pcalau12i

That's... the point. That's like, literally the entire point I am making. It makes no sense to be "anti-AI" because AI is such an incredibly broad spectrum of technology. It's fine to be critical of specific applications of AI (indeed, there are many examples of AI making things worse or even being used for evil) but being "anti-AI" in an absolute sense is an incredibly dogmatic and entirely unreasonable position and I am utterly appalled so many people here are unironically trying to defend it.

No one uses the term ANN because most people don't know what it means so it's not good for marketing, so AI is used in its place, but it refers to the same kind of technology. Machine learning isn't a good replacement precisely for the reason you say: it is broad and includes things that aren't ANNs and would not fit under what is generally understood to be AI. If a person bought a piece of tech that said it is powered by AI and used something like a k-means clustering algorithm they probably would feel a bit ripped off and would expect something with an actual AI model that does intelligent processing, they would expect something that could take advantage of an AI accelerator, which is the consumer-end name for a piece of hardware that does AI inferencing, which is specific to ANNs!

It is just undeniably true that when "AI" is used in the overwhelming majority of articles, papers, etc these days people very specifically have ANNs in mind. If you deny this you are just denying factual reality, you are denying that 2+2=4 and that point you are being too unreasonable to carry on the discussion with. I am going to tap out of this discussion as none of y'all are being reasonable in the slightest and stretching to the moon to look for "gotchas" to justify a reactionary anti-technology stance and refusing to listen to someone with background in this field.

The AI Derangement Syndrome mind virus seems to impervious to reason and people will come up with any excuse to justify it. I refuse to engage with this further. Stop replying to me, I do not care to engage further. I do not want to argue with 4 people at once trying to pull out excuses to why it's somehow evil for China to invest in technology because muh AI scawy. If you are willing to be educated to understand why this technology is important, educated from someone who has a computer science degree and works in this field, then I can teach you, but none of you want to learn and just want to play word games to justify your anti-AI hysteria and I have no interest in engaging with this.

AI is largely used interchangeably with an ANN. Sometimes companies might use it even more broadly than ANN for marketing purposes, but if you actually go take a class in AI at university you will be learning about ANNs.

They are all artificial neural networks, which is what "AI" typically means... bro you literally know nothing about this topic. No investigation, no right to speak. You need to stop talking.

• ANNs used to accelerate discoveries in the material sciences
• ANNs used in USPS
• ANNs used in making breakthroughs in protein folding
• ANNs used to stabilize plasma in fusion reactors
• ANNs used to coordinate infrastructure megaprojects

The "intelligence" part in artificial intelligence comes from the fact that these algorithms are very loosely based on how what makes biological organisms intelligent: their brains. Artificial neural networks (as they are more accurately called) use large numbers of virtual neurons with different strengths of neural connections between the neurons sometimes called their "weights" and the total number of different connections is referred to as the "parameter" count of the model.

You do a bit of calculus and you can figure out how to take training data to adjust sometimes billions of parameters in an ANN in order to make the artificial neural network spit out more accurate answers given the training data. You repeat this process many times with a lot of data and eventually the ANN will fine-tune itself to find patterns in the dataset and start spitting out better and better answers.

The benefit of ANNs is precisely that they effectively train themselves. Imagine writing a bunch of if/else statements to convert text in an image to written text. It would be impossible because there's quadrillions of different ways an image can look and have the same text, if it's taken at a different distance, different writing style, under different lighting conditions, etc. You would be coding for forever and would never solve it. But if you feed an ANN millions of pictures of written text alongside images of that written text under all these different conditions, you can do a bit of calculus with a lot of computational power and what you will spit out is the fine-tuned weights for an ANN that if you pass in a new image it will be able to identify the text.

Technology is fascinating but sadly you seem to have no interest in it and I doubt you will even read this. I only write this for others who may care.

Also, yes, computer vision is also based on ANNs. I have my own AI server with a couple GPUs and one of the tasks I use it for is optical character recognition which requires you to load the AI model onto the GPU for it to run quickly, otherwise it is rather slow (I am using paddleocr). If the image I am doing OCR on is in a different language then I can also pass it through Qwen to translate it. If you ever setup a security system in your home, these often will use AI for object recognition. It's very inefficient to record footage all the time, but many modern security systems you can tell them to record footage only when they see a moving person, or a moving car. Yes, this is done with AI, you can even buy an "AI hat" for the Raspberry Pi that was developed specifically for computer vision and object identification.

