351–360 of 388 posts
Earlier quoted context omitted.
That's assuming the AI really wants to make a lot of paperclips though
I'd say a hallmark of an intelligent agent is knowledge seeking. You can only learn so much without venturing out into space.
Earlier quoted context omitted.
Fusion by 1990 with a bit of extra investment promised in 1972 https://www.science.org/doi/10.1126/science.175.4027.1194.b I remember tons of articles from that time arguing the same. Then the attention shifted to "fifth-generation computers".
I don't think this contradicts the AGI prediction though (nor the "Fusion by 1990 with a bit of extra investment" prediction for that matters) https://external-preview.redd.it/LkKBNe1NW51Wh-8nLSTRdQtTha2... This chart shows how much people in the 1970s estimated should be invested to have fusion by 1990, to have it by 2000s and to "never" have it. We ended up spending below the "never" amount for research over four d…
Earlier quoted context omitted.
Grid scale energy storage is basically solved once you realize that hydrogen storage is an option already. It's in the same boat as when photovoltaics got very cheap, but skeptics continued to deny that possibility for some years afterwards.
>once you realize that hydrogen storage is an option already With unavoidable ~30% efficiency (or ~70%) energy loss, requiring an overcapacity factor of 2-3...
As 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…
I disagree with you. > Essentially all we've done is pushed the basic model proposed by linear regression to it's absolutely limits No, we haven't pushed linear regression to its limits. If it was only linear regression, it wouldn't work. Neural networks need a non-linearity to model complex things. The beauty is that given an infinite series of nonlinearities, one can model any mathematical function. In practice we…
1) Given an infinite series of nonlinearities, one can model any mathematical function to an arbitrary degree of precision. This has been proven and is accepted by any working mathematician.
2) The human brain / intelligence can be simulated by a sufficiently complex mathematical function. It is somewhat accepted that simulating the human brain is sufficient for intelligence. Disagreements usually boil down to: It won't have a soul, you can't simulate biological / quantum processes on digital computers, or something abut Qualia I don't really understand,
3) How big / complex is the function that we need to get to AGI / simulate human or above intelligence? TBD, it seems like experts disagree but many working in the field have been surprised by the capabilities that GPT has and we are likely closer to AGI than anyone thought 2 years ago.
As 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…
It's all moot until we can agree on a definition of intelligence. I don't necessarily think it's relevant to the discussion whether an AI is truly intelligent or not. What matters is whether the technology is good enough to fool an "intelligent" human. It's good enough to do that today. I'd say we would have to train a "dumber" model that can respond with a simple "im not sure" instead of the typical responses we get…
We did agree on a definition intelligence! For 50 years, the Turing test was the unquestioned threshold beyond which machines would be considered "intelligent". The simple fact of the matter is that we've reached that point, but folks remain unimpressed, and so have set about moving the goalposts.
I am of the opinion that, when the dust settles, the point in time which will be selected as "the moment we achieved Artificial Intelligence", or even rudimentary AGI, will not be in the future, but in the past. This will be true because, once we take a step back, we'll remember that our definition of intelligence should not be so strict that it it excludes a significant percentage of the human population.
Consider this--is there any reasonable definition of intelligence which excludes Chat GPT 4.0, but which includes all human beings who fall in the bottom ~5% of IQ?
Earlier quoted context omitted.
Quantum effects don't. There are rules but results are within range random.
Quantum mechanics works the same way every time. Being random doesn't mean they're not working the same way every time, the distribution never changes.
The other few things possible in next few years for past few years are: 1. Fusion 2. Self driving cars. 3. Ubiquitous, generally available and stable 8192 bit quantum computers. 4. High energy density grid scale batteries. Just few more "this maybe the... what could revolutionize..." articles away. What we forget is that it is in these people's business interest (executives making such statements) to state the exagge…
I think we'll have full-blown AGI before fusion and before world-changing levels of quantum computing. Which is to say, we won't be around to see fusion and world-changing quantum :( But there's still time for robo-taxis!
Earlier quoted context omitted.
I disagree with you. > Essentially all we've done is pushed the basic model proposed by linear regression to it's absolutely limits No, we haven't pushed linear regression to its limits. If it was only linear regression, it wouldn't work. Neural networks need a non-linearity to model complex things. The beauty is that given an infinite series of nonlinearities, one can model any mathematical function. In practice we…
Andrej Karpathy, the director of AI at tesla, published an AMAZING video about how the current models are built which I think everyone in the tech space should watch. It is intuitive, easy to follow and quite frankly the best video I have seen on the topic. Here is the link: https://www.youtube.com/watch?v=kCc8FmEb1nY&list=FL2tbfd7UpJ... If you conclude, after watching this, that this is all intelligence is - namely…
Earlier quoted context omitted.
Why would anything emerge?
Well, because they do: https://en.wikipedia.org/wiki/Large_language_model#/media/Fi... If you're asking "Why does anything emerge?", I don't think anyone really knows.
Why would any of these emerge?
As 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…
If we’re talking about something like agency or free will, that’s trickier.
The problem in this space is people use terms that nobody agrees on.