AI (as it currently exists) will never be "creative" in the sense that it can only imitate or interpolate between past works. At some point we'll recognize that AI generated works are boring and predictable. AI will probably never invent a new musical genre or artform since it can only reproduce or recombine works from the past. I wonder what happens when the internet is so full of AI generated slop that that the only things worth "training" on were made in the time before AI generation became a thing. Will AI generations be full of dated references to a time gone by?
People may say it increases creativity but I see it more as lowering the bar to produce things. The same could be said for a lot of inventions like photography made producing images easier and I'm sure a lot of portrait painters lost their jobs to photographers. I think the danger is that we may see a very rapid erosion of jobs in the creative space that won't be easy to transition into new fields which I feel will have a detrimental effect on our society.
I'm struggling to understand why an LLM even needs to be involved in this at all. Can't you write a script that takes the last 10 slack messages and checks the github status for any URLs and adds an emoji? It could be a script or slack bot and it would work far more reliably and cost nothing in LLM calls. IMO it seems far more efficient to have an LLM write a repeatable workflow once than calling an LLM every time.
This reminds of when Adam Wathan admitted that LLMs really helped his workflow due to automating the process for turning SVG's into react components... something that can be handled with a single script rather than calling an LLM every time like you mentioned.
That depends on the content of the SVGs.. Of course you can write a script to do a very literally kind of conversion of regardless, but in practice a lot of interpretation would be required, and could be done by an LLM. Simple case is an SVG that's a static presentation of a button; the intended React component could handle hover and click states and change the cursor appropriately and set aria label etc. For anything but trivial cases a script isn't going to get you far.
Maybe the audience is not developers at all?
Someone that does not know anything about computers and computation might not comprehend how easy or complex a given task is.
For a whole class of people, checking a key in a json object might be as complex and difficult as creating a compiler. Some of those are in charge of evaluating progress and development of software.
Here's the magic, by now everyone can understand that prompting and receiving an answer is easy.
> “For a whole class of people, checking a key in a json object might be as complex and difficult as creating a compiler.”
Ugg. I think this is me. I’m self taught (never once made a compiler in a course or class) and making scripts for ETL at work mostly from CSV input. And JSON/APIs are aggravating to me.
I’ve yet to see the Matrix in JSON data structures (Is it storage? Is it wire protocol?). I can follow _examples_ in documentation, but struggle to put parts together from Swagger or some documentation to get the data view I need. For a while I thought some kind of UML diagramming projects would do it for me—to see the Forest and the trees—but the answer was not there.
So, yes, if I can “vibe” code with ChatIA to get over the mental structural hump to make the right joins and calls, I’m all in.
It's just a standardised way to represent data structures in text. You can then save that text to a file for storage, or send the text over the wire for data transfer. As long as everyone involved knows they're saving/loading or talking JSON then everyone knows exactly how to read/write the data.
It is a very literal representation of (specifically JavaScript, but generally any) data-structures in text.
Right. Now the problem for me is these structures don’t come with maps. They’re also like relational databases. If you have to add the mixin calls, how do you got them all? Or know you’ve reconstructed the data model correctly? Where’s the blueprint?
Are you sure about that? They say "full 3D shape geometry, texture, and layout" which doesn't preclude it being a splat but maybe they just use splats for visualization?
On their paper they mentioned using an "latent 3D grid" internally, which can be converted to mesh/gs using a decoder. The spatial layout of the points shown in the demo doesn’t resemble a Gaussian splat either
The linked article of the grandparent says "mesh or splats" a bunch, and as you said their examples wouldn't work if it were splats. I feel they are clearly illustrating it's ability to export meshes.
> Valve actually tried with the first Half-Life game in a decade, and even that didn't work.
