I think there are few plausible reasons not mentioned.
We are still early in the tech. 2021 LLMs were not fit for any real purpose outside zany Madlibs. The tech has only really gotten fit for purpose since GPT4 and coherent models only got cheap and fast enough since llama 2 (July 2023). Even then smart models only got affordable around Deepseek v2 (May 2024) which was the first gpt4 level model to consistently serve subdollar per million tokens. You need at least a GPT4 level model to make a really interesting game. That's like a blink of time in game making time, indie games have 2-3 year cycles at best and AAA are like 5+. Even now fast, tool native interaction is only just coming online and there are no cheap models for that. Really fun game playing AI needs something like that to be magical.
There are AI games they just don't look like AI games neal.fun's Infinite Craft made a boat load of money and was very popular and it was powered by a Llama backend. Character ai and it's dozen copycats are like online storytelling/roleplay things. These are fairly popular. That could be a game or like a game platform or maybe it's more like fanfic. It has no fail state but people have made variants with fairly complex rules and states. Skryim and a few other games have pretty popular AI mods that let you add in NPC interactions that can talk, see, and even change their interactions with AI. The Skryim one Mantella has more than ~100k downloads.
We don't really have a northstar yet. Making a good game is really hard and usually takes someone doing something weird and clever. Minecraft is obvious in hindsight but many games on the way failed to make 'legos on the computer' fun. Incorporating AI breaks all the 'rules' of gaming. It operates slowly, it has high potential to break the rules and it has nearly unrecoverable failure states. Indie devs haven't figured out the best way to handle this and I suspect most big companies have just washed their hands of the whole thing until the AI gets faster, cheaper, and more stable.
The B2C case here seems off to me. The market of people who are going to pay the high price tag, have enough storage, and tolerate the extreme limitations and slowness of these machines seems to be: the rich, the elderly, and people with physical disabilities. The latter two categories come with a huge number of liability and regulatory costs that I think most of these companies are not willing to handle. That does not feel like a huge market.
I'll have a stronger belief in these things for consumers when we start seeing B2B adoption. Hotels have routine, highly structured cleaning tasks; hospitals have a need for extra strength, have highly structured cleaning tasks, and need to stock items; grocery stores have highly structured cleaning tasks and need to stock. Hospitals at least could tolerate the slowness of these things.
Without any B2B adoption it's hard to not see this as Roombas all over again. Cool for people who like it but low impact and still a toy 20 years later. I think generative AI makes these things better, though still perhaps struggles with long task adoption, but if you look at their movement they are still slow, weak, cognitively inflexible, and unstable. Maybe this tech is accelerating in some way I don't see and I'd love to be proven wrong here.
Visual anagrams popped up last year using similar, though simpler methods to those in the posted. Flips, internal rotations, rearrangements, color negatives etc.
[0]
Diffusion illusions did something similar at about the same time but with puzzles and multi-image color layer mixing. Some of the double puzzles they made are a lot of fun. They have great explainer videos [1] on that site including with Steve Mould.
Also there are diffusion double/hidden images using qrmonster, illusion diffusion, control net, or img2img that have been making the rounds. [2] for a random example. These work by using a fine-tuned diffusion model[3] to take an image and use it as a structuring element at various levels of following to a generated image. To see these illusions, squint or move the screen away. These are quite a bit more popular and easy to make than the other methods so many more examples show up around the internet.
You are right, current CRISPR systems tends to accumulate in the liver. Most CRISPR companies have shifted their focus to the liver over time because it's easiest to deliver there. Most viruses people use to target other organs are not large enough to carry CRISPR and lipid nanoparticles with CRISPR seem to like ending up in the liver and are hard to deliver at dose to hit other organ systems. It has been one of the big struggles of CRISPR companies. That being said, this is a huge deal and very encouraging.
As to the FDA stance, it tends to be more willing to go ahead with compassionate uses like this when it's clearly life or death.[1]
While this seems true and I hope these companies get a large fine and some regulatory action takes place to wipe out these third party apps, I'm kind of surprised at the corporate learned helplessness here.
