All the vids have that instantly recognizable GenAI "sheen", for the lack of a better word. Also, I think the most obvious giveaway are all the micro-variations that happen along the edges, which give a fuzzy artifact.
Somebody took AI video editing, and in a year or two, we're already at entire MMA rabbit holes of fake videos.
Commenting mostly as a personal evidence reference of how crazy the World Wide Web has gotten from anecdotal sources.
I won't argue wether text to speech qualifies as an AI but I agree they must be making bank.
Might also be an AI voice-changer (i.e. speech2speech) model.
These models are most well-known for being used to create "if [famous singer] performed [famous song not by them]" covers — you sing the song yourself, then run your recording through the model to convert the recording into an equivalent performance in the singer's voice; and then you composite that onto a vocal-less version of the track.
But you can just as well use such a model to have overseas workers read a script, and then convert that recording into an "equivalent performance" in a fluent English speaker's voice.
Such models just slip up when they hit input phonemes they can't quite understand the meaning of.
(If you were setting this up for your own personal use, you could fine-tune the speech2speech model like a translation model, so it understands how your specific accent should map to the target. [I.e., take a bunch of known sample outputs, and create paired inputs by recording your own performances of them.] This wouldn't be tenable for a big low-cost operation, of course, as the recordings would come from temp workers all over the world with high churn.)
But the people who position themselves to profit from the energy consumption of the hardware will profit from all of it: the LLMs, the image generators, the video generators, etc. See discussion yesterday: https://news.ycombinator.com/item?id=41733311
Imagine the number of worthless images being generated as people try to find one they like. Slop content creators iterate on a prompt, or maybe create hundreds of video clips hoping to find one that gets views. This is a compute-intensive process that consumes an enormous amount of energy.
The market for chips will fragment, margins will shrink. It's just matrix multiplication and the user interface is PyTorch or similar. Nvidia will keep some of its business, Google's TPUs will capture some, other players like Tenstorrent (https://tenstorrent.com/hardware/grayskull) and Groq and Cerebras will capture some, etc.
But at the root of it all is the electricity demand. That's where the money will be made. Data centers need baseload power, preferably clean baseload power.
Unless hydro is available, the only clean baseload power source is nuclear fission. As we emerge from the Fukushima bear market where many uranium mining companies went out of business, the bottleneck is the fuel: uranium.
Trial and error content-creation using generative AI, whether or not it creates any real-world value, consumes a lot of electricity.
This electricity demand is likely to translate into demand for nuclear power.
When this demand for nuclear power meets the undersupply of uranium post-Fukushima, higher uranium prices will result.
More supply and lower prices will result.
Not unlike the recent few years in (say) lithium, anticipated demand surged exploration and development, actual demand didn't meet anticipated demand and a number of developed economicly feasible resources were shuttered .. still waiting in the wings for a future pickup in demand.
Look at how long NexGen's Rook 1 Arrow is taking to develop (https://s28.q4cdn.com/891672792/files/doc_downloads/2022/03/...). Spend an hour listening to what Cameco said in its most recent conference call. Look at Kazatomprom's persistent inability to deliver the promised pounds of uranium, their sulfuric acid shortages and construction delays.
Uranium mining is slow and difficult. Existing demand and existing supply are fully visible. There's a gap of 20-40 million pounds per year, with nothing to fill the gap. New mines take a decade or more to develop.
It is not in the slightest like lithium.
Would two decades in global exploration geophysics and being behind the original incarnation of https://www.spglobal.com/market-intelligence/en/industries/m... count?
> Once you know the facts we can talk again.
Gosh - that does come across badly.
When someone compares uranium to lithium, I know I'm not talking to a uranium expert.
All the best to you, and I'll try to be more polite in the future.
Clearly you're a better expert.
> when someone compares uranium to lithium, I know I'm not talking to a uranium expert.
It's about boom bust and shuttering cycles that apply in all resource exploration and production domains.
Perhaps you're a little too literal for analogies? Maybe I'm thinking in longer time cycles than yourself and don't a few years of lag as anything other than a few years.
