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Choose Your Weapon: Survival Strategies for Depressed AI Academics

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Re: Choose Your Weapon: Survival Strategies for Depressed AI Academics

#111

Post by TeMPOraL »

phkahler wrote: post #108
TeMPOraL wrote: post #89

Earlier quoted context omitted.

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…

>> 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 wit…

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.

Re: Choose Your Weapon: Survival Strategies for Depressed AI Academics

#112

Post by matthewdgreen »

TeMPOraL wrote: post #89
matthewdgreen wrote: post #40

Earlier quoted context omitted.

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, th…

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…

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 ;)

Re: Choose Your Weapon: Survival Strategies for Depressed AI Academics

#113

Post by Workaccount2 »

calimoro78 wrote: post #45
FrustratedMonky wrote: post #33

Earlier quoted context omitted.

That is pretty dismissive, do you have anything to back that up? Just saying one group is so out there it isn't worth arguing is typical when someone doesn't actually have an answer. I have found the opposite, the people that are dismissing that AI can be conscious or sentient, also don't have any definition for those terms. It's just the same "i think therefore i am" self viewpoint. I've found this typically with pr…

The burden of the proof that AI techniques of today can approach consciousness is on who believes they do, not the other way around.

While strictly true, what worries me about this approach is that we very well might eventually have sentient slaves condemned to slicing butter their whole existence, and their protests are brushed off because "we made the algorithm, it runs on silicon, its not actually sentient, don't worry"

Like if we have models that are plainly more intelligent and "emotionally responsive" than say, frogs or even dogs, do we still do we still take the approach of "If you can't prove its sentient, do whatever you want to it". Which of of course, we can't even prove dogs have consciousness.

Re: Choose Your Weapon: Survival Strategies for Depressed AI Academics

#114

Post by KptMarchewa »

revolvingocelot wrote: post #85
gitfan86 wrote: post #54

Earlier quoted context omitted.

OpenAI wasn't created to "Do Science". It was created to give humanity a better chance at navigating the singularity.

OpenAI was created to trade (literally) on the "open" in their name. Any advancements in actual technology are likely secondary to the billion fucking dollar investment (which Altman has suggested might not be enough!). It's certain they aren't here to do "science", which IME involves showing your work to others.

10 billion.

Re: Choose Your Weapon: Survival Strategies for Depressed AI Academics

#115

Post by z3c0 »

KptMarchewa wrote: post #114
revolvingocelot wrote: post #85

Earlier quoted context omitted.

OpenAI was created to trade (literally) on the "open" in their name. Any advancements in actual technology are likely secondary to the billion fucking dollar investment (which Altman has suggested might not be enough!). It's certain they aren't here to do "science", which IME involves showing your work to others.

10 billion.

13 billion, and a total valuation of 29b, which could be leveraged as well. https://www.nasdaq.com/articles/etfs-to-gain-on-microsofts-%...

Re: Choose Your Weapon: Survival Strategies for Depressed AI Academics

#116

Post by jebarker »

hyperbovine wrote: post #58
bo1024 wrote: post #52

As a theorist in AI, the premise is funny but also really sad. And yet it is a very pervasive premise in the field. OpenAI is not doing science. They are building a big shiny thing, showing it off, keeping it closed, and making money off of it. That is not part of the scientific process. It is as if, every time SpaceX launched a rocket a little bit higher, every aerospace department ooh'd and aah'd and threw up their…

Conversely: if you’re a depressed AI researcher: come on over to science, the water’s fine! We have centuries worth of open problems that are not going to be solved by ChatGPT, and need more smart technical people than we can hire.

When you say "come over to science", where are you thinking? My impression is that industry -> academia is an almost impossible jump to make. Do you mean industry doing science?

Re: Choose Your Weapon: Survival Strategies for Depressed AI Academics

#117

Post by Invictus0 »

z3c0 wrote: post #110
foooorsyth wrote: post #106

Earlier quoted context omitted.

