Q: “Whats wrong with tkinter?!?!”

A: It sucks, that’s what!

How you act throughout your life, is how your son will act in his life. And it might seem like at times he doesn’t care or is above it, but he will ultimately do how Dad did it. This isn’t taught so much as it is imprinted. for me at least, that means: what will your son teach you? You will see just how much it matters.

Maybe an analogy is that it’s like LLM training…the better the training data, the better model. You are the training data. Remember that! And congratulations.

For the record, I think this project was an excellent presentation, I loved the interactive elements snd the meteorite (or is that a meteorwrong?) to come across the page. This is about as good of a use of LLM's as I have seen. I didn't see anything about it in the article, but does anyone know what future plans are for this particular project, or is that a wrap? I didn't see I the Where This Stands section anything about future search topics. This really feels like a time where finding the question is every bit as important as finding an answer to that question.

Do you use AI much? Not a trick question. What is your usage like? Reason I ask is recall OpenAI hyping GPT-2 with the same language as they are these new models. Doom and gloom. It sells. It gets attention. But use these systems enough and you start to become very familiar with their capabilities, and they are just so limited, and that's not even talking about how they lose the thread on long tasks. I understand that agency expands that a little, but not by much, really. When you use these systems a lot, it tends to be easier to see through the hype.

The real threat is, and will be for some time I think, people. Guard rails are not for AI. Guard rails are for people. All of this talk we are seeing in this space, this security chip being no exception, is treating a symptom, not a cause. People are what we should be focusing on, how, I have no idea, I couldn't even begin to guess how, but I can at least see that the actual issue is that the first thing some people want to do with it is cause harm and havoc. There is our sign. And we are trying to moderate the capabilities of the tool that can do harm in bad hands at a granular level when that means also moderating the same thing that can do good in that same tool. We aren't trying to moderate the hands that are handling the tool. And we need to be doing more of that. Any time we misapply constraints and focus on the shadow of the thing, and not the object casting the shadow, we are always going to be one step behind, whether it's AI or anything else. So I don't attribute the dangers of AI to AI itself, I attribute it to people.

Additionally, on AI building AI and AI just wiping us out; AI can do that for itself for rule-based things, up to god level I reckon. Things like Go, etc. And to be fair, AI research is partly like that, code either runs or it doesn't, so I get why that paper is worried. But where it needs data to actually succeed, it is limited by the amount of data that exists. And we, us, people, are who creates that data. AI and humans have more of a symbiotic relationship than people seem to realize. There are several papers that go into detail on this:

Models trained on their own output degrade: https://www.nature.com/articles/s41586-024-07566-y They need fresh real data every generation or they go downhill: https://arxiv.org/abs/2307.01850 Reusing the same data loses value after a few passes: https://arxiv.org/abs/2305.16264 And the stock of human-written text is finite: https://arxiv.org/abs/2211.04325

So it's not that I think it's 0%. I just think the explosion story underrates how much it still needs us, and that the damage in the near term is going to come from people, not the AI deciding on its own.

The problem isn't even the AI, the problem is the people in charge of the AI. This is a fabricated crisis

It’s hard to respect a piece of work or take it seriously when it feels like the person who made it didn’t think it was worth giving much consideration. You’re asking someone to stop, look, and care about your poster and whatever it represents, but you haven’t given them much reason to believe you cared about it yourself. If you can’t take it seriously, how should we? It isn’t really about whether it took five minutes. It’s about whether there was any thought behind those five minutes.

So, I read this article as if it had the following title: “If you insist on using an LLM to generate visuals, you still have to put some thought into them.” But then, that starts to defeat the point for anyone who was using an LLM to avoid that effort in the first place, doesn’t it? You can make the process easier, but you can’t just assume that having produced something means you’ve made something worth sharing.

At the end of the day, I think there are valid uses for LLMs and there are uses that don’t add much of anything. As a consumer of content, the ones I respond to tend to be the ones where the LLM’s involvement isn’t the most obvious thing about the work. I want to see the person’s idea come through. A very light touch, not a deluge. This can absolutely apply to creating visuals, but it requires judgment, revision, and a willingness to do more than accept the first thing you’re given.

And it seems like a lot of people aren’t willing to put in that effort. That’s fine. Their work will continue to be distinguished by that lack of care, while people who are willing to put more into it will stand out. The initial novelty only carries something so far. Once you start picking up on the familiar patterns, that sense of something being slightly off becomes hard to ignore.

So, moving forward, I’m not so sure human creativity and input are things that can simply be outdone and made irrelevant. Maybe they’re an additional layer that keeps giving us somewhere further to go. An LLM can imitate what people have already brought to the table, but given access to that same LLM, we can bring our own experiences, ideas, and judgment into the mix. The tool gives us a starting point. I don’t see why we should assume it also determines how far we can take it.