You are well-prepared to familiarize yourself with the current supply/demand situation. It's time to "make bank", just like you did in 2007... only more so. The 2007 spike was during an oversupplied uranium market and mainly driven by financial actors.
I invite you to begin by listening to any recent interview with Mike Alkin.
Good night and enjoy your weekend.
That said, this channel has been producing videos well before ChatGPT3.5/4 so at the very least they probably started with human written scripts.
I feel like it might actually be quite complex for AI to pull up the perfect clips and edit them with the script, including timing and everything. Maybe it could be made automatic, but nonetheless it would be a complex process and I don't think possible few years ago. I know Gemini and possibly some others can analyze video if fed to them, but I'm still skeptical that this channel in particular would have done it, when they have always had this frequency of uploads and similar tone.
Also I think there's far better TTS now with ElevenLabs and others so it could be made much more human like.
The image had all the telltale signs of being AI generated (too much detail, the lights & shadows were the wrong scale, the focus of the lens was odd for the kind of photo, etc). I checked that other group, and sure enough, they claim to be about sharing "miniature dioramas" but all they share is AI-generated crap.
And in the original group, which I'm a member of and is full of people who actually create dioramas -- let's say they are "subject matter experts" -- nobody suspected anything! To them, who are unfamiliar with AI art, the photo was of a real hand-made diorama.
At this point, looking at a big tech SoMe feed I would expect that everything is, or at least could be, gen AI content.
I could see it being pretty shocking if I hadn't, but I honestly can't imagine how I'd miss that.
> I could see it being pretty shocking if I hadn't, but I honestly can't imagine how I'd miss that.
The point of the video wasn't to count correctly, but to see the gorilla
That is also how magicians work, drawing your attention to one particular thing, hiding the secret of the trick from you, sometimes even in plain sight, like in the video.
Or pickpockets, who might bump into you and picking your pocket at the same time, where your attention is focussed on the sudden impact, keeping your attention away from your walled being taken.
Essentially, send me a video of something I care about and I will only look for that thing. Most people are not detectives, and even most would-be detectives aren’t yet experts.
Who even needs AI generated videos when you can just act out absurdity and pretend it's real?
There's a morbid path from the grainy Iraq war and earlier shaky footage, through IS propaganda which at the time had basically the most intense combat footage ever released to the Ukraine war. Which took it to the morbid end conclusion of endless drone video deaths and edited clips 30+ mins long with day long engagements and defending.
And yes, to answer your belief that there is none - there is loads of "cinematic body cam footage out there now".
I still don't know if it's autogenerated from the original video or recreated manually but yeah it's pretty realistic for the first few seconds.
https://www.theverge.com/2017/12/4/16732912/ufc-video-stream...
Fake images play into that, but they don't need to be AI generated for that to be true, it's been true forever.
The only regret I have about that is losing video as a form of evidence. CCTV footage and the like are a valuable tool for solving crimes. That’s going to be out the window soon.
Does this create difficulties if you want to modify the raw video data in any way? Yes it does, even if you just want to save it in a different lossy compression level or format. But these problems aren't insurmountable. Essentially, provenance info can be added for each modification, signed by the entity that made the change, and the end viewer can then decide if they trust the full certificate chain (just as they do now with HTTPS).
Recently someone said here that it's noticable that videos from CCTV cameras are often filmed with a phone or camera on a screen instead of using the original video, and people were speculating that it might be hard or impossible to get access to the original recording because of bureaucracy or something, but that recording a playback on a screen with a phone or camera or something is then often allowed. Maybe they also do this partly so that the original can't be easily messed with by other people.
But yeah if you can verify that a certain video was filmed at a certain time by a certain camera, that is great. Of course the companies providing these cameras should be trustworthy, and that the camera's are actually really sending what they actually record, and that the company itself doesn't mess with the original recordings.
I recall an article posted 1-2 years ago about a camera company (Kodak? Can't remember) which was starting to offer something along these lines.
>the companies providing these cameras should be trustworthy, and that the camera's are actually really sending what they actually record, and that the company itself doesn't mess with the original recordings.
