I’m getting this shit from everywhere I’ve ever lived. I can normally ignore my phone for the most part, but I’m actually waiting on updates about the health of a relative. And this shit needs to stop. No one’s changing their mind cause of a text, guys. No one’s like, “Oh yeah, is it time to vote?” Please, just stop.

Is it Elmo?

Yeah, they (used to) send ya a csv with all your posts/comments. Got mine before I overwrote and deleted, 17 years worth. I trained an llm on it. It may or may not be writing this.

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Thank you. For as much as this post comes up, I hope people are at least getting an education.

Just looking at the mental space of the three fanboy types… Mac seems the chilliest to hang with.

e: lol, case in point

On a scale of 1-10, with 1 being Inconsequential and 10 being Paramount, how important is hierarchy to the healthy functioning of a group?

I honestly don’t know why more people aren’t excited about A.I. succeeding us.

Claude for President

This is another good use case for gAI. Copy/paste the comment into a GPT and tell it to re-write the content at the desired reading or technical level. Then it’s available for follow-up clarification questions.

That’s how I communicate my intention to pay a parking ticket. “Bowing to regulatory pressure”

Sure, thanks for your interest. It’s an incomplete picture, but we can think of LLMs as an abstraction of all the meaningful connections within a dataset to a higher dimensional space - one that can be explored. That alone is an insane accomplishment that is changing some of the pillars of data analysis and knowledge work. But that’s just the contribution of the “Attention is All You Need” paper. Many implementations of modern generative AI combine LLM inference in agentic networks, with GANs, and with rules-based processing. Extracting connections is just one part of one part of a modern AI implementation.

The emergent properties of GPT4 are enough to point toward this exponential curve continuing. Theory of mind (and therefore deception) as well as relational spatial awareness (usually illustrated with stacking problems) developed solely from increasing the parameter count describing the neural network. These were unexpected capabilities. As a result, there is an almost literal arms race on the hardware side to see what other emergent properties exist at higher model sizes. With some poetic license, we’re rending function from form so quickly and effectively that it’s seen by some as freeing and others as a sacrilege.

Some of the most interesting work on why these capabilities emerge and how we might gain some insight (and control) from exploring the mechanisms is being done by Anthropic and by users at Hugging Face. They discovered that when specific neurons in Claude’s net are stimulated, everything it responds with will in some way become about the Golden Gate Bridge, for instance. This sort of probing is perhaps a better route to progress than blindly chasing more size (despite its recent success). But only time will tell. Certainly, Google and MS have had a lot of unforced errors fumbling over themselves to stay in what they think is the race.

I’m happy to take the time to alter your perspective, if you are open to new information.

Did you watch Breaking Bad?

The Queen Bean is an excellent name for a coffee shop.

I understand this perspective, because the text, image, audio, and video generators all default to the most generic solution. I challenge you to explore past the surface with the simple goal of examining something you enjoy from new angles. All of the interesting work in generative AI is being done at the edges of the models’ semantic spaces. Avoid getting stuck in workflows. Try new ones regularly and compare their efficacies. I’m constantly finding use cases that I end up putting to practical use - sometimes immediately, sometimes six months later when the need arises.

Very interesting! I’ll check it out. Thanks for the recommendation.