"The studios and creators who thrive in this new landscape will be those who can effectively harness AI’s capabilities while maintaining the human creativity and vision that ultimately drives the art of cinema."
It is in many ways thrilling to see this come to life, and I couldn't agree with you more.
> "The studios and creators who thrive in this new landscape will be those who can effectively harness AI’s capabilities while maintaining the human creativity and vision that ultimately drives the art of cinema."
..Just somehow several years on, these optimistic statements still all end up being in the future tense, somehow for all the supposed greatness and benefits, we still dont see really valuable outputs.
A lot of us do not want more of the "CONTENT" as envisioned by corporate ghouls who want their employees or artists to "thrive" (another word kidnapped by LinkedIn-Linguists). The point is not in the speed and easiness of generation of outputs, visual and sound effects etc. The point is the artists interpretation and their own vision, impressions etc. Not a statistical slop which "likely" fits my preferences (i.e. increases my dopamin levels).
> Later iOS 26 beta releases show Apple reducing transparency and adding blur effects for better readability.
This is a beta release. It is a work in progress. When iOS 7’s betas came out the reaction was similarly negative. I would suggest we wait and see what the system evolves into; by the time we get to iOS 27 I am quite sure that Apple will have found the right balance.
I've observed that some people like to gatekeep others from being allowed to criticize the beta, but then when the final version releases they gatekeep criticizing that too because "you should have given feedback during the beta!"
It is just a way people try to shutdown being critical of Apple's stuff in general. It is tiresome.
When Apple is dropping press releases trumpeting their "meticulously crafted" design that "makes even the simplest of interactions more fun and magical", it is not persuasive to cry "beta!"
Even the screenshots in the press release - Apple's best foot forward - were criticized more or less immediately; it's not like the problems with the design are rare edge cases.
Apple clearly owns the decision to go this route. No one forced them to announce it before refining it internally. It remains to be seen how drastically they will have to walk it back. Whether or not they rework it enough to reduce complaints, I can't see how they can call it a win in the end. They will anyway.
As for being "sure" Apple will find the right balance, they never fixed usability regressions in macOS introduced in the last redesign. And they have ~10 weeks to fix all this.
Ah. So they're reducing transparency to get better readability... If reducing transparency increases readability, then wouldn't the best readability occur at zero transparency?
Readability is obviously good for something. Is transparency?
I am quite sure that "the right balance" is zero transparency, but only about 99% sure Apple won't "find" that.
iOS 7 remains to this day a common meme among Apple developer community regarding design going too far, so naturally finding the right balance is kind of questionable.
But so is alpha, which is where looniness gets to live without judgement. Beta is supposed to be polished and working well, except where there are explicit warnings of incomplete or sketchy functionality. I.e. small areas that are still alpha.
Which is the opposite of how Apple framed "liquid glass" in the beta.
Apple lowered the bar on its beta. Strong feedback is how customers suggest a course correction at a higher level.
A fascinating situation is facing polymarket - Zelenskyy was pictured wearing a suit (as described by the media) at the NATO summit on June 25th but this market is still unresolved. It appears there’s lots of argument here and that this market is set to be test of the platform.
This video[1] seems to give some insight into what the process actually is, which I believe is also indicated by the output token cost.
Whereas GPT-4o spits out the first answer that comes to mind, o1 appears to follow a process closer to coming up with an answer, checking whether it meets the requirements and then revising it. The process of saying to an LLM "are you sure that's right? it looks wrong" and it coming back with "oh yes, of course, here's the right answer" is pretty familiar to most regular users, so seeing it baked into a model is great (and obviously more reflective of self-correcting human thought)
So it's like the coding agent of gpt4. But instead of actually running the script and fix if it gets error, this one check with something similar to "are you sure". Thank for the link.
The comments on this thread are interesting. I use Laravel extensively. For big applications, serving lots of users. It works when the application is relatively complex, and the ecosystem is second to none. Need to just throw it up on a server? There's Forge[1]. A better CI/CD process? Envoyer[2]. Want serverless? Not for me, but Fathom[3] use it to deal with >100Ms of hits a day; there's Vapor[4]. All three of those are Laravel developed and maintained solutions.
If I'm throwing something small together then sure, I'll maybe use Flask or something lightweight[5]. But Laravel is very good for nearly every use-case where you intend to actually build something.
Then there's the bigger question: if you're building to meet a business use case, or well, to make money, then why wouldn't you use the most complete scaffold possible? I'd say Laravel is that. If it's too much of a pain to do something in PHP I can just stick in a call to a python file or really whatever language I want. But for the basics? A db? Auth? and lots of other stuff that I never want to personally build again? Yeah, give me Laravel everyday.
