AI: First New UI Paradigm in 60 Years?

Yes, but I'm saying it's not an accident - I've mentioned mechanisms like culture, social customs, and education, which we've been using in some form for all our recorded history. I should've probably added violent conflicts within and between tribes/groups, too, which also acted to reduce variance, by culling the more volatile and less agreeable people. People today are "extremely close in both opinion and ability" because for the past couple thousands years, generation by generation, we've been busy reducing the variance of individuals.

EDIT: keeping high-variance individuals locked up safely away is just one of the methods we use, specifically to deal with outliers. It too traces back to the dawn of recorded history - shunning, expelling individuals from the tribe (which often meant certain death), sending them to faraway lands, or forcing them into war, were other common means past societies used to eliminate high-variance outliers.

As for authority, it's a separate topic - I argue that hierarchical governance is an artifact of scale: it's necessary to coordinate groups past certain size (~Dunbar's number), when our basic social intuitions are no longer up to the task. But the first level of hierarchy can handle only so many people, and if you want to coordinate multiple such groups, you need to add another layer... and that's how, over time, human societies scaled from tribes of couple dozen people, to nation states of hundreds of millions.

Even as the focus is usually on the national governments, the entire hierarchy is still there - you have states and lands/vovoidships/counties with their own governance, then another level for a major city and surrounding villages, then yet another level in each individual village, and one or two levels in the city itself, etc. We don't often pay attention to it, but the hierarchy of governance does reach down, in some form, all the way to groups of couple hundred people or less.

There is no magic "paint the picture I want" button in Photoshop. There is a magic button in AI tools. This magic button does in one request what would take dozens or hundreds of commands without the magic button.

It very rarely does. And the more specific the request, the more work you have to do to get, at best, close to what you want. You may need to train your own model or blend models and mess with ControlNet. And then you have to do more work to fix stuff like fingers and eyes. In order to really use AI to its full potential (which still isn't, IMO, fully up to the capabilities of the software or artists it's intended to replace) you have to understand how it works, how the various filters work, how models work, etc.

Once you learn most of the really good AI generated art you find online is still heavily edited in Photoshop by actual artists you realize it's anything but magic.

>> This magic button does in one request what would take dozens or hundreds of commands without the magic button.

> It very rarely does.

No, it always does. You don't have to press the button 45 times in the morning and 67 times in the afternoon and 24 in the evening. You press it once, and you get a result.

What you read was: "This magic button does in one click what you want". That is not what I wrote, and not what the author argues.

> And the more specific the request, the more work you have to do to get, at best, close to what you want. You may need to train your own model or blend models and mess with ControlNet. And then you have to do more work to fix stuff like fingers and eyes.

Yes, you're repeating the author's point well: the current systems have bad usability in regards to how a user expresses their intentions. That does not change that the system produces one complex result with one button click.

Let's imagine there does exist a function for evaluating truth - it takes in a statement and produces whether that statement is "true" (whatever "true" means). Let's also say it does that perfectly.

We train the LLM. We keep training it, and training it, and training it, and we eventually get a set of weights where our eval runs only make it produce statements where the truth-function says they are truthful.

We deploy the LLM. It's given an input that wasn't part of the evaluation set. We have no guarantee at all that the output will be true. The weights we chose for the LLM during the training process are a serendipitous accident: we observed that they produced truthy output in the scenarios we tested. Scenarios we didn't test _probably_ produce truthy output, but in all likelihood some will not, and we have no mathematical guarantee.

This remains the case even if you have a perfect truth function, and remains true if you use deterministic inference (always the most likely token). Your comment goes even further than that and asserts that a mostly-accurate function is good enough.