Fair points, thank you for the very thoughtful feedback! "Alexandrian who found area from mere sides" is a bad hint because it is fairly obscure, and really just a history trivia check. And the image is a bit random. You can view the explanation for the hint by clicking the ? button in the upper right corner after a word is completed if you are curious about the models reasoning.
As a bit of an explanation I generated these before Nano banana pro came out, and at the time I made a large comparison grid for various image and text models. For this style qwen image performed very well. LLM wise I started with 5.1 and updated to 5.2. Of course with the rate of model release my choices are pretty much already obsolete... Expense is also a factor for a hobby project, and NB pro is 7.5x more expensive than Qwen image.
Yeah understandable. If you've got the hardware for it, consider looking into Flux.2 Dev with the Turbo LoRA. It's probably the best-in-class for sheer prompt adherence right now, though Qwen-Image is still very respectable.
And of course, we should see Qwen-Image 2.0 become open-weight in 4-6 weeks as well - so good things on the horizon!
I tried this a few months back with claude 3.5 writing cadquery code in cline, with render photos for feedback. I got it to model a few simple things like terraforming mars city fairly nicely. However it still involved a fair bit of coaching. I wrote a simple script to automate the process more but it went off the rails too often.
I wonder if the models improved image understanding also lead to better spatial understanding.
Yeah, I definitely see using this for literate programming. Not quite sure the best way to organize it. Maybe use a static site compiler to auto host documentation version.
The typical Markdown answer to needing indentation preserved is the "code fence" (triple backquotes ```), though I imagine the problem with that is that Obsidian by default stops dealing with Wikilinks inside fenced code. I don't know Obsidian that well, but maybe there's a way to use a code fence and have it support Wikilinks inside?
A different direction to explore might be to explore proportional font coding techniques that rely less on whitespace. Lisp can be a good language to play with those ideas given whitespace isn't syntactic. Though idiomatic Lisp has certainly relied on semantic whitespace in coding styles for a very long time.
> I imagine the problem with that is that Obsidian by default stops dealing with Wikilinks inside fenced code
Exactly. Interestingly enough autocomplete is still triggered by [[ inside of a code block which is kind of funny. So writing code blocks works fine, it's just that they won't display with links.
> A different direction to explore might be to explore proportional font coding techniques that rely less on whitespace.
I'm definitely open to proportional font coding techniques being interesting, but in this case with all leading indentation unusable I doubt they'd be enough to get a normal experience. Unless you only write assembly so you can stick to the left margin <taps forehead>.
It was a stupid and shameful tweet. The thing that elevates this above a drunk tweet though are the "real life" postcards. Should we ignore the fact that the postcards seem very much a false flag?
Have you tried using ChatGPT to study? It's pretty incredible for learning, particularly interactive exploration. The point of this plugin is to give ChatGPT access to reliable information to help it be more accurate and informative, as well let it provide real sources so you can verify and continue learning. MIT OpenCourseWare is a great resource (the quality of the teachers is amazing), but it isn't easy to find what you want, and it lacks the interactive component of a real class. I made this to try combining the strengths of both.
This is a shame as bing search api was much better documented, and supported (at least as of a few years ago). Google switched api specs and dropped features at an even faster rate than their consumer facing products.
I feel like this headline is deceptive. This is a Jax reimplementation, and it was released a year ago. It is a cool library though. The basic operation of muzero is very simple, but training it efficiently is tricky.
Mog: “What truly is fire? The divine blessing stolen by Prometheus? Concentrated Phlogiston? The element of change? Is it not madness to seek to create something we don’t even have a good definition of?”
Grog: “Grog rubs two sticks together” Lowers voice and looks around furtively “really hard.”
Fire is warm and it produces light, and it's easily observable in nature, it's a "thing" which humans could easily describe and recognize, before reproducing it. Someone was rubbing two sticks together, and noticed things became warmer, and warmer as they went, starting to feel like the warmth of fire and it happened.
In the case of AGI, I don't feel like we have a definition of done, so it seems kind of crazy to be chasing it / throwing money at it.
