I promise this question is asked in good faith. I do not currently see the point of generative AI and I want to understand why there’s hype. There are ethical concerns but we’ll ignore ethics for the question.
In creative works like writing or art, it feels soulless and poor quality. In programming at best it’s a shortcut to avoid deeper learning, at worst it spits out garbage code that you spend more time debugging than if you had just written it by yourself.
When I see AI ads directed towards individuals the selling point is convenience. But I would feel robbed of the human experience using AI in place of human interaction.
So what’s the point of it all?
Video generators are going to eat Hollywood alive. A desktop computer can render anything, just by feeding in a rough sketch and describing what it’s supposed to be. The input could be some kind of animatic, or yourself and a friend in dollar-store costumes, or literal white noise. And it’ll make that look like a Pixar movie. Or a photorealistic period piece starring a dead actor. Or, given enough examples, how you personally draw shapes using chalk. Anything. Anything you can describe to the point where the machine can say it’s more [thing] or less [thing], it can make every frame more [thing].
Boring people will use this to churn out boring fluff. Do you remember Terragen? It’s landscape rendering software, and it was great for evocative images of imaginary mountains against alien skies. Image sites banned it, by name, because a million dorks went ‘look what I made!’ and spammed their no-effort hey-neat renders. Technically unique - altogether dull. Infinite bowls of porridge.
Creative people will use this to film their pet projects without actors or sets or budgets or anyone else’s permission. It’ll be better with any of those - but they have become optional. You can do it from text alone, as a feral demo that people think is the whole point. The results are massively better from even clumsy effort to do things the hard way. Get the right shapes moving around the screen, and the robot will probably figure out which ones are which, and remove all the pixels that don’t look like your description.
The idiots in LA think they’re gonna fire all the people who write stories. But this gives those weirdos all the power they need to put the wild shit inside their heads onto a screen in front of your eyeballs. They’ve got drawers full of scripts they couldn’t hassle other people into making. Now a finished movie will be as hard to pull off as a decent webcomic. It’s gonna get wild.
And this’ll be great for actors, in ways they don’t know yet.
Audio tools mean every voice actor can be a Billy West. You don’t need to sound like anything, for your performance to be mapped to some character. Pointedly not: “mapped to some actor.” Why would an animated character have to sound like any specific person? Do they look like any specific person? Does a particular human being play Naruto, onscreen? No. So a game might star Nolan North, exclusively, without any two characters really sounding alike. And if the devs need to add a throwaway line later, then any schmuck can half-ass the tone Nolan picked for little Suzy, and the audience won’t know the difference. At no point will it be “licensing Nolan North’s voice.” You might have no idea what he sounds like. He just does a very convincing… everybody.
Video tools will work the same way for actors. You will not need to look like anything, to play a particular character. Stage actors already understand this - but it’ll come to movies and shows in the form of deep fakes for nonexistent faces. Again: why would a character have to look like any specific person? They might move like a particular actor, but what you’ll see is somewhere between motion-capture and rotoscoping. It’s CGI… ish. And it thinks perfect photorealism is just another artistic style.
What doesn’t exist yet, but is obviously possible, is automatic tweening. Human animators spend a lot of time drawing the drawings between other drawings. If they could just sketch out what’s going on, about once per second, they could probably do a minute in an hour. This bullshit makes that feasible.
We have the technology to fill in crisp motion at whatever framerate the creator wants. If they’re unhappy with the machine’s guesswork, they can insert another frame somewhere in-between, and the robot will reroute to include that instead.
We have the technology to let someone ink and color one sketch in a scribbly animatic, and fill that in throughout a whole shot. And then possibly do it automatically for all labeled appearances of the same character throughout the project.
We have the technology to animate any art style you could demonstrate, as easily as ink-on-celluloid outlines or Phong-shaded CGI.
Please ignore the idiot money robots who are rendering eye-contact-mouth-open crowd scenes in mundane settings in order to sell you branded commodities.
For the 99% of us who don’t know what tweening is and were scared to Google it in case it was perverted, it’s short for in-betweening and means the short frames of an animation in-between two main scenes
Have you seen this? There was another paper, but I can’t remember the name of it right now.
I had not. There’s a variety of demos for guessing what comes between frames, or what fills in between lines… because those are dead easy to train from. This technology will obviously be integrated into the process of animation, so anything predictable Just Works, and anything fucky is only as hard as it used to be.
I think this is the other one I remember seeing.
I use it in a lot of tiny ways for photo-editing, Adobe has a lot of integration and 70% of it is junk right now but things like increasing sharpness, cleaning noise, and heal-brush are great with AI generation now.
So what’s the point of it all?
