What should we tell our students?
terrytao.wordpress.com
terrytao.wordpress.com
You can't really swear off looking at problems solved by AI, or keep moving away from fields whenever AI starts contributing, and have a viable career.
Today's frontier models, are everybody's local models in the near future.
If this were a change that had expected stalls or reprieves it would be different.
Doing mathematics looks to be the next chess, er, I mean protein folding challenge. The latter's computation demands have dropped significantly.
When things really start going downhill, if we are still in the current mess, it will grease the the wheels, and the brakes.
Yes. The problem isn't that there won't be any problems. The problem will be the rate of general progress and solutions that at least some awareness is required of, to reliably identify a good new unsolved problem will just keep getting more challenging. And then an attempt needs to be make, to solve it in a very short time.
There is always another race to run too, but increasingly slow runners don't find that translates to winning.
This change is not going to slow down, it is going to speed up. Machines will be doing math systematically, checking off the meta-math theorems that ensure axiom combinations are covered, that the search does not stop where theorems are not exhausted, and does stop where it can be proven they are. Humans will never operate at that level.
His end goal was one of the things released in the recent OpenAI publications. It took a model 3.5 hours to do.
Things are wild now on math and theoretical faculties now.
And for theoretical fields labs don’t need human data - synthetic one works well if not better.
The best thing you can do if you’re a scientist, imho - wrapup whatever grant you have now asap with ai, use ai to get more grants if possible, and spend the rest od the year learning what new science you can do with the new tools and participate in creating the new paradigm in your field.
That’s when you’re an established scientist. If you’re new in the field then this is the most exciting time you could wish for - an opportunity to make a name for yourself.
How could that be true? I am a mathematical rube, but surely data that works "better" than real data should be extremely suspect?
Why do we ever use PCs, smartphones, Internet, electricity, medicine and so on? Why do we live? For what?
Sometimes HN is crazy and out of touch.
Most vote-based forums like Reddit end up like that
To not bury your head in the sand hoping this will go away, because it won't.
The student you're copying from won't be sitting next to you for the rest of your career. The LLM probably will be, and you'll be probably expected to use it, so why not use it here as well?
All your peers are doing it too, and look at all the cool things they're doing while you're struggling with the basics. And what if the LLMs keep improving at a high rate? What if being able to use them efficiently turns out to be a more important skill than what you're being taught anyways?
Because you're paying a lot of money and opportunity cost to learn how to do something. No one needs the assignment. It's there for you to learn. You were given the assignment for you. Any adult should be able to understand this. We're not talking about 7 year olds asking why they need to learn to multiply when a calculator can do it better than them. These are 20 year olds. If they don't have the maturity for this, a university should not be accepting them.
Being able to use LLMs efficiently isn't a specific skill. It's a reflection of your ability to articulate what you want, which is a reflection of your understanding of the world, which is what you're in school to build. Terence Tao can get an LLM to do math better than I can. I can probably get one to build software better than he can.
And you can still use one to do cool things like your peers. Just not your assignments, the purpose of which is literally to teach you the basics that you're struggling with and that you're there to learn in the first place.
This is like why don't you copy out of the back of the book or look up proofs on the internet. It's all there, but doing that completely misses the point of why you're there.
Eh? As Greg K-H mentioned in that video from a few days back about how very disappointing Fable was when compared to its astronomical hype, LLMs that one can run locally are quite good enough for a great many tasks... including bug hunting in the Linux kernel. And -as LLM boosters keep saying- they're only keep getting better, right?
We absolutely should be telling people to stop using the LLMs from the major LLM manufacturers. Those manufacturers have spent so many billions of dollars on this project, made so many promises that they're going to have no way to keep, and now that they're running into resistance, [0] they're holding all of humanity hostage unless they're permitted to get intimately involved in the creation of new laws and regulations especially for them. [2]
Even if one only considers their recent threats to humanity, it's clear that these are not companies that deserve any of our hard-earned money.
[0] Some of that resistance comes from their ever-more-sharply-increasing cost to produce the next performance increment, some from folks asking the probing questions about their promises, claims, and business practices that should have been asked years ago, some from ongoing State AG's court cases in regards to their illegal conduct, and some from folks who -unsurprisingly- don't want enormous warehouses that suck up quite notable amounts of power [1] but provide dreadfully little revenue to the areas that house them in their communities, and still others who are starting to realize that benefits of cloud LLMs aren't worth the tradeoff of being unable to afford a new personal computer.