I didn't say say I'm against it, but does seem like a crazy way to go about it. Keep producing models that one day might mimic intelligence as we know it?
Grog could define fire in a very real way even back then, which is how he knew he’d so easily created it . It is hard for us to even know intelligence (the one we mean in AGI) when we see it, much less create it, no?
We can define intelligence in a very real, practical way now. We see and identify intelligence all the time in humans and in animals and in AI. We may not be perfect at identifying it (just like grog might mistake a rising sun for a forest fire), but we don't need a perfect mathematical or philosophical definition that we all agree on to create it. We just need to rub sticks together really hard.
The person you originally replied to and I disagree that we have any real, practical definition. I can recognize what humans and to an extent animals do as intelligent, but haven’t seen a definition that separates that intelligent behavior from them. I have never seen anything that’s been called ai do something I could call intelligent in that animal-like sense (though some have been impressive in the same way Google / page rank was impressive when it first came out)
So, I don’t see why rubbing these statistical model sticks should suddenly burst into intelligence, but I’m open to seeing convincing reasoning on that at some point. I wouldn’t invest time or energy in the meantime and like that original poster, think it’s kinda insane to if my goal was to see human-like intelligence emerge outside of humans
It is interesting that you can write thirteen posts on the topic without being able to define it.
It also seems very odd that you can differentiate between some things that you think are intelligent, and some things that you think definitely are not, yet you are incapable of extracting any sort of goal from that knowledge.
If you could tell us your criteria, perhaps we could help you with that...
I’m simply very curious about the subject, it’s super important :)! Given that, I’m also frustrated with what seems like a popular lack of critical thought and curiosity on the specifics.
In these comments, when I’ve talked about an intelligence I can distinguish, I’ve been talking about human / animal intelligence. AGI implies an intelligence independent of that, so I’m asking about the specifics there - what are we calling intelligence if not “what humans do”?
If we are calling it just that, then I’d argue everything I know about how these models do things is very different from what I know of how humans approach the specific tasks the models are built against. And I’ve read that that’s intentional. So, even with that sort of definition I don’t see how it follows that these approaches are on any linear path to AGI (maybe nonlinear if we learn limits and such from mistakes).
I’ve since read more of the article (it’s long, huh?) I like the framework they use from Roitblat in Section 2 - and again, don’t see how LLMs and such are on the road to fulfilling those criteria.
Fair enough, though I feel you are a bit too eager to push back against ideas that go counter to your initial thoughts. Of course, because I hold differing opinions, you could reasonably object that it is just what I would say!
I have a different idea of what AGI means: in my view, it is a retronym created in the 1980s in order to refer to AI of the sort Turing envisioned (which was more or less "what humans do") and differentiate it from things that were then being called AI, such as IBM's Deep Blue, which were mostly brute force applied to conceptually narrow problems.
You mentioned Roitblat's framework, and I would draw your attention to one aspect of it: it is not just a list of things that humans do, but those things which humans do considerably better than other animals, yet for all of them, there are other species that do them to some extent. As an evolutionist, I suppose there was a relatively recent time in the past when some of our ancestors or sibling species (all now extinct) had some or all of these skills to some intermediate level. In this view, intelligence is not an all-or-nothing concept, and achieving some of it is still progress.
Here's a view which you may not have seen: the pace of progress in AGI has not been constrained by an inability to define what we want, but by the pace at which we see ways to make what we see we need. For example, it is clear that current LLMs have a problem with truth, but it is not clear from what has been made public so far that anyone has a solution. Some people think that what's being done now with LLMs, but more of it, will be enough to get us to what will be generally accepted as AGI; I am skeptical, but I am willing to be persuaded otherwise if the evidence warrants it.
As a bit of an explanation I generated these before Nano banana pro came out, and at the time I made a large comparison grid for various image and text models. For this style qwen image performed very well. LLM wise I started with 5.1 and updated to 5.2. Of course with the rate of model release my choices are pretty much already obsolete... Expense is also a factor for a hobby project, and NB pro is 7.5x more expensive than Qwen image.