I'm sure she'll be happy to strap you in front of a camera.

Next time I'm reincarnated I'm going to opt for being a member of the privileged class.

The Bluesky Mir is coming ;)

Huh? You're upset that Jack left Bluesky because people there didn't like him? I'm confused.

Sounds like Jack went full Kanye, and got booted out of Bluesky.

He didn't get "booted", but it was a rather amusing situation, where he dropped a bunch of seed money on Jay's project to make Bluesky... only to find out that the vast majority of the people who flocked there don't actually like him, and weren't afraid to let him know ;)

1) You don't open to more people than you have the capacity to serve.

2) They do not use the same backend. Bluesky's backend is specifically designed to fix Mastodon's design flaws that make it so annoying.

3) Bluesky is growing far faster than Mastodon.

The real market is investors. He's seeking a $6B valuation on x.ai, which is just nonsensical vs. what they're offering.

How is a flow chart...

You stopped after reading two word and ignored every other word in the post. *eyeroll*

Predicting words is just about resonance

The word "resonance" is a non-sequitur in that sentence. You might as well have written, "Predicting words is all about Australopithecus."

For me it's the Final Fantasy II trap.

As a kid, my first run through Final Fantasy II, I had gotten like halfway through when I hit a fairly difficult area, and I was getting tired of the fights, so rather than spending time leveling up and whatnot before going there, I just increasingly started making a habit of running away from enemies. And it worked great, I got further and further and further, really quickly. But my level correspondingly fell further and further behind what it should have been for the area, to the point where ultimately I could no longer beat the bosses and advance further.

If they want us to try their coin, they should hand out some free samples.

He comes up with the most mind-bogglingly stupid ideas based on how twisted his conception of reality has gotten. Basically, in his reality, news articles are probable lies, but people who get lots of likes on Twitter are probable truths.

Good to see we're abandoning the premise that the logic behind LLMs is "simple".

LLMs, these immensely complex models, function basically as the most insane flow chart you could imagine. Billions of nodes and interconnections between them. Nodes not receiving just yes-or-no inputs, but any degree of nuance. Outputs likewise not being yes-or-no, but any degree of nuance as well. And many questions superimposed atop each node simultaneously, with the differences between different questions teased out at later nodes. All self-assembled to contain a model of how the universe and the things within it interact.

At least, that's for the FFNs - the attention blocks add in yet another level of complexity, allowing the model to query a latent-space memory, which each FFN block then outputs transformed for the next layer. The latent space memory being.... all concepts in the universe that exist, and any that could theoretically exist between any number of existing concepts. These are located in an N-dimensional space, where N is hundreds to thousands. The degree of relationship between concepts can be measured by their cosine similarity. So for *each token* at *each layer*, a conceptual representation of somewhere in the space of everything that does or could exist is taken, and based on all the other things-that-does-or-could exists and their relative relations to each other, are transformed by the above insane-flow-chart FFN into the next positional state.

Words don't exist in a vacuum. Words are a reflection of the universe that led to their creation. To get good at predicting words, you have to have a good model of the underlying world and all the complexity of the interactions therein. It took achieving the Transformers architecture, with the combination of FFNs and an attention mechanism, along with mind-bogglingly huge scales of interactions (the exponential interaction of billions of parameters), to do this - to develop this compressed representation of "how everything in the known universe interacts".

What is "a few" about 316 billion parameters, let alone before you exponentiate their interactions?

What is "simple" about the extremely complex interactions of 316 billion parameters?

Honestly I question how much of the training was actually done with Twitter data.

It certainly was a source, but I seriously doubt it was the only data or even majority of the data.

Either way, doesn't really matter with RAG. RAG doesn't have to "know" much, just how to summarize things others have written.