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?
People keep meaning different things when they say “Generative AI”. Do you mean the tech in general, or the corporate AI that companies overhype and try to sell to everyone?
The tech itself is pretty cool. GenAI is already being used for quick subtitling and translating any form of media quickly. Image AI is really good at upscaling low-res images and making them clearer by filling in the gaps. Chatbots are fallible but they’re still really good for specific things like generating testing data or quickly helping you in basic tasks that might have you searching for 5 minutes. AI is huge in video games for upscaling tech like DLSS which can boost performance by running the game at a low resolution then upscaling it, the result is genuinely great. It’s also used to de-noise raytracing and show cleaner reflections.
Also people are missing the point on why AI is being invested in so much. No, I don’t think “AGI” is coming any time soon, but the reason they’re sucking in so much money is because of what it could be in 5 years. Saying AI is a waste of effort is like saying 3D video games are a waste of time because they looked bad in 1995. It will improve.
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.
I treat it as a newish employee. I don’t let it do important tasks without supervision, but it does help building something rough that I can work on.
shitposting.
Need some weidly specific imagery about whatever you’re going on about? It got you covered
I wrote guidelines for my small business. Then I uploaded the file to chatgpt and asked it to review it.
It made legitimately good suggestions and rewrote the documents using better sounding English.
Because of chatgpt I will be introducing more wellness and development programs.
Additionally, I need med images for my website. So instead of using stock photos, I was able to use midjourney to generate a whole bunch of images in the same style that fit the theme of my business. It looks much better.
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Money. It’s always about money. But more seriously, I also wonder what’s the point since all my interactions with GenAI have been disappointment after disappointment. But I read Dev saying that it’s great at creating drafts
I use it to help with programming and writing. Not as a way to have something so it for me but as something that can show me how to do something I am stuck on or give me ideas when Im drawing a blank.
Kinda like an interactive rubber duck. Its solutions arent always right or accurate but it does help me get past things I struggle with.
Here’s some uses:
- skin cancer diagnoses with llms has a high success rate with a low cost. This is something that was starting to exist with older ai models, but llms do improve the success rate. source
- VLC recently unveiled a new feature of using ai to generate subtitles, i haven’t used it but if it delivers then it’s pretty nice
- for code generation, I agree it’s more harmful than useful for generating full programs or functions, but i find it quite useful as a predictive text generator, it saves a few keystrokes. Not a game changer but nice. It’s also pretty useful at generating test data so long as it’s hard to create but easy (for a human) to validate.
There was a legitimate use case in art to draw on generative AI for concepts and a stopgap for smaller tasks that don’t need to be perfect. While art is art, not every designer out there is putting work out for a gallery - sometimes it’s just an ad for a burger.
However, as time has gone on for the industry to react I think that the business reality of generative AI currently puts it out of reach as a useful tool for artists. Profit hungry people in charge will always look to cut corners and will lack the nuance of context that a worker would have when deciding when or not to use AI in the work.
But you could provide this argument about any tool given how fucked up capitalism is. So I guess that my 2c - generative AI is a promising tool but capitalism prevents it from being truly useful anytime soon.
I use it to sort days and create tables which is really helpful. And the other thing that really helped me and I would have never tried to figure out on my own:
I work with the open source GIS software qgis. I’m not a cartographer or a programmer but a designer. I had a world map and wanted to create geojson files for each country. So I asked chatgpt if there was a way to automate this within qgis and sure thing it recommend to create a Python script that could run in the software, to do just that and after a few tweaks it did work. that saved me a lot of time and annoyances. Would it be good to know Python? Sure but I know my brain has a really hard time with code and script. It never clicked and likely never will. So I’m very happy with this use case. Creative work could be supported in a drafting phase but I’m not so sure about this.
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’.
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
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”.
Best use is to ask it questions that you’re not sure how to ask. Sometimes you come across a problem that you’re not really even sure how to phrase, which makes Googling difficult. LLM’s at least would give you a better sense of what to Google