[1] Note carefully that I made no reference to water usage. The only counterargument you can make here is that 500MW -> 2GW is not a quite notable amount of power.
At this point; I see no other scenario than having LLM doctors, LLM judges, etc. No matter the quality. We will get replaced. And if they do a worse job, nobody cares. If they do a better job, also nobody cares. Maybe you can pay more and get high effort model as your doctor.
These companies might not be the ones, my expectation is that they will be scrapped for their pieces, the losses distributed to the bagholders/taxpayers. But some version of them will.
This might be pure cyncisism with no positive value, but really I think we have no mechanisms left to stop going down on this trajectory.
I am afraid that ship has already sailed ... These companies might not be the ones, my expectation is that they will be scrapped for their pieces ...
We *absolutely* should be telling people to stop using the LLMs from the major LLM manufacturers.
...homeboy here is behaving as if there aren't experts in this world who are paid to do and publish research and other experts who are paid to collate, analyze, vet, and publish that research for other domain experts to learn from. [0]
I'm fine with my doctor reading from expert-vetted documents. Hell, I'm more than fine with my doctor going through expert-vetted checklists; most of the time what's wrong with you can be easily figured by going through a few good checklists. I'm not fine with my doctor relying on a lossy-compressed database with a absentminded librarian that has a penchant for people-pleasing bolted on top.
I'm absolutely not against the use of ML systems in safety-critical domains like medicine, but I am absolutely against the use of LLMs in those domains.
[0] One might choose to retort with some variant of "But all that expert work is so expensive!". I would retort: "First, without that work the data fed into the LLM is catastrophically unreliable. Second, have you bothered to look at how much the major LLM manufacturers have spent over the past five years or so? I suspect that it's more money than has been spent on medical research 'meta analysis' over the past fifty years."
What I meant is different though. I fully expect to no longer have access to human doctors in a decade or two. Instead, LLMs will be the doctors. Likely government accredited ones. And my point is that in time, whether the LLM doctor is better or worse will not matter. Same with judges, same with the surveillance camera evaluators and private message examiners. I am saying we no longer have any mechanisms to stop these things from happening.
Maybe there should be a class on how how to use LLMs too.
But learning computer science using LLMs all the time will be like learning to ride a bike using training wheels, and never taking them off.
I expect people that read things here to be literate. i choose optimism.
Or like learning to add fractions by looking up the answer key instead of actually struggling to figure it out.
In a way it's on the student to make sure whatever they're doing, they actually understand it, or come exam season the gaps in their knowledge sans Claude will become quite obvious.
However, to say they "should never use an LLM" entirely is going to make them unemployable.
One natural step of AI usage, the way I see it, is that even mediocre researchers can use AI as a harness to become prolific researchers.
So if you're part of a pure "human only" researchers that publish 1/10th of what the rest are doing, how are you going to survive?
The more prolific researchers will eat your grants for lunch.
This is exactly the same thing a lot of software devs are going through now. AI models have lifted up EVERYONES ability to produce code, so there's simply no premium anymore for those that will only code by hand. And the people paying their salary are asking why they aren't more productive, when even business analyst Joe is pumping out new products left and right.
It's just stubbornness, in the end, that is manifesting as a kind of gatekeeping Luddism. I see the same thing in my Mastodon feed from folks in the software engineering world, particularly people who have a great love for writing code and hold it as a core part of their identities. I understand being irritated at a machine having been trained on (potentially, inter alia) your work with no compensation to you, and now it is perhaps better at (some of) what you do than you are; or maybe companies are now making a lot of money off of something that benefited from your contributions; but I find it extremely improbable that we will be able to refuse our way out of further progress now that so much money has been invested and so much momentum has been gathered.
I don't think I feel any discomfort about a machine being better at anything that I can do than I am at it. Maybe it's because I'm a mediocre person. I personally think that if there's something that I'm better at than a machine, then we need to build a better machine. Imagine what I'd be able to do if I used that machine!
