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.

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.

"Grok's differentiator from other AI chatbots like ChatGPT is its exclusive and real-time access to X data." That's just RAG. Retrieval Augmented Generation. All Grok is doing is acting as a summarizer. This is something you can do with an ultra-lightweight model, you don't need a 314B param monster.

Also, you don't need an X Premium subscription to "get access" to Grok, since its weights are public. To "get access" to an instance running it, maybe.

I've not tried running it, but from others who have, the general consensus seems to be: it's undertrained. It has way more parameters than it should need relative to its capabilities. Kinda reminiscent of, say, Falcon.

I also have an issue with "A snarky and rebellious" LLM. Except people using them for roleplaying scenarios (where you generally don't want a *fixed* personality), people generally don't want it inserting some sort of personality into their responses. As a general rule, people have a task they want the tool to do, and they just want the tool to do it. This notion that tools should have "personalities" is what led to Clippy.

Global mean surface temperature for one.

What?

We haven't been keeping records for billions of years.

The combustion of fossil fuels is relevant because it is releasing carbon from hundreds of millions of years ago back into the atmosphere. Which is rapidly warming the globe. Man-mads is relevant because no one else is doing that.

I agree that climate change is only one cause impacting the observed reduction in populations tracked by the living planet index. There's also habitat loss, overexploitation and pollution.

But you're also claiming that none of the reduction is populations is attributable to climate change?

Because that's going to be difficult to defend. Nearly every studied ecosystem is being impacted by climate change. To quote the summary for policymakers of the 2023 IPCC report:

It is causing it. Devastating drought in Amazon result of climate crisis, study shows

I agree with that.

The invasion of Ukraine has contributed. So has climate change. Climate change is producing and will continue to produce a decrease in crop quality and yields..

Which populations of which animals are you including in "here"?

The recent living planet report. Download the English version from this page.If you're interested in the details, the technical supplement is here.

There are regions where they are increasing due to climate change. Most noticeably. the boreal forests. However, you do get an increase in the geographical and temporal extents of drying at the edge of where Hadley cells cause drying. So both fires in places like Canada and Russia as well as in places like Southern USA are being exacerbated by global warming.

Forest management seems normal here. Such a general statement needs links.

I'm more talking about the kind of drought where plants are under water stress because of lower rainfall or increased evaporation. Leaving especially dry vegetable matter that, with increased heat waves, lead to especially intense wildfires.

Apparently so.