Jack000

None of my relatives who were alive in 1969 have ever heard of the US moon landings. The information was totally suppressed at the time and in the intervening decades they simply had no reason to seek it out.

The younger generations think it’s a hoax but are more uncertain because they know that any information about the event is likely to be propaganda

None of the things you listed mean much when it comes to ethno-nationalism. There are a lot of Chinese that love American brands but still hate the US for nationalist reasons.

Among my relatives I would say all are anti-US. About 5-6 of them vehemently so and want the war to start immediately.

I grew up in China and if you think there’s less propaganda in China compared to the US I don’t know what to tell you.

It's heavy and the extra encumbrance is not great for general purpose computing, though I never felt the need for extra screen real estate while I'm coding, watching youtube, sending emails etc.

That said I still use my AVP regularly, it's a great home theater system for 4k HDR content that's portable for travel. I've owned other headsets like the valve index but the novelty of 3d gaming wore off pretty quickly.

This is kind of the visual equivalent of asking an LLM to count letters. The failure is more related to the tokenization scheme than the underlying quality of the model.

I'm not certain about the specific models tested, but some VLMs just embed the image modality into a single vector, making these tasks literally impossible to solve.

Chess is a great example because it highlights the subtle difference between LLMs and agents. What GPT3.5 does is not quite playing chess but creating realistic chess moves that a human might make.

An LLM could play chess though, all it needs is grounding (by feeding it the current board state) and agency (RF to reward the model for winning games)

I think a lot of the confusion on whether LLMs can think stems from the fact that LLMs are purely models of language and solve intelligence as a kind of accidental side-effect.

The real problem that an LLM is trying to solve is to create a model that can enumerate all meaningful sequences of words. This is just an insane way of approaching the problem of intelligence on the face of it. There's a huge difference between a model of language and an intelligent agent that uses language to communicate.

What LLMs show is that the hardest problem - of how to get emergent capabilities at scale from huge quantities of data - is solved. To get more human-like thinking, all that is needed is to find the right pre-training task that more closely aligns with agentic behavior. This is still a huge problem but it's an engineering problem and not one of linguistic theory or philosophy.

In pixel space a convnet uses pixel-wise convolutions and a pixel-kernel. If you represent a vector image as a polygon, the direct equivalent to a convolution would be the Minkowski sum of the vector image and a polygon-kernel.

You could start off with a random polygon and the reverse diffusion process would slowly turn it into a text glyph.

I think this approach isn't ideal because you're representing pixels as 150x150 unique bins. With only 71k fonts it's likely a lot of these bins are never used, especially at the corners. Since you're quantizing anyways, you might as well use a convnet then trace the output, which would better take advantage of the 2d nature of the pixel data.

This kind of reminds me of dalle-1 where the image is represented as 256 image tokens then generated one token at a time. That approach is the most direct way to adapt a causal-LM architecture but it clearly didn't make a lot of sense because images don't have a natural top-down-left-right order.

For vector graphics, the closest analogous concept to pixel-wise convolution would be the Minkowski sum. I wonder if a Minkowski sum-based diffusion model would work for svg images.

> what will LLMs ever do for us?

Hallucinations are an engineering problem and can be solved. Compute per dollar is still growing exponentially. Eventually this technology will be widely proliferated and cheap to operate.

A doctor for every person, a teacher for every child, available any time and for free.

As Yann Lecun said, “in high dimension, there is no such thing as interpolation. In high dimension, everything is extrapolation.”

It’s trivial to come up with a prompt that doesn’t exist in the dataset. To generalize, the model cannot memorize.

The foreign buyers tax, the empty home tax, and now the total ban on foreign buyers introduced this year has had zero effect on home prices. It's time to stop blaming foreigners.

The solution is to build more and more quickly.