this post was submitted on 25 Dec 2023
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We really need to stop calling things "AI" like it's an algorithm. There's image recognition, collective intelligence, neural networks, path finding, and pattern recognition, sure, and they've all been called AI, but functionally they have almost nothing to do with each other.
For computer scientists this year has been a sonofabitch to communicate through.
But "AI" is the umbrella term for all of them. What you said is the equivalent of saying:
All of the things you've mentioned are correctly referred to as AI, and since most people do not understand the nuances of neural networks vs hard coded algorithms (and anything in-between), AI is an acceptable term for something that demonstrates results that comes about from a computer "thinking" and making ~~shaved~~ intelligent decisions.
Btw, just about every image recognition system out there is a neural network itself or has a neural network in the processing chain.
Edit: fixed an autocorrect typo
I think you're fighting a losing battle.
You're right, but so is the previous poster. Actual AI doesn't exist yet, and when/if it does it's going to confuse the hell out of people who don't get the hype over something we've had for years.
But calling things like machine learning algorithms "AI" definitely isn't going away... we'll probably just end up making a new term for it when it actually becomes a thing... "Digital Intelligence" or something. /shrug.
It isn't human-level, but you could argue it's still intelligence of a sort, just erstatz
I dunno... I've heard that argument, but when something gives you >1000 answers, among which the correct answer might be buried somewhere, and a human is paid to dig through it and return something that looks vaguely presentable, is that really "intelligence", of any sort?
Aka, 1 + 1 = 13, which is a real result that AI can and almost certainly has recently offer(ed).
People are right to be excited about the potential that generative AI offers in the future, but we are far from that atm. Also it is vulnerable to misinformation presented in the training data - though some say that that process might even affect humans too (I know, you are shocked, right? well, hopefully not that shocked:-P).
Oh wait, nevermind I take it all back: I forgot that Steven Huffman / Elon Musk / etc. exist, and if that is considered intelligence, then AI has definitely passed that level of Turing equivalence, so you're absolutely right, erstatz it is, apparently!?
This problem was kinda solved by adding AGI term meaning "AI but not what is now AI, what we imagined AI to be"
Not going to say that this helps with confusion much 😅 and to be fair, stuff like autocomplete in office soft was called AI long time ago but it was far from LLMs of now
Enemies in Doom have AI. We've been calling simple algorythms in a handful lines of code AI for a long time, the trend has nothing to do with languege models etc.
AI = "magic", or like "synergy" and other buzzwords that will soon become bereft of all meaning as a result of people abusing it.
There's whole countries that refer to the entire internet itself as Facebook, once something takes root it ain't going anywhere
Computer vision is AI. If they literally want a robot eye to scan their cluttered pantry and figure out what is there, that'll require some hefty neural net.
Edit: seeing these downvotes and surprised at the tech illiteracy on lemmy. I thought this was a better informed community. Look for computer vision papers in CVPR, IJCNN, and AAAI and try to tell me that being able to understand the 3D world isn't AI.
You're very wrong.
Computer vision is scanning the differentials of an image and determining the statistical likelihood of two three-dimensional objects being the same base mesh from a different angle, then making a boolean decision on it. It requires a database, not a neutral net, though sometimes they are used.
A neutral net is a tool used to compare an input sequence to previous reinforced sequences and determine a likely ideal output sequence based on its training. It can be applied, carefully, for computer vision. It usually actually isn't to any significant extent; we were identifying faces from camera footage back in the 90s with no such element in sight. Computer vision is about differential geometry.
Computer vision deals with how computers can gain high level understanding of images and videos. It involves much more than just object reconstruction. And more importantly, neural networks are a core component is just about any computer vision application since deep learning took off in the 2010s. Most computer vision is powered by some convolutional neural network or another.
Your comment contains several misconceptions and overlooks the critical role of neural networks, particularly CNNs, which are fundamental to most contemporary computer vision applications.
Thanks, you saved me the trouble of writing out a rant. I wonder if the other guy is actually a computer scientist or just a programmer who got a CS degree. Imagine attending a CV track at AAAI or the whole of CVPR and then saying CV isn't a sub field of AI.
Shouldn’t there be a catch all term to explain the broader scope of the specifics?
Science is a broad term for multiple different studies, vehicle is a broad term for cars and trucks.
Machine learning?
Glorified chatbots. Tops. But definitely not something with any kind of intelligence.
Yesterday I prompted gpt4 to convert a power shell script to Haskell. It did it in one shot. This happens more and more frequently for me.
I don't want to oversell llms, but you are definitely underselling them.
"Computer Science"
So people think of programming instead?
Those are all very specific intelligences. The goal is to unite them all under a so-called general intelligence. You're right, that's the dream, but there are many steps along the way that are fairly called intelligence.