is being incentivized by markets or other factors to raising his productivity, or has at his disposal an internet-connected, modern computer, another business nearby that also optimizes production of one of his raw materials / business requirements, and all the tools and practical manuals and human resources and expertise to use them.
Long story short, there’s a huge difference between “Someone invented these automated farming tools and techniques, and I know they exist” and “I have the practical ability to obtain an automated farming vehicle, construct or obtain a facility complete with tools and materials for adjustment so I can raise livestock, contacts who also have resources like trucks (who in turn have contacts with means to sell them fuel), and contacts who can transform and distribute my products.”
The former is what you have when something is “public domain” and you take the time to propagate all the information about it. The latter, and all the infrastructure and step-by-step work required to get there, is what you need before the economic growth kicks in.
I believe the latter was being referred to by “advances in science and technology”.
Could you more clearly define the “presence” of S&T? Your examples like “automated farming tools are practical to obtain” sounds like a way of restating “both the availability of S&T and the economy are strong”, which does indeed imply that the economy will be strong, but “A ⇒ B” would have been a much more useful theory than “A && B ⇒ B”.
You’re making some argument that you think is implied by what you’ve said, but that I can’t see. I don’t see how the US of 2 generations ago having a high GDP is inconsistent with growth being a result of science and technology, unless you imagine science and technology are the same all over the world at a given time, which would be a strange thing to imagine.
(Side note: “Technology” here include organizational and management techniques.)
The use of science and technology isn’t the same all over the world at a given time, but the availability is remarkably close, don’t you think? What are the less developed countries left out on? ITAR-controlled products, trade secrets, and patents? For everything else they have access to the exact same journals.
Perhaps your side note is what’s critical: are there organizational and management techniques which are available in the United States but which we’ve successfully kept a secret internationally? Are multi-generational trade secrets the critical part of science and technology?
Or would other countries grow much faster if they just fully used public domain technology, but there’s some other factor X which is preventing them from using it? If the latter, then what is X, and wouldn’t it be a better candidate explanation for disparate economic growth?
This is a reasonable observation; yes, it is not obvious why every nation can’t jump straight to modern-nation productivity.
There are plenty of places in Africa where water purification is a great new technology, and plenty of places in China where closed sewage lines would be a great new technology. Why don’t they use them?
The stories I hear from very-low-tech countries usually emphasize cultural resistance. One guy installed concrete toilets in Africa, and people wouldn’t use them because concrete had negative connotations. People have tried plastic-water-bottle solar water purification in southeast Asia, and some concluded (according to Robin Hanson) that people wouldn’t put plastic bottles of water on their roof because they didn’t want the neighbors to know they didn’t have purified water. Another culture wouldn’t heat-sterilize water because their folk medicine was based on notions of what “hot” and “cold” do, and they believed sick people needed cold things, not hot things. There are many cases where people refused to believe there are invisible living things in water. (As Europeans also did at first.)
(Frequent hand-washing and checklists are technologies that could save many thousands of lives every year in US hospitals, but that are very difficult to get doctors to adopt.)
But lots of low-tech countries can’t afford anything that they can’t build themselves. How much of modern technology can be built with materials found on-site without any tools other than machetes, knives, and hammers? Mosquito netting is very valuable in some places, but impossible to manufacture in a low-tech way.
My short answer is that there are a variety of obstacles to applying any technology in a low-tech nation. But growth is only possible either by finding more resources, or by using existing resources more efficiently, and using resources more efficiently = technology.
If it were possible to have growth without technology—let’s say 1% growth every 10 years—then a society with medieval technology, and no technological change, would eventually become as productive per person as today’s modern countries. And that’s physically impossible, just using energy calculations alone. There may be other necessary conditions, but tech improvement is absolutely necessary.
Those are excellent answers; thank you.
Do you really thing that things like Good Governance don’t have anything to do with economic growth? Science doesn’t help you much if a competitor pays a corrupt official to shut your business down.
What do you mean by this? We have plenty of composers and musicians today, and I’d bet that many modern prodigies can do the same kinds of technical tricks that Mozart could at a young age.
Good question, though doing technical tricks at a young age does not make one Mozart. I don’t mean that we don’t have 1000 composers as good as Mozart or Beethoven. I mean we don’t have 1000 composers recognized as being that good. We may very well have 10,000 composers better than Mozart, but we’re unable to recognize that many good composers.
This is conflated with questions of high versus pop art andd accidents of history. Personally, I’m open to the idea that Mozart represents a temporary decline in musical taste—a period between baroque and romantic when people ate up the kind of pleasant, predictable pop music that Mozart churned out. He wrote some great stuff, but I think the bulk of what he wrote is soulless compared to equally-prominent baroque or romantic music.
I’d be really interested in reading more about this.
If you email philgoetz at gmail, I’ll send you a draft.
Er, or not. The number of publications per scientist has risen dramatically, but so has the number of authors per paper. I don’t know if these cancel each other out.
Compare the complexity of F=MA to string theory. The difficulty of science is going up by orders of magnitude as the low hanging fruit are eaten.
We have over 1000 genres of music. Sure, not everyone can be recognised as the best musician of a generation by definition, but I think we could arguably be producing 1000 times more good music than at the time of Mozart.
Don’t forget to compare their usefulness as well X-)
Good post.
First of all, knowledge is partially ordered. A bunch of lesser-known results were required before Einstein could bring together the mathematical tools and physics knowledge sufficient to create relativity. True enough, this finding may have come much later, if not for Einstein, but dozens of others built predecessor results that also required great insight.
Similarly, we should not decry the thousands of biologists who have been cataloging every single protein, its post-translational modifications and its protein-protein interactions in exhaustive detail. Some of this work requires a great deal of cleverness each time.
A portion of the phenomenon you are talking about can be addressed by referencing Kuhn: We have periods of normal science, with many people giving input, and a building tension where twenty pieces fall into place and (frequently interdisciplinary) thinkers visit a problem for the first time.
In other cases the critical breakthrough has to be facilitated using new tools that generate new breakthroughs. When these tools require advances in component technology, you have a large number of engineers, testers and line workers feeding their talents into discoveries for which only a few get credit sometimes.
If these components require days or months of “burn-in” testing to judge their reliability, a superintelligence might have limited advantage over people in reducing the timeline.
Sometimes discovery relies on strings of experiments which by their nature require time and cannot be simulated. Our current knowledge of human biology requires that we follow patients for many years before we know all of the outcomes from a drug treatment.
Initially, at least, a superintelligent drug developer would still have to wait and see what happens when people are dosed the drug over the course of many years.
If a cosmic event can only be observed once in a decade, a superintelligence would not have the data any sooner, short of inventing some faster-than-light physics we do not have today.