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I think your response is very narrow in defining what a "really cool problem" is. If in the 1970s you imagined that the cool problem in ECE/CS was "building eve

I think your response is very narrow in defining what a "really cool problem" is. If in the 1970s you imagined that the cool problem in ECE/CS was "building ever-more-powerful computer processors" then you basically got smoked by Intel and other industry labs. But there's a different way to look at it. Because industry went off and solved the now-"boring" problem of building ever-more-powerful computer processors, the people left behind in academia got to invent machine learning, public-key cryptography, modern coding theory, distributed systems... and so on. What makes academic research valuable is not the freedom of "I get to plan my own day", but the freedom of "I get to work on the weird problems that industry doesn't even realize are problems."

oldsecondhand 4y ago
Public key cryptography was invented in the 70s, commercialized in the 80s. In machine learning we already had back-propagation in the 1970, but had to wait another 40 years for Intel and Nvidia to create ever more powerful processors to tackle useful problems.
numbers_guy 4y ago
OK, I agree with you. But this sort of struggle that you highlight is common in many aspects of research. As a researcher you are conflicted between your curiosity instinct to explore new paths, and your desire to also have big results. Ideally you would have a balance between the two, although you can definitely make a career out of only one.
TeMPOraL 4y ago
The "cool problem" is cracking the mystery of intelligence, of consciousness. Building a general AI. Until recently, this was mostly an academic pursuit, and people expected it to take decades or more. But now, suddenly, it's highly likely the problem will be cracked within a decade, and it will be done by corporate R&D teams, by means of scaling up the transformer architectures. So I get how they feel - one of the coolest problems ever instantly went from being something anyone could hope to contribute to, to something almost no one can. You can't match the compute to do the things OpenAI & Google & friends do. And if you happen to stumble on something related that can be explored without access to obscene amounts of capital, guess what, the corporate research teams will notice it, and they can do it better than you, and then they can the idea much further than you ever could.
- phkahler 4y ago
  >> But now, suddenly, it's highly likely the problem will be cracked within a decade, and it will be done by corporate R&D teams, by means of scaling up the transformer architectures. I'll counter that. In the end we need AI that can do training AND inference on edge devices out in the real world. A good (and possibly profitable) example would be robotic pets that can learn (even to understand words) and interact with their owners like real animals, but don't need to go to the vet or eat and poop. Big companies relying on huge compute resources are not even aiming at this type of thing. They're too busy using their "scale" to even bother looking at smaller but interesting methods or solutions.
  - TeMPOraL 4y ago
    That's fair, and I'd agree in any other case, but This Time Is Different™. If (when) someone cracks this problem, they will be able to apply compute to every other problem. Some marketer convinces some C-suite that the world needs smart robotic pets? Why, here's access to ChatGPT-8, make it so. Just don't burn more than half a billion dollars in a week on it. That's of course if, by this point, we aren't in a middle of a futile scramble to avoid getting extincted by ChatGPT-7 that someone left in self-play mode and forgot to turn off before going on vacation. Point being, general AI is general. Even at extreme expenditure of resources, the closer the corporations get to it, the more problems they can put it to - including, eventually, the problem of optimizing itself. Already today people are using current-gen models to assist in developing next-gen models; this trend will only continue, until at some point you'll be able to let the model self-improve, mostly unsupervised. I imagine the compute costs per AI value delivered will drop like a stone then.
- matthewdgreen 4y ago
  Has industry "cracked the mystery of consciousness"? Has anyone at OpenAI even made a plausible start at explaining what the hell is going on inside these LLMs that enables them to produce such human-like comprehension? To me these are the really exciting questions. From what I can tell, the team at OpenAI (following after Google/Deepmind etc.) are simply mashing the pedal to the floor to get bigger and better models from their existing techniques, and then tuning the resulting black box to make it produce more "useful" answers. And that's fine! That's precisely what an industry lab is expected to do: they have the resources to do the training and the need to get products in front of paying customers as quickly as possible to justify it. And frankly with top AI engineers getting paid millions of dollars and Google/Meta tight behind you, emphasizing results is the most viable strategy. If "turn the needle on the box to the right" is giving you good answers, why would you waste a $1-$5m-salary engineer on academic questions like "why does the box do that?" And yet, asking questions like "why does the box do that?" is the reason technology didn't stop at the steam engine. I suspect that finding the answer to those questions won't immediately sell enterprise licenses, but will be very important. And the answers will probably fall to someone who's making $30k/year in a graduate program. ETA: Of course, it may turn out that "turn the needle on the box to the right" is enough to obsolete all human researchers, in which case I'll be wrong about this. But it'll hardly matter in that case ;)

oldsecondhand 4y ago

Public key cryptography was invented in the 70s, commercialized in the 80s. In machine learning we already had back-propagation in the 1970, but had to wait another 40 years for Intel and Nvidia to create ever more powerful processors to tackle useful problems.

