Earlier quoted context omitted.
If you were to try to build such a simulation, given the natural limits of real hardware, wouldn’t some form of relativity be a necessary feature of the simulation to avoid segfaults, rather than something simply to take into account while constructing the hardware?
In what way is that not "taking it into account?"
Google DeepMind CEO says some form of AGI possible in a few years
361–370 of 388 posts
Re: Google DeepMind CEO says some form of AGI possible in a few years
#362Earlier quoted context omitted.
> These things are not possible with just words I don't think this is the case at all. Language is how we encode and communicate ideas/concepts/practicalities; with sufficient data, the links are extractable just from the text.
I don't see how two examples I gave are possible with just text. They require understanding spatial relationships between objects and their physical properties.
What GPT does is not "text" - although it centers that as the interface - but "symbols". The billions of parameters express different syntaxes and how they relate to each other. That's why GPT can translate languages and explain things using different words or as different personas.
So when we ask it to solve a spatial problem, we aren't getting a result based on muscle memory and visual estimation like "oh, it's about 1/3rd of the way down the number line". Instead, GPT has devised some internal syntax that frames a spatial problem in symbolic terms. It doesn't use words as we know them to achieve the solution, but has grasped some deeper underlying symbolic pattern in how we talk about a subject like a physics problem.
And this often works! But it also accounts for why its mathematical reasoning is limited in seemingly elementary ways and it quickly deviates into an illogical solution, because it is drawing on an alien means of "intuiting" answers.
We can definitely call it intelligent in some ways, but not in the same ways we are.
Re: Google DeepMind CEO says some form of AGI possible in a few years
#363Earlier quoted context omitted.
I don't think it is necessarily important how the human brain works. An interesting metaphor here is that aeroplanes don't work in a similar way to birds, the end goal is flight, not having wings that flap and are covered in feathers.
Precisely. LLM’s are an engineered system. Artificial. What I keep hearing from the AGI folks is that we’re on the verge of replacing humans. That these systems, “think,” on their own and will be superior to us in every way: dangerous even! I highly doubt they will be a danger on their own. Bing isn’t going to decide one day that it thinks you’ve been a bit distant lately and doesn’t want to answer your query until y…
And what happens when someone designs an algorithm to run a company by itself and make money? Or to orchestrate military strategy? Or to be any sort of general purpose intelligence that tries to achieve complex goals? Once these systems prove themselves useful, there is going to be intense pressure for companies to make them smarter, and for governments to use them to gain advantages in geopolitics and military applications.
Yes, people wielding AGI is dangerous, AND it has a lot of potential to be dangerous all by itself, as soon as it becomes smarter than us. When we make something more intelligent than we are, I fear we might as well start a clock and start taking bets on how long humanity will be a) relevant and b) alive. I hope I'm wrong, that alignment turns out to be easier that I think, and that these machines turn out to be gentle, benevolent stewards of humanity, guiding us into the future. Or at least, that we have more time than I think to work on the problem.
By the way, about your scenario of Bing not answering your query until you apologize -- have you read the transcripts of Sydney? https://plainenglish.io/blog/bing-chats-sydney-do-you-believ.... It's not dangerous, except to people with existing mental health issues, but if you can see how unpredictable this tech can be when it's not smart, how do we expect to keep it on a particular safe path when it's a new form of machine intelligence smarter than we are?
Is there any way that a less intelligent species can control a significantly more intelligent one for any more than a very short time? Or even if not controlling the smarter species, just maintaining control of their own future. It feels as likely as designing a working perpetual motion device, to me.
Re: Google DeepMind CEO says some form of AGI possible in a few years
#364Earlier quoted context omitted.
"People don't need the gigantic amount of input data that ChatGPT needs to learn" Sure they do. Humans rely on tons of audio and video before they can even read (or walk).
But I have learned to program and do complex math having read and analyzed ridiculously less source material on those subjects (and in fact in the case of programming having seen very little code as I mostly only had reference manuals at the time).
Re: Google DeepMind CEO says some form of AGI possible in a few years
#365Earlier quoted context omitted.
Ah so it is! I guess my point was that even this simple function is enough, you don't need any 'magic' beyond that. In particular, this continuous function can be used (infinitely) to represent discontinuous functions - the step function being the usual example. That is the more interesting and relevant mathematical fact I think.
Well, yes. The 'magic' is the nonlinearity. It's because the composition of linear (actually also affine) functions is still just linear (affine). You don't get any additional power by combining many of them - which is also the reason why linear functions are so well understood and easy to work with. You give sprinkle in a tiny nonlinearity (e.g. x^2 instead of x) and suddenly you can get infinite complexity by weigh…
Except you can "compose" affine functions using Horner's schema to get any polynomial... No need to sprinkle tiny non linearities. It's a buffet. Grab as much as you need.
Re: Google DeepMind CEO says some form of AGI possible in a few years
#366As someone who has worked in the field of AI/ML for quite awhile now, the problem with current AGI predictions is ML hasn't done anything new since the 80s (or arguably earlier). At the end of the day all ML is using gradient descent to do some sort of non-linear projection of the data on to a latent space, then doing some relatively simple math in this latent space to perform some task. Personally I think the limits…
You miss the point. Just make humans dumber and more predictable. Problem solved. (This isn't that hard since humans will adapt to the stupidity level of the "AI" they use daily on their own without special social engineering efforts.)
Re: Google DeepMind CEO says some form of AGI possible in a few years
#367Earlier quoted context omitted.
We do not have sufficient knowledge of what reasoning entails to be able to tell whether there's any meaningful distinction between human reasoning and LLMs. It's likely there is some difference. It's not clear whether there's any major qualitative difference. Claiming we can tell that there's a distinction that merits saying people are reasoning and LLMs are not, is "hallucination" to me. It's making a claim there i…
It may be true that we don't have a real fundamental understanding of what human reasoning involves but I think we can be pretty certain that it's something different from just stringing words together.
E.g. ChatGPT can (badly) perform maths or symbolically evaluate code that is guaranteed not to be in its input in a way that would be very hard to explain as "just stringing words together".
Here is an example where I loosely describe a language that is a mix of an INTERCAL-inspired "come from" construct and Ruby, and then give it an example program and asks it what it will output:
> We're going to write a program in a "new" language that is a mix of Ruby and INTERCAL. We're going to take the "come from" statement and use it to allow "hijacking" the return of a function. Furthermore, we're going to do it conditionally. "come from if " will execute the following block if was executed and is true. In , "result" can be used to refer to the result of executing .
>
> Can you deduce what this program will print, and why?
>
> def foo(str)
> str
> end
>
> come from foo if result == "hi" do
> print "foo returned hi"
> end
>
> puts "1:"
> puts foo("some string")
> puts "2:"
> foo("hi")
I explicitly avoided passing strings that'd indicate the expected output. There's an ambiguity introduced by passing a block rather than just letting the statement stand by itself (passing a block might indicate I'd like the statement to return at the end of the block). This was unintended from my side. I intended execution to just continue after the "come from" block, as that is consistent with INTERCAL. I'll get back to that ambiguity.Here's ChatGPT(GPT4)'s output:
> Sure, let's analyze this program step by step. The new language is a mix of Ruby and INTERCAL, using the "come from" statement to conditionally hijack the return of a function.
>
> Here's the program:
[snipped a verbatim copy of the program I input for brevity] > 1. The `foo` method is defined, taking a single argument `str` and returning it.
>
> 2. The "come from" statement is introduced with a condition to hijack the return of the `foo` method if the `result` is equal to "hi".
>
> 3. The program outputs "1:".
>
> 4. The program calls `foo("some string")` and outputs its result, which is "some string". The "come from" statement is not executed because the result is not equal to "hi".
>
> 5. The program outputs "2:".
>
> 6. The program calls `foo("hi")`. Since the result is "hi", the "come from" statement is executed, and "foo returned hi" is printed. Note that the return value of the `foo` method is not printed here, as there's no `puts` statement wrapping the call to `foo("hi")`.
>
> So, the output of the program will be:
>
> ```
> 1:
> some string
> 2:
> foo returned hi
> ```
This is what I intended.This isn't a very complex problem, but it's the kind of problem that requires stepping outside how you're used to think to the point where I'd like to e.g. pose it as an interview question, but I'd worry about using it as I know from experience of conversations with people about INTERCAL's "come from" that a surprising number of people struggle with reasoning about it.
I asked a followup question:
> "come from" alters the control flow, and prevents a method call from returning to its call site. Suggest a couple of implementation and specification issues specific to dealing with the control flow and its implications.