Back in the day McDonald's or one of their fast food competitors would have built their own frozen potato pipeline, made a massive marketing gimmick about cheapest fries and shattered this cartel quickly. It sounds like the companies are taking a high margin and the farmers would love to sell to anyone else. But it feels like the current managers at these fast food companies have gotten so used to outsourcing every part of production they lack the knowledge/remit to even try to set up a competing supply line.
It feels like only it's only efficient to focus core competencies if the people in charge of those stay smaller. Given how big companies can squeeze suppliers I see how they would end up consolidated. But if everyone is doing one thing and consolidating horizontally to negotiate better it becomes kind of red queen race.
Maybe when you have a multi-billion dollar supply chain and complex contract structure you can't just learn to do something new to solve a problem anymore.
The small players can't do much. As mentioned in the article, either potatoes and DIYing are cheaper or they aren't, but medium to large firms presumably could/should do something.
McDonald's has switched between Simplot and McCains a half dozen times in the last ten years. In Australia, they even use both. They have their own pipelines that they require the company matches, already, and companies compete to have them as a customer. The fast food company holds the power in that particular relationship.
McDonald's rejected Simplot's "Innate" in 2020, a GMO potato that they spent about a decade developing. Which basically turned the entire project into a loss. It's one of the main reasons that the Lanthrop plant is closing.
I believe McDonald's does have their own pipelines for various ingredients, including beef, in countries where it's necessary for uniformity, outside the US. I'd have to assume they've looked at the numbers and determined that having their own potato to french fry pipeline would not shave enough cost off a happy meal to lure enough customers from their competitors to make it worthwhile. Shattering the cartel might not be in their interest.
McDonalds doing those things in other countries doesn't necessarily show that the parts of their organization responsible for their US operations are competent enough to do the same. Assuming that whatever the corporation is doing presently must be the result of careful rational economic analysis seems like a fallacy of some sort. Just world fallacy? Optimally Run Corporation fallacy.
Upvoted, and I wish I could upvote you twice. Because I did commit a logical fallacy. I can't assume that a decision was made wisely, or informedly, just because it was made by a giant organization with information we're not privy to. People in such organizations are just as incompetent as everyone else.
It’s unlikely they are able to gouge McDonald’s and the large companies. Even the article mentioned that the big competition is for those massive contracts. It’s much more likely that McDonald’s and co can shop around, negotiate a great price, and maintain margins.
It’s the mom and pops, the regional suppliers that can’t do anything, and likely pay much higher prices than the megacorps.
Indeed, it is surprising that you would be able to pull this stunt on such a huge buyer as McDonalds.
Here's an alternative theory that will disappoint the readers of Jacobian - potatoes are a commodity and commodity prices drive inflation. A bad crop, expensive fertiliser, a ground war in one of the largest potato-exporting countries in the world (Ukraine) - all these things would cause suppliers to increase prices in lockstep.
Inflation can be good cover for price collusion, sure, but the reason why it's such good cover is that its effects are almost indistinguishable without a smoking gun. Lets see what the FTC investigation brings up.
(Another note - inflation inflates profits as well as prices.)
Just to augment what you are saying... all potatoes aren't created equally either... size, starches, variety, organic... there are places around North America where certain kinds of potatoes are grown that are completely unsuitable for french fries but might be fine for retail. Even among Russets, for example - you see smaller ones bagged up for retail, but larger ones are often sold loose as "bakers." And sometimes those same big beautiful Russets are undesirable for fries because they are inconsistent sizes, which can be problematic for processing.
> It sounds like the companies are taking a high margin
Are we really sure that's the case?
I bet that mcDonalds has much better people focused on this problem than the Jacobin, and considering how much fries they sell, they have people on it for sure.
Maybe they know more about this market than we do, and have actually found that the prices are not as unreasonable as they seem?
