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Evan Prodromou 🇨🇦🇺🇸🇬🇷🇵🇸

evanp.me/pollfaq#never

Poll FAQ

I do a lot of polls on my account on the Fediverse. I get the same questions or requests multiple times, so I made this FAQ to make it easier to reply. Q: Why do you do so many polls? A: I like to …
Evan Prodromou (Evan Prodromou's Blog)
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Andres Jalinton

@boz
This and also the current speed of the climate change.
Hardware costs for one (it's true that LLM companies can incur into massive amounts of debt to finance it but not forever)
Food and unstable weather after, will for sure disrupt people's life and their will to work.
@Valerio Bozz
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R × P

that’s fair. I’m probably just too primed for people playing definitional games about AI. “AI is better than humans at this one specific task, therefore -[rhetorical sleight-of-hand], therefore all the predictions from Bostrom’s book are coming true.” But that’s not on you, that’s on me for not leaving those parts of Reddit sooner. 😛
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bignose

> LLMs are great at a lot of things that computers haven't done well previously.

I hear this claim a lot, but it's never substantiated verifiably.

In fields where I understand the LLM output, it is unreliable plagiarism or unreliable garbage.

In fields where I need to rely on others more knowledgeable, those who *aren't incentivised to hype the LLM* report LLMs are unreliable plagiarism or unreliable garbage.

They're okay at language patterns. What are you saying they're great at?

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Evan Prodromou 🇨🇦🇺🇸🇬🇷🇵🇸

@me

Explain! What part is impossible?

You don't think it's possible for an entity to be more intelligent than a human?

Or is it not possible for a *constructed* entity to be more intelligent than a human?

Or are *humans* not smart enough to make a constructed entity that is more intelligent than a human?

@Jonathan Lamothe
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Aljoscha Rittner (beandev)

In the context of "AI = LLM", never. In context of "AI = whatever we will find as technology" we will need decades to have the technology to simulate at least a simple brain (much simpler than a human brain).

The reason is that the neural networks that we map in silicon today make up only a fraction of a brain. Countless elements that make up our thinking, our consciousness, and our self-awareness are missing. Even our senses, which are necessary for these, can currently only be represented in a rudimentary way.

Currently, we only emulate knowledge through high speed and parallelization. But we have clear limits in this regard with ressources.

And knowledge is not intelligence. It is also only simulated.

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Evan Prodromou 🇨🇦🇺🇸🇬🇷🇵🇸

@beandev you should check your math on that argument!

It's not going to take decades to have hardware and software systems with complexity equivalent to the number of neurons (86 billion) or synapses (100 trillion) in a human brain. Frontier models have about 0.5 trillion parameters, roughly equivalent to synapses. That's about 2 OOM from humans.

I think it's very interesting to ask what the difference between true intelligence and simulated intelligence is, though.

@Aljoscha Rittner (beandev)
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Aljoscha Rittner (beandev)

Thanks to the hint about the math. I completely forgot it 😅.

It would be nice to go deeper in the science behind neuronal networks, simulations and models and what that means. If you count transistors and compare it to the number of neurons, your math is completely wrong. Additionally you need synaptic connections. All the current simulations are based on reduced and simplified models. Typically only the electrical signal way, binary switches, heavily reduced synaptic connections, and spike signal population (to reduce energy, parallelization, and computation power). We are far away.

However, a nice read about the mathematics behind it, here a nice article:
golem.de/news/maschinentraeume…

It's in German, but you can translate it. Well worth reading. 👍

Golem

www.golem.de
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Huey

It's impossible to answer this without specific definitions of "AI" and "superintelligence". If AI is something that can write text and draw pictures in a way that is superficially like human output, and superintelligence means that it can do that faster than a human, then we've got it already. If not, then the answer is some time between tomorrow and never, depending on those definitions.
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Joshua Chalifour

for a long time I've been strongly opposed to the possibility of AGI or superintelligence as mostly just simulation that people conflate with what humans do. I still find the arguments pretty unconvincing and usually neglecting a lot of the human experience but the past few years have made me question more the nature of what humans are doing BECAUSE of the patterns that make AI work. I'm intrigued by the regularity and depth of patterns exhibited across the outputs of human thinking.
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David Penfold

@fdrc

The point is that current LLM tech doesn't have the capacity for understanding truth or accuracy in the way we do. It's a statistical probability process. It's getting refined in some pretty cool ways, but that fundamental inability to actually understand is just dealt with crudely by harnesses in order to try to attain some form of deterministic approach, and that will never scale computationally to encompass anything approaching the human experience and breadth of understanding/intuition etc.

