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DeepMind: A Generalist Agent

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Re: DeepMind: A Generalist Agent

#141

Post by stnmtn »

hgomersall wrote: post #121
2bitencryption wrote: post #10

given that the same model can both: 1. tell me about a cat (given a prompt such as "describe a cat to me") 2. recognize a cat in a photo, and describe the cat in the photo does the model understand that a cat that it sees in an image is related to a cat that it can describe in natural language? As in, are these two tasks (captioning an image and replying to a natural language prompt) so distinct that a "cat" in an im…

I think the critical question here is does it have a concept of cattyness? This to me is the crux of a AGI: can it generalise concepts across domains? Moreover, can it relate non-cat but cat-like objects to it's concept of cattyness? As in, this is like a cat because it has whiskers and pointy ears, but is not like a cat because all cats I know about are bigger than 10cm long. It also doesn't have much in the way of…

I don't disagree with you, and I think that what you're saying is critical; but it feels more and more like we are shifting the goalposts. 5 years ago; recognizing a cat and describing a cat in an image would be incredible impressive. Now, the demands we are making and the expectations we keep pushing feel like they are growing as if we are running away from accepting that this might actually be the start of AGI.

Re: DeepMind: A Generalist Agent

#142

Post by phreeza »

abeppu wrote: post #130
axg11 wrote: post #22

Slowly but surely we're moving towards general AI. There is a marked split across general society and even ML/AI specialists between those who think that we can achieve AGI using current methods and those who dismiss the possibility. This has always been the case, but what is remarkable about today's environment is that researchers keep making progress contrary to the doubter's predictions. Each time this happens, th…

I think a key problem is our understanding of the quality of an ML system is tied to a task. Our mechanism of training is tied to a loss, or some optimization problem. The design, training, and evaluation of these systems is dependent on an externally provided definition of "correct". But this seems structurally different from how we or even less intelligent animals operate. DALL-E may make "better" art than most hum…

> Methodologically, how do we create agents that aren't just good at several tasks, but make up their own tasks, "play", develop changing preferences for different activities (I think this is more than just "exploration"), etc? Even a dog sometimes wants to play with a toy, sometimes wants to run and chase, sometimes wants to be warm inside. We don't "score" how well it plays with a toy, but we take its desire to play as a signs of greater intelligence than, e.g. a pet iguana which doesn't seem to have such a desire.

This doesn't sound like it would be so hard to do if you have an agent or ensemble of agents that can already do it. What you probably really want is this behavior to somehow emerge from simple ground rules, which is probably a lot harder.

Re: DeepMind: A Generalist Agent

#143

Post by Hellicio »

ajmurmann wrote: post #32
axg11 wrote: post #22

Slowly but surely we're moving towards general AI. There is a marked split across general society and even ML/AI specialists between those who think that we can achieve AGI using current methods and those who dismiss the possibility. This has always been the case, but what is remarkable about today's environment is that researchers keep making progress contrary to the doubter's predictions. Each time this happens, th…

> a world where AI is indistinguishable, or far superior, to human intelligence I think the part about being "indistinguishable from human intelligence" is potentially a intellectual trap. We might get to it being far superior while still underperforming at some tasks or behaving in ways that don't make sense to a human mind. An AI mind will highly likely work completely differently from humans and communicating with…

Only if you don't assume that consciousness comes from complexity.

The physical ability of an animal to see better/different/faster doens't matter as we do not compare / seperate us from animals by those factors. We seperate us by consciousness and it might get harder and harder to shut down a PC on which a ML model is running which begs you not to do it.

Re: DeepMind: A Generalist Agent

#144

Post by underdeserver »

stnmtn wrote: post #141
hgomersall wrote: post #121

Earlier quoted context omitted.

I think the critical question here is does it have a concept of cattyness? This to me is the crux of a AGI: can it generalise concepts across domains? Moreover, can it relate non-cat but cat-like objects to it's concept of cattyness? As in, this is like a cat because it has whiskers and pointy ears, but is not like a cat because all cats I know about are bigger than 10cm long. It also doesn't have much in the way of…

I don't disagree with you, and I think that what you're saying is critical; but it feels more and more like we are shifting the goalposts. 5 years ago; recognizing a cat and describing a cat in an image would be incredible impressive. Now, the demands we are making and the expectations we keep pushing feel like they are growing as if we are running away from accepting that this might actually be the start of AGI.