numbers_guy 4y ago

OK, I agree with you. But this sort of struggle that you highlight is common in many aspects of research. As a researcher you are conflicted between your curiosity instinct to explore new paths, and your desire to also have big results. Ideally you would have a balance between the two, although you can definitely make a career out of only one.

TeMPOraL 4y ago

The "cool problem" is cracking the mystery of intelligence, of consciousness. Building a general AI. Until recently, this was mostly an academic pursuit, and people expected it to take decades or more. But now, suddenly, it's highly likely the problem will be cracked within a decade, and it will be done by corporate R&D teams, by means of scaling up the transformer architectures. So I get how they feel - one of the coolest problems ever instantly went from being something anyone could hope to contribute to, to something almost no one can. You can't match the compute to do the things OpenAI & Google & friends do. And if you happen to stumble on something related that can be explored without access to obscene amounts of capital, guess what, the corporate research teams will notice it, and they can do it better than you, and then they can the idea much further than you ever could.

phkahler 4y ago
>> But now, suddenly, it's highly likely the problem will be cracked within a decade, and it will be done by corporate R&D teams, by means of scaling up the transformer architectures. I'll counter that. In the end we need AI that can do training AND inference on edge devices out in the real world. A good (and possibly profitable) example would be robotic pets that can learn (even to understand words) and interact with their owners like real animals, but don't need to go to the vet or eat and poop. Big companies relying on huge compute resources are not even aiming at this type of thing. They're too busy using their "scale" to even bother looking at smaller but interesting methods or solutions.
- TeMPOraL 4y ago
  That's fair, and I'd agree in any other case, but This Time Is Different™. If (when) someone cracks this problem, they will be able to apply compute to every other problem. Some marketer convinces some C-suite that the world needs smart robotic pets? Why, here's access to ChatGPT-8, make it so. Just don't burn more than half a billion dollars in a week on it. That's of course if, by this point, we aren't in a middle of a futile scramble to avoid getting extincted by ChatGPT-7 that someone left in self-play mode and forgot to turn off before going on vacation. Point being, general AI is general. Even at extreme expenditure of resources, the closer the corporations get to it, the more problems they can put it to - including, eventually, the problem of optimizing itself. Already today people are using current-gen models to assist in developing next-gen models; this trend will only continue, until at some point you'll be able to let the model self-improve, mostly unsupervised. I imagine the compute costs per AI value delivered will drop like a stone then.
matthewdgreen 4y ago
Has industry "cracked the mystery of consciousness"? Has anyone at OpenAI even made a plausible start at explaining what the hell is going on inside these LLMs that enables them to produce such human-like comprehension? To me these are the really exciting questions. From what I can tell, the team at OpenAI (following after Google/Deepmind etc.) are simply mashing the pedal to the floor to get bigger and better models from their existing techniques, and then tuning the resulting black box to make it produce more "useful" answers. And that's fine! That's precisely what an industry lab is expected to do: they have the resources to do the training and the need to get products in front of paying customers as quickly as possible to justify it. And frankly with top AI engineers getting paid millions of dollars and Google/Meta tight behind you, emphasizing results is the most viable strategy. If "turn the needle on the box to the right" is giving you good answers, why would you waste a $1-$5m-salary engineer on academic questions like "why does the box do that?" And yet, asking questions like "why does the box do that?" is the reason technology didn't stop at the steam engine. I suspect that finding the answer to those questions won't immediately sell enterprise licenses, but will be very important. And the answers will probably fall to someone who's making $30k/year in a graduate program. ETA: Of course, it may turn out that "turn the needle on the box to the right" is enough to obsolete all human researchers, in which case I'll be wrong about this. But it'll hardly matter in that case ;)