@Fedo
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Fedo

@davep@infosec.exchangeTo be honest the second is not a good case at all, reproducing is not something we control, we kinda just go with the flow, maybe we can twitch some little things when we’re lucky but it’s more biology making us than the other way around isn’t it?
You’re right by saying we are be able to make things work even if we don’t truly understand what’s going on under the hood. I guess cooking is a good mundane example. And I feel this is what they’re trying to to with AI, like if they were trying to make AGI like if it was a loaf of bread “just a couple of petabytes more and AGI will leaven, let me cook bro”.

I just think this specific field of knowledge needs something more than limitless injection of capital providing infinite computing power to process all stolen data humans can put together, maybe a philosophical approach less similar to a 60s-fashion space race or Third Reich experiments on human

@David Penfold
This entry was edited (yesterday, 11:19 PM)
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trisweb

@davep Define magic.

In other words, yes, quite possibly some form of "magic" compared to what we think today. There's so much we don't know about how the brain works, and so much has evolved in tangled complex ways over millions of years of evolution. There could be quantum effects involved in sentience for all we know. It could be much more difficult.

@David Penfold
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malte

I will go with something a little bit arbitrary, but fun: the early 1970s with the invention of the first pocket-sized electronic calculators. These AI had extraordinary intelligence and could do very difficult calculations at lightning speed. Almost whatever multiplication or division with many decimals you threw at it, these AI would give you the answer in an instant. And the answer was always right.
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malte

These AI super intelligent machines couldn't read my mind, couldn't design a beautiful house, touch my shoulder in just the right way to comfort me or facilitate a consensus-meeting with culturally different people. It had a very specific kind of artificial intelligence and was super good at that. I still rely on this kind of AI superintelligence on a regular basis!
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Evan Prodromou 🇨🇦🇺🇸🇬🇷🇵🇸

@muddle

You're right! I try to get a lot out of the 4 choices Mastodon gives me.

evanp.me/pollfaq#never

On the topic of people who say never: nobody has to prove it to me! But I'm interested in why they say it. What part of artificial super intelligence do they think is impossible?

I think for a lot of people, they just assume that what tech billionaires say is by default a crock of shit. It's a good instinct!

Poll FAQ

I do a lot of polls on my account on the Fediverse. I get the same questions or requests multiple times, so I made this FAQ to make it easier to reply. Q: Why do you do so many polls? A: I like to …
Evan Prodromou (Evan Prodromou's Blog)
@muddle 🥣
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Evan Prodromou 🇨🇦🇺🇸🇬🇷🇵🇸

@muddle I'm asking about super intelligence because it's been in the news lately.

cbsnews.com/news/ai-superintel…

I don't think it's particularly loaded, except as you pointed out, I ask about when and not whether. I think doing both in one question is hard, so I asked the question I was interested in.

Humans are "close to being outsmarted" by superintelligence, AI expert says

Rapid developments in AI are prompting concerns about "superintelligence." Here's why some experts are worried.
Mary Cunningham (CBS News)
@muddle 🥣
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Melia Sand, Ash to Ash

Unknown, but current hardware and software can only barely clear a minimal bar of cognition; forget any kind of intelligence. Conversational LLMs piggy back on human mentalisation cognitions to create the illusion of a mind, but it's even less real than the "people" that used to live in my head when I was a teenager.

We don't even have neuromorphic electronics that can remotely function like a nervous system. We can perform basic cognitions on neuromorphic hardware, but there is no reason to believe that anything like intelligence will magically emerge if you throw enough transistors at the problem.

A further problem is the gordian knot of trying to understand the brain, from its underlying physics to needing to understand the dynamical aspects of its anatomy; currently we're mostly stuck in correlating environmental or internal, conscious stimuli to metabolic activity in particular regions. Lesion studies are more problematic in terms of the accuracy of anatomical knowledge they provide.

Side thought, I think LLMs show one of the biggest weaknesses of attempting to create true AI. As soon as we have a system that can mimic the form of human speech, we are biased to inferring a mind into it through mentalisation cognition. It is going to be an important problem to be able to prove a priori that a system that produces language is doing so as a result of spontaneous self-reflection and motivated by social cognition.

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Melia Sand, Ash to Ash

just as an aside, I think it's important to distinguish cognition and intelligence, since obviously even trivial neural nets have some level of cognition, as do life forms that don't even have a nervous system, or only exist as single cells.

As to whether hardware needs to be brainlike, I lean towards the existence proof, but also with the proviso that neural net software is too abstract and deliberately elides potentially important physical properties of a brain. Some examples:
* neurons have chemical signalling that propagates at the speed of sound, in a volume, beyond the synaptic transmission.
* neurons have internal state due to their epigenome.
* neurons have complex, mesh-like connections between anatomical regions.
* neural information is inherently sensitive to time in a variety of ways, including those mesh-like connections.
* non-neural tissue in the brain also affects neuronal behaviour.
* there are unknown nonclassical properties of neurons.

Maybe you could throw enough matrix munching transistors at a neural net and get a life-like mind that is superior to life in all domains, but I doubt it, not with current software models of neural nets. They are only able to reproduce particular kinds of cognitions, and certainly nothing like a mind.