To reduce wages.
Instead of using tech to reduce work and allow humans to thrive and make art, we use tech to make art and force humans into long hours of drudgery and repetitive bitch work just because CEOs like to watch other people suffer I guess.
Idea generation.
E.g., I asked an LLM client for interactive lessons for teaching 4th graders about aerodynamics, esp related to how birds fly. It came back with 98% amazing suggestions that I had to modify only slightly.
A work colleague asked an LLM client for wedding vow ideas to break through writer’s block. The vows they ended up using were 100% theirs, but the AI spit out something on paper to get them started.
Those are just ideas that were previously “generated” by humans though, that the LLM learned
Those are just ideas that were previously “generated” by humans though, that the LLM learned
That’s not how modern generative AI works. It isn’t sifting through its training dataset to find something that matches your query like some kind of search engine. It’s taking your prompt and passing it through its massive statistical model to come to a result that meets your demand.
I feel like “passing it through a statistical model”, while absolutely true on a technical implementation level, doesn’t get to the heart of what it is doing so that people understand. It’s using the math terms, potentially deliberately to obfuscate and make it seem either simpler than it is. It’s like reducing it to “it just predicts the next word”. Technically true, but I could implement a black box next word predictor by sticking a real person in the black box and ask them to predict the next word, and it’d still meet that description.
The statistical model seems to be building some sort of conceptual grid of word relationships that approximates something very much like actually understanding what the words mean, and how the words are used semantically, with some random noise thrown into the mix at just the right amounts to generate some surprises that look very much like creativity.
Decades before LLMs were a thing, the Zompist wrote a nice essay on the Chinese room thought experiment that I think provides some useful conceptual models: http://zompist.com/searle.html
Searle’s own proposed rule (“Take a squiggle-squiggle sign from basket number one…”) depends for its effectiveness on xenophobia. Apparently computers are as baffled at Chinese characters as most Westerners are; the implication is that all they can do is shuffle them around as wholes, or put them in boxes, or replace one with another, or at best chop them up into smaller squiggles. But pointers change everything. Shouldn’t Searle’s confidence be shaken if he encountered this rule?
If you see 马, write down horse.
If the man in the CR encountered enough such rules, could it really be maintained that he didn’t understand any Chinese?
Now, this particular rule still is, in a sense, “symbol manipulation”; it’s exchanging a Chinese symbol for an English one. But it suggests the power of pointers, which allow the computer to switch levels. It can move from analyzing Chinese brushstrokes to analyzing English words… or to anything else the programmer specifies: a manual on horse training, perhaps.
Searle is arguing from a false picture of what computers do. Computers aren’t restricted to turning 马 into “horse”; they can also relate “horse” to pictures of horses, or a database of facts about horses, or code to allow a robot to ride a horse. We may or may not be willing to describe this as semantics, but it sure as hell isn’t “syntax”.
There is no point. There are billions of points, because there are billions of people, and that’s the point.
You know that there are hundreds or thousands of reasonable uses of generative AI, whether it’s customer support or template generation or brainstorming or the list goes on and on. Obviously you know that. So I’m not sure that you’re asking a meaningful question. People are using a tool to solve various problems, but you don’t see the point in that?
If your position is that they should use other tools to solve their problems, that’s certainly a legitimate view and you could argue for it. But that’s not what you wrote and I don’t think that’s what you feel.
I know they are being used to, and are decently good for, extracting a single infornation from a big document (like a datasheet). Considering you can easily confirm the information is correct, it’s quite a nice use case
For coding it works really well if you give it examples like “i have code that looked like this … And i made it to look like this … If i give you another piece of code that’s similar to the first can you convert it to the second for me”. Been great to reduce the amount of boring grunt work so I can focus on the more fun stuff
In C#, when programming save/load in video games, it can be super tedious. I am self taught and i didnt have the best resources, so the only way i could find to ensure its saving the correct variables was to manually input every single variable into a text file. I dont care if its plaintext, if people want to edit their save then more power to them. The issue is that there are potentially tens of hundreds of different variables that need to be saved for the gamestate to be accurately recreated.
So its really nice that i can just copy/paste my classes into gpt and give it the syntax for a single variable to be saved, then have it do the rest. I do have to browse through and ensure its actually getting all the variables, but it turns a potentially mindnumbing 4 hour long process into maybe a 20 minute one thats relatively engaging.
Also if you know a better way lmk. I read that you can simply hash the object into a text file and then unhash it, but afaik unhashing something is next to impossible and i could never figure it out anyways.
You could encrypt and decrypt it with keys.
Or you can do something simple like scramble the letters like a cypher, still able to edit manually but it wouldn’t be as readable and obvious what everything does.
