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AGI is far from inevitable

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Re: AGI is far from inevitable

#151

Post by kylecazar »

ryandvm wrote: post #112
Gehinnn wrote: post #6

Basically the linked article argues like this: > That’s because cognition, or the ability to observe, learn and gain new insight, is incredibly hard to replicate through AI on the scale that it occurs in the human brain. (no other more substantial arguments were given) I'm also very skeptical on seeing AGI soon, but LLMs do solve problems that people thought were extremely difficult to solve ten years ago.

> but LLMs do solve problems that people thought were extremely difficult to solve ten years ago Agreed. I would have laughed you out of the room 5 years ago if you told me AI's would be writing code or carrying on coherent discussions on pretty complex topics in 2024. As far as I'm concerned, all bets are off after the collective jaw drop that the entire software engineering industry did when we saw GPT4 released. W…

Are capabilities truly 'emerging'? Or are we just observing new competencies/applications we hadn't previously considered.

Re: AGI is far from inevitable

#152

Post by [deleted] »

riku_iki wrote: post #148
famouswaffles wrote: post #133

Earlier quoted context omitted.

>How can you not understand the difference between "humans are not absolutely perfect or reliable at this task" and "LLMs by their very nature cannot perform this task"? Because anyone who has said nonsense like "LLMs by their very nature cannot do x" and waited a few years has been wrong. That's why GPT-3 and 4 shocked the research world in the first place. People just have their pre-conceptions about how they think…

> Because anyone who has said nonsense like "LLMs by their very nature cannot do x" and waited a few years has been wrong. That's why GPT-3 and 4 shocked the research world in the first place. there are some benchmarks which show fundamental inability of LLM perform certain tasks which human can, for example add 100 digits numbers.

[deleted]

Re: AGI is far from inevitable

#153

Post by famouswaffles »

riku_iki wrote: post #148
famouswaffles wrote: post #133

Earlier quoted context omitted.

>How can you not understand the difference between "humans are not absolutely perfect or reliable at this task" and "LLMs by their very nature cannot perform this task"? Because anyone who has said nonsense like "LLMs by their very nature cannot do x" and waited a few years has been wrong. That's why GPT-3 and 4 shocked the research world in the first place. People just have their pre-conceptions about how they think…

> Because anyone who has said nonsense like "LLMs by their very nature cannot do x" and waited a few years has been wrong. That's why GPT-3 and 4 shocked the research world in the first place. there are some benchmarks which show fundamental inability of LLM perform certain tasks which human can, for example add 100 digits numbers.

> there are some benchmarks which show fundamental inability of LLM perform certain tasks which human can, for example add 100 digits numbers.

fundamental inability ? No. Current Sota LLM (4o, claude, gemini) woes with arithmetic is not a transformer weakness never mind a large language modelling one. Those benchmarks show that those particular models have problems with accuracy on that many digits, not that LLMs fundamentally cannot be accurate on that many digits.

https://arxiv.org/abs/2405.17399

https://arxiv.org/abs/2307.03381

https://arxiv.org/abs/2310.02989

Ultimately, numbers are represented in GPT like systems in a pretty weird way. That way affects how well they can learn to do things like arithmetic and counting and the like but that way isn't a necessary way. You don't have to represent numbers like that.

Re: AGI is far from inevitable

#154

Post by riku_iki »

famouswaffles wrote: post #153
riku_iki wrote: post #148

Earlier quoted context omitted.

> Because anyone who has said nonsense like "LLMs by their very nature cannot do x" and waited a few years has been wrong. That's why GPT-3 and 4 shocked the research world in the first place. there are some benchmarks which show fundamental inability of LLM perform certain tasks which human can, for example add 100 digits numbers.

> there are some benchmarks which show fundamental inability of LLM perform certain tasks which human can, for example add 100 digits numbers. fundamental inability ? No. Current Sota LLM (4o, claude, gemini) woes with arithmetic is not a transformer weakness never mind a large language modelling one. Those benchmarks show that those particular models have problems with accuracy on that many digits, not that LLMs fun…

> https://arxiv.org/abs/2405.17399

they built specialized model which is after bunch of trickery still has 99% accuracy(naive model had very low accuracy) on very simple deterministic algo. I also think most of the accuracy came from memorization of training set(model didn't provide intermediate results, and started failing significantly at sligtly larger input). In my book it is fundamental inability to learn and reproduce algorithm.

They also demonstrated that transformer can't learn sorting.

Re: AGI is far from inevitable

#155

Post by famouswaffles »

riku_iki wrote: post #154
famouswaffles wrote: post #153

Earlier quoted context omitted.

