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Comment by vlovich123 | Hacker News Reader

I don’t think I follow this argument. AI has been dropping in costs and complexity the more engineering time is spent on it. It seems like the bottleneck is humans creating new AI techniques right? If an AI is capable of developing new AI techniques unsupervised, isn’t that by definition the singularity? Heck doesn’t even need to be unsupervised. If it can even do most of the heavy lifting for a human I feel like that would put us into runaway territory.

Granted we’re a long way away from that and likely we’d need an AI that could come up with its own hypotheses for new AI models to make this truly minimal cost and that feels even farther away. But I don’t think I follow the claim that each improvement step change requires to much massive extra scaling as it seems not match what we’ve seen over the past few years (granted I could be misinformed - I’m applying a 10k view spectator and maybe my own mental model here is flawed).

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I consider the singularity to be the point at which the certainty of our predictions about our future becomes close to zero. By this definition I reckon we are already in the singularity.

It’s not necessarily bad. The problem with the singularity is that that we can’t tell if it’s bad or not.

Certainty about predictions of the future has always been close to zero. If you take a person from an appropriate time and ask them what the future will look like in 1000, 100 or even 20 years then their predictions will bear little resemblence to what actually occurs.

As humans we have a tendancy to make linear future predictions based on past observations. Over the timescales that matter significant effects occur from previously unseen kinds of events that become important via interactions with other events.

The important measurement would be what the length of the event horizon - the length of time before our predictions rapidly decrease to zero certainty. In a singularity we would expect that length of time to decrease close to zero. What has it been historically? I would claim that 5-10 years is a difficult period to make meaningful predictions about. I think that 20 years has proven to be very difficult but possible in the past. I am unaware of any 100+ year predictions that have landed with better than random chance.

Interestingly I think we are entering a period where 5 years will be the upper bound, and even predictions over shorter 2-3 year timespans are going to become difficult.

I would say that we are really saying the same thing. I did intend to imply “short term predictions”.

However I feel that these LLMs are not like the internet or the release of the iPhone. We’ve gone in a very short time from LLMs in the lab to ChatGPT to passing the bar exam.

Considering the rate of research that’s being published and the fact that what we see today is generally at least many months behind the state of the art, I don’t feel that we can even have any certainty about the next 6 months.

Ah, I did not grasp that reading your comment - then we are saying the same thing.

Yes, I think that progress has become rapid enough that we can't predict six months out. I suspect that we are only 1-2 inventions away from something quite large and transformative. Obviously LLMs have already created a lot of excitement and opened up new areas of content generation already, but I think something larger is coming.

Any exponential growth in tech makes it hard for us to predict the outcome. By that metric we’ve been in that phase for some 70 years since the invention of the transistor. The world today looks nothing like the world in the 50s and the transistor is a huge part of that reason. And that is true - that was the technological singularity and we’ve been in that world ever since.

When people today talk about the AI singularity though it’s a slightly different definition from the more general technological singularity which is that the AI itself is delivering that improvement with no input from humans.

It's hard to predict some things now, but the vast majority of things are as easy to predict as they've ever been, or we wouldn't be reasonably sure that when we took a step on the sidewalk that our feet would touch the ground, or that when we opened a door leading in to a house there'd be a house on the other side, or that cars, airplanes, and other machines mostly work as designed, that food is nutritious and satiates hunger, etc, etc, etc..

Prediction for the overwhelming majority of things is still reasonably easy, or we wouldn't be able to survive.

We're very, very far from a singularity if it really means that "our predictions about our future becomes close to zero".

>or we wouldn't be able to survive.

If you want to keep surviving you have to be 100% correct in your ability to predict the correct choice. Now the thing is the vast majority of predictions you have to make about anything are not necessarily life or death and/or rapidly changing. LIke you said, cars are still cars, and food is still food.

The problem is the change can be massively abrupt. Watching the use of drones in Ukraine is seemingly a decent example of this. Small cheap drones are being used in mass as surveillance platforms, for artillery targeting, and even for direct enemy bombing. They cost of defending against them seems to be far higher than the cost of using them to attack.

Now this isn't exactly unpredictable in itself. Where is gets more questionable about predictions, is what is it going to look like when AI is given direct control of swarms of these things rather than individuals with an xbox controller? Suddenly decades of battle field planning is out the window and there is a massive shakeup in fighting capabilities.

> If an AI is capable of developing new AI techniques unsupervised, isn’t that by definition the singularity?

AI is not capable of developing new AI techniques unsupervised.

Honestly the lowest hanging fruit should be the ability for LLMs to generate their own textual training data that leads to an improvement in model abilities, since that is what they are supposed to be good at. Until then, it's still garbage in garbage out.

> AI is not capable of developing new AI techniques unsupervised.

Today. Hence we're not in the singularity.

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