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Evan Prodromou 🇨🇦🇺🇸🇬🇷🇵🇸

Thanks everyone! I have to admit that I'm not sure. Here's my best guess.

One way to guess at whether a system can handle intelligence is to estimate the complexity of that system. The human brain has 86 billion neurons and about 100 trillion synapses, the connections between the neurons. So, we could make a claim that systems won't become intelligent until they're at least that complex. This is Ray Kurzweil's estimation method.

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Evan Prodromou 🇨🇦🇺🇸🇬🇷🇵🇸

I think it's possible that it's never. We only have one example of known intelligence, and a lot of interesting close calls, including not only some pretty smart mammals, but also eusocial insects as collective intelligences. We just don't know enough about intelligence to say what kinds of systems it can arise in, and what kind of systems it can't.
This entry was edited (today, 2:02 AM)
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Evan Prodromou 🇨🇦🇺🇸🇬🇷🇵🇸

I don't think we know for sure a lot about intelligence. We don't know that it can be implemented on silicon chips, but we also don't know if it can't. We don't know if it has to work like brains do, or if it could work in entirely different ways. We don't even know exactly how our own minds arise from our brains. We don't know if we can replicate intelligence without understanding our own first.
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Evan Prodromou 🇨🇦🇺🇸🇬🇷🇵🇸

I'm pretty dubious about claims that superintelligence is so immanent, and so dangerous, that LLM technology should be restricted to only a few trusted companies and the US government. That sounds like some self-serving bullshit by companies that want to consolidate economic power by partnering with a protectionist government.
This entry was edited (today, 2:00 AM)
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Evan Prodromou 🇨🇦🇺🇸🇬🇷🇵🇸

All that said, AI is in a very different place than it was at the beginning of my working career, during the "AI Winter" of the early 90s. Deep learning really changed a lot. We use AI techniques now a lot more, for a lot more areas of applicability. And LLMs are really impressive.
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Evan Prodromou 🇨🇦🇺🇸🇬🇷🇵🇸

There have been previous generations of AI technology that hit walls -- the planning systems of the 1950s, the expert systems of the 1980s. It's possible that LLMs will hit some kind of wall -- that it won't be possible to get to superintelligence with that method.
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Evan Prodromou 🇨🇦🇺🇸🇬🇷🇵🇸

Other humans at least have the same kinds of bodies as I do, so by the principle of mediocrity (I'm probably not an exception), if I have a mind other humans probably do too.

We can't assume that with machine intelligence. It is by definition very different from me. Everything I know about AI suggests that what's going on inside the machine is very different from what goes on inside me.

oldguycrusty reshared this.

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Chris Alemany🇺🇦🇨🇦🇪🇸

I think, and hope, the biggest impediment to that exponential growth of “intelligence” will be raw resources. Power, material, and water.

These data centres are already hoovering up precious minerals and complex components so much that it is causing serious shortages. They are already consuming magnitudes of electricity to require their own generation, or sucking power from communities. They are already consuming so much water that concerns are rising for capacity and environmental impact.

And then there is the financial capital.

We have never seen a technology this *hungry* before. Short of turning humanity itself into batteries, I don’t think there will be true intelligence before these issues are solved. So my answer would be beyond 2050.

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malte

I agree we don't know much about intelligence - in the way that we tend to confuse it with other potentially very different phenomena. One thing that often gets confused with it is self-consciousness. Another thing that is not synonymous with either of those is intention. Psychologists working on those concepts have interesting things to say about all three, but it doesn't get through in the public yet.
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Rob Ricci

I'm not convinced that they will keep growing at that rate - I think we are fairly well along the road of diminishing returns for dollars-to-model-quality.

Yes, models are getting better. I agree.

But I don't think that it's currently true that if you spend 10x as much, you train a model that is 10x better. I'm not going to pretend that I know what the constants of this function are, but I think they are sub-linear. I think that a scenario of ever-increasing amounts of money spent for ever-decreasing marginal improvement is going to make further linear increase in models unlikely.

I'm also not convinced that number of neurons are a great proxy for intelligence - both in terms of comparison to synapses, and in terms of linear scaling. At some point, it used to be easy to convert more transistors on a chip into a commensurate amount of computing but now ... it's not so linear. I don't know if there is such a point of diminishing return for neural nets, and if there is, what it would be but - my hunch is that it likely exists. There are not a lot of things that scale linearly forever.

Thanks for this thought-provoking thread!

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Evan Prodromou 🇨🇦🇺🇸🇬🇷🇵🇸

@davep I don't know if I can provide a citation that params play the same role in LLMs that synapses play in biological brains. I only used it by very bare analogy; they are the "connections" between parts of the network. And from the little I know, I think they're much less complex than biological synapses.
@David Penfold

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