I have the same optimism as Prof Tao. That said, I can understand why so many mathematicians have been so upset or stressed out. It turned out much of the mathematical work is about clever combination of existing methods - this already requires enormous amount human ingenuity and years of dedicated learning. Unfortunately, or maybe fortunately, AI can be very good at knowledge transfer and finding combination of existing ideas to solve seemingly impossible problems. Even though mathematicians are extremely smart and capable, only a small number of them are capable of truly inventing "alien ideas", discovering new ground-breaking mathematical structures, or coming up with new problem-solving techniques. That is, AI can eat many mathematicians' cake. That said, I'm still hopeful. Mathematicians still understand mathematics deeply. If someone can prompt AI to solve an important problem, that person is more likely a good mathematician than an average joe like me. So, I think mathematicians do have a bright future: leverage AI, and make more and bigger math discoveries. It's still the same north star: we must know, and we shall know. It's just that with AI, we will know sooner and more.
This is not written by Tao, but by Álvaro Lozano-Robledo.
> At any given time in the history of mathematics, there have been mathematicians … whose mental capacity for mathematics seems completely super human (e.g., the owner of this blog, among many others).
That said, in the past couple of month, AI has gained a lot of ground on the 'project management' front in terms of planning capability for a lot of simple to medium-complexity projects. It still needs a lot of help for more complex projects. Just today, it almost fooled me into making a major structural mistake but thankfully I asked the right question and saved myself a lot of future problems; it was essentially dancing around a critical point and giving me the illusion that the critical point had been addressed but only when I phrased my question in a particular way, I understood the core issue was not addressed.
I asked Claude what it thought about the technique. It hated it. It loathed the idea that I was not sticking with traditional methods that were pointed to in papers and pre-existing open source packages. Even after talking through the idea and how it was better and showing it the results, it was weirdly hesitant to admit it was a valid method, simply because it seemed nervous that the idea was novel.
I don’t have a real moral to the story, I just thought I would add my anecdote here.
> With the latest ChatGPT models, these problems are more equivalent to homework questions: the answer is `in the back of the book.’ I am not discovering new solutions. Instead, I am working on problems whose answer exists and is simply waiting to be retrieved by a user of the model. In fact, I mentioned a problem that I was interested in working on to my advisor and he informed me that he and a collaborator had completely resolved it using ChatGPT – they have no plans to write up the result, so it will sit there until another `researcher’ pulls the proof slot machine.
There is another good point that the most tenured researchers have a sense of what problems are most worth exploring and therefore are more likely to feel excitement than younger researchers:
> I have also heard the contention that math research has `gotten more exciting,’ mainly from established researchers. They have decades of open problems that they care deeply about and want to see resolved. I have no such problems.
As a student, I am actively making decisions which will shape my career for the next forty or so years. At a minimum a discussion like this should acknowledge the possibility that the current rate of AI progress continues apace. I understand the desire to be encouraging, but the best preparation for students involves the consideration of possibilities that current mathematicians negligently paper over.
Of course it will get better at exposition than humans. And it will prompt itself in due time.
Do you know where “the ball” is going? Do these math students? Do the professors? It’s easy to say a vague nothing platitude like “target where the ball is going to” but such empty vague platitudes do not help anyone and just waste time.
I assumed the student ultimately wants a career that is financially viable.
> Then what?
Literally anything else?
Ironically, this very attitude could lead to AI creating one of the biggest slowdowns in progress in history.
It's this one, but different careers will diminish at different rates. Mathematics was already not financially well rewarded, and current AI is basically better than everyone in the field.
> this very attitude could lead to AI creating one of the biggest slowdowns in progress in history
I think there are enough young people with pre-AI experience that we'll probably develop superintelligence before they retire, so I don't really think a near-term lack of fresh talent will result in any significant slowdown.
Then why are frontier labs ploughing millions into racing human math researchers when they're already cash-strapped? What's the ROI for that?
> we'll probably develop superintelligence before they retire.
That ship has sailed:
https://www.whitehouse.gov/presidential-actions/2026/09/inau...
Human-crafted religious items and artwork may be more appealing than machine-crafted replicas.
Mathematics was historically "just a hobby". Much theoretical work is still "just a hobby". But, for the past few hundred years, humans that better understood the theory could find very profitable applications.
