Submitted by EntireContext t3_zasjrg in singularity
Me I'm still going with AGI by 2025. This seems really reasonable to me knowing that (presumably) ChatGPT is not even powered by GPT-4 and seeing its capabilities through conversation and pair-programming.
​
What are your thoughts?
we also have people like joscha bach and yoshua bengio working on alternative networks like generative flow networks that learn by sampling whatever data available unlike deep learning that needs a lot of traning dataset, almost like how humans learn.
I saw that headline but didn't go deep into it. It's real progress, not hype? How much efficiency gains? How long before they can implement it?
And aren't neural nets super complex already with all those billions of parameters?
To my very limited understanding, you need huge servers to run complex neural networks because the interaction needs to be solved using numeric integration, with a symbolic solution this restriction disappear, opening the path to running this networks on less powerful servers, maybe even personal computers and phones.
This doesn't hold for the networks currently in use, only if we want to more closely simulate human brains. There is no real indication yet that we can train these better or that they work better.
What about utilizing quantum computers? Or is that not applicable
what does that mean
ChatGPT just seems like a polished up version of GPT-3. Nothing wrong with it, but I think people are making a little bit much of it.
100%. It’s really weak at a couple of subjects.
Same. GPT2 and GPT3 were impressive steps. This is clearly still GPT3.+
Yeah I’m not getting the hype. There’s a bot on character.ai called Gabriel that seemed just as capable and far less restricted. I’m sure there are certain things ChatGPT can do that blow the other bot out of the water but my “holy shit” moment was definitely from my first interactions with the Gabriel bot, which kind of spoiled ChatGPT for me.
No. ChatGPT didn't show anything unexpected. Memory (working and episodic) is still isn't there.
It remembered previous prompts when I talked about them.
Make sure that the prompt is 2000-3000 words away from the question.
No idea what the context window is, but at the end of the day they can just increase it....
It's already commercially useful right now. It doesn't need more context window to be more useful (although the context window will continue to increase) but only more qualitative intelligence.
It's not feasible to increase context window due to quadratic growth of required computations.
> It doesn't need more context window to be more useful
It needs memory to be significantly more useful (as in large-scale disruptive) and, probably, other subsystems/capabilities (error detection, continual learning). Its current applications require significant human participation and scaling alone will not change that.
I mean the context window will increase with incoming models. GPT-1 had a smaller context window than ChatGPT.
It will increase but the size of increases will slow down without major breakthroughs. You can't predict the rate of future progesss solely based on the rate of past progress in the short term.
You guys take the "exponential growth" stuff way too seriously. All that refers to is technological growth over human history itself, but every time scale doesn't follow the exact same growth patterns. If they did we'd have already reached the singularity a long time ago.
Bottlenecks sometimes occur in the short term and the context-window problem is one such bottleneck.
Nobody doubts that we can solve it eventually, but we haven't solved it yet.
There are potential workarounds like using external memory systems, but that is only a partial workaround for enabling more modest context-window increases. External-memory systems are not feasible for AGI because they are way too slow and do not scale well dynamically, not to mention they are separate from the neural network itself.
In the end, we either need an algorithmic breakthroughs or quantum computers to solve the context-window problem as it relates to AGI. An algorithmic breakthrough is more likely to happen before quantum computers become viable. If it doesn't, then we may be waiting a long time for AGI.
Look into the concept of computational complexity if you want to better understand the issue we are dealing with here.
They can’t just increase it. The context window’s time complexity is O(n^2) which means the amount of compute needed per token added grows exponentially.
This is an architectural constraint of transformers. We’ll either need a better algorithm than transformers, or a way to encode/decode important information to, say, a database and insert it back into the prompt when it’s required
Well they will make a better algorithm than transformers then (which have already been improved to performers and whatnot).
At any rate, I still see AGI in 2025.
Based on what?
The difference between what was possible in 2019 and what the models can do now.
Back when GPT-2 was out it could barely produce coherent sentences.
This GPTChat model does make mistakes, but it always speaks in a coherent way.
I do agree with this comment. It’s feasible that long term memory isn’t required for AGI (though I think it probably is) or that hacks like reading/writing to a database will be able to simulate long term memory.
I think it may take longer than 2025 to replace transformers though. They’ve been around since 2017 and we haven’t seen any real promising candidates yet.