I agree. We can't guarantee any of these things, but on the bright side, the incentives are pointing in the right direction to make self-interested companies choose to behave the right way.
It will complicate things and make the hardware more expensive, so I doubt it will sweep through all consumer camera tech unless the "Is this photo real?" question becomes a crisis. There's also the fact that it would be possible to give individual cameras different private keys, with certificates signed by the manufacturer: This would enable non-repudiation (you would not be able to plausibly deny that you had taken a particular photo/video), which has potentially big upsides but also privacy downsides. I think that could be solved by giving the user the option of signing with their unique camera private key (when the user wants to prove to others that they took the photo themselves) or with the manufacturer's key (when they want to remain anonymous).
Similarly, while Photoshop made it easier it happened at a time where technical advances made the problem harder because everyone’s standards for photos went up dramatically, and so producing a realistic fake was still a slow process for a skilled worker.
Now, it’s increasingly available to everyone and that means that we’re going to see a lot more scams and hoaxes as people without artistic talent or willingness to invest time can make realistic fakes even for minor things. That availability is transformative enough to merit the concern we’ve been seeing here.
The ultimate compression is to reduce the video clip to a latent space vector representation to be rendered on device. :)
Just give us a few more revs of Moore’s law for that to be reasonable.
edit: found a patent… https://patents.google.com/patent/US11388416B2/en
There's an entire pattern of reels that are basically just ripped-off-content with enough added noise to (I presume) avoid content detection filters. Then the comments have links to scam sites (but are labelled as "the IMDB page for this content").
That's something that can be fixed in a future release or you can fix it right now with some filters in post in your pipeline.
This has been the default excuse for the last 5+ years. I won't hold my breath.
AI in general is from 1950, or more generally from whenever the abacus was invented. This very website runs on AI, and always has. I would implore us to speak more exactly if we’re criticizing stuff; “LLMs” came around (in force) in 2023, both for coherent language use (ChatGPT 3.5) and image use (DALLE2). The predecessors were an order of magnitude less capable, and going back 5 years puts us back in the era of “chatbots”, aka dumb toys that can barely string together a Reddit comment on /r/subredditsimulator.
> AI so far has given us ability to mass produce shit content of no use to anybody
Good AI goes largely undetected, for the simple reason that it closely matches the distribution of non-AI content.
Controversial aside: This is same bias that results in non-passing trans people being representative of the whole. Passing trans folk simply blend in.
All criminals seem so incredibly stupid that you can’t understand why anyone would ever try since they all are caught? The smart ones don’t get caught and no one ever hears about them.
Today we have these realistic videos that are still in the uncanny valley. That's insane progress in the span of a year. Who knows what it will be like in another year.
Let'em cook.
2 years ago we had decent video generation for clips
7 months ago we have Sora https://news.ycombinator.com/item?id=39393252 (still silence since then)
With these things, like DALL-E 1 and GPT-3, the original release of the game changer often comes ca. 2 years before people can actually use it. I think that's what we're looking at.
I.e. it's not as fast as you think.
And isn’t this model open source…? So we get access to it, like, momentarily? Or did I miss something?
When the Will Smith one was released, it was kind of a parody though. Tech had already been able to produce that level of "quality" for about 2 years at the time of it's publishing. The Will Smith one is honestly something you could have created with Disco Diffusion in early 2021, I used to do this back then...
2022 saw: https://makeavideo.studio/ (coherent, but low res - it was possible to upscale at extreme expense) https://sites.research.google/phenaki/ https://lumiere-video.github.io/
It was more like 18-20 months ago sorry so early 2023, but https://runwayml.com/research/gen-1 was getting there as was https://pika.art/home - Sora obviously changed the game, but I would say these two were great.
The only interesting questions now have nothing to do with capability but with economics and raw resources.
In a few years, or less, clearly we'll be able to take our favorite books and watch unabridged, word-for-word copies. The quality, acting, and cinematography will rival the biggest budget Hollywood films. The "special effects" won't look remotely CG like all of the newest Disney/Marvel movies -- unless you want them to. If publishers put up some sort of legal firewall to prevent it, their authors, characters, and stories will all be forgotten.