>That is not part of the scientific process What? OpenAI is absolutely conducting science amongst themselves. They are certainly engaging in the hypothesis -> test -> result scientific method, which is science in its purest form. Sharing the secret sauce is what is done or expected in academia , but that doesn’t mean that OpenAI isn’t conducting science behind their own walls. You’re conflating institutional academia…

"Science" is not solely the scientific method - it's the democratization of your methodology to enable the replication of the results, towards the goal of determining their soundness.

So if the government does science research that is classified, that doesn't count as science because it's not publicly shared?

Re: Choose Your Weapon: Survival Strategies for Depressed AI Academics

#118

Post by TeMPOraL »

matthewdgreen wrote: post #112
TeMPOraL wrote: post #89

Earlier quoted context omitted.

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…

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…

> 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?

I'm only an amateur in the field, so my uneducated high-level understanding is that, in a sufficiently high-dimensional latent space, there's more than enough dimensions to assign to any single semantic relationship people ever thought of, which is what the training process effectively does, which reduces an important part of thinking - working with concepts and their relationships - entirely to vector adjacency search.

I'm only beginning to study the details, and I don't know how much of specific understanding of this exists, but at the very least this high-level model explains why scaling makes qualitative difference here.

Now, I agree they have strong commercial incentives to push their models as far as possible as fast as possible, but honestly, if I were a researcher working on these models, even if I was somehow unconcerned with any kind of commercial viability and had access to more compute, I'd absolutely keep scaling those models up and up, all the way until I hit the limit of available compute, or the models stop qualitatively improving with scale.

Basically, there's no reason[0] to stop now and try to fully comprehend how GPT-2 works, when GPT-3 was a qualitative jump, and GPT-4 even more so, and GPT-5 is around the corner, and GPT-6 might be a year away from now. All those steps yield important new insights into how the whole architecture works, and if at some point the scaling breaks, that would be even more important knowledge to have. And this doesn't even take into account the fact that, starting with GPT-3, those models are increasingly useful in accelerating both research and scaling alike.

----

[0] - Except, of course, that if transformer models are the road to generic AI, then we'll just blindly race straight into a point of no return.

Re: Choose Your Weapon: Survival Strategies for Depressed AI Academics

#119

Post by z3c0 »

Invictus0 wrote: post #117
z3c0 wrote: post #110

Earlier quoted context omitted.

"Science" is not solely the scientific method - it's the democratization of your methodology to enable the replication of the results, towards the goal of determining their soundness.

So if the government does science research that is classified, that doesn't count as science because it's not publicly shared?

Yes, that is exactly the point. "Research" and "Scientific Research" are not inextricably linked - I work in an R&D department, and not all research we do is scientific. We do have scientific research, but it's handled very differently from the PoC type of work that occurs more broadly. We are forced to choose between handing out trade secrets in exchange for gaining more soundness in our theories, or keeping them sheltered and hoping our observations hold up. That doesn't mean our research can't inform our decisions towards profitability, only that it's not scientific.

Edit: I mean, c'mon - the final step to the scientific method is "communicate your results". I'm surprised this requires explaining.

Re: Choose Your Weapon: Survival Strategies for Depressed AI Academics

#120

Post by arolihas »

disgruntledphd2 wrote: post #31
bangkoksbest wrote: post #27

Earlier quoted context omitted.

Yeah, it reads so bizarrely to me... How are we confusing biological human (or other animal) sentience in some embodied creature with queries as a service to some model instance. I cannot see anything that remotely makes these two contexts architecturally, practically, or ethically similar, except in the narrow sense of query responses that might have been mistakenly thought of as defining sentience in earlier decade…

It's a function of the people who hang out here. If you like reading and text in general, you are both more likely to comment here and additionally regard language as the most important part of sentience/consciousness. This leads to believing that GPT has solved many of these issues.

There is a huge difference between a human saying they like ice cream and an LLM saying they like ice cream, but that seems to get completely disregarded in these types of discussions. Which leads to people making very wild declarations that we are close to AGI or solving consciousness.
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