In fairness most PhD topics people work on these days, outside of the select few top research universities in the world, are obsolete before they begin. At least from what my friends in the field tell me.
Counter-anecdata of one: On the other hand, one of the research teams of which I've been a member after my PhD was basically inventing Linux containers (in competition with other teams). Industry caught up pretty quickly on that. Still, academia arrived first.
edit Rephrased to decrease pedantism.
Could you give us more detail? It sounds intriguing.
All these things have been available in academia for a long time now. Even languages such as Rust or Scala, that offer cutting edge (for the industry) type systems, are mostly based on academic research from the 90s.
For comparison, garbage-collectors were invented in the 60s and were still considered novelties in the industry in the early 2000s.
Perhaps looking at the proceedings of ICFP and POPL can help?
Will this effect the job market (both academic and commercial) for these folks? It's very hard to say. Clearly lots of value will be generated by the new generation of models. There will be a lot of catchup and utilisation work where people will want to have models in house and with specific features that the hyperscale models don't have (for example constrained training sets). I'm wondering how many commercial illustrators have had their practices disrupted by Stable Diffusion? Will the same dynamics (what ever they are) apply for the use of LLM's?
Pretty hard disagree. Even if your NLP PhD topic is looking at hypotheses on underlying processes about how languages work (and LLMs can't give you this insight), 9 times out of 10 it's with an eye for some sort of "applicability" of this for the future. GPT-4 just cut off the applicability parts of this for huge swaths of NLP research.
A touch of understatement.
Their value just went up tremendously, even if their PhD thesis got cancelled.
Easily millionaires waiting to happen.
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edit: Can't respond to child comment due to rate limit, so editing instead.
> That is not how it works at all.
Speak for yourself. I'm hiring folks off 4chan, and they're kicking ass with pytorch and can digest and author papers just fine.
People stopped caring about software engineering and data science degrees in the late 2010's.
People will stop caring about AI/ML PhDs as soon as the challenge to hire talent hits - and it will hit this year.
Hired in industry. That's the opposite. I've had a friend who had to hide that they had a PhD to be hired...
For a plain SWE role a Ph.d might be a disadvantage here too, but for anything ML related it is mandatory from what I can see.
As someone who hired for this in general we'd use PhD (or maybe a Masters degree) as a filter by HR before I even saw them.
It's true that a PhD doesn't guarantee anything though. I once interviewed a candidate with 2 PhDs who couldn't explain the difference between regression and classification (which was sort of our "ok lets calm your nerves" question).
Almost all the time, they're shitty startups, where bankruptcy is a matter of time, run by overpromising-underdelivering grifter CTOs pursuing a get-rich-quick scheme using whatever is trendy right now -crypto, AI, whatever has the most density on the frontpage-.
That was a few years ago, though.
The demand for AI/ML will fast outstrip available talent. We'll be pulling students right out of undergrad if they can pass an interview.
I'm hiring folks off Reddit and 4chan that show an ability to futz with PyTorch and read papers.
Also, from your sibling comment:
> Maybe it is also a matter of location. I am in Germany.
Huge factor. US cares about getting work done and little else. Titles are honestly more trouble than they're worth and you sometimes see negative selection for them in software engineering. I suspect this will bleed over into AI/ML in ten years.
Work and getting it done is what matters. If someone has an aptitude for doing a task, it doesn't matter where it came from. If they can get along with your team, do the work, learn on the job and grow, bring them on.
Chris Olah was at OpenAI but is now one of the founders of Anthropic but doesn't have any degree (he joined Google Brain after dropping out of his undergrad degree).
I think they are pretty exceptional though - most people on the ML team do AFAIK. Perhaps on the infrastructure side the backgrounds are different.
We've tried many time to work with CSIRO (the NSF of Australia) and it's fallen flat. They love impressive resumes and nothing else. I'm having a chat with their "Director of ML" who's never heard of the words "word2vec" or "pytorch" before. (And I'm a UX designer!)
I think at most corporate firms you'll end up running into more resume stuffers than people who actually know how to use ML tools.
Reminds me of Computer Security academic and professional pedigree where these people aren't even programmers
We could not "just use ChatGPT" because the answers have to be correct, or students could take legal action against the university in situations where they'd give an answer in an exam that the chatbot gave them and that might be wrong. It's exciting for sure to compare e.g. ChaptGPT with other approaches, for a set of controlled benchmark questions such as:
-- snip --
Q: What is the A* algorithm?
A: The A* algorithm is a popular heuristic search algorithm used in pathfinding and graph traversal. It is an extension of Dijkstra's algorithm, but uses a heuristic function to guide the search towards the goal node, which can make it more efficient than Dijkstra's algorithm in certain situations.
The algorithm works by maintaining a priority queue of nodes to be explored, where the priority of each node is the sum of its actual cost from the starting node (g-score) and an estimate of the cost to reach the goal node (h-score), based on a heuristic function. The heuristic function must be admissible, meaning it never overestimates the actual cost to the goal node.
At each step, the algorithm selects the node with the lowest priority from the queue and expands it, generating its neighboring nodes and computing their priority scores. The algorithm then adds these nodes to the queue if they have not been explored before or if a better path to them has been found.
If the goal node is reached, the algorithm terminates and returns the path from the starting node to the goal node. If the queue becomes empty before the goal node is reached, then there is no path from the starting node to the goal node.
Overall, the A* algorithm is a powerful and widely used algorithm that has many applications in fields such as robotics, video games, and logistics.
-- snip --Right now, GPT 4 would earn a top 10% SAT score, implying that it can give very high quality answers on a range of scholarly topic. This output is basically free.
Nobody that's beholden to even mild economic pressures is going to pay for an expensively constructed 100% solution if they can have the 99% solution for free.
And aren’t PhDs supposed have a theoretical underpinning?
To be honest, that's a sh*tty PhD to begin with, it doesn't bring anything good and really worthwhile to the world, quite the contrary.