That’s my general understanding as well, but it isn’t a large conceptual leap to go from real-time selection of pretrained “z-vectors” to real-time generation of the same. The larger conceptual breakthrough, with demonstration of its effectiveness, is the big success here.

While not a large conceptual leap, the real-time generation of "z-vectors" is not cheap in terms of compute or data requirements, the latter of which I see as the main issue. How are you going to generate the vector from a single real-time input?

I still have yet to see anything that dissuades me from agreeing with Yann LeCun when he says Transformers are fundamentally limited. We won't get creativity, reasoning, or even move past hallucinations without a major breakthrough

How do the o3 results fit in context of this perspective?

They do not change it, from what I have seen, o3 is more hype and marketing than a meaningful step towards models which can exhibit real creativity and reasoning as humans perform it (rather than perceive it, which is the root of the hype)

For example, a small child is completely capable of being told "get in the car" and can understand, navigate, open the door, and get in, with incredibly little energy usage (maybe about the amount of a single potato chip/crisp)

Now consider what I have been working on recently (1) evaluating secops tools from both a technical and business perspective (2) prototyping and creating an RFC for the next version of our DX at the org. They are very far from this capability because it involves so many competing incentives, trade offs, and not just the context of the current state of code, but also the history and vision. Crafting that vision is especially beyond what a foundation in transformers can offer. They are in essence an averaging and sequence prediction algorithm

These tools are useful, even provide an ROI, but by no means anywhere close to what I would call intelligent.

Would love to know if you know any other papers like:

Faith and Fate: Limits of Transformers on Compositionality https://arxiv.org/abs/2305.18654

Maybe the analogy is something with gold mining. We could pretend that the machines that mine gold are actually creating gold. Pretending the entire gold mining sector is instead a discovery of alchemy.

Maybe the way alchemy kind of leads to chemistry is the analogy that applies?

I don't even know if that is right though.

The intelligence is in the training data. The model then is extracting the intelligence.

We can't forget Feynman's ideas here that we aren't going to make a robot cheetah that runs fast. We will make a machine that uses wheels. Viewing things through the lense of a cheetah is a category error.

While I agree completely with you we very well both might be completely and utterly wrong. A category error on what intelligence "is".

The interesting thing here is that the human brain also seems to use pretrained ... things. For vision, use the visual subsystem. For hearing, use the auditory subsystem. For movement ... you get the point. Plus you can combine these pretrained ... things, so for example for complex movement, like balancing on a tightrope, multiple subsystems are used (try standing on one leg with your eyes closed).

Z-vectors are of course nothing like the subsystems in your brain, but general the approach is certainly similar to how the brain works.

> things

Senses?

For sight and hearing, yes, but is "language use" a sense?

In the strict sense, no, but as a system of communication, yes; organisms need some form of sensory perception to communicate or 'sense' language.

Sort of. According to the text they can use multiple z-vectors (sets of weights that select for parts of the system to be used to answer a specific question) simultaneously, using a "simple optimization algorithm" to determine the relative weight for each of these vectors.