(It does make a leap from computation to intelligence though.)
The problem I have with exponential is that you can’t have an exponential difference between two data points. Exponential is a measure of growth which can’t be seen with just two data points. However orders of magnitude can accurately describe the difference between two data points. Even though it is somewhat ambiguous as well (powers of 2,10,16?) orders of magnitude makes more sense than exponential.
I would love to hear other's experiences with PhDs in the context of the essay's example.
Leonard Mlodinow has written two books that you might find both resonant and uplifting:
Feynman's Rainbow: A Search for Beauty in Physics and in Life , and The Drunkard's Walk: How Randomness Rules Our Lives.
Furthermore -- if you went to grad school and learned something, either about the subject or yourself, then the effort was not a failure. In the long game, it isn't about the degree nor the piece of paper.
Looking back, diversifying my work and finding short term projects likely would have innoculated me against the effects of the (biggest) problem I faced.
After leaving, I have had few regrets (aside from lessons learned). I have had fun jobs, travelled, and had far less stress holding me down. Less money, sure, but more of the really important things.
https://news.ycombinator.com/item?id=17843735
https://news.ycombinator.com/item?id=17519881
https://news.ycombinator.com/item?id=16725676
https://news.ycombinator.com/item?id=16541610
https://news.ycombinator.com/item?id=15911873
https://news.ycombinator.com/item?id=14675362
A key connecting idea in all of them is this reference to "Disciplined Minds": http://disciplinedminds.tripod.com/ "In this riveting book about the world of professional work, Jeff Schmidt demonstrates that the workplace is a battleground for the very identity of the individual, as is graduate school, where professionals are trained. He shows that professional work is inherently political, and that professionals are hired to subordinate their own vision and maintain strict "ideological discipline." The hidden root of much career dissatisfaction, argues Schmidt, is the professional's lack of control over the political component of his or her creative work. Many professionals set out to make a contribution to society and add meaning to their lives. Yet our system of professional education and employment abusively inculcates an acceptance of politically subordinate roles in which professionals typically do not make a significant difference, undermining the creative potential of individuals, organizations and even democracy. Schmidt details the battle one must fight to be an independent thinker and to pursue one's own social vision in today's corporate society."
However, even given that experience of enforced ideological conformity to a social power structure (which some people may find an unexpected horror while others go "meh, so what else is new?"), academia is an increasingly bad deal for reasons explained by David Goodstein (the vice-provost of CalTech testifying to Congress in the 1990s on why the economics of academia have changed since the 1970s and peer review is starting to fail as a consequence) and Philip Greenspun (explaining why most women he has met are too socially savvy and socio-economically astute to play the PhD game with plans for an academic career):
http://www.its.caltech.edu/~dg/crunch_art.html "We must find a radically different social structure to organize research and education in science after The Big Crunch. That is not meant to be an exhortation. It is meant simply to be a statement of a fact known to be true with mathematical certainty, if science is to survive at all. The new structure will come about by evolution rather than design, because, for one thing, neither I nor anyone else has the faintest idea of what it will turn out to be, and for another, even if we did know where we are going to end up, we scientists have never been very good at guiding our own destiny. Only this much is sure: the era of exponential expansion will be replaced by an era of constraint. Because it will be unplanned, the transition is likely to be messy and painful for the participants. In fact, as we have seen, it already is. Ignoring the pain for the moment, however, I would like to look ahead and speculate on some conditions that must be met if science is to have a future as well as a past."
http://philip.greenspun.com/careers/women-in-science "This is how things are likely to go for the smartest kid you sat next to in college. He got into Stanford for graduate school. He got a postdoc at MIT. His experiment worked out and he was therefore fortunate to land a job at University of California, Irvine. But at the end of the day, his research wasn't quite interesting or topical enough that the university wanted to commit to paying him a salary for the rest of his life. He is now 44 years old, with a family to feed, and looking for job with a "second rate has-been" label on his forehead. Why then, does anyone think that science is a sufficiently good career that people should debate who is privileged enough to work at it? Sample bias. ... What about personal experience? The women that I know who have the IQ, education, and drive to make it as professors at top schools are, by and large, working as professionals and making 2.5-5X what a university professor makes and they do not subject themselves to the risk of being fired. With their extra income, they invest in child care resources and help around the house so that they are able to have kids while continuing to ascend in their careers. The women I know who are university professors, by and large, are unmarried and childless. By the time they get tenure, they are on the verge of infertility. ..."