That is no longer true. Computers have solved mathematics, like they did chess two decades ago. A human will never find a better application than a computer, just like they will never find a better chess move. Yet people still play chess, and people still make money tutoring chess.
That is the future of humans in mathematics. Mathematics will become mathematics competitions. It will be just another intellectual sport. Sure, a sport that teaches valuable life lessons, but still "just a game". If you are going into mathematics today, your future career is as a competition mathematics coach.
> What specific direct advice would you give then if you could talk to someone who is currently studying math?
Do it for personal satisfaction, but find another way to pay the bills.
FWIW, I did like that the article at least acknowledged that the fundamental question is whether LLM-based approaches will eventually "max out", i.e. will they be limited to the "convex hull of ideas outlined in literature" as the article out it.
If LLMs do eventually hit a wall, then great, humans will still have a role to play. If not, though, we're all completely fucked - none of this nonsense about "humans managing agents" or providing "unique human insight" and what not, as agents will be more than capable of managing themselves and providing superior insight.
Manual labor and trades is likely to be one of the last things to go, so the whole "dropout of school and go into trades" crowd may have been more correct than ever, though their original reasoning was not.
Why are meta glasses such a big deal? Because they need training data for manual labor.
Realistically the outcome of highly capable advanced AI is an economic dark age more than anything else.
Robots automating everything and humans not needing to work was the stuff paradise fantasies were made of.
Sure, it would be the end of capitalism (which, I guess you could describe as "economic dark age"), but people these days have so little imagination what life outside of capitalism looks like that ideal societies sound like a horror story.
Communism was a horror for multiple reasons, but two big ones were: the planners didn't have the data or ability to actually plan (the interactions in a market economy did that much better), and not doing the work you were assigned was a crime (that is, slavery).
AGI and AGLabor solves both these problems. If you don't need workers, you don't need to coerce them into work. And planning and massive data collection (and management in general) sounds like something AGI would work very well at. The conceit that the lower classes are disempowered before the capitalists never struck me as well founded.
And jobs that require humans’ responsibility.
People can do people stuff, machines can do machine stuff. And market will react accordingly.
Meta glasses are a terrible example for the case you're making a point about: 90+% of whatever video they'll capture will be trash data.
It'd be much more efficient to set up dedicated sites just for producing the very same training data you're claiming they harvest.
Yes, this is likely true. Plumber & Electrician on existing construction will likely take a long time (relatively speaking) to automate. And even if we end up being able to do it, it could be that humans will be the cheapest option when it comes to a lot of manual jobs especially given that there will be a large supply of unemployed humans in many scenarios.
Especially if the timeline is 40 years (as opposed to, say... 4.) I have no doubt AI-assisted robotics development would have solved the plumber and electrician jobs in a decade or two.
But I’m not sure it will take 40 years. I’m quite sure Astra could figure it out, it’s just too slow and robots are too weak. The speed will improve, and robots may lag shortly thereafter but by not much. I give it 1-2 years for AI, and 5-10 years for robots tops (perhaps even quicker).
It's also quite likely that the next big era for humanity will be the space era. And that stands to create the biggest labor boom in human history. It could also happen far faster than many might expect, even moreso as there will be competition between countries, especially as those left with only terrestrial claims will see their influence relatively wane. Space jobs and industry can also, in some ways, act as a more productive Works Progress Administration [1] to compensate for any temporary economic instability that LLMs might bring along.
[1] - https://en.wikipedia.org/wiki/Works_Progress_Administration
The honest answer is via politics not education, but this debate is often had in "apolitical" circles that try their best to ignore this.
Not needing to work is a neat future that might be ahead of us. But it could unlock some new negatives as well as positives.
https://youtube.com/shorts/CaJd88zh1h0?feature=shared
Free money and universal healthcare thanks to the abundance of Ai is where "they," say we are headed. Thanks to Ai ravaging our civil society we are use to.
The second reason is that we often prefer humans to do it even if a machine can do it faster / cheaper. Sometimes, it's a status thing. For example, some people will buy Ikea furniture, some will have it custom made by a local craftsman. In fact, it's sort of the hallmark of the upper middle class that you can spend more for that human touch. Cupcakes from an artisanal bakery, private banker, etc.
Now, I don't think there's enough people who want to pay extra for human-made software. From the current trends in the industry, my impression is that we don't value our craft, so it would be surprising if others did. For mathematicians, I don't know; it's a bit painful to watch.