Half Life Alyx is still considered to be one of the best VR games ever made and one that is still consistently recommended to new users even years after release. IMO people buy hardware because of the exclusive content. If a standard game console came out and it only had one AAA game on it, I probably wouldn't bother buying it. But if there were 3-4 games that looked really interesting it starts to look more worth the investment. Playing VR games takes a lot of committment (time / physical space / $$$) so the payoff has to be worth it or you'll lose people. With the huge amount of money spent on R&D for new hardware I think it's a valid argument to say that maybe funding content would have been a better investment in terms of ensuring platform growth.
Also, side note but not every game requires free motion. Plenty of hits had no movement or teleport etc. A lot of these were completely new (sub-)genres that didn't exist or hit the same as they would in a traditional pancake game. Plus lots of kids seem unaffected by free movement (maybe as high as 50% of users by my rough estimate).
Another way to look at it is parallel processing vs sequential processing.. our brains can make a judgement call about a thousand subtle variables and data points that we can't exactly put our fingers on unless we really dig into it, which we usually label as 'feelings', using the parallel part of our brain. The sequential (logical) part can only consider a limited number of variables at a time. I don't think either mode of thinking is inherently worse (we need both), but in our society the feelings part has traditionally been discounted as being 'illogical' by academics.. I think AI has shown us that parallel processing is actually incredibly important to thinking.
But back to the original post, I think 'having good taste' and knowing when something feels like the right solution is one of those hard to define qualities that can make the difference between average and great products (and has far reaching effects in any business).
I always like to say we aren’t rational, we _rationalize_. Much of our decision making process is subconscious / vibes / “system 2”, but we also have a strong need for a sensible narrative structure to our lives. So what hack did nature come up with? Let us make the gut decision based on a bunch of soft heuristics, then rationalize it and wrap it into a sensible narrative before it reaches our conscious mind. Lets us use our efficient system 2 thinking most of the time while avoiding all that messy cognitive dissonance that would arise from a conscious awareness of how chaotic such a system would be at the scale of… oh, say, a global civilization of such creatures ;)
All rational thought depends on its axioms/premises, and there's no rational way to define a new axiom - by definition they are asserted from scratch, so you need to depend on gut feeling to choose a good axiom over a bad one.
Rationalism "only" works to discard or modify some subset of your axioms when you discover that they lead to incompatible conclusions; which is a good outcome if you want to achieve a consistent theory, of course; but it doesn't help in selecting one consistent theory over a competing one. Again, those preferences are led by emotions.
I haven't see any details on how OpenAI's model works, but the tokens it generates aren't directly translated into pixels - those tokens are probably fed into a diffusion process which generates the actual image.. The tokens are the latent space or conditioning for the actual image generation process.
> I haven't see any details on how OpenAI's model works
Exactly. People just confidently make things up. There are many possible ways, and without details, "native generation" is just a marketing buzzword without clear definition. It's a proprietary system, there is no code release, there is no publication. We simply don't know how exactly it's done.
Open AI have both said it's native image generation and autoregressive. It has the signs of it too.
It's probably an implementation of VAR (https://arxiv.org/abs/2404.02905) - autoregressive image generation with a small twist. Rather than predict every token at the target resolution directly, start with predicting it at a small resolution, cranking it higher and higher until the desired resolution.
I agree with this. I keep harping on this, but we are sold automation instead of a power tool. If you have domain knowledge in the problem that you are solving, then LLMs can become an extremely valuable aid.
Similar to a developer who copy-pastes sections of code from StackOverflow and puts their faith in it being correct. The bigger issue with LLMs is that it's easier to be tricked into thinking you actually understand the code when your understanding may actually be quite superficial.
He's not arguing that no jobs will be displaced, he's arguing that jobs will change, engineering may become more reliable, new types of software jobs may be created.
People may say it increases creativity but I see it more as lowering the bar to produce things. The same could be said for a lot of inventions like photography made producing images easier and I'm sure a lot of portrait painters lost their jobs to photographers. I think the danger is that we may see a very rapid erosion of jobs in the creative space that won't be easy to transition into new fields which I feel will have a detrimental effect on our society.