Of course we are. This is what technological progress is.

Re: DeepMind: A Generalist Agent

#145

Post by momojo »

Barrin92 wrote: post #138
axg11 wrote: post #22

Slowly but surely we're moving towards general AI. There is a marked split across general society and even ML/AI specialists between those who think that we can achieve AGI using current methods and those who dismiss the possibility. This has always been the case, but what is remarkable about today's environment is that researchers keep making progress contrary to the doubter's predictions. Each time this happens, th…

I don't think many people were making the claims that AI can't solve any scientific problems or can't perform creative work at all. That sounds like a big strawman. Before ML was getting big there were AI systems that created art. What sceptics have actually been saying is that the first step fallacy still applies. Getting 20% to a goal is no indication at all that you're getting 100% to your goal, or as its often pu…

Self driving cars come to mind as well. I remember 2015, when my friends would debate the self-driving Trolley problem over lunch. We were worried if society was ready for an owner-less car market; I seriously wondered if I would have to have a license in the future, or if I should keep it just in case.

Re: DeepMind: A Generalist Agent

#146

Post by Hellicio »

mhitza wrote: post #50
axg11 wrote: post #22

Slowly but surely we're moving towards general AI. There is a marked split across general society and even ML/AI specialists between those who think that we can achieve AGI using current methods and those who dismiss the possibility. This has always been the case, but what is remarkable about today's environment is that researchers keep making progress contrary to the doubter's predictions. Each time this happens, th…

I'm skeptical because we are building black boxes. How do you fix something you can't reason about? These billion parameter boxes are outside the reach of your everyday developers. In terms of cost of propping up the infrastructure makes them tenable only for megacorps. Most of us aren't moving goal posts, but are very much skeptic at the things we are being oversold on. I personally think we are still far away from…

They are blackboxes for the normal user the same way as a smartphone is a blackbox.

Non of my close family understands the technical detail from bits to an image.

There are also plenty of expert systems were plenty of developers see them as blackboxes. Even normal databases and query optimizations are often enough blackboxes.

As long as those systems perform better as existing systems, thats fine by me. Take auto pilot: As long as we can show/proofe good enough that it drives better than an untrained 18 year old or 80 year old (to take extremes, i'm actually quite an avg driver myself), all is good.

And one very very big factor in my point of view: We never ever had the software equivilent of learning. When you look at Nvidia Omniverse, we are able to simulate those real life things so well, so often in such different scenarios, that we are already out of the loop.

I can't drive 10 Million KM in my lifetime (i think). The cars from Google and Tesla already did that.

Yesterday at the google io, they showed the big 50x Billion parameter network and for google this is the perfect excuse to gather and put all of this data they always had into something they now can monetarize. No one can ask google for money now like the press did (Same with Dall-E 2)

I think its much more critical that we enforce/force corporations to make/keep those models free for everyone to use. unfortunate i have no clue how much hardware you need to run those huge models.

Re: DeepMind: A Generalist Agent

#147

Post by yldedly »

Barrin92 wrote: post #138
axg11 wrote: post #22

Slowly but surely we're moving towards general AI. There is a marked split across general society and even ML/AI specialists between those who think that we can achieve AGI using current methods and those who dismiss the possibility. This has always been the case, but what is remarkable about today's environment is that researchers keep making progress contrary to the doubter's predictions. Each time this happens, th…

I don't think many people were making the claims that AI can't solve any scientific problems or can't perform creative work at all. That sounds like a big strawman. Before ML was getting big there were AI systems that created art. What sceptics have actually been saying is that the first step fallacy still applies. Getting 20% to a goal is no indication at all that you're getting 100% to your goal, or as its often pu…

The notions that are crucial for distinguishing between intelligence and what large NNs are doing, are generalization and abstraction. I'm impressed with DALL-E's ability to connect words to images and exploit the compositionality of language to model the compositionality of the physical world. Gato seems to be using the same trick for more domains.