This is correct under present day thinking.
But imagine the accounting AI of twenty years hence. If it can reliably do the books, reduce and prevent fraud, significantly better than any human ever could, why would you still need the human in that loop?
In the present day it's a legal requirement, not to mention a practical requirement given present AI capabilities. That doesn't mean it still will be in the future.
To put it another way, if the human is on the hook for the agent, but the agent is proven by a decade+ of statistical evidence to be way less mistake/fraud prone than the human, what's the point of the human there?
What we're seeing so far is consistent with the training data being the upper bound for capabilities. They get better at recall / synthesis / reasoning over the corpus, but they don't, for example, acquire trans-human ethics; they're at best as ethical as we are, except not grounded by the fear of consequences. A perfectly-behaved, perfectly-moral LLM is not a given in 20 years, not unless your position is that there's room for unbounded, exponential self-improvement without any loss of fidelity. In that case, we'll probably have problems more pressing than the outlook for accounting jobs.
A lot of people put a lot of their self-worth into their work and that’s a good thing that won’t change.
But nothing is certain. I am trying to avoid overspecialization and remain flexible, just as you advise. If this path fails I might become a paramedic or physiotherapist.
Go into things expecting you might have to change careers.
The sports analogy holds; the future location of the ball may be uncertain, but that is the student's target, and not overcommitting to the present location of the ball. The commenter is expressing disappointment that the commentary seems to be reacting to the present too much.
I think the problem is the reverse. We no longer can make good predictions for four years ahead, certainly not eight.
And yet, people have to make choices - so they need advice and some kind of hope of a guess.
> you might have to change careers.
I hope this is some dust dry humor!
In the UK only about 30-40% of students end up working in an area related to their degree.
The people who have a realistic plan that works out for them are a minority.
Sure you wouldn’t predict what you would work on exactly. And there were better tools, better communication and so on sure, but no disruptive innovations or seismic shifts really.
Some of those complaining today are the ones who keep saying "LLMs are just stochastic parrots predicting the next word", "how can AI do math if they can't even count the number of 'r's in strawberry?", etc.
It doesn't take a genius to realize AI will start getting better and possibly become a "threat" even before all this mathocalypse stuff happened.
For all the arrogance the hard sciences have, in the end they are just as human as their humanities counterparts. They just thought they were above becoming worthless because science was elevated above the softer sciences.
But the ball is flying out of the court and into the abyss.
Don't go classically into that qubit night.
But the post does do this: "[...] even if their capacity becomes far superior, there will always be a need for mathematicians at all levels to guide research in paths that make sense for humans to walk (not run)."
> Of course it will get better at exposition than humans.
I'm not so sure about this, because I don't know how one would train towards better exposition as it's not easy to check for good or bad exposition at scale.
I expect it will remain true into the future (10+ years). I have moderately high confidence (75% or higher) in this prediction.
I use frontier AI models in my work all the time. I think they accelerate my work by helping me understand faster and prompt better.
The models are most useful and most productivity-enhancing in the hands of experts and in the area of their expertise.
I don’t expect a jobs apocalypse, not even in math.
It's definitely (again, to the article's credit as it points out) up in the air whether LLMs have inherent limitations. But I can't fathom how someone can say "I don’t expect a jobs apocalypse, not even in math." Because if AI does end up surpassing humans, what exactly will there be left for humans to do? And even if they don't surpass humans, there will still be a jobs apocalypse. Probably the majority of people today work in jobs where AI can surpass their performance.
Simple case in point: years ago I used TurboTax to do my taxes, but I eventually needed to hire a CPA because I had some complicated situations that TurboTax couldn't handle, and I also needed some tailored advice. I ended up finding a great CPA. Now though, AI agents can literally do 100% of the job I hired my CPA to do, including asking me the right questions and offering advice. I'm sure there may still be tasks for a CPA in the corporate world, but for personal taxes, I literally can't imagine a CPA providing value over what an AI agent can provide. And to emphasize, I would have probably considered that a ludicrous statement a year ago with all the mistakes LLMs made. But so many of those mistakes have been fixed, and you can get better-than-human performance by having agents check each other's work. And AI will only get better when tax season rolls around next year.
