Just like DEI, sustainability efforts, I predict we will see new initiatives for forced hiring of Juniors.
Implementation can differ (e.g. ratio of interns vs total headcount and so on), but it is the time for governments to intervene and force corporations to train people, humans are resource for the government, they need to polish that resource to thrive.
> I’m not sure where that will leave students who start with no research experience.
What is wrong with this guy? Of course he knows where that will leave those students. Why did he even choose to be in the business of developing people? Nobody forced him. Anyway, the ladders were pulled up in 2020–2021.
It's one thing to be forced to use the damn things, but this guy gives it very serious thought, much more than others I've seen and known, he even writes a science.org article about it, and ultimately chose wrong.
It says a lot about US academic culture that they think in terms of hiring. There is an important educational commitment requirememt to the role of professor, at least in Europe. Hiring is to the betterment of your own goals and almost orthogonal to the educational mission. A lot of unethicalities fond their root in this schizophrenic mission statement of doing professional competitive scientific research and at the same time education of graduates.
On the contrary, my experience of US academia has been that (graduate) students are very much students, who take a lot of classes, are graded seriously with the possibility of failing, are mentored rigorously (the author even says "the classic one hour a week meeting", which I also witnessed there), and in fact enroll in a program more than they are hired directly.
I did my PhD in France where we were legally employees like any other and did 100% research with like 100 hours training over the three years which could be 5min MOOCs counting for hours or classes the professors would sign us off on. We were hired by a specific researcher for a specific topic, unlike US students who join a broader program and explore their own directions more. My mentoring was drinking coffee with my advisor and colleagues and the odd e-mail exchange the day before turning in a paper.
I believe Germany and quite a few other European countries are similar. Any country that does 3 years PhDs is bound to cut on the student part of things.
Over here in Germany, professors' job is "research and teaching". According to the internet, the author's university is a publicly funded university as well. I can see how AI can make you faster on the research side, but you give up 100% of the teaching/developing people part.
As a tax payer, I am very concerned if the people I fund with my taxes to do a job unilaterally declare they are no longer going to do the half of it.
You've clearly never worked the academic sector. Calm down most researchers are hyper focused on their research productivity because at times 90%+ of their time is consumed by teaching for months at a time. This is an almost universal constant for all decent institutes globally.
Taking on extra cheap labor in the form of grad students used to be the only way to do this but every single time this turns into onboarding someone for months to get weeks of work out of them. Great when you can hide it in your other side of your job, but most of the time you can't...
In the classic division, "teaching" consists in giving undergraduate classes, and "research" consists in the whole spectrum between working all on your own and managing a PhD factory (3+ students a year).
So this article is really not saying anything controversial in the strictly ontological side of things, in fact it's already a relatively common stance to prefer supervising few (or, more rarely, none at all) students.
This researcher is saying "when I consider hiring someone as a workhorse, I might prefer AI instead"; what's the harm in that? Too many PhD students are used as disposable cheap labor, seeing little personal growth in their PhD journey and being generally neglected and abused.
This is something that will have to be solved through the way research is funded.
At least for publicly funded work, it was always an assumption that you would need students to hit some goal; so by funding it you would get both the outcome, and more people skilled in that field. If the scope of what one team/senior can handle has grown with ai, we will either need explicit staff numbers as a requirement or bigger scope to the point where the ai can't handle it.
Or we find that AI can do so much the whole system implodes...
>In the process, they may bypass the valuable experience of struggling through early tasks and learning from their mistakes. Students, I worry, could simply become an intermediary between the raw idea and the AI’s output.
Even if all AI progress grinds to a permanent halt today, there's already enough utility in its current capability to force these questions. As a result, how we train and educate graduates and young people needs to change.
I have no doubt you need to have actual experience to be able to ensure AI output is at a production standard but if we accept that reality, then a shift in how we educate and train young people could make an enormous difference in ensuring employers still see value in hiring people with no real commercial work experience.
Students are not only workers, they are also disciples of your work and, once forced to read it, will likely use it in the future even when they leave your lab.
Even completely egoistically replacing students with AI is shooting yourself in the foot in the long term.
I think also at the end of your career in science, you are going to care a lot more about the people you've uplifted and turned into flourishing scientists themselves vs however many papers you've gotten your name under. At least for every professor I've come to be close with in my years in academia that is certainly the case.
We measure scientific output as nr of publications. And that is the cause of bs like this.
These institutions have a duty to educate humanity. PhDs are also supposed to be able to help the public understand complicated science. To guide ethical decisions.
But no, we measure the number papers, and not even their quality (very well).
It's all a matter of incentive alignment, what gets measured gets done. The state of academic science is sad in most places. This contemplation by OP being case and point.
Why not hire a graduate and empower them to use AI? Much better interfacing with an actual human who will then go and do the work using all AI tools at their disposal.
> Why not hire a graduate and empower them to use AI?
A lot of my work with AI involves questions where I have an intuitive direction and sense of the data or model, but where explaining why takes almost as much work as doing it. (Commonalities: weird interdisciplinary nexuses and idiosyncratic data sources.) Adding a human translator, much less someone without field experience, seems worse than giving the task to a human or AI wholesale.
Where humans still reign supreme is in interacting with other humans. Paradoxically, this might make grad students’ roles attending staff meetings as their professors’ proxies and/or filling out paperwork.
10 years from now, the people that stopped hiring novices and juniors are going to be deeply regretting their past decisions. The people that kept hiring are going to be working with their newly-promoted-to-senior colleagues and be making significantly more progress than those that didn’t keep hiring.