> there are some benchmarks which show fundamental inability of LLM perform certain tasks which human can, for example add 100 digits numbers. fundamental inability ? No. Current Sota LLM (4o, claude, gemini) woes with arithmetic is not a transformer weakness never mind a large language modelling one. Those benchmarks show that those particular models have problems with accuracy on that many digits, not that LLMs fun…

> https://arxiv.org/abs/2405.17399 they built specialized model which is after bunch of trickery still has 99% accuracy(naive model had very low accuracy) on very simple deterministic algo. I also think most of the accuracy came from memorization of training set(model didn't provide intermediate results, and started failing significantly at sligtly larger input). In my book it is fundamental inability to learn and re…

They did not build a specialized model. They changed the embeddings. You can do this for any existing model.

>They also demonstrated that transformer can't learn sorting.

They did not demonstrate anything of the sort.

Re: AGI is far from inevitable

#156

Post by riku_iki »

famouswaffles wrote: post #155
riku_iki wrote: post #154

Earlier quoted context omitted.

> https://arxiv.org/abs/2405.17399 they built specialized model which is after bunch of trickery still has 99% accuracy(naive model had very low accuracy) on very simple deterministic algo. I also think most of the accuracy came from memorization of training set(model didn't provide intermediate results, and started failing significantly at sligtly larger input). In my book it is fundamental inability to learn and re…

They did not build a specialized model. They changed the embeddings. You can do this for any existing model. >They also demonstrated that transformer can't learn sorting. They did not demonstrate anything of the sort.

What you are saying contradicts to my reading of publication.

Re: AGI is far from inevitable

#157

Post by etempleton »

gizmo686 wrote: post #114
ryandvm wrote: post #112

Earlier quoted context omitted.

> but LLMs do solve problems that people thought were extremely difficult to solve ten years ago Agreed. I would have laughed you out of the room 5 years ago if you told me AI's would be writing code or carrying on coherent discussions on pretty complex topics in 2024. As far as I'm concerned, all bets are off after the collective jaw drop that the entire software engineering industry did when we saw GPT4 released. W…

> It turns out that the larger these models get, the more unexpected emergent capabilities they have, so I'm mostly in the camp of thinking AGI is just a matter of time and resources. AI research has a long history of people saying this. Whenever there is a new fundamental improvement, it looks like you can just keep getting better results by throwing more resources at it. However, eventually we end up reaching a poi…

It is hard for me to not think that given the current approach it will always be almost there, but not quite good enough to be more than a nice assistant or tool that has to be meticulously built and maintained by a company. Take, for example, self driving vehicles, we have been 5 years out for 25 years. That is not to say there has not been significant progress, but to go from a really powerful driver assist to full self driving has still not really come to full fruition and that is kind of a best case scenario for AI/ machine learning.

That is just one very narrow task that basically anyone can do regardless of intelligence or talent. It takes less than a year to train a distracted 16 year old to do it, but three decades to train an AI and even then you probably need to hand tune it for specific locations because it won’t know what do in unusual road layouts.

I think we will get there with driverless vehicles, but only because it is being tailored by humans to handle all of the edge cases. Self driving cars, if truly self driving with no user intervention, will of course be worth it in the end. The trillions of dollars spent will eventually add countless dollars in added efficiency and unlimited revenue for those who get there first, but how many applications can we really say that is true for? That the decades of development and training it takes to remove humans will be worth the initial investment?

I guess my contention is that the current development path seems unlikely to ever achieve AGI and so instead you will have to do heavy customization to get anything that is much more useful than what we have now.

Re: AGI is far from inevitable

#158

Post by famouswaffles »

riku_iki wrote: post #156
famouswaffles wrote: post #155

Earlier quoted context omitted.

They did not build a specialized model. They changed the embeddings. You can do this for any existing model. >They also demonstrated that transformer can't learn sorting. They did not demonstrate anything of the sort.

What you are saying contradicts to my reading of publication.

Their method does not require building specialized models from scratch (you can but you don't have to) and they did not prove transformers can't learn sorting. If you think they did, then you don't understand what it means to prove something.

Re: AGI is far from inevitable

#159

Post by AlotOfReading »

avazhi wrote: post #106
oska wrote: post #99

Earlier quoted context omitted.

> I don’t think any serious man would suggest that AGI is impossible Plenty of ppl would suggest that AGI is impossible, and furthermore, that taking the idea seriously (outside fiction) is laughable. To do so is a function of what I call 'science fiction brain', which is why I found it ironic that you'd used another device from science fiction to opine about its inevitability.

Happy for you to cite some thinkers who are on record as saying it’s impossible. I’ll wait.

Not the OP, but Searle of Chinese room fame came up with the aforementioned argument to demonstrate just that.

Re: AGI is far from inevitable

#160

Post by riku_iki »

famouswaffles wrote: post #158
riku_iki wrote: post #156

Earlier quoted context omitted.

What you are saying contradicts to my reading of publication.

Their method does not require building specialized models from scratch (you can but you don't have to) and they did not prove transformers can't learn sorting. If you think they did, then you don't understand what it means to prove something.

In my books what they built (specialized training data + specialized embeddings format) is exactly specialized model. You can disagree of course and say again that I don't understand something, but discussion will be over.
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