It's not like "should I get a PhD" is a new question. It's not unheard of for unpredictable events to drastically affect the career prospects of PhDs in my field. During my lifetime:
1. Mandatory retirement of professors was ruled illegal. While good for civil rights, it created a 10+ year gap in faculty retirements.
2. End of the cold war.
3. Transition of college teaching from tenured professors to gig workers, aka "adjuncts."
The one constant during this time was the perpetual optimism of the faculty for the employment prospects of PhDs. "There will always be a need for physicists." My dad, also a PhD, confirmed that this goes back as early as the 1950s.
I would add one question to the student's letter: What are the ethics of AI and its owners?
This is a huge, and likely permanent, change.
https://philip.greenspun.com/careers/women-in-science
Basically, it's arguing that science (really academia) is generally an awful career path for most Americans.
AI is never to take the place of understanding, knowledge, and education.
It's best that AI sycophants smarten up.
> will math academia be big enough and accessible enough for anyone with sheer passion (despite not being the brightest mind in the field) to pursue a career in, or will it inevitably shrink such that it will only really be accessible to the brightest minds?
Could someone with a recent math PhD experience tell me if it this hasn't been so for years? Competition is incredibly high because people have to compete with every "brightest mind" from all around the world. PhDs are overproduced relative to the number of permanent positions. It's not a question that you only start to think about after you've seen LLMs do mathematics, and if that's the case the student should be made fully aware of what he's getting into. I don't want to call the original post "dishonest" but I wish this was addressed here without the LLM angle. However, it was written by a tenured professor, so he did say the only thing he realistically could.
I'm with you, doing a PhD was already a risky choice. Now it's even more risky.
My own suspicion/guess is that AI can break out of the bounds of the convex hull in math, and that’s going to become apparent soon.
We studied maths because it is interesting, because it teaches you how to think, you often pair it up with something that's more employable like economics or software development, or you go for a teaching career. Unless you're one of the few people who are heavily invested in cutting edge research I don't even see how new research tools change the profession.
An undergraduate maths education isn't going to change because you have people with computers churn out 400 page proofs. It's like being worried about 30 move Stockfish opening theory if you're a club level chess player.
But it's a construction based on very basic properties of the real world, at least on those properties we accept and perceive as very general and we use them as building blocks.
I don't necessarily think that that is true, as almost every social science has been turned into maths one way or another, beginning with Economics. Heck, even mundane stuff like sports (think football/soccer) has now been absorbed by data, which you can see as applied maths.
Yes, I do know that advanced maths doesn't currently explain Arteta's system at Arsenal but we're generally talking about that same semantic area of interest, i.e. if the interest on maths were to subside as a result of AI taking that interest away from us then we will have no more data/stats-focused Artetas in the future (and possible no AI-like thingie also).
I’m saying that this wouldn’t have happened had our society not become “mathematics-ised” at some point (I’m talking about said society’s technical and scientific elites, of course). I’m also saying that outsourcing our “mathematics-mind” to AI (which seems the current process we’re now part of) will not allow for similar “mathematics-ing” to take place into the future. I’ll come with an even stronger claim and say that we risk losing the “mathematics-ing” we now associate with most of our sciences.
In a future where learning math has about the same economic value as learning to play chess or the flute, you can see why many mathematicians aren’t very excited about their chosen career paths.
At least for now someone still has to decide what to prove and why. Like why are you trying to prove that thing to begin with? Presumably it's a step along some journey, right? Maybe the journey is where you need to start deriving your satisfaction from, then.
I’m frankly a bit disappointed that Tao published the blog post I linked to above.
Telling OpenAI they shouldn’t test frontier math on their internal models is just plain nuts and illogical. It’s surprising that the advanced math community can lack so much logic.
> At least for now someone still has to decide what to prove and why. Like why are you trying to prove that thing to begin with? Presumably it's a step along some journey, right? Maybe the journey is where you need to start deriving your satisfaction from, then.
Beautifully said. I had been thinking about the same thing and the analogy b/w CS and mathematics and these were some that I had found:
1.) to prove/disprove from the proofs that OAI created, you needed an mathematician to do so and OAI had to withdraw three mathematical proofs.[0]
But it was only because an expert within the field could verify if it was true or not, I feel as if software engineering is the same as well. We are/can be paid to prove/disprove if a software is working as intended or not.