From a completely different angle, see also Carol Dweck's research and recommendations about a "Growth Mindset": https://en.wikipedia.org/wiki/Carol_Dweck#Mindset_work "Her key contribution to social psychology relates to implicit theories of intelligence, per her 2006 book Mindset: The New Psychology of Success. According to Dweck, individuals can be placed on a continuum according to their implicit views of where ability comes from. Some believe their success is based on innate ability; these are said to have a "fixed" theory of intelligence (fixed mindset). Others, who believe their success is based on hard work, learning, training and doggedness are said to have a "growth" or an "incremental" theory of intelligence (growth mindset). Individuals may not necessarily be aware of their own mindset, but their mindset can still be discerned based on their behavior. It is especially evident in their reaction to failure. Fixed-mindset individuals dread failure because it is a negative statement on their basic abilities, while growth mindset individuals don't mind or fear failure as much because they realize their performance can be improved and learning comes from failure. These two mindsets play an important role in all aspects of a person's life. Dweck argues that the growth mindset will allow a person to live a less stressful and more successful life. ... Dweck advises, "If parents want to give their children a gift, the best thing they can do is to teach their children to love challenges, be intrigued by mistakes, enjoy effort, and keep on learning. That way, their children don't have to be slaves of praise. They will have a lifelong way to build and repair their own confidence." Dweck warns of the dangers of praising intelligence as it puts children in a fixed mindset, and they will not want to be challenged because they will not want to look stupid or make a mistake. She notes, "Praising children's intelligence harms motivation and it harms performance.""
The other assumption was based on the determinism of the machine. As far as I understand, the brain is not a deterministic computer. We don’t really understand how our brains work at all, but they definitely don’t work in any way shape or form to how we understand a computer to work, this leaving even more possibility for interpretation to an opposite conclusion.
Lastly, what about all the evidence of people who actually did accomplish exponentially more work than others? We have the benefit of the hindsight to check that real quick and, yup, I’d say 100% there are people who have done it. Elon, Jobs, Gates, etc...
However, I’d agree with the author if they argued that we can’t predict who will be exponentially smarter. To do that, we would have to simulate the future or have an algorithm that can tell us, which obviously presents some contradictions.
I think we all just sort have to wait and see.
This whole analogy I realized after thinking about it is just computer science baby babble.
Fundamentally it comes down to not formally specifying the problem (not saying I can, I can’t and neither can anyone else that I’m aware of)
People always want to cling to quantitative interpretations of qualitative problems, and then declare QED, despite the whole thing being predicated off a false premise to begin with (like the thing we’re discussing is an appropriate candidate for an algorithmic interpretation to begin with).
And without a formal specification of the problem, we have no formal way of checking our solution.
Secondly, if you're so smart, why didn't you write this essay, and refute it yourself?
Next step: we're a simulation in a universe which has ways to violate complexity bounds we hit with quantum computers
Snark omitted, here's an alternate idea of "genius":
The word refers to a kind of spirit or daemon (same root as Genii). Consider the 'daimonion (literally, a "divine something")' of Socrates:
https://en.wikipedia.org/wiki/Daemon_(classical_mythology)#S...
> In ancient Rome, the genius (plural in Latin genii) was the guiding spirit or tutelary deity of a person, family (gens), or place (genius loci).
https://en.wikipedia.org/wiki/Genius
> Jinn, also Romanized as djinn or Anglicized as genies (with the more broad meaning of spirits or demons, depending on source), are supernatural creatures in early pre-Islamic Arabian and later Islamic mythology and theology.
https://en.wikipedia.org/wiki/Jinn
To be a genius is to be favored or assisted by one of more of these invisible people.
I don't know if they can solve any problem in NP in polynomial time but they can apparently predict winning lottery numbers which suggests to me that there must be something interesting going on from a computational complexity POV.
[0] https://en.wikipedia.org/wiki/The_Emperor%27s_New_Clothes
To borrow a metaphor from a recent HN thread about an eponymous paper called "How to recognize AI snake oil" [1], there's an "incomplete and crude but useful breakdown" on you can apply towards AI problems: genuine and useful progress in perception, imperfect but improving work in automating judgment, and fundamentally dubious attempts to predict social outcomes.
Let's think about where the idea of a theory of mind based on computational complexity to determine "smartness" lands -- it's certainly not stimulus detection, it's certainly not automating judgment, but it is about predicting or modeling social outcomes. I would say that this application Wolfram's idea is fundamentally dubious. Because of this, it's hard for me to say that the premise, argument or conclusion of this essay is anything but fundamentally dubious.
To at least leave a useful suggestion: this essay is missing an adequate definition and exploration of what "smart" is, why it's a facet of human nature and history, and what issues the concept causes. To the author, I'd recommend starting off by building a better foundation there before jumping to conclusions that are hard to take seriously.
2. That snake-oil AI paper guy, right though he may be here, you know he's jealous of Bitcoin, right? Wrote a whole academic paper trying to justify how Bitcoin wouldn't exist w/o academia. If he was so smart, why didn't he write Bitcoin himself?
3. If this was so obvious and you're so smart, why didn't you write it yourself?
Look, just because I don't buy your argument doesn't mean I buy the premise that "genius" exists the way it's constructed in society. I find that pretty fallacious too, but for different reasons. I don't think you're going to get to the core of why genius is a flawed concept with a strained analogy to computational complexity (which doesn't work) rather than by digging into historical context. I think if you focus on that, you'll probably make more progress on coming up with something interesting to say.
2. Commenters criticize article.
3. Author responds to criticism with " why didn't you write this yourself" over and over.
Wow..
AKA, some people are solving classes of problems in approximately the most efficient way (or relatively more efficient way) and most people are computing solutions in exponentially inferior ways.
But there may be thresholds, such as working memory needed, because the skill or knowledge cannot be decomposed further due to interconnections.
It seems likely there exist some potential skills or knowledge that require more working memory than any human has, had or could ever have.