That's a good point, I assumed the farmers in the article were correct on getting squeezed, but individual producers rarely have a good view on the market. And McDonald's et al has been getting flack lately for the price of fries increasing. But looking at the potato price charts it's a pretty frothy market with a huge spike in 2023[0]. Maybe they've decided the suppliers are playing fair enough, at least with them. Or perhaps they are waiting to see if the frozen prices track the commodity price down before they decide to try to do something, I didn't realize the drop the article talks about was so recent.
> Back in the day McDonald's or one of their fast food competitors would have built their own...
Where does Costco, which famously went vertical to make its own hot dogs, source its fries? (Admittedly, that was for their food court. And I don't recall that they've got fries on their food court menu.)
This was probably an okay idea terribly implemented. GenAI creators on social media kind of sense.
Neurosama, an AI streamer, is massively popular.
Silllytavern which lets people make and chat with characters or tell stories with LLMs feeds Openrouter 20 million messages a day, which is a fraction of it's totally usage. Anecdotally I've have non tech friends learn how to install Git and work an API to get this one working.
There are unfortunately tons of secretly AI made influencers on Instagram.
When Meta started these profiles in 2023 it was less clear how the technologies were going to be used and most were just celeb licensed.
I think a few things went wrong. The biggest is GenAI has the highest value in narrowcast and the lowest value in broadcast. GenAI can do very specific and creative things for an individual but when spread to everyone or used with generic prompts it start averaging and becomes boring. It's like Google showing its top searches: it's always going to just be for webpages. Making an GenAI profile isn't fun because these AIs don't really do interesting things on their own. I chatted with these they had very little memory and almost no willingness to do interesting things.
Second, mega corps are, for better or worse, too risk averse to make these any fun. GenAI is most wild and interesting when it can run on its own or do unhinged things. There are several people on Twitter who have ongoing LLM chat rooms that get extremely weird and fascinating but in a way a tech company would never allow. Silllytavern is most interesting/human when the LLM takes things off the rails and challenges or threatens the user. One of the biggest news stories of 2023 was an LLM telling a journalist it loved him. But Meta was never going to make a GenAI that would do self-looping art or have interesting conversations. These LLMs probably are guardrailed into the ground and probably also have watcher models on them. You can almost feel that safeness and lack of risk taking in the boringness of the profiles if you look up the ones they set up in 2023. Football person, comedy person, fashion person, all geared to advice and stuff safe and boring.
I suspect these things had almost zero engagement and they had shuttered most of them. I wonder what Meta was planning with the new ones they were going to roll out.
Meta's platforms are already filled with AI slop content farms that drive clicks and engagement for them.
I have a FB account for marketplace, and unsubscribed from all my pages and friends. If I log in, my feed is a neverending stream of suggested rage bait, low quality AI photos, nonsensical LLM "tips" on gardening and housekeeping.
The posts seem to attract tens of thousands of reactions and comments from seemingly real people.
Absolutely, and in picking collabs with people who are willing to work with the weirdness and make it funny. Vedal is definitely a fantastic creator to make it work so well and the amount of fine-tuning and tweaking he must do must be unreal. But I think it still shows there is some hunger for this type of content, though you are probably correct that it still needs to be curated, gardened, worked with, and sometimes faked.
This was probably an okay idea terribly implemented.
No, I'd vote terrible idea terribly implemented so good (that it failed).
The argument for GenAI chatbots in culture has to be more than "people like it".
The worst possible GenAI is one that manages to be "better" than the standard sterile, moronic homogenized celebrity that everyone already likes. And sure, like any computer program, a GenAI can be randomly "interesting" but this kind of thing is quite shallow imo.
Aspersions aside, the content’s actually typically pretty involved and has a lot to speak for itself, it’s not low-effort content that one would typically associate with AI.
Laid bare, it’s generally a variety comedy show of a human host and AI riffing off each other, the AI and the chat arguing and counter-roasting each other with human mediation to either double down or steer discussions or roasts in more interesting directions, a platform for guest interviews and collaborations with other streamers, and a showcase of AI bots which were coded up by the stream’s creator to play a surprising variety of games. There’s a lot to like, and you don’t need to be on “that bit of the bell curve” to enjoy a skilled entertainer putting new tools to enjoyable use.