I can definitely see a scenario where GPT-5 or 6 has prompts built into is training data which are designed to teach it to utilize database read/writes.
Imagine it says hello to you after seeing your name only once six months ago. It could have a read database token which has sub-input tokens to fetch your name from a database based on some sort of identifier.
It could probably get really good at doing this too if it’s actually in the training data.
Eventually, I could see the model using its coding knowledge to design the database/promoting system on its own.
Eventually, but without any knowledge of specific breakthroughs that will happen very shortly, your 2025 estimation is an uninformed guess at best.
I don't see a need for specific breakthroughs. I believe the rate of progress we've been seeing since 2012 will get us to AGI by 2025.
Well you can believe whatever you want but you're not basing those beliefs on anything substantive.
Honestly, the rate of progress since 2012 has been very slow. It's only in the past few years that things have picked up substantially and that was only because of recent breakthroughs with transformer models.
That's kind of how the history of AI progress has worked. We typically have breakthroughs that lead to a surge in progress that eventually plateaus and then stalls for a while as bottlenecks are reached and then eventually a new breakthrough is reached and there is another surge in progress.
It's not guaranteed there will be another plateau before AGI, but we're gonna need new breakthroughs to get there, because as I said, we are approaching bottlenecks with the current technology that will slow down the rate of progress.
That's not necessarily a bad thing, by the way. Our society isn't currently ready to handle AGI. It's good to have some time pass to actually integrate the new technology rather than developing it faster than we can even use it.
[removed]
its not gonna be popular, but no - even after testing ChatGPT its pretty obvious that its just evolution, not revolution
by evoluting gpt-3 we're not getting to AGI, but thats already well known; i am looking forward to gpt-4 though
It's not even an evolution, it's just finetuning of GPT3 for a particular use case. Nothing ChatGPT does can't be done with regular GPT3. It just works differently out of the box. Meanwhile, there are many things regular GPT3 can do that ChatGPT can't do.
Regular GPT3 is like an operating system and ChatGPT is like an application running on that operating system.
I agree that it's evolution and not revolution. It feels like that evolution finally pushed it over the "actually useful" threshold, which makes it sort of a revolutionary advancement.
What it can do with code is pretty astounding ?
I haven't changed my timeline at all given that ChatGPT is literally just GPT3 that has been fine tuned to be more conversational out of the box.
I think what's happening is a lot of people who haven't really explored the potential of GPT3 itself are now becoming aware of it since ChatGPT is free to use (for now) and easier to use.
Base GPT3 is still much more impressive as it is much more versatile. It can do everything ChatGPT can and a lot more. It just takes a little bit more set up work.
no, if anything what I've observed with chatgpt, as well as the drama surround stablediffusion 2.0, the singularity will not be publicly noticeable or available in public consumer products. these applications are demonstrating a negative feedback where arbitrary limitations become more necessary for increasingly social (not technical) reasons. additionally, chatgpt is like a snapshot of everything that's been said in the past, and whatever it spits out sounds convincingly authoritative but has no certain accuracy for basic logic & reasoning (like incorrect math). I suspect that further iterations will be more convincing, perhaps even frighteningly "informative", but sussing out errors and inaccuracies will just get proportionately more demanding for the human domain experts. it does spit out a lot of code, but give it a complex enough prompt, and the code will abruptly end. there's might be a subscription service that matches the work being done to serve up output. I still suspect that the advances in AI will accelerate for quite a while, and only past a certain threshold (maybe 2030 or later) will a collection of humans procure a novel methodology that self-evidently produces all the necessary reasoning and self-awareness that an AGI would require. until then, there is 0% chance that we build an AI that builds an AI, so on and so forth, that would actually reach another stage of complexity. in all likelihood, AGI is closest to those that have the most scaled computational facilities with the most optimized ASICs and the widest distribution of feedback mechanisms. this does not 100% overlap with current academic work using AWS and other cloud compute.
Current methods can solve maths though. A paper from November showed a net that solved ten International Mathemtical Olympiads problems. It's not like transformers can't do math. And ChatGPT wasn't trained to do math.