The article states that "They scan an image through a thin slit up to 2,000 times a second", whereas it has been widely reported that it is actually 40,000 times a second[1]
The article probably wasn't wrong, for when it was first written. This is a curious internet thing - this article is a decade old and has been updated incrementally to keep it somewhat relevant, however because it's about tech that keeps advancing it ends up being a misleading source.
If you look at all of the sources, they're from January 2014 but because the article is undated it leads you to think it's is correct. It's an interesting problem. An old textbook is clearly an old textbook, but a website can just have modern CSS applied, dates removed and there is no apparent guide to the freshness of the article. Internet problems.
I've been bullish[1] on this as a major aspect of generative AI for a while now, so it's great to see this paper published.
3D has an extremely steep learning curve once you try to do anything non-trivial, especially in terms of asset creation for VR etc. but my real interest is where this leads in terms of real-world items. One of the major hurdles is that in the real-world we aren't as forgiving as we are in VR/games. I'm not entirely surprised to see that most of the outputs are "artistic" ones, but I'm really interested to see where this ends up when we can give AI combined inputs from text/photos/LIDAR etc and have it make the model for a physical item that can be 3D printed.
I have to be a soggy blanket person, but there are some pretty strong reasons why 3D generative AI is going to have a much shallower adoption cycle than 2D. (I founded a company that was likely the first generative AI company for 3D assets)
1. 3D is actually a broad collection of formats and not a single thing or representation. This is because of the deep relation between surface topology and render performance.
2. 3D is much more challenging to use in any workflow. Its much more physical, and the ergonomics of 3DOF makes it naturally hard to place in as many places as 2D
3. 3D is much more expensive to produce per unit value, in many ways. This is why, for example, almost every indie web comic artist draws in 2D instead of 3D. In an ai first world it might be less “work” but will still be leaps and bounds more expensive.
In my opinion, the media that have the most appeal for genAI are basically (in order)
- images
- videos
- music
- general audio
- 2D animation
- tightly scoped 3D experiences such as avatars
- games
- general 3D models.
My conclusion from being in this space was that there’s likely a world where 3D-style videos generated from pixels are more poised to take off than 3D as a data type.
You skipped an important market segment. Porn. The history of technology for making and distributing images, from cave paintings to ancient Rome through the Renaissance, Betamax, Photoshop, Tomb Raider and Instagram filters, has always been driven by improving the rendering of boobs.
At this point GLTF seems pretty darn good and seems broadly usable. It embeds the mesh, textures, animations right in a single file. It can represent a single model or a scene. I also has a binary format.
3d need not be so complicated! We've kinda made it complicated but a simplification wave is likely coming.
The big unlock for 3d though will have to be some better compression technology. Games and 3d experiences are absolutely massive.
USD was designed for letting very large teams collaborate on making animated movies.
It's actually terrible as an interchange format. e.g. The materials/shading system is both overengineered and underdefined. It is entirely application specific.
For the parts of USD that do work well, we have better and/or more widely supported standards, like GLTF and PBR.
>I've been bullish[1] on this as a major aspect of generative AI for a while now, so it's great to see this paper published.
Me too. My first thought when seeing 2D AI generated images was that 3D would be a logical next step. Compared to pixels, there's so much additional data to work with when training these models I just assumed that 3D would be an easier problem to solve than 2D image generation. You have 3D point data, edges, edge loops, bone systems etc and a lot of the existing data is pretty well labeled too.
I'm excited to see the main deterrents to Indy gave dev: art and sound, get AI'd away. A single developer could use an off-the-shelf game engine and some AI generated assets (perhaps combining with whatever they can buy cheap) to develop some fun games.
Still, getting from still models to something that animates is necessary:(
This is exactly what I’m not excited for: indie game discovery is already hard enough, Steam widening the floodgates has not been a positive experience. Reducing the effort to create games even further is going to DoS the indie game market as we see the same studios that pump out hundreds of hentai games suddenly able to broaden their audiences significantly.
I think having modern game engines reducing the need for game programmers to almost zero caused much of this, but it also resulted in some interesting games when artists could create games without a need to hire programmers.
It will be interesting to see if AI art (and AI 3D models) will mean that we see interesting games instead created by programmers without having to hire any artists.
What I do not look forward to is the predictable spam flood of games created without both artists and programmers.
To be fair, this is already the case on all the platforms, as you can easily put together a game with free assets from the assetstores (or pay a few dollars for pretty high quality assets). For every standard game genre you can imagine I'm sure there are thousands of generic games released every year on every playform (don't have any real numbers but I get that feeling...)
