> .. we can abstract, and formalise, and prove, and demonstrate. You can't do that just by predicting. That's a power over and beyond modelling data.
I think we will find this not to be the case. And of course, many people like me do think that the ability to predict well enough implies the ability to do everything else.
Since I wrote my reply, I found two other posts on HN that seem relevant to that if you want to read more:
Here is a quote from that last one - I am still reading it since it is long, but
> ... I call this the prediction orthogonality thesis: A model whose objective is prediction can simulate agents who optimize toward any objectives, with any degree of optimality (bounded above but not below by the model’s power).*
> This is a corollary of the classical orthogonality thesis, which states that agents can have any combination of intelligence level and goal, combined with the assumption that agents can in principle be predicted. A single predictive model may also predict multiple agents, either independently (e.g. in different conditions), or interacting in a multi-agent simulation. A more optimal predictor is not restricted to predicting more optimal agents: being smarter does not make you unable to predict stupid systems, nor things that aren’t agentic like the weather.
>> I think we will find this not to be the case. And of course, many people
like me do think that the ability to predict well enough implies the ability
to do everything else.
That's a debate that's been going on for a long time. For a while I've kind of
waded in it unbeknownst to me, because it's mainly a thing in the philosophy
of science and I have no background in that sort of thing. So far I only got a
whiff of a much larger discussion when I watched Lex Friedman's interview with
Vladimir Vapnik. Here's a link:
Vapnik basically says there's two groups in science, the instrumentalists, who
are happy to build models that are only predictive, and the realists, who want
to build explanatory models.
To clarify, a predictive model is one that can only predict future
observations, based on past observations, possibly with high accuracy. An
explanatory model is one that not only predicts, but also explains past and
future observations, according to some pre-existing scientific theory.
For me, it makes sense that explanatory models are more powerful, by
definition. An explanatory model is also predictive, but a predictive model is
not explanatory. And once an explanatory model is found, once we understand
why things turn out the way they do, our ability to predict also improves,
tremendously so.
My favourite exmaple of this is the epicyclical model of astronomy, that
dominated for a couple thousand years. Literally. It went on from classical
Greece and Rome, all the way to Copernicus, who may have put the Earth in the
center of the universe, but still kept it on a perfect circular orbit with
epicycles. Epicycles persisted for so long because they were damn good at
predicting future observations, but they had no explanatory power and, as it
turned out, the whole theory was mere overfitting. It took Kepler, with his
laws of planetary motion, to finally explain what was going on. And then of
course, Newton waltzed in with his law of universal gravitation, and explained
Kepler's laws as a consequence of the latter. I guess I don't have to wax
lyrical about Newton and why his explanatory, and not simply predictive,
theory, changed everything, astronomy being just one science that was swept
away in the epochal wave.
So, no, I don't agree: prediction is what you do until you have an
explanation. It's not the final goal, and it's certainly not enough. The only
thing that will ever be enough is to figure out how the world works.
It’s an interesting debate to bring up, but I think not really the same kind of prediction without explanation I am talking about.
The second post I linked turned out to be really interesting, it both aligns with my thoughts while also adding new ideas and concepts. It makes a distinction between GPT and the agents it simulates, the simulator and the simulacra.
A good enough simulator can simulate an entity capable of explaining lots of things, depending on the limits of the simulator.
I think we will find this not to be the case. And of course, many people like me do think that the ability to predict well enough implies the ability to do everything else.
Since I wrote my reply, I found two other posts on HN that seem relevant to that if you want to read more:
AI vs. AGI vs. Consciousness vs. Super-intelligence vs. Agency https://secondbreakfast.co/ai-vs-agi-vs-consciousness-vs-sup...
Simulators (Self-supervised learning may create AGI or its foundation) https://generative.ink/posts/simulators/
Here is a quote from that last one - I am still reading it since it is long, but
> ... I call this the prediction orthogonality thesis: A model whose objective is prediction can simulate agents who optimize toward any objectives, with any degree of optimality (bounded above but not below by the model’s power).*
> This is a corollary of the classical orthogonality thesis, which states that agents can have any combination of intelligence level and goal, combined with the assumption that agents can in principle be predicted. A single predictive model may also predict multiple agents, either independently (e.g. in different conditions), or interacting in a multi-agent simulation. A more optimal predictor is not restricted to predicting more optimal agents: being smarter does not make you unable to predict stupid systems, nor things that aren’t agentic like the weather.