And if we can spend $100 of compute and get something I described above, why wouldn't Disney et al throw $500m at something to get even more out of it, and charge everyone $50? Or maybe we'll all just be zoo animals soon (Or the zoo animals will have neuralink implants and human level intelligence, then what?)
That would be a boring movie.
I don't think so at all. You're thinking a movie is just the end result that we watch in theaters. Good directing is not a text prompt, good editing is not a text prompt, good acting is not a text prompt. What you'll see in a few years is more ads. Lots of ads. People who make movies aren't salivating at this stuff but advertising agencies are because it's just bullshit content meant to distract and be replaced by more distractions.
But at the same time, while it is indeed true that the end result is far more than simply just making good images, LLMs are weird interns at everything — with all the negative that implies as well as the positive, so they're not likely to produce genuinely award winning content all by themselves even though they can do better by asking them for something "award winning" — so it's certainly conceivable that we'll see AI indeed do all these things competently at some point.
I'm also expecting, before 2030, that video game pipelines will be replaced entirely. No more polygons and textures, not as we understand the concepts now, just directly rendering any style you want, perfectly, on top of whatever the gameplay logic provided.
I might even get that photorealistic re-imagining of Marathon 2 that I've been wanting since 1997 or so.
This isn't an intentional "feature" of these models; rather, it's kind of an inherent part of how such models work — they learn associations between tokens and structural details of images. Artists' names are tokens like any other, and artists' styles are structural details like any other.
So, unless the architecture and training of this model are very unusual, it's gonna at least be able to give you something that looks like e.g. a "pencil illustration."
But overall the physics are surprisingly good. In the videos from text we a person moving covered in a bedsheet, a mirror doing vaguely mirror-like things, a monkey moving in water and creating plausible waves, shadows moving over a 3d object with the sloth in the pool and plausible fire. Those are all classic topics to tackle in computer-generated graphics, all casually handled by a model that isn't explicitly trained on physical simulation.
In a twist of irony it's the simplest of those (the mirror) that's the most obviously wrong.
There were movies with horrible VFX that still sold perfectly well at the time.
But then, a lot of people have financial reasons to ignore the problem. What's too bad, because it's hindering the creation of stuff that are actually useful.
These models do seem like they could be great photorealism/stylization shaders. And they are also pretty good at stuff like realistic explosions, fluid renders etc. That stuff is really hard with CG.
You are spot on. I've been involved in creating technologies used by film and video creators for decades, so I have some understanding of what would be useful to them. The best video AIs I've seen only seem capable of replacing some stock video clip creation because, so far, I haven't seen any ability to maintain robust consistency from shot to shot and scene to scene. There's also no granular control other than the text prompt. At first glance, these demos are very impressive but when you try to map the capability shown to a real production workflow for a movie, TV show or commercial, they're not even close because they aren't even trying to solve the problem.
To be clear, I think it's probably possible to create a video AI that would be truly useful in a real production workflow, it's just that I haven't seen anything working in that direction yet.
Yeah it's really hard to get across to a lot of folks that are really amped up about these tools that what they're focused on refining is not getting them any closer to their imagined goal in most professional workflows. This will be great right off the bat for what most developers would need images for-- making a hero image for a blog post, making a blurb of video for a background, or a joke, or making assets for their video game that would never cut it for a non-cheapo commercial project but are better than what they'd have been able to cobble together themselves. But those workflows are fundamentally so different from the very first steps in the process. It's a larger-scale version of trying to explain to no-compromise FOSS zealots 20 years ago that Gimp was nowhere near able to replace Photoshop in a professional toolkit because they're completely disinterested in taking feedback about professional use cases, and that being able to write your own filters in Perl doesn't really help graphic designers-- well 20 years later, the gap is as wide as it ever has been, and there are even more people, almost exclusively FOSS nerds with to professional visual work experience, that insist it's better.