But that's riding on human-created abstractions, rather than creating abstractions. In terms of practical consequences, that means these systems won't learn new things unless humans learn then first and provide ample training data.

But someday we will develop systems that can learn their own abstractions, and teach themselves anything. Aligning those systems is imperative.

Re: DeepMind: A Generalist Agent

#148

Post by Jensson »

VikingCoder wrote: post #133
Jensson wrote: post #116

Earlier quoted context omitted.

> What set of tasks would you, right now, consider to be demonstrative of "intelligence" if a computer can do them? Be able to apply for, get and hold a remote job and get paid for a year without anyone noticing, or something equivalent to that. I said this many years ago and it still hasn't happened. The people who are moving the goalposts aren't the sceptics, it is the optimists who always move the goalposts to exa…

Why must it apply for a job, rather than just DO a job? But maybe some combination of this [1] and this [2] would do it. If you want to know about a computer actually DOING a remote job for a year without anyone noticing, I'll conclude with many links [a-i]. [1] : https://thisresumedoesnotexist.com/ (Sorry for the bad certificates.) [2] : https://www.businessinsider.com/tiktoker-wrote-code-spam-kel... [a] : An origin…

> Why must it apply for a job, rather than just DO a job?

Because being able to manage a business relationship is a part of the job. If you could show an AI which got a job, then wrote a simple script that automated the AI's job and then coasted for a year that would be fine, but your links are just humans doing that, I want an AI that can do that to consider it intelligent.

But thanks for demonstrating so clearly how AI proponents are moving goalposts backward to make them easy to meet.

Re: DeepMind: A Generalist Agent

#149

Post by Veedrac »

hgomersall wrote: post #121
2bitencryption wrote: post #10

given that the same model can both: 1. tell me about a cat (given a prompt such as "describe a cat to me") 2. recognize a cat in a photo, and describe the cat in the photo does the model understand that a cat that it sees in an image is related to a cat that it can describe in natural language? As in, are these two tasks (captioning an image and replying to a natural language prompt) so distinct that a "cat" in an im…

I think the critical question here is does it have a concept of cattyness? This to me is the crux of a AGI: can it generalise concepts across domains? Moreover, can it relate non-cat but cat-like objects to it's concept of cattyness? As in, this is like a cat because it has whiskers and pointy ears, but is not like a cat because all cats I know about are bigger than 10cm long. It also doesn't have much in the way of…

If you've seen much DALL-E 2 output, it's pretty obvious they can learn such things.

Example: https://old.reddit.com/r/dalle2/comments/u9awwt/pencil_sharp....

Re: DeepMind: A Generalist Agent

#150

Post by gonehome »

gurkendoktor wrote: post #140
gonehome wrote: post #27

Earlier quoted context omitted.

The closer we get, the more alarming the alignment problem becomes. https://intelligence.org/2017/10/13/fire-alarm/ Even people like Eric Schmidt seem to downplay it (in a recent podcast with Sam Harris) - just saying “smart people will turn it off”. If it thinks faster than us and has goals not aligned with us this is unlikely to be possible. If we’re lucky building it will have some easier to limit constraint like…

To be fair, ants have not created humanity. I don't think it's inconceivable for a friendly AI to exist that "enjoys" protecting us in the way a friendly god might. And given that we have AI (well, language models...) that can explain jokes before we have AI that can drive cars, AI might be better at understanding our motives than the stereotypical paperclip maximizer. However, all of this is moot if the team develop…

Yeah, I'm not arguing alignment is not possible - but that we don't know how to do it and it's really important that we figure it out before we figure out AGI (which seems unlikely).

The ant example is just to try to illustrate the spectrum of intelligence in a way more people may understand (rather than just thinking of smart person and dumb person as the entirety of the spectrum). In the case of a true self-improving AGI the delta is probably much larger than that between an ant and a human, but at least the example makes more of the point (at least that was my goal).

The other common mistake is people think intelligence implies human-like thinking or goals, but this is just false. A lot of bad arguments from laypeople tend to be related to this because they just haven't read a lot about the problem.

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