We don’t have the same problems LLMs have.

LLMs have zero fidelity. They have no - none - zero - model of the world to compare their output to.

Humans have biases and problems in our thinking, sure, but we’re capable of at least making corrections and working with meaning in context. We can recognise our model of the world and how it relates to the things we are saying.

LLMs cannot do that job, at all, and they won’t be able to until they have a model of the world. A model of the world would necessarily include themselves, which is self-awareness, which is AGI. That’s a meaning-understander. Developing a world model is the same problem as consciousness.

What I’m saying is that you cannot develop fidelity at all without AGI, so no, LLMs don’t have the same problems we do. That is an entirely different class of problem.

Some moon rockets fail, but they don’t have that in common with moon cannons. One of those can in theory achieve a moon landing and the other cannot, ever, in any iteration.

If all you’re saying is that neural networks could develop consciousness one day,, sure, and nothing I said contradicts that. Our brains are neural networks, so it stands to reason they could do what our brains can do. But the technical hurdles are huge.

You need at least two things to get there:

1. Enough computing power to support it.
2. Insight into how consciousness is structured.

1 is hard because a single brain alone is about as powerful as a significant chunk of worldwide computing, the gulf between our current power and what we would need is about… 100% of what we would need. We are so woefully under resourced for that. You also need to solve how to power the computers without cooking the planet, which is not something we’re even close to solving currently.

2 means that we can’t just throw more power or training at the problem. Modern NN modules have an underlying theory that makes them work. They’re essentially statistical curve-fitting machines. We don’t currently have a good theoretical model that would allow us to structure the NN to create a consciousness. It’s not even on the horizon yet.

Those are two enormous hurdles. I think saying modern NN design can create consciousness is like Jules Verne in 1867 saying we can get to the Moon with a cannon because of “what progress artillery science has made in the last few years”.

Moon rockets are essentially artillery science in many ways, yes, but Jules Verne was still a century away in terms of supporting technologies, raw power, and essential insights into how to do it.

You’re definitely overselling how AI works and underselling how human brains work here, but there is a kernel of truth to what you’re saying.

Neural networks are a biomimicry technology. They explicitly work by mimicking how our own neurons work, and surprise surprise, they create eerily humanlike responses.

The thing is, LLMs don’t have anything close to reasoning the way human brains reason. We are actually capable of understanding and creating meaning, LLMs are not.

So how are they human-like? Our brains are made up of many subsystems, each doing extremely focussed, specific tasks.

We have so many, including sound recognition, speech recognition, language recognition. Then on the flipside we have language planning, then speech planning and motor centres dedicated to creating the speech sounds we’ve planned to make. The first three get sound into your brain and turn it into ideas, the last three take ideas and turn them into speech.

We have made neural network versions of each of these systems, and even tied them together. An LLM is analogous to our brain’s language planning centre. That’s the part that decides how to put words in sequence.

That’s why LLMs sound like us, they sequence words in a very similar way.

However, each of these subsystems in our brains can loop-back on themselves to check the output. I can get my language planner to say “mary sat on the hill”, then loop that through my language recognition centre to see how my conscious brain likes it. My consciousness might notice that “the hill” is wrong, and request new words until it gets “a hill” which it believes is more fitting. It might even notice that “mary” is the wrong name, and look for others, it might cycle through martha, marge, maths, maple, may, yes, that one. Okay, “may sat on a hill”, then send that to the speech planning centres to eventually come out of my mouth.

Your brain does this so much you generally don’t notice it happening.

In the 80s there was a craze around so called “automatic writing”, which was essentially zoning out and just writing whatever popped into your head without editing. You’d get fragments of ideas and really strange things, often very emotionally charged, they seemed like they were coming from some mysterious place, maybe ghosts, demons, past lives, who knows? It was just our internal LLM being given free rein, but people got spooked into believing it was a real person, just like people think LLMs are people today.

In reality we have no idea how to even start constructing a consciousness. It’s such a complex task and requires so much more linking and understanding than just a probabilistic connection between words. I wouldn’t be surprised if we were more than a century away from AGI.

Little Brother is a novel about a future dystopia where copyright laws have been allowed free rein to destroy people’s lives.

It’s legislated that only “secure” hardware is allowed, but hardware is by definition fixed, which means that every time a vulnerability is found - which is inevitable - there is a hardware recall. So the black market is full of hardware which is proven to have jailbreaking vulnerabilities.

Just a glimpse of where all this “trusted”, “secure” computing might lead.

As a short video I saw many years ago explained on the concept: “trust always depends on mutuality, and they already decided not to trust you, so why should you trust them?”

Edit: holy shit, it’s 15 years old, and “anti rrusted computing video dutch voice over” (turns out the guy is German actually) was enough to find it:

https://www.lafkon.net/work/trustedcomputing/

Faster to apply and much harder to remove.

Interesting article, but you have to be aware of the flipside: “people said flight was impossible”, “people said the earth didn’t revolve around the sun”, “people said the internet was a fad, and now people think AI is a fad”.

It’s cherry-picking. They’re taking the relatively rare examples of transformative technology and projecting that level of impact and prestige onto their new favoured fad.

And here’s the thing, the “information superhighway” was a fad that also happened to be an important technology.

Also the rock argument vanishes the moment anyone arrives with actual reasoning that goes beyond the heuristic. So here’s some actual reasoning:

GenAI is interesting, but it has zero fidelity. Information without fidelity is just noise, so a system that can’t solve the fidelity problem can’t do information work. Information work requires fidelity.

And “fidelity” is just a fancy way of saying “truth”, or maybe “meaning”. Even as conscious beings we haven’t really cracked that issue, and I don’t think you can make a machine that understands meaning without creating AGI.

Saying we can solve the fidelity problem is like Jules Verne in 1867 saying we could get to the moon with a cannon because of “what progress artillery science has made during the last few years”. We’re just not there yet, and until we are, the cannon might have some uses, but it’s not space technology.

Interestingly, artillery science had its role in getting us to the moon, but that was because it gave us the rotating workpiece lathe for making smooth bore holes, which gave us efficient steam engines, which gave us the industrial revolution. Verne didn’t know it, but that critical development had already happened nearly a century prior. Cannons weren’t really a factor in space beyond that.

Edit: actually metallurgy and solid fuel propellants were crucial for space too, and cannons had a lot to do with that as well. This is all beside the point.