Rendering the assets by AI or buying them from the asset store is not going to change the number of generic games put out there I think, maybe AI gen can make some of them a bit more unique at best.
Passable art is common. Original and interesting game mechanics are exceedingly rare, and will continue to be. The relationship between passable art and throwaway games is like that between bland AI content and marketing blogs.
Really good games will still employ really good artists.
This is my point exactly, but even passable art takes some time to create. I’m not excited for the very-soon-to-arrive tide of VNs, deckbuilders, and JRPGs made with effectively 0 time or effort.
I’ve never understood the effort = quality view of art. Just because someone spent thousands of hours does not mean it is good art. And plenty of great art is executed quickly.
It seems as odd to me as bemoaning the way word processors let people write novels without even being good typists.
Picasso produced 50,000 paintings in his career[1], about two per day every day. So probably considerably more on some days.
It’s harder to find data on great art from relative novices. But consider the opposite — how much bad art is there from people who put their 10,000 hours or whatever in? I’m willing to believe some correlation between time spent and quality, but I am not willing to believe that tools that make artists more efficient necessarily reduce quality.
I mean, part of my job is hiring illustrators and designers. I can tell by looking at a portfolio whether someone has put in their (slightly metaphorical) "10,000 hours". And much of that has nothing to do with execution or the tools they used. In fact, thinking that execution and tooling make them better is often a red flag.
What I look for is that the artist knows what they want and that the ideas they're putting on the page are thoughtful, coherent, original, and well-executed in a style that's unique enough to justify hiring them personally. And the ability to hone ideas into visual form is not innate, nor have I ever seen it successfully done by someone who didn't spend countless hours trying and failing first.
For example, upper management, who spend time looking at and approving art pieces, almost never understand that altering them is going to make them worse. "Add something here" or "take this out", generally undermine the piece when coming from someone not trained and experienced. Writing prompts is much the same as being a manager. You never get exactly the result you expect for what you asked, but that is also because you did not have the exact vision in your own mind of how it would look before it was executed.
Practice is about developing that vision. Once you have that vision, execution is the easy part, and you don't really need a tool to draw it for you. In any case, the tool will not draw it the way you see it.
So yes, a songwriter who's written tons of songs can suddenly write a good one in 30 minutes. Most of my best songs were written longhand with no edits. That happens sometimes after writing hundreds of songs that you throw away.
Similarly, I've been coding for 25 years. Putting my fingers on the keys and typing out code is the easy part. I don't need copilot to do that for me. I don't really need a fancy IDE. What practice gives is the ability to see the best way to do something and how it fits into a larger project.
If a tool could read the artist's mind and draw exactly what the artist sees, it would be crystal clear that 10,000 hours of trial and error in image-making results in a thought process that makes great art possible (if the artist is capable of it at all). The effort is mostly in the process of developing that mental skill set.
But this is a category that didn't exist yet. So who knows what people without art skills or budgets might do? Probably nothing, but maybe one in ten thousand actually isn't garbage. Just like music at the advent of digital home recording. The market is already so flooded it hardly matters.
I'm an artist and a gentleman coder and I'm disgusted and offended by careless work. But I don't think I need to die on the hill of stopping infinite crappy game mills from having access to infinite crappy art.
[edit]
I'm also just bitter after years working on pretty great original art / mechanics driven casual games that only garnered tiny devoted fan bases, and so I assume that when it comes to the kinds of long tail copycat games you're talking about, especially with AI art, no one's going to bother playing them anyway.
Lol, yeah, the main deterrent/obstacle to indie game dev has little to do with actual development, and machine generated content is actively making that worse.
So we should not improve production methods, because it will give us more things for less effort?
Just let the market sort it out. I for one can't wait for the next Cyriak or Sakupen, that can wield the full power of AI assistance to create their unique vision of what a game can be.
Autodesk has been building practical 3d models for years with generative design. I have to imagine it's only getting better with these recent advances, but I'm not immersed in the space.
I'd be interested in seeing a similar analysis but with a slight twist:
We use (in production!) a prompt that includes words to the effect of "If you don't get this right then I will be fired and lose my house". It consistently performs remarkably well - we used to use a similar tactic to force JSON output before that was an option, the failure rate was around 3/1000 (although it sometimes varied key names).
I'd like to see how the threats/tips to itself balance against exactly the same but for the "user"
"The studios and creators who thrive in this new landscape will be those who can effectively harness AI’s capabilities while maintaining the human creativity and vision that ultimately drives the art of cinema."
It is in many ways thrilling to see this come to life, and I couldn't agree with you more.