That said, it's nearly as hard to get this across to ADs who are like "what do you mean this shot is going to take you 3 days? I just made these stills which are like 70% there in midjourney in 10 minutes."
> To be clear, I think it's probably possible to create a video AI that would be truly useful in a real production workflow, it's just that I haven't seen anything working in that direction yet.
I think that neural networks, generally, are already fantastically useful in tools like Nuke's Copycat node. Nobody misses masking frame-by-frame if they don't have to do it. But prompt-based tools? Nah. If even 200 words in a prompt was enough information to convey work that needed to be done, why do creative workflows need so many revisions and why are there so many meetings with sketches and mood boards and concept art among career professionals? Text prompts are great for people that are working with a medium they don't really know how to create in because the real artistic decisions are already made by the artists whose artwork was ingested into the models. If you don't understand that level of nuance, you don't see how unbelievably consequential it is to the final product, and not having granular control of it seems nearly inconsequential. Most professionals look at it and see a toy because they know it will never be capable of making what they want it to make.
Yes, I agree. You've highlighted the distinction I should have included of "prompt-based".
There's a vast gulf between these AI-researcher-based concept demos on one side and the NN-based features slowly getting implemented in real production tools. Like you, I've found it challenging to have constructive conversations about AI tooling with anyone not versed in real production workflows. To anyone with real industry experience it's obvious that so far these demos don't represent a threat to real production workflows or the skilled career professionals making a good living. It's not that they're not threatening, they're just threatening to replace a different type of job entirely. If you're one of the poor souls in an off-shore locale doing remote low-end piece-work like manning a stock photo/video clip farm or doing >$100 per piece gigs on Fivver - then, yeah, you should feel "threatened".
A meta-point I try to make in these conversations is that, at least so far, every actual paying creative job I've seen AI threaten are, IMHO, work I wouldn't wish on my worst enemy. These are low-paid entry-level sweatshop gigs and everyone doing them aspires to do something else as soon as they can. The two analogies I use are: 1) How the "threat" of robotics to jobs is actually playing out. So far, in industrial applications robots are replacing Amazon warehouse and manufacturing assembly line workers, literally today's equivalent of 1920s sweatshop work. Much like the heart-wrenching videos of children in Calcutta earning pennies sifting through junk piles for metal scraps, it'll be a better world when robots replace those jobs and humans have jobs designing, installing, programming and servicing the robots. Likewise in consumer robotics applications, so far, the robots in our house only vacuum the floors, change the cat litter box, and wash the dishes/clothes. Growing up my family spent a couple years living in Asia in the 1970s and we actually had a "wash ama" who came twice a week and washed our clothes manually with a washboard and a tub. Sounds quaint but in reality it was grueling labor. She was a lovely lady but I'm glad Maytag replaced that job.
The second analogy I often use is observing that self-driving cars are mainly a threat to Uber and Lyft drivers who often barely earn minimum wage and have no job security to start with. Career professionals actually working in real video and film production workflows feel as "threatened" by prompt-based AIs as Formula 1 drivers feel about self-driving cars. Why does current F1 champion Max Verstappen never get asked how he feels about AI self-driving cars coming for his job? :-) As you observed, anyone who understands the thousands of creative choices which comprise any shot in a quality film doesn't even see these prompt-based AI demos as relevant. Once you've heard a skilled cinematographer, colorist or director of photography spend over an hour deconstructing and debating the creative choices made in single shot or scene from a film, it's hard to even imagine these demos as a threat to that level of creative skill. But being able to crudely copy the traits of a composite of a thousand exemplars of the craft without understanding any of the interactions between those thousands of creative choices does make for impressive demos. Even though the fidelity of the crude copy is amazing, the fact is such shots are a random puree of a thousand different creative choices pulled from a thousand different great shots. That's the root of what unskilled people call the "AI-clip sheen". It won't be easy to eliminate from prompt-based clip generators because the nature of the NN is it doesn't understand the interactions of all those subtle creative choices it's aping. Mashing together one cinematographer's lens choice from one shot with another cinematographer's filter choice from another shot with a third cinematographer's film stock choice from another film and a colorist's palette from a fourth unrelated work and then training the output filter only against broad criteria like "looks good" or "like a high-quality art film" is not a strategy that, IMHO, will ever produce a true threat to skilled top-level production workflows.