Literally if you ever take a course in AI, one of the first things you learn is OCR, because it's one of the earliest examples of AI being useful. There is literally a famous dataset with its own Wikipedia page called MNIST because so many people who learn how AI work often first learn to build a simple one that can do OCR on handwritten digits that they are tasked with training on the MNIST dataset.

I'm also surprised your hatred is towards large language models specifically, when usually people who hate AI despise text-to-image models. You do know that "AI art" generators are not LLMs, yes? I find it odd someone would despise LLMs, which actually have a lot of utility like language translation and summarization, over TTIMs, which don't have much utility at all besides spitting out (sometimes...) pretty pictures. Although, I assume you don't even know the difference since you seem to not know much about this subject, and I doubt you will even read this far anyways.

You are, you're opposed to automation technology, that's literally Luddism, which is a form of anti-communism. What positions are you even trying to defend? "I'm not anti-technology, I just oppose automation!" Like, the overwhelming majority of new technology is developed to increase labor productivity, which means to increase the degree in which tasks are automated. To oppose automation is to oppose the overwhelming majority of new technologies.

AI is just one of many automation technologies. You realize USPS is largely ran on AI? Automation is a major backbone to our economy. But, oooh, there's no "soul" in OCR software or something so we have to go backwards and bring back whole warehouses of people who decipher the text on letters and put them into a computer and can't have it done automatically because muh AI scawy. We have gotta burn all the huge breakthroughs in medical science such as with protein folding and in material science that were discovered through AI because muh AI scawy and lacks a soul or something. We have to abandon research in nuclear fusion technology because all recent breakthroughs in plasma stabilization have come through AI automation.

Do you know what it means to develop the productive forces? It means to improve productivity, which requires continually improving automation and semi-automation (by that I mean, tools that partially automate things but may still require some supervision). We will never reach a higher stage communist society without automation and semi-automation, i.e. without constantly improving labor productivity.

I hope you never in your life use the speech recognition feature on your phone, like writing text messages by speaking it. I hope you never in your life use a translation app like Google Translate or DeepL. Otherwise you are a hypocrite for using the evil soulless scawy AIs.

Just get off the AI derangement syndrome forums that are convincing you to hate tech and realize technology is just a tool which can be good or bad depending upon its application and you do not need to have a generalized opinion on it as a whole. It's like saying "I don't know what to think of knives." It's just a weird statement. Knives are just knives, you can use them for bad things like stabbing people or good things like cutting up some peppers to go in hot pot. No need to have an opinion on knives in general. Same with AI.

well to be fair the USA does use it positively in many ways as well, USPS is largely ran on AI.

AGI isn't real, it's largely a buzzword without a rigorous definition. We will continue to gradually improve the quality of artificially intelligent systems as we improve the hardware and make more progress in understanding intelligence, but there will not be some turning point where there is a sudden explosion in progress from AI when we cross some non-existent AGI threshold. It will just continue to gradually improve over time.

I don't see how one can reconcile being anti-technology with being a Marxist. You'd be better served in an anarchist community.

So usually this is explained with two scientists, Alice and Bob, on far away planets. They’re each in the possession of a particle that is entangled with the other, and in a superposition of state 1 and state 2.

This "usual" way of explaining it is just overly complicating it and making it seem more mystical than it actually is. We should not say the particles are "in a superposition" as if this describes the current state of the particle. The superposition notation should be interpreted as merely a list of probability amplitudes predicting the different likelihoods of observing different states of the system in the future.

It is sort of like if you flip a coin, while it's in the air, you can say there is a 50% chance it will land heads and a 50% chance it will land tails. This is not a description of the coin in the present as if the coin is in some smeared out state of 50% landed heads and 50% landed tails. It has not landed at all yet!

Unlike classical physics, quantum physics is fundamentally random, so you can only predict events probabilistically, but one should not conflate the prediction of a future event to the description of the present state of the system. The superposition notation is only writing down probability amplitudes of the likelihoods of what you will observe (state 1 or state 2) of the particles in the future event that you go to the interact with it and is not a description of the state of the particles in the present.

When Alice measures the state of her particle, it collapses into one of the states, say state 1. When Bob measures the state of his particle immediately after, before any particