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> It's also not obvious at all it's a bubble It's breathtakingly obvious: 1. Large numbers of people getting poached from academic programs by industry. 2. "

> It's also not obvious at all it's a bubble It's breathtakingly obvious: 1. Large numbers of people getting poached from academic programs by industry. 2. "This time it's different" is what we've said during every bubble ever. 3. Lots of money chasing a few ideas. The only thing that's not clear about this bubble is when it's going to pop. People who didn't see the potential of the internet in 1996 missed out. People who got into it in 2000 were left holding the bag.

notyourday 9y ago
I think you are missing the scale mentioned in the article: The industry is paying five times what universities are paying. Not Fifty percent. Not double. Five times. Professors/administrators whining about it are pure scum unless they themselves are making one fifth what they would be making in a different university that offered them a job purely because they love this specific college doing the specific research or teaching. What is really pissing off these professors is that with their worker bees gone they themselves would need to do the work - something they are not quite used to.
- sevensor 9y ago
  > I think you are missing the scale mentioned in the article: > The industry is paying five times what universities are paying. Not Fifty percent. Not double. Five times. Nope, not missing that. You don't think this happened in 1999-2000? I agree with you that grad students are ruthlessly exploited. I've seen that firsthand. I routinely discourage people from doing a Ph.D. for that reason. And if you're not too far along, taking a terminal master's and leaving for industry is a solid option, especially because this AI bubble won't last forever. But if, like the one student in TFA, you have just one year left, quitting grad school is not a great idea. Now you have, at best, an awkwardly long stretch of grad school on your CV with a master's degree on the end of it, and at worst, no degree at all to show for your pains. At that point, just suck it up and finish the degree.
  - notyourday 9y ago
    > Nope, not missing that. You don't think this happened in 1999-2000? I do. I'm yet to see people who jumped away from academic CS tracks at the time being upset about their decisions. I do know of dozens degreed people in their late thirties reporting to C students while making quarter of the salary of the same C students.
    - YeGoblynQueenne 9y ago
      CS and AI are different subjects. As a CS dropout, your job is to write code. You can get quite competent at that without a higher education course. You can't do that with AI. The amount of material you need to get on top of is really something else, especially if you want to be able to understand the foundations of what you're doing- which stretch back several decades before statistical machine learning, popular as it is today.
      - notyourday 9y ago
        It is very naive to think that AI programs in most of universities is anything other than writing code and pretty lousy one. Do you know that it was possible to get a PhD in genetic sequencing not a very long time ago from very serious schools? That's right, what today can be done by a random student or a random outsourcing shop in a third world country used to be a PhD worthy specialty. In 5 years today's ML/AI PhDs would be in the same peculiar position as PhDs of genetic sequencing.
        YeGoblynQueenne 9y ago
        >> It is very naive to think that AI programs in most of universities is anything other than writing code and pretty lousy one. Are you speaking from experience? Because my own experience from my degree and Master's AI courses and lectures is completely different to what you describe. Particularly during my Master's, code was only incidental; the meat and potatoes of each and every lecture was theory. It depends of course on the quality of the teaching in a given institution, but, for instance, you can find here the lecture notes from Oxford's Deep NLP course: https://github.com/oxford-cs-deepnlp-2017/lectures https://github.com/oxford-cs-deepnlp-2017/lectures As you will see, it's anything but just code, or lousy such. Of course, that's Oxford, their teaching is world-class- but most AI courses I've found on the internet are similar in scope. Really, it's pointless trying to teach AI with "just code" - because in AI you can't really code much, unless you understand the theory. >> In 5 years today's ML/AI PhDs would be in the same peculiar position as PhDs of genetic sequencing. I think what you're saying is that in 5 years, people knowing Tensor Flow or Torch will be a dime a dozen. Perhaps- but someone will still need to figure out some way to acquire new knowledge. Also, it bears repeating that AI is not just statistical machine learning and statistical machine learning itself is not just deep learning. AI is a broad field with communities focused on many different subjects. Just as an example you can find information on, easily, try probabilistic programming.
yequalsx 9y ago
It may appear obvious to you but you don’t really know that there is a current AI bubble. There’s an economics adage that seems apt: Economists have predicted 15 of the last 10 recessions.
eli_gottlieb 9y ago
>3. Lots of money chasing a few ideas. That's the real problem. There are only so many fundamental things you can do with deep convnets as function inducers. There's a lot of money to be made by applying those to a thousand different things, but even that has to taper off at some point.
- tMcGrath 9y ago
  I suppose it's limited by the size of 'the set of things you can do with functions'. This seems like a large enough set that I wouldn't be worried about hitting its limits too soon...
  - eli_gottlieb 9y ago
    More like "the set of things you can do with functions, where you've got a strong prior on the functions being things like spacial mappings with certain invariances." So basically more like, "functions for vision problems."
    - tMcGrath 9y ago
      You are absolutely right that a lot of work to date (especially in applications and metrics-driven research) has focused on 'functions for vision problems'. But this is far from the only idea - that's the point I'm trying to make. For instance what about learning value functions for reinforcement learning (e.g. AlphaGo)? Or natural language processing? These are definitely not vision problems, or if you believe that they are then 'functions for vision problems' is actually a pretty huge class! The universal approximation theorem backs up my claim [0] - we can approximate arbitrary functions with neural networks. I think this theorem is overemphasised in practice: we don't generally want to approximate arbitrary functions, we _want_ to encode specific prior information into the function we approximate, as you rightly say. But that doesn't mean that we have to do so, or that we only have one idea about what functions to encode, or even how to encode them. [0] https://en.wikipedia.org/wiki/Universal_approximation_theorem https://en.wikipedia.org/wiki/Universal_approximation_theore...
msla 9y ago
> Large numbers of people getting poached from academic programs by industry. You say this like someone who thinks academia deserves those people and industry is "stealing" them.
nopinsight 9y ago
The market for autonomous vehicles (land, air, water) alone could become larger than the entire IT industry in 2000. When you include robots for industry (e.g. Sewbots, Warehouse robots, ...) and household (e.g. Aibo, Amazon Echo, ...) it is clear that the markets are potentially much larger than their current sizes and will grow with improvements in AI. Recent papers in AI and Deep Learning have shown that it is possible to train robots to adapt to unpredictable environment and human interactions. This will realize many new potential applications in the physical space over the next decade. Although the fundamentals are the same, the variations are legion and could become highly complex as the environment dictates. It takes a few years at least to become an expert in the field with experience in practical applications. Not everyone possesses a cognitive toolkit necessary to become one either. There will not be an oversupply of experts in AI and ML within the next five or even ten years.
deleted 9y ago
[deleted]
bobdole1234 9y ago
Investors were left holding the bag. The rest of us just kept working and earning.

notyourday 9y ago

I think you are missing the scale mentioned in the article: The industry is paying five times what universities are paying. Not Fifty percent. Not double. Five times. Professors/administrators whining about it are pure scum unless they themselves are making one fifth what they would be making in a different university that offered them a job purely because they love this specific college doing the specific research or teaching. What is really pissing off these professors is that with their worker bees gone they themselves would need to do the work - something they are not quite used to.

sevensor 9y ago
> I think you are missing the scale mentioned in the article: > The industry is paying five times what universities are paying. Not Fifty percent. Not double. Five times. Nope, not missing that. You don't think this happened in 1999-2000? I agree with you that grad students are ruthlessly exploited. I've seen that firsthand. I routinely discourage people from doing a Ph.D. for that reason. And if you're not too far along, taking a terminal master's and leaving for industry is a solid option, especially because this AI bubble won't last forever. But if, like the one student in TFA, you have just one year left, quitting grad school is not a great idea. Now you have, at best, an awkwardly long stretch of grad school on your CV with a master's degree on the end of it, and at worst, no degree at all to show for your pains. At that point, just suck it up and finish the degree.
- notyourday 9y ago
  > Nope, not missing that. You don't think this happened in 1999-2000? I do. I'm yet to see people who jumped away from academic CS tracks at the time being upset about their decisions. I do know of dozens degreed people in their late thirties reporting to C students while making quarter of the salary of the same C students.
  - YeGoblynQueenne 9y ago
    CS and AI are different subjects. As a CS dropout, your job is to write code. You can get quite competent at that without a higher education course. You can't do that with AI. The amount of material you need to get on top of is really something else, especially if you want to be able to understand the foundations of what you're doing- which stretch back several decades before statistical machine learning, popular as it is today.
    - notyourday 9y ago
      It is very naive to think that AI programs in most of universities is anything other than writing code and pretty lousy one. Do you know that it was possible to get a PhD in genetic sequencing not a very long time ago from very serious schools? That's right, what today can be done by a random student or a random outsourcing shop in a third world country used to be a PhD worthy specialty. In 5 years today's ML/AI PhDs would be in the same peculiar position as PhDs of genetic sequencing.
      - YeGoblynQueenne 9y ago
        >> It is very naive to think that AI programs in most of universities is anything other than writing code and pretty lousy one. Are you speaking from experience? Because my own experience from my degree and Master's AI courses and lectures is completely different to what you describe. Particularly during my Master's, code was only incidental; the meat and potatoes of each and every lecture was theory. It depends of course on the quality of the teaching in a given institution, but, for instance, you can find here the lecture notes from Oxford's Deep NLP course: https://github.com/oxford-cs-deepnlp-2017/lectures https://github.com/oxford-cs-deepnlp-2017/lectures As you will see, it's anything but just code, or lousy such. Of course, that's Oxford, their teaching is world-class- but most AI courses I've found on the internet are similar in scope. Really, it's pointless trying to teach AI with "just code" - because in AI you can't really code much, unless you understand the theory. >> In 5 years today's ML/AI PhDs would be in the same peculiar position as PhDs of genetic sequencing. I think what you're saying is that in 5 years, people knowing Tensor Flow or Torch will be a dime a dozen. Perhaps- but someone will still need to figure out some way to acquire new knowledge. Also, it bears repeating that AI is not just statistical machine learning and statistical machine learning itself is not just deep learning. AI is a broad field with communities focused on many different subjects. Just as an example you can find information on, easily, try probabilistic programming.

yequalsx 9y ago

It may appear obvious to you but you don’t really know that there is a current AI bubble. There’s an economics adage that seems apt: Economists have predicted 15 of the last 10 recessions.

eli_gottlieb 9y ago

>3. Lots of money chasing a few ideas. That's the real problem. There are only so many fundamental things you can do with deep convnets as function inducers. There's a lot of money to be made by applying those to a thousand different things, but even that has to taper off at some point.

tMcGrath 9y ago
I suppose it's limited by the size of 'the set of things you can do with functions'. This seems like a large enough set that I wouldn't be worried about hitting its limits too soon...
- eli_gottlieb 9y ago
  More like "the set of things you can do with functions, where you've got a strong prior on the functions being things like spacial mappings with certain invariances." So basically more like, "functions for vision problems."
  - tMcGrath 9y ago
    You are absolutely right that a lot of work to date (especially in applications and metrics-driven research) has focused on 'functions for vision problems'. But this is far from the only idea - that's the point I'm trying to make. For instance what about learning value functions for reinforcement learning (e.g. AlphaGo)? Or natural language processing? These are definitely not vision problems, or if you believe that they are then 'functions for vision problems' is actually a pretty huge class! The universal approximation theorem backs up my claim [0] - we can approximate arbitrary functions with neural networks. I think this theorem is overemphasised in practice: we don't generally want to approximate arbitrary functions, we _want_ to encode specific prior information into the function we approximate, as you rightly say. But that doesn't mean that we have to do so, or that we only have one idea about what functions to encode, or even how to encode them. [0] https://en.wikipedia.org/wiki/Universal_approximation_theorem https://en.wikipedia.org/wiki/Universal_approximation_theore...

msla 9y ago

> Large numbers of people getting poached from academic programs by industry. You say this like someone who thinks academia deserves those people and industry is "stealing" them.

nopinsight 9y ago

The market for autonomous vehicles (land, air, water) alone could become larger than the entire IT industry in 2000. When you include robots for industry (e.g. Sewbots, Warehouse robots, ...) and household (e.g. Aibo, Amazon Echo, ...) it is clear that the markets are potentially much larger than their current sizes and will grow with improvements in AI. Recent papers in AI and Deep Learning have shown that it is possible to train robots to adapt to unpredictable environment and human interactions. This will realize many new potential applications in the physical space over the next decade. Although the fundamentals are the same, the variations are legion and could become highly complex as the environment dictates. It takes a few years at least to become an expert in the field with experience in practical applications. Not everyone possesses a cognitive toolkit necessary to become one either. There will not be an oversupply of experts in AI and ML within the next five or even ten years.

deleted 9y ago

[deleted]

bobdole1234 9y ago

Investors were left holding the bag. The rest of us just kept working and earning.