I don’t really know, but I think it’s mostly to do with pentagons being under-represented in the world in general. That and the specific way that a pentagon breaks symmetry. But it’s not completely impossible to get em to make one. After a lot of futzing around, o1 wrote this prompt, which seems to work 50% of the time with FLUX [pro]:

An illustration of a regular pentagon shape: a flat, two-dimensional geometric figure with five equal straight sides and five equal angles, drawn with black lines on a white background, centered in the image.

Fun Fact: It is very difficult to get any of the image generators to make a pentagon.

I think it was Perplexity. Moved to using Flux since that cuddly monstrosity.

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

It’s an old game, but Pixel Junk: Monsters is a ton of fun as a co-op tower defense. Don’t bother with the sequel - s’bad.

As a chaote, it honestly didn’t register as satire. But if it is, it’s spot on.

This type of segmentation is of declining practical value. Modern AI implementations are usually hybrids of several categories of constructed intelligence.

Fair weather friends to AI crack me up.

Ironically, if they’d used an LLM, it would have corrected their writing.

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

e: lol, case in point

I like to think about the spacefaring AI (or cyborgs, if we’re lucky) that will inevitably do this stuff in our stead, assuming we don’t strangle them in the cradle.

What makes the “spicy autocomplete” perspective incomplete is also what makes LLMs work. The “Attention is All You Need” paper that introduced attention transformers describes a type of self-awareness necessary to predict the next word. In the process of writing the next word of an essay, it navigates a 22,000-dimensional semantic space, And the similarity to the way humans experience language is more than philosophical - the advancements in LLMs have sparked a bunch of new research in neurology.

Not OP, but speaking from a fairly deep layman understanding of how LLMs work - all anyone really knows is that capabilities of fundamentally higher orders (like deception, which requires theory of mind) emerged by simply training larger networks. Since we don’t have a great understanding of how our own intelligence emerges from our wetware, we’re only guessing.

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.