Anecdata of one: I finished my PhD about 20 years ago in programming language theory. I created something innovative but not revolutionary. Given how slowly industry is catching up on my domain, it will probably take another 20-30 years before something similarly powerful makes it into an industrial programming language. Counter-anecdata of one: On the other hand, one of the research teams of which I've been a member…
> something as powerful as what I created Could you give us more detail? It sounds intriguing.
I developed a new static analysis (a type system, to be precise) to guarantee statically that a concurrent/distributed system could fail gracefully in case of (D)DoS or other causes of resource exhaustion. Other people in that field developed comparable tools to statically guarantee algorithmic space or time complexity of implementations (including the good use of timeouts/resource sandboxes if necessary). Or type system-level segregation between any number of layers of classified/declassified information within a system. Or type systems to guarantee that binary (byte)code produced on a machine could find all its dependencies on another machine. Or type systems to prove that an algorithm was invariant with respect to all race conditions. Or to guarantee that a non-blocking algorithm always progresses. Or to detect deadlocks statically. etc.
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.
I think education goal for people shifted. I teach my kids to be flexible and embrace the change. Invest in abilities that transfer well to various things you could be doing during your life. Be a problem solver. In the future -- forget about cosy job you can be doing for the rest of your life. You no longer have any guarantees even if you own the business and even if you are farmer. What you absolutely don't want is…
> What you absolutely don't want is spend X years at uni learning something, and then 5-10 years into your "career" finding out it was obsoleted overnight and you now don't have plan B.
That seems to be running directly opposite of the current trend of admin assistant jobs requiring 2 years specialized admin assistant diplomas. Tech (and I would guess the world of the business MBA) is a unique space where people are learning and changing so quickly, but for a lot of those outside the bubble things seem to be calcifying and requiring more and more training at the expensive of the worker.
When I was studying Computational Linguistics I kept running into the unspoken question: given that Google Translate already exists, what is even the point of all of this? We were learning all these ideas about how to model natural language and tag parts of speech using linguistic theory so we could eventually discover that utopian solution that would let us feed two language models into a machine to make it perfectl…
I learnt some very basics of computational linguistics since it was related to a side project. I kept wondering why people were spending huge amounts of resources into tagging and labelling corpora of thousands of words, while to me it seems that in theory it should be possible to feed wikipedia (of a certain language) into a program and have it spit out some statistically correct rules about words and grammar.
I guess the same intuition led to these new AI technologies...
I tried translating something from English to German (my native language) yesterday with ChatGPT4 and compared it to Microsoft Translate, Google Translate and DeepL. My ranking: 1. ChatGPT4 - flawless translation. I was blown away 2. DeepL - very close, but one mistake 3. Google Translate - good translation, some mistakes 4. Microsoft Translate - bad translation, many mistakes I can understand the panic.
During my master's degree in data science, we had several companies visit our faculty to recruit students. Not a single one was a specialized NLP company, but many of them had NLP projects going on. Most of those projects were the usual "solution looking for a problem to solve". Even those projects that might have had _some_ utility, would have been way more effective to buy/license a product than to develop an in-ho…
>> "solution looking for a problem to solve" I wonder if this is a bad as everyone thinks. When a new technology arrives which is not completely understood, isn't the right approach to try to find some applications for it? Sure, most will fail, but some valid use cases will likely emerge. I'm pretty sure almost all technologies at some point were solutions looking for a problem to solve. Examples include the internet…
R&D is fraught with risk, but some risks are more rewarding than others. These companies don't just sit on useless knowledge. Take Google who now sits as a "loser" in the current AI "competition"; their projects are far from worthless. Because they've built up expertise, they're now in a very good position to overtake Microsoft on AI, even though they currently seem a bit behind. (And frankly on many fields they're already far ahead.) So OK, perhaps the behemoth that is Google is a bad example, but I still think the same thing is true for smaller companies. If you just read the news, you would think that a technological race like this only has one winner, but that just isn't true. Even quote unquote "worthless projects" can help increase the understanding and expertise in quite important areas, that while not "worth" anything currently, may still have huge value in the future. The only way to know, is to stay in the race.
During my master's degree in data science, we had several companies visit our faculty to recruit students. Not a single one was a specialized NLP company, but many of them had NLP projects going on. Most of those projects were the usual "solution looking for a problem to solve". Even those projects that might have had _some_ utility, would have been way more effective to buy/license a product than to develop an in-ho…
>> "solution looking for a problem to solve" I wonder if this is a bad as everyone thinks. When a new technology arrives which is not completely understood, isn't the right approach to try to find some applications for it? Sure, most will fail, but some valid use cases will likely emerge. I'm pretty sure almost all technologies at some point were solutions looking for a problem to solve. Examples include the internet…
> I wonder if this is a bad as everyone thinks.
I think it is. If they actually do end up finding a problem to solve, that would be serendipitous but I imagine the vast majority of the time they find themselves in the business of trying to convince the rest of us to buy a thing that we don’t need. And while the latter may drive the economy to some degree as I get older I detest it more and more.
During my master's degree in data science, we had several companies visit our faculty to recruit students. Not a single one was a specialized NLP company, but many of them had NLP projects going on. Most of those projects were the usual "solution looking for a problem to solve". Even those projects that might have had _some_ utility, would have been way more effective to buy/license a product than to develop an in-ho…
> Honestly, I can't wait for GPT and other productivity tools to wrech havock upon the tech labour market. Some people in tech really need to be taken down a notch or two.
You have to remember that when these sorts of things happen, the ones who get "taken down" in ways that actually affect their lives are invariably the ones who already have the least. The ones who "need" that takedown will be just fine, unless they've made incredibly stupid investment decisions.
As one of the comments on reddit posts - it's not just big tech companies, but also entire university teams which feel the goalposts moving miles ahead all of a sudden. Imagine working on your PhD on chat bots since start of 2022. Your entire PhD topic might be irrelevant already...
This is where it pays off to be researching something completely esoteric rather than something immediately applicable. I mostly scoffed at such research in the past, but now I see the value of it. The guy researching QML algorithms for NLP is not panicking yet, I think.
All these people don't understand how hireable and desirable they are now. They need to get out of academia and plugged into AI positions at tech companies and startups. Their value just went up tremendously, even if their PhD thesis got cancelled. Easily millionaires waiting to happen. --- edit: Can't respond to child comment due to rate limit, so editing instead. > That is not how it works at all. Speak for yoursel…
That is not how it works at all. You won't get hired if you don't have the academic pedigree in the first place. That means a completed Ph.D and good publications in good journals.
Hired in academia? Sure.
Hired in industry. That's the opposite. I've had a friend who had to hide that they had a PhD to be hired...
Everyone here is saying that people can simply transition easily into startups and other big companies. To a certain extent that's true, but what exactly are they going to do? As technology consolidates into one or two major LLM's, likely only accessible by API, I feel most orgs would be better served by relying heavily on finetuning or optimizing those for their purpose. Previous experience with NLP certainly helps…
That's definitely a risk.
With a PhD in the domain, I consider myself pretty good at (a subset of) distributed programming. But these days, when companies hire for distributed programming, they seem to want developers who know a specific set of tools and APIs. I'm more suited at reimplementing them for scratch.