It produced a detailed explanation of how it'd require stack unwinding, pointed out that you'd need to define how it is affected by dynamic dispatch in Ruby (e.g. which method should it actually intercept?), and gave an example of a scenario where "come from" could affect exception handling that I frankly hadn't thought of. If someone gave an answer like it did to this in a technical interview, I'd hire them.I then followed up with one more:
> Are there other options to unwinding the stack? E.g. when the block passed to "come from" exits, what are our options for where code execution will continue?
It correctly ascertains that returning to the original call site is another option, and then deduced that this would make "come from" behave like an aspect-oriented "around" mechanism. It also produced several more esoteric suggestions and explained why they'd be a bad idea.Because of looking through this I recognised that the block I'd added to the "come from" syntax made it somewhat ambiguous and might hint at different semantics, I asked this:
> Does the fact I passed a block to the "come from" statement give any hints as to which of these options I might have intended?
ChatGPT replied: > Yes, the fact that you passed a block to the "come from" statement does provide some hints about your intended behavior. It suggests that you might want the "come from" statement to behave like a method interceptor or an "around" aspect, executing the block of code when the specified condition is met and then returning control back to the original call site. [snipped it pointing out that matches one of the options it had described before and copying it's previous explanation]
I was hopeful it'd pick up on this when specifically asked, but I'm frankly surprised it did. It's a subtlety of how idiomatic Ruby differs from INTERCAL, and something I didn't think about myself when I wrote the example, and so given I told it the "come from" was inspired by INTERCAL, it was right to assume at first that my intent was for it to pass through. But it's also right to recognise that the block given might imply other intent (and indeed either this construct should be named something else, like "around" or "after", or the block ought to go)If this is just "stringing words together", then I know a lot of developers who are bad at just stringing words together.
Re: Google DeepMind CEO says some form of AGI possible in a few years
#368Earlier quoted context omitted.
But I have learned to program and do complex math having read and analyzed ridiculously less source material on those subjects (and in fact in the case of programming having seen very little code as I mostly only had reference manuals at the time).
Before you learned how to code from a book, you had to learn how to read and write English. You also had to learn how to follow instructions, how to imbibe and compose information etc. How many books and hours of instruction did that take?
>Let us consider the GPT-3 model with 𝑃 =175 billion parameters as an example. This model was trained on 𝑇 = 300 billion tokens. On 𝑛 = 1024 A100 GPUs using batch-size 1536, we achieve 𝑋 = 140 teraFLOP/s per GPU. As a result, the time required to train this model is 34 days.
https://arxiv.org/pdf/2104.04473.pdf
I'm not sure expressing brain capacity in FLOPs makes much sense, but I'm sure if it can be expressed in FLOPs, the amount of FLOPs going to learning for a normal human is less than that.
Re: Google DeepMind CEO says some form of AGI possible in a few years
#369Earlier quoted context omitted.
>I think ChatGPT is good evidence of this. What evidence do we have that this isn't how intelligence works? People don't need the gigantic amount of input data that ChatGPT needs to learn. However I'm not sure what exactly "this" is you and GP are referring to, and it may be possible to improve existing ideas so that it works with less input data.
This is actually a Noam Chomsky linguistics question: how much of language is innate/genetic and how much is learned. The common perception has been that children aren't exposed to enough data to arrive at their grammatical language skills, implying there's some proto language built in. Comparative analysis of languages has looked for what aspects are truly universal but there's actually not a lot of concrete true un…
It doesn't necessarily mean it's fundamentally different, but it doesn't mean it is comparable either. Geoff Hinton doesn't think the brain does backpropagation. Training a neural net uses backpropagation. So if Hinton is correct then saying "it just took a longer training period" while brains doesn't learn like our current neural nets is glossing over a lot of things.
Re: Google DeepMind CEO says some form of AGI possible in a few years
#370Earlier quoted context omitted.
>I think ChatGPT is good evidence of this. What evidence do we have that this isn't how intelligence works? People don't need the gigantic amount of input data that ChatGPT needs to learn. However I'm not sure what exactly "this" is you and GP are referring to, and it may be possible to improve existing ideas so that it works with less input data.
"People don't need the gigantic amount of input data that ChatGPT needs to learn" Sure they do. Humans rely on tons of audio and video before they can even read (or walk).
For reading, the same applies. Our brains are equipped with many of the foundational aspects required for reading, and we only _learn_ a part what is necessary for the skill of reading.
Unlike computer models, brains are no tabula rasa. So we don't need the same input as computer models to learn.