I didn't find its limits in terms of web development at least. It's a capable pair-programmer. Of course I guess it can't create innovative new hardcore algorithms that are state-of-the-art in complexity, but I didn't expect it to do that.
maybe I'm misunderstanding, but if you don't expect state-of-the-output or, for lack of a better term, gain of function from the output of these current AI, how do you see our approach to the singularity being shortened based on the current consumer product. as far as the math olympiad reference, I'm assuming you're referencing Minerva or something at the same level. Again, it doesn't show completely error-free answers, it just shows a sequence of words & algorithms that are statistically adjacent enough to be convincing. it should be expected that if olympiad (or college level) question sets were available in the training data, then the bot can just recall the answers as complete chunks without "thinking".
GPTChat is state-of-the art in terms of what's available as a general conversational model. It's obviously not state-of-the-art at everything though, because it can't solve IMO problems in maths for example.
When you answer any question, what you do is give a sequence of words rhat are statistically adjacent enough to be convincing...
I would disagree with your point about how we answer questions, we optimize for comprehensively sound and valid answers, not for statistical adjacency. If someone says a whole bunch of techno-jargon or other word salad just to sound convincing, the wisdom of the crowds is already powerful enough to call that redundant. Likewise, the wisdom of the crowds can break GPTChat and there's already actively collected techniques to "jailbreak" the application.
My point is that a general conversational model is a gimmick at this point, and likewise GPT4 is already prescribed to have limitations like being text-centric and is not multimodal. It'll definitely being uncannily entertaining as a conversational homunculus, but a homunculus does not a singularity make.
When the code abruptly ends, that's just because OpenAI put a limit on the length of the output, not because it can't generate more code.
If you ask "Can you write the second part of the code, starting from let foo = bar", for example, it will print out the rest of the code starting at the line you mention.
thanks, just tried it with some ps5.js samples
I was quite optimistic we'll get AGI in 2020's. But I want to see some partial breakthrough in a year or two, or 2030's will become more realistic...
Don't get me wrong - ChatGPT can do some impressive things. Solving Advent of code tasks. Fixing buggy code given a stack trace, etc...
But there are cases where it just fakes it and gives a convincingly looking wrong answer.
It has no way of verifying the answer though. You have to tell it the errors that you get in your code so that it can output better code.
What you want is a model that can write perfect code with no way of testing it. That will ve possible I think, just not right now
Yeah, I wasn't that impressed when it generated some code for a first Advent of Code task, since I've already seen it on some video on Twitter. But then I told it "your code is wrong, here's stacktrace", it explained the problem, suggested fix, and my jaw dropped.
I don't think any improvements in existing models changes the AGI landscape. Existing architectures perfected to 99.99% accuracy gets you a bunch of narrow/weak super intelligent models and not AGI. If you had millions of those for every economically useful task, that would pass for AGI.
R&D needs to max out on existing architectures before they will seriously branch out and search the possibility space for something that will get you a proper learning algorithm.
If you want AGI, you will need the R&D community to realize existing models won't get them what they want and they need to explore elseware.
ChatGPT is sometimes smart with code and gives cool ideas, but because it is from Open Ai and I see that it has a filter, unfortunately I think that if we depend on these big companies we will still have to wait a little longer.
Yeah the filters are just horrible.
That we are allowing the screeching karens of the world to slow down the progression to agi, (which will end human suffering at unprecedented scale) is just spirit destroying.
Wish we could take all that power being used to keep things like btc up, to train models, and create truly free and open ai..
I am glad that someone here sees how AGI is actually good.
[deleted]
No, it's cool, but in line with what I expected.
Starting to think GPT-4 might qualify as an AGI. The following generation definitely will. After that, it’ll also be interesting to follow the long march of algorithmic efficiency and compute density towards AGI that can be run locally at high speed.
Words yesterday, more credible words today, even more credible words tomorrow.
Nothing multimodal. Just words. Zero progress.
Credibility is the first goal of a liar.
You can't lie if you're stupid. You can't fake knowing math, or knowing how to program, or knowing how to talk. Either you do or you don't.
What is AGI?
artificial gay intelligence
I don't see how ChatGPT is better than davinci-003 GPT-3, from my experience it's worse and more limited.
UBI
Imaginary_Ad307 t1_iynk0ou wrote
Also the differential equation modeling interaction between neurons has been solved last November, clearing the path for very complex neural networks without bottlenecks due to numeric integration. So I am with you AGI is going to be a reality very soon.