At the same time, as you observed, NN's are already delivering tremendous value eliminating labor-intensive, repetitive manual production work like frame-by-frame rotoscoping and animation tweening, work no one actually in the industry is sorry to see humans being relieved of. While I think NN-based features in production tools will continue to expand the use cases they can assist, I'm not sure AI tools will ever completely replace high-skill production professionals. I've already mentioned the technical challenges based on how NNs work but even if these challenges are someday overcome, there's a more fundamental limitation which is economic. Although feature film, network-level television and high-end commercials have massive cultural reach and are huge industries, the overall economic value of the entire technical production workflow and related tooling isn't as large as most people imagine. From Panavision cameras, Zeiss film lenses and Adobe Premiere to Chapman camera cranes, Sachtler tripods and Kinoflow lights, it's a relatively small industry with no unicorn-level startups. Even assuming one could license all the necessary content and manually tag it, it's hard to imagine a viable business plan which justifies investing the hundreds of millions required to recruit top-level AI researchers, thousands of H100 GPUs, etc to create and train a tool that could really replace the top 1000 career production pros working in Hollywood. There are so many other markets AI can target which are potentially far more lucrative than high-end film and video production workflows. Even the handful of blockbuster Summer tent pole movies made each year that cost $200M to make only spend somewhere around $10M or $20M on production labor and tooling below the department head level. That's not enough money to fund AI replacement anytime in the foreseeable future. The total addressable market of high-end film and video production just isn't big enough to be an attractive target for investors to fund going after it.
My biggest hope for the professional use of these things is in post-render pre-comp polishing for simulations and pyro. They're so good at understanding patterns and having smooth transitions that they can make a nonsense, physically absurd combination of images blend together perfectly... one of my favorites was a background guy's nose in a sepia toned video was neatly melded into a distant oncoming train. I think that could be really great for smoothing out volume textures and things like that. Given, that probably has more to do with my specialty than anything.
My main problem is that I'm just starting out my career in this field after switching from a decade of python dev work, and then doing some visual design before going to art school where I graduated at the top of my program having mostly concentrated in making cool shit at the Houdini/UE confluence. Two years ago everyone was saying "holy crap you've got the golden skillset," and now everyone's like "oof... hang in there... I guess..." Even aside from the strike aftermath, nobody in the market has any idea what to do right now, especially with juniors, let alone a really weird mixture of junior + senior dev that I am with a few contracts under my belt and a ton of really solid coding experience, but nothing really impressive in the industry itself. Who fucking knows. I think a lot of people in charge of hiring are waiting for a moment where it's going to just be sort of obvious what they need to do, and don't want to hire people into FTEs that are going to be eliminated through ai efficiency gains in 6 months. I don't have a lot of insight into the hiring side of the business though.
I have yet to find examples of this
* https://www.reddit.com/gallery/1fvs0e1
* https://old.reddit.com/r/StableDiffusion/comments/1fak0jl/fi...
- The monkey in hotspring video, if not for its weird beard...
- The koala video I would have mistaken for hollywood-quality studio CGI (although I would know it's not real because koalas don't surf... do they?)
- The pumpkin video if played at 1/4 resolution and 2x speed
- The dog-at-Versailles video edit
If the videos are that good, I'm sure I already can't distinguish between real photos and the best AI images. For example, ThisPersonDoesNotExist isn't even very recent, but I wouldn't be able to tell whether most of its output is real or not, although it's limited to a certain style of close-up portrait photography.
Not to take away from your point but it's more limited than one might think from this phrase. As an exercise, open that page and scroll so the full image is on your screen, then hover your mouse cursor within the iris of one of the eyes, refresh and scroll again. (Edit: I just noticed there's a delayed refresh button on the page, so one can click that and then move their mouse over the eye to skip a full page refresh.) I've yet to see a case where my mouse cursor is not still in line with the iris of the next not-person.