This sounds misguided. In the little experience I had, I've seen that models get basic knowledge so absolutely wrong that giving them any sort of independence will not result in publications that positively impact a professor's reputation, or contribute to science. Or at least the reviews and papers I read that had AI content did not give me the impression that we should have more of this. And they require much more supervision, with the added issue that they cannot learn in the long term through your interactions, and without the enjoyment of teaching something to someone.
They're really good at finding papers though. Perhaps because navigating search engines has become a pain. Perhaps this will be the case in the future, but saying you're tempted right now is like saying you're being tempted to replace your HPC with quantum computers. It's a bit early.
> The issue is not whether my students are valuable. In the long run, they are invaluable. The issue is that their value emerges slowly, whereas AI delivers immediate returns.
I had the thought that it's more like hiring only autistic/on-the-spectrum employees that will on whims do exactly what their interpretation was, or possibly worse literally what you said without considering further consequences.
This is morally wrong and it should be embarrassing to publish such an article.
It doesn’t surprise me to see such articles coming from academia, in which juniors are treated like dirt to such an extreme that is unimaginable in any other industry, save for maybe Michelin star cuisine.
The title is not relevant to the article, not even for a single line. The author straightup assumes, does not answer the 'why', cause I was here to give Lady Lovelace argument to Turing, that you would NEVER (hire an ai instead of a student) unless you making directionless slop. You can share goals, but not the vision, and mission is different. Ai learns from experience, humans are needed to build that experience due to their extremely large 'context windows' going as deep as the constant evolution of the DNA(as long as it serves human-centric goals, which circles back to the mission part).
The article really is about "education seems directionless without economic goals", and again as comments have pointed out, it only seems so.
It's interesting that this dilemma (of getting quick and easy wins) is occurring at multiple levels. Even as a junior researcher, its often tempting to hand off actuall thinking and reasoning about one's research to AI (e.g. blindly accepting AI code) to quickly make 'progress'.
Apparently the same question is being asked at different levels and abstractions...
42 comments
[ 1.7 ms ] story [ 71.5 ms ] threadImplementation can differ (e.g. ratio of interns vs total headcount and so on), but it is the time for governments to intervene and force corporations to train people, humans are resource for the government, they need to polish that resource to thrive.
What is wrong with this guy? Of course he knows where that will leave those students. Why did he even choose to be in the business of developing people? Nobody forced him. Anyway, the ladders were pulled up in 2020–2021.
I did my PhD in France where we were legally employees like any other and did 100% research with like 100 hours training over the three years which could be 5min MOOCs counting for hours or classes the professors would sign us off on. We were hired by a specific researcher for a specific topic, unlike US students who join a broader program and explore their own directions more. My mentoring was drinking coffee with my advisor and colleagues and the odd e-mail exchange the day before turning in a paper.
I believe Germany and quite a few other European countries are similar. Any country that does 3 years PhDs is bound to cut on the student part of things.
As a tax payer, I am very concerned if the people I fund with my taxes to do a job unilaterally declare they are no longer going to do the half of it.
So this article is really not saying anything controversial in the strictly ontological side of things, in fact it's already a relatively common stance to prefer supervising few (or, more rarely, none at all) students.
This researcher is saying "when I consider hiring someone as a workhorse, I might prefer AI instead"; what's the harm in that? Too many PhD students are used as disposable cheap labor, seeing little personal growth in their PhD journey and being generally neglected and abused.
At least for publicly funded work, it was always an assumption that you would need students to hit some goal; so by funding it you would get both the outcome, and more people skilled in that field. If the scope of what one team/senior can handle has grown with ai, we will either need explicit staff numbers as a requirement or bigger scope to the point where the ai can't handle it.
Or we find that AI can do so much the whole system implodes...
Even if all AI progress grinds to a permanent halt today, there's already enough utility in its current capability to force these questions. As a result, how we train and educate graduates and young people needs to change.
I have no doubt you need to have actual experience to be able to ensure AI output is at a production standard but if we accept that reality, then a shift in how we educate and train young people could make an enormous difference in ensuring employers still see value in hiring people with no real commercial work experience.
Even completely egoistically replacing students with AI is shooting yourself in the foot in the long term.
These institutions have a duty to educate humanity. PhDs are also supposed to be able to help the public understand complicated science. To guide ethical decisions.
But no, we measure the number papers, and not even their quality (very well).
It's all a matter of incentive alignment, what gets measured gets done. The state of academic science is sad in most places. This contemplation by OP being case and point.
A lot of my work with AI involves questions where I have an intuitive direction and sense of the data or model, but where explaining why takes almost as much work as doing it. (Commonalities: weird interdisciplinary nexuses and idiosyncratic data sources.) Adding a human translator, much less someone without field experience, seems worse than giving the task to a human or AI wholesale.
Where humans still reign supreme is in interacting with other humans. Paradoxically, this might make grad students’ roles attending staff meetings as their professors’ proxies and/or filling out paperwork.
You may be able to go fast with AI, but you can only go far with humans.
We are definitely seeing a lot of anti-human behavior around AI adoption, because all anyone seems to care about is going fast
However, the person is also sufficiently self-aware to share their thought process (and candid about its shortsightedness).
To me, that made it worth reading (even though it has a sad message).
> The issue is not whether my students are valuable. In the long run, they are invaluable. The issue is that their value emerges slowly, whereas AI delivers immediate returns.
I had the thought that it's more like hiring only autistic/on-the-spectrum employees that will on whims do exactly what their interpretation was, or possibly worse literally what you said without considering further consequences.
It doesn’t surprise me to see such articles coming from academia, in which juniors are treated like dirt to such an extreme that is unimaginable in any other industry, save for maybe Michelin star cuisine.
The article really is about "education seems directionless without economic goals", and again as comments have pointed out, it only seems so.
Apparently the same question is being asked at different levels and abstractions...