The first is the lofty intellectual ideal of solving a major problem, winning a Nobel, and getting your name in the history books. The second is the daily grind of reading other people's papers, providing peer review, writing grant proposals, and generally interfacing with other humans as a cog in a grand machine. The second is in some sense "easier" than the first. It's a lot more work, takes a lot more time, but it's a relatively straightforward (if often tedious) process that pretty much anyone can do with enough diligence. The first is a lot more fun, can often be done while showering, but is also fraught with risk and dependent on luck. You have to find just the right problem at just the right time under just the right circumstances. You can spend your time slogging, or you can spend your time buying intellectual lottery tickets and hope that lightning strikes, but you can't do both, at least not at at the same time.
The good news is that engaging in the daily slog is often (but not always) good preparation for and improves the odds of having lightning strike. So as a practical matter, that is often a good place to start. You might feel as if you're wasting your time reading everybody else's bullshit papers instead of writing the next Nobel prize winner yourself, but you're not. You're actually an essential part of the process even if you don't end up with the glory. And some day you just might be facing a really hard problem and go, "Wait a minute, this seems kinda like that thing I remember Dr. Arglebargle talking about three years ago, except that he missed this one detail..." and that's when the magic happens.
I think that's it for most PhD students. Look at recent papers from your advisor (or their co-authors). Once you understand that specialized area well-enough, you should be able to find an epsilon that can be added, or to apply some results to a different use case. A 2-3 papers in somewhat decent conferences should be enough to be able to graduate.
Most people won't solve interesting open problems. They will find questions that nobody has asked before, so they can be the first one to answer it.
If you want to do high-impact research in the long run, you need to have a strong foundation, which means a solid bed of high-impact papers to point to when you need funding, opportunities, etc.
For instance, my team (in a big tech company) has people with very diverse backgrounds. Some tasks require more expertise, but most of the work will fit a generalist profile. Some teams who are more "researchy" have probably more PhDs. I feel doing a PhD gave me more time to learn things, while my current position is more fast paced and I can't afford to spend 2 weeks just learning new things.
Financially, it didn't make sense to do a PhD. It's not even super clear what I learned is helpful now but I'm glad I've had this experience. It gave me a bit more perspective and culture of the field. I don't regret doing it. However, I regret going into academia. Being a professor didn't suit me for many reasons. I'm happier in industry.
Even academia is willing to look past a lack of a degree for the right person -- my ex-wife's PhD advisor's advisor didn't have a PhD himself (but did foundational work in the field).
These seems like more than a generation ago. Things were very different back then. I think now attaining a PhD (or enrolling in one) is the first step towards even starting out in foundational research. Academia today seems to be more about restricting access to the Ivory tower, rather than democratizing it.
While this feeds into the founder kool-aid we all drank as founders, it also speaks volumes of truth to me to this day: I rejected where biology/ecology was going even when supposedly favourable paradigms--Al Gore's inconvenient truth and Carbon Credits were a thing as I was wrapping up my undergrad and I was appalled by both--were standing in front of progress and the most obvious solutions if you could take the small amount of time to familiarize yourself with emerging technology outside your gradschool bubble. Something I don't think academics are actually capable for fear of seeming to reveal themselves as hyper-specialized in one aspect only rather than the polymath they may seem/convinced themselves that they are.
Also, it is my experience most academics are tone-deaf and incredibly petty individuals: a visit with the tenured faculty was enough for me to run for the hills of the debt-laden, financial crisis impacted work-force.
I still visit my Biochemistry professor from time to time, who did well getting into Bitcoin, when I launched my startup working with the Hemp Industry; and I'm super proud of playing a part in that since we both got hit hard during the 2008 financial crisis. But I'm glad I turned down his offer to work in his lab and get my Masters and went through boot-strapping hell to launch my startup after working in many Industries just to get by.
It was interesting white boarding about the bio-chemistry of CBD (and other cannabnoids for that matter) infused drinks/food I could pitch to my clients as profitable avenues of growth after struggling to make it to class and 'only get a B-' because I just didn't 'fit' (read: rebellious) the school model at all despite having both honors and letters of recommendation to join two labs.
I'm in Grad school now (almost 20 years since starting my undergrad) for CompSci now, but only because I'm working to transition into Big Data and I already work on the large Data Centers spurred on by the AI hype; luckily it's a non-thesis program for experienced and working professionals (I'm currently on assignment for a sub-contractor in WY).
Yep, their egos get inflated from interacting with students all the time.
If you have been teaching the same subject for 10 or more years, and you are constantly interacting with people who are learning it for the first time and struggling, it's easy to become arrogant.