Or you can can encode it, same issue as the last but they’ll have to know what it was encoded with to decode it before editing.
Or you can just turn it into bytes so the file is more awkward to work with.
You could probably mix a bunch of these together if you care enough. U don’t think any are THE standard and foolproof but they’re options
The goal isnt to encrypt the data, i dont care if its plaintext. The goal is to find a way to save an object in c# without having to save each individual variable.
Oh, in that case serialise it into json. Just use the json serialiser in system.text. it can turn any object in c# into a json object and you can deserialise them back into objects too.
Sorry i misinterpreted what you were asking for.
Yeah, that sounds a lot easier. Thanks
AI saves time. There are few use cases for which AI is qualitatively better, perhaps none at all, but there are a great many use cases for which it is much quicker and even at times more efficient.
I’m sure the efficiency argument is one that could be debated, but it makes sense to me in this way: for production-level outputs AI is rarely good enough, but creates really useful efficiency for rapid, imperfect prototyping. If you have 8 different UX ideas for your app which you’d like to test, then you could rapidly build prototype interfaces with AI. Likely once you’ve picked the best one you’ll rewrite it from scratch to make sure it’s robust, but without AI then building the other 7 would use up too many man-hours to make it worthwhile.
I’m sure others will put forward legitimate arguments about how AI will inevitably creep into production environments etc, but logistically then speed and efficiency are undeniably helpful use cases.
As some witty folks have put it, LLMs can’t give you anything truly, interestingly new when all they’re capable of is some weighted average of what’s already there. And I’ll be clear in saying I hate with the force of a tsunami the way AI is being shoved at us by desperate CEOs, and how it’s being used to kill labor, destroy copyright law, increase income inequality, destroy the environment, and increase the power of huge corporations headed by assholes like Altman and Musk. But AI is getting pretty good at that weighted-average-of-what’s-out-there, and a lot of the work done in several industries can benefit from that. For me, one of the great perversities or tragedies of AI is that it could be a targeted, useful tool but, instead, it’s a hammer to further erode freedom. Even the coders, editors, advertisers, educators, etc. using it to do their jobs are participating in a short-term selloff of their profession to their CEOs, shareholders, etc. at the expense of large numbers of their colleagues or potential colleagues who will now never get jobs.
It’s like if someone invented the wheel and Sam Altman immediately patented it and sold it to Raytheon.
i’ve written bots that filter things for me, or change something to machine-readable formats
the most successful thing i’ve done is have a bot that parses a web page and figures out the date/time in standard format, gets a location if it’s listed in the description and geocodes it, and a few other fields to make an ical for pretty much any page
i think the important thing is that gen ai is good at low risk tasks that reduce but don’t eliminate human effort - changing something from having to do a bunch of data entry to skimming for correctness
I have personally found it fantastic as a programming aid, and as a writing aid to write song lyrics. The art it creates lacks soul and any sense of being actually good but it’s great as a “oh I could do this cool thing” inspiration machine
In the context of programming:
- Good for boilerplate code and variables naming when what you want is for the model to regurgitate things it has seen before.
- Short pieces of code where it’s much faster to verify that the code is correct than to write the code yourself.
- Sometimes, I know how to do something but I’ll wait for Copilot to give me a suggestion, and if it looks like what I had in mind, it gives me extra confidence in the correctness of my solution. If it looks different, then it’s a sign that I might want to rethink it.
- It sometimes gives me suggestions for APIs that I’m not familiar with, prompting me to look them up and learn something new (assuming they exist).
There’s also some very cool applications to game AI that I’ve seen, but this is still in the research realm and much more niche.
shitposting.
Need some weidly specific imagery about whatever you’re going on about? It got you covered
I use it to re-tone and clarify corporate communications that I have to send out on a regular basis to my clients and internally. It has helped a lot with the amount of time I used to spend copy editing my own work. I have saved myself lots of hours doing something I don’t really like (copy-editing) and more time doing the stuff I do (engineering) because of it.
I have a very good friend who is brilliant and has slogged away slowly shifting the sometimes-shitty politics of a swing state’s drug and alcohol and youth corrections policies from within. She is amazing, but she has a reading disorder and is a bit neuroatypical. Social niceties and honest emails that don’t piss her bosses or colleagues off are difficult for her. She jumped on ChatGPT to write her emails as soon is it was available, and has never looked back. It’s been a complete game changer for her. She no longer spends hours every week trying to craft emails that strike that just-right balance. She uses that time to do her job, now.
I hope it pluralizes ‘email’ like it does ‘traffic’ and not like ‘failure’.