2.) for someone to be that said mathematician who disproved it, he had to learn the basics of mathematics and multiple branches of it to then perhaps specialize in one thing that he most strongly resonated with and within all this learning, there was some struggle definitely involved. They had to learn algebra etc.,
this analogy can also extend to how we teach children algebra/calculations and other things even though we have had calculators for a long time, yet, we teach children how to do calculations because it is still valuable enough and either teaching maths can help them perhaps in future make a mathematician or it can help them be less reliant on simply calculators and more confident on on the spot calculations and help them within this skill.
As knowing calculation has become the norm rather than exception, even though we have calculators. In fact knowing how to do calculation by hand can perhaps better help you write a problem to calculator. Knowing the technical aspects of CS can help you express a problem to AI with much more depth and effectiveness as well.
You haven't tried the latest gen of frontier models, I take it? These things aren't just hype.
1. There was no overhiring in 2019.
2. It's much worse at the entry level than the overall industry.
3. We're years out of COVID. It's not relevant anymore.
God, the absolute decimation that is going to come for middle income countries.
Hell, right now the people quitting frontier labs are rediscovering trust and safety isues, like language parity problems, except all the work being done is in the first world and exported back to the rest.
[...]
> After I finished writing this blog post, and had already sent it to Terry, OpenAI released a huge treasure trove of results in mathematics [including] the resolution of the so-called quasi Riemann Hypothesis
We ought to all be careful about underestimating the speed and magnitude of the change that is coming.
> if you are a student who is passionate to learn what is new and what is left to do, then a PhD is definitely the right path for you
This is an awful lot of confidence to put behind career advice in a wildly changing world. Markets are real and tradeoffs bite. We're not in gay communist space utopia yet.
SWE is a capitalistic game and Academia seems to be a game of status ?
Regardless, they are not going to make it if they remove LLMs.
Someone, somewhere will use SOL/Opus and mog the big boys.
So this Calculus class was 100% online. I discovered only at finals week that the instructor was firmly based at a distant satellite campus.
The class was brutal to me, and I needed a lot of time in the tutoring hall just to barely grasp concepts as they paraded past us. I used my calculator in good faith.
I discovered that there were websites that would solve integrals and complex equations, but I recognized that as outright cheating, so I resisted those tools.
However, the online LMS was set up with frequent quizzes with unlimited attempts. Not being penalized for attempts meant that I could brute-force every answer for every quiz throughout the course and get perfect scores. Though I felt kinda evil, I did just that.
When the final exam came around, our phantom remote instructor suddenly expected everyone to show up on her campus in-person. This was absurd to someone who was (1) on FAFSA funds and (2) riding the bus, and when I protested, the solution was a proctored session in the Disability office, all alone.
I solved every last question on that thing with paper and pencil and I required every single minute of the extended 3-hour limit they granted to me. I got an A- or whatever final grade.
Perhaps I didn’t deserve it, because all those learings drained out of my skull within 3 months, but it was a textbook example of gaming the system without strictly cheating, and since I was not aspiring to a math career, who cares?
The department deleted the Calculus requirement shortly after I finished that semester.
This is a great moment to be a hassadeur, a madmen, somebody who wants to jump of the cliff with just a rope on his feet.
If you are that- great times- otherwise- good luck.
If you are one of those who try to drag down people oustanding, congrats on the team effort. Now try that with a machine..
What actual industries where you could get a phd in, died?
If I was to ever suggest one, it would be Philosphers.
Yet they have found ways to get tenure and/or other jobs for as long as the field exists.
“42.”
But no one could understand what the answer even meant. So they designed a computer to build the question itself again, and that was Earth. Then the story begins with Earth being destroyed because of a cosmic highway problem (I won’t write more since that would be a spoiler). In the opening background of this work, I found it interesting that after calculating for 7.5 million years, they didn’t even know what they had originally been asking. The story now feels similar to that story from back then.
You should also tell them “better luck next time” as they have to create and find their own luck unfortunately.
This game was meant to be unfair because the ones who have “won” want to keep it unfair for others, and cannot stand losing due to complete greed.
The truth is they (students) have to find a way to outsmart the incumbents. The best advice is to find your own strategy and listen to no-one.