I'll go 1 step further and say that most of the advice academics give out in terms of learning has been counterproductive for me.
There have been some exceptions, but I have been disappointed for the most part.
The rich person life back in the day was having personal tutors at a young age teaching you Latin, Greek, rhetoric, mathematics, music, and so on. Then if you were really good you'd just work on whatever problems you fancied, while the rest of the family probably took care of the fortune. That's my very limited understanding for how it worked for some anyway. They would correspond with other scientists, but mostly focus on their work. There's some advantage to the university system, but the administrative beauracracy is intense. Writing government grants or proposals and so on is an enormous time suck. If you didn't need money, you could focus 100% on whatever you want.
> Working independently, Wolfram published a widely cited paper on heavy quark production at age 18 and nine other papers. Wolfram's work with Geoffrey C. Fox on the theory of the strong interaction is still used in experimental particle physics.
> In 1983, Wolfram left for the School of Natural Sciences of the Institute for Advanced Study (IAS) in Princeton.
That doesn’t make sense. PhD salaries in most places can barely support one person , if that.
I wonder sometimes, how many of the people arguing endlessly and religiously about concurrency primitives realize that they are debating “religious” texts from the same prophet, Sir Tony Hoare. But as you say, the lack of immediacy does seem to mess with our heads.
So I think both options really depend on what you can deliver and in what timeframe.
I think you might be being too harsh on yourself.
That is sort of the point of a liberal arts education broadly, no? It's not a trade school (although I did spend my 20s at a glorified one and worked in what really was a fancy service industry gig - the law - that is partially obstructed by the sort of things at stake that we deal with, but is fundamentally a service and very much one that is customer-facing. I was one of the first few to quit from those in my graduating class that I kept touch with as well.) Owners of sports franchises, worth billions of dollars, fail to understand concepts like this. Those in charge of large companies, with externalities being constraints that we might not see in fairness but still having control of the narrative more or less, fail to demonstrate this. The government and policymakers either ignore or are ignorant of this on more occasions than one can count. Although it's hard to say what would've happened in the alternative, the initiative does alter the dynamics one would eventually have with their new situation, speaking personally. At least I'm not routinely putting in 75-80 hour weeks which is liberating in its own way even if it was routine enough that few bat an eye when put in that position at the start, judging from what my friends mostly ended up doing and not doing.
But the imposter syndrome thing does haunt as well. It takes a while to realize that one hears about plane crashes because planes not crashing do not make the news, after all, and the actual rate of crashes, incidents, and near-misses can be and usually ends up haphazardly reported at best if one doesn't dig into the official quotidian, and jargon-filled language of official reports, and I don't think that it's part of society's expectation for the average person even if it's both an apt analogy and something to look up if one has the time.
Right. I got an MSCS from Stanford in 1985. That was about when it was becoming clear that expert systems were a dead end. The Stanford faculty was heavily invested in expert systems at the time, and were in serious denial. This was the beginning of the "AI Winter". I could see this, because I'd been doing proof of correctness work, where machine-powered predicate calculus could deliver results. It was clear to me that those methods were too brittle for the real world, although they worked fine on the rigid world of computer programs. Hammering the real world into predicate calculus only works if you already almost know the answer. (Drew McDermott's essay, "Artificial Intelligence meets Natural Stupidity" is the classic commentary on that.)
There were some neural net people around, but they were stuck, too, partly because they needed a few more orders of magnitude more compute power before that approach started working. Neural nets had been around for decades, and on a good day in 1970 you could recognize handwritten numerals, slowly. Computer power wasn't increasing that fast in those days. In 1970, there were 1 MIPS mainframes, and in the early 1980s, there were 1 MIPS VAXes. Success with neural nets lay many orders of magnitude in compute and several decades ahead.
So, instead of academia, I did some stuff for a little company called Autodesk.
I never expected computer science to be captured by the ad industry.
When I started my PhD I read this text as well. Of course I was very motivated of doing only important research. After some time I even found a nice research topic and worked on it for over two years. Even my advisor pointed out that it was a very novel and foundational idea.
At the same time, a colleague published three papers in top conferences. Their approach was basically to look at publications from the previous conference, apply some delta that they had an advantage over other teams and publish this in the next round.
Reviewers were happy because their publications already got cited and they understood the topic well.
Whereas my topic used an algorithm from the seventies that reviewers had to revisit. My topic didn't fit so well into the overall conference trend and so I still have way less citations than those incremental works.
When someone asks me if it's worth doing a PhD I tend to say that you need to like the direction in which science is going. If you don't like it and are rather rebellious, it's better to start a company.