Readit NewsBack to ML models?
However, I have extreme skepticism when it comes to the applicability of this finding. Based on what they have written, they seem to have created a universal (maybe; adaptable at the very least) constraint-satisfaction solver that learns the rules of the constraint-satisfaction problem from a small number of examples. If true (I have not yet had the leisure to replicate their examples and try them on something else), this is pretty cool, but I do not understand the comparison with CoT models.
CoT models can, in principle, solve _any_ complex task. This needs to be trained to a specific puzzle which it can then solve: it makes no pretense to universality. It isn't even clear that it is meant to be capable of adapting to any given puzzle. I suspect this is not the case, just based on what I have read in the paper and on the indicative choice of examples they tested it against.
This is kind of like claiming that Stockfish is way smarter than current state of the art LLMs because it can beat the stuffing out of them in chess.
I feel the authors have a good idea here, but that they have marketed it a bit too... generously.
What is the justification for this? Is there a mathematical proof? To me, CoT seems like a hack to work around the severe limitations of current LLMs.
Edit: I apologies, the author has pre-gpt posts that use em dashes so likely it’s part of their writing style.
This is not helped by the fact that she pushes an interpretation of quantum mechanics viewed as fringe at best. Her takes on modern physics seem typically disingenuous or biased.
Right now it hasn't amounted to anything useful, other than Shor's and 'experiments' and promises and applications that are no better done on a GPU rack right now.
Wondermoss was a spectacular piece of tech. Every single forest scene and every single piece of vegetation in Brave is made using Wondermoss, and it was all procedural- when you'd open up a shot from Brave in Menv30, you'd see just the characters and groundplane and very little else, and then you'd fire up the renderer and a huge vast lush forest would appear at rendertime. The even cooler thing was that since Brave was still using REYES RenderMan, iq took advantage of the REYES algorithm's streaming behavior to make Wondermoss not only generate but also discard vegetation on-the-fly, meaning that Wondermoss used vanishingly little memory. If I remember correctly, Wondermoss only added like a few dozen MB of memory usage at most to each render, which was insane since it was responsible for like 95% of the visual complexity of each frame. One fun quirk of Wondermoss was that the default random seed was iq's phone number, and that remained for quite a number of years, meaning his phone number is forever immortalized in pretty much all of Pixar's films from the 2010s.
iq is one of the smartest and most inspiring people I've ever met.
Since noone has many qubits, typically physical qubits are compared as opposed to virtual qubits (the error corrected ones).
The other key figures of merit are the 1-qubit and 2-qubit gate fidelities (basically the success rates). The 2-qubit gate is typically more difficult and has a lower fidelity, so people often compare qubits by looking only at the 2-qubit gate fidelity. Every 9 added to the 2-qubit gate fidelity is expected to roughly decrease the ratio of physical to virtual qubits by an order of magnitude.
In architectures where qubits are fixed in place and can only talk to their nearest neighbours, moving information around requires swap gates which are made up of the elementary 1 and 2-qubit gates. Some architectures have mobile qubits and all-to-all connectivity, so their proponents hope to avoid swap gates, considerably reducing the number of required 2-qubit gates required to run an algorithm, thus resulting in less errors to deal with.
Some companies, particularly ones on younger architectures, but perhaps with much better gate fidelities, argue that their scheme is better by virtue of being more "scalable" (having more potential in future).
It is expected that in the future, the overall clock speed of the quantum computer will matter, as the circuits we ultimately want to run are expected to be massively long. Since we're far away from the point where this matters, clock speed is uncommonly brought up.
In general, different architectures have different advantages. With different proponents having different beliefs of what matters, it was once described to me as each architecture having their own religion.
TL;DR: the two key stats are number of qubits and 2-qubit gate fidelity.
A delayed choice setup is not too dissimilar than a Bell inequality violation experiment. The weirdness there is that you can set things up such that no signal can travel between the systems being measured, and yet the outcomes are more correlated than any classical joint state can be.
So the conclusion is that either locality fails (i.e. it’s not true that outcomes on one side are independent of how you measure the other side) or realism fails (i.e. you can’t assign values to properties before the measurement, or in other words a measurement doesn’t merely “reveal” a pre-existing value: the values pop into existence in a coordinated fashion). Both of these options are crazy, and yet at least one of them must be true.