> This was probably an okay idea terribly implemented. GenAI creators on social media kind of sense.
It boggles my mind that there are people who think this is a good/ok idea. From a human perspective, all it does is pulls the mind ever closer to fictional imaginative world rather than encouraging real life interactions which I believe is inherently wrong no matter what business strategy is wrapped around it.
Valve actually backed off the ban and clarified their stance [0]. Now they allow AI art by the same standards as non-AI art if it is pre-generated and expect certain guardrails with regards to content and copyright if live generated. While their initial banning was probably over the top they did get better.
Linkin Park used it in 4 videos recently and seemed to use a combination of img2img and leaning into the glitchy style. [0] (These are 9-11 mo old so using much older models and techniques)
Peter Gabriel used it extensively in some recent music videos. The artists behind it also leaned into the glitchy style but are probably using some pre trained stuff to keep style.[1]
A writer/creative named Austin McConnell used AI art to make a 50 minute anime short to help market a book he wrote using AI [2]. Not sure how he kept consistency but this video got some flak and he has another video addressing his techniques.
Corridor crew did a second video which is a lot better but still a lot of work. [3]
I think a lot of projects using it are still kind of in the spec stage and are usually using a combination of loras, clip, generating multiple angles, and aggressive use of img2img and controlnets. And a few companies (Scenario [4] etc) are working on consistency. I think you won't see a ton of big projects using it yet because the tech is still early days and the early versions were really a bear to work with.
A lot of people on YouTube are using it like theyd use stock art (YouTube thumbnails, backgrounds, ads, or story boards for writing focused/story YouTubes).
There is still a stigma so a lot of people using it aren't announcing their use broadly, so I've found I usually have to stumble on their projects. Also, a lot of major companies aren't using it for that reason. I know Wizards of the Coast has had some arguments about that recently. Also consistency is still a problem as you mention which limits it's use in bigger projects.
Video games I think will be the first place we'll see it widely accepted. As people have been using AI generation techniques for background tools for like 20 years, see speedtree.
Seems super fast, some are saying 600x faster [0], than than the version made off of Google's paper. But it is a little less accurate. Point clouds are less useful but some on Reddit and the authors have tools to try to convert to meshes [1][2]. It does feel like stable diffusion level generation of good 3d assets is right around the corner. It will be interesting to see which tech wins out, whether it's some variant of depth estimation like sd2 and non ai tools can do, object spinning/multi angle view like Google's tool does, or whatever this tool does.
> The main problem with mesh generation from stuff like this is that usually the topology is a mess and needs a lot of cleanup to be useuable. It's not quite so bad for static non deforming objects but anything that needs to be animated deforming or that is organic looking would likely need retopologizing by hand.
>
> That's one of the worst parts of 3D modeling so it's like you're getting the AI to do the fun part and leaving you to do all the boring cleanup process.
From [1]. Seems like there is a pattern of "AI asked to generate final results with only final results to learn from, immediately asked for the apple in the picture" in AI generators. I suppose lack of specialization in application domains of NNs is a deliberate design choice for these high-profile projects, in a vague hope of simulating emergent behaviors as seen in the nature and avoiding to be another expert system(while being one!), but that attitude seems limiting usefulness, here and again.
People developing these models are very aware of what 3D workflow is like.
The issue is that image->point cloud training data is very easy to get, whereas image or point cloud -> clean 3d mesh training data is very hard to get in unconstrained domains.
Generating point clouds is where the state of the art is now. That doesn't mean that the whole field isn't entirely aware that text->3d mesh unlocks many more capabilities.
Seems like video game engines and the like would be useful ways to get lots of 3d models to corresponding point cloud data. What's the blocker to doing that? The models shown on that page look like 3d graphics circa 2000's or earlier.
I agree that random sampling surfaces of 3D meshes seems like a reasonable way to generate synthetic data for mesh > point cloud.
Without knowing a dang thing about AI, it feels like the problem moreso lies in:
1. Math related to topology: vertices, faces, edges, tri vs quad etc
2. Different topologies for the same object are better for different use cases. Rendering, skinning, morphing, physics etc all have different optimal topologies, and the definition of optimal varies based on workflow and scene specifics or even the human who has skills based on certain topological preferences. In other words, I'm not sure how much of 3D workflows are standardized even -- getting the topological data for workflows is no easy task, and it's not super usable until the model output can plug right into a workflow and the existing DCC ecosystem.
text2img generates a static asset, text2mesh is far more interesting beyond just the static rendering part which is where mesh topology becomes a big sticking point.
* There isn't software that generates point clouds from video games. This should be solvable but AFAIK hasn't been done yet.
* The diversity of models in video games is much lower than the real world
* Games use a bunch of techniques to reduce the poly count while making assets look like they are high poly (eg texture mapping). It's unclear what should be generated here.
Or ask CG designers, under consent and with credits, for data recordings of intermediate steps. Same for illustrations. It almost seems like circumventing experts is the point.
Don't human designers do image or point cloud -> clean 3D mesh in an iterative manner? I see it will be significantly more computationally expensive to iteratively deform a cube to a tree by NN, but I don't see why it isn't a solution.
the thing is that it's been shown times and times again (with chatGPT for example) that you can get really pretty good results by giving massive amounts of final results to the model. This approach is better by far than anything we've ever had in either text AI or image generation AI
It’s a fun demo. Worth to note that on mobile it didn’t include any button to download the generated point cloud data itself, at least not that I could find. Might be the same on desktop also.
Additionally I think the amount of time taken depends on the amount of visitors. I had to wait about 7 minutes for it to finish.
Too many users, I don't know Hugging Face's rules but they seem to limit how much each demo can use to a ceiling. When I ran it originally it was like 12 people using it, looks like the queue is now around 300 and Hugging Face doesn't spin up more instances. That being said the model is relatively small and can be run locally with at least 5 GB of VRAM according to the Stable Diffusion subreddit.
This is amazing but Google/OpenAI haven't released their models and don't seem to plan to. There is stable diffusion which was released and is probably slightly less good but still good if anyone wants to mess with it.
There is a huggingface instance, Collab notebooks, and local running notebooks here. [1] on the stable diffusion subreddit.
Also someone has packaged an exe that runs it with no fuss on computers with Nvidia GPUs that they posted on the media synthesis subreddit[0]
In my limited testing this compares ok to Dalle2. Style shifting works slightly less well and it's hard to force it away from normal images but with a little work it tends to be more accurate to your prompt.
We are still early in the tech. 2021 LLMs were not fit for any real purpose outside zany Madlibs. The tech has only really gotten fit for purpose since GPT4 and coherent models only got cheap and fast enough since llama 2 (July 2023). Even then smart models only got affordable around Deepseek v2 (May 2024) which was the first gpt4 level model to consistently serve subdollar per million tokens. You need at least a GPT4 level model to make a really interesting game. That's like a blink of time in game making time, indie games have 2-3 year cycles at best and AAA are like 5+. Even now fast, tool native interaction is only just coming online and there are no cheap models for that. Really fun game playing AI needs something like that to be magical.
There are AI games they just don't look like AI games neal.fun's Infinite Craft made a boat load of money and was very popular and it was powered by a Llama backend. Character ai and it's dozen copycats are like online storytelling/roleplay things. These are fairly popular. That could be a game or like a game platform or maybe it's more like fanfic. It has no fail state but people have made variants with fairly complex rules and states. Skryim and a few other games have pretty popular AI mods that let you add in NPC interactions that can talk, see, and even change their interactions with AI. The Skryim one Mantella has more than ~100k downloads.
We don't really have a northstar yet. Making a good game is really hard and usually takes someone doing something weird and clever. Minecraft is obvious in hindsight but many games on the way failed to make 'legos on the computer' fun. Incorporating AI breaks all the 'rules' of gaming. It operates slowly, it has high potential to break the rules and it has nearly unrecoverable failure states. Indie devs haven't figured out the best way to handle this and I suspect most big companies have just washed their hands of the whole thing until the AI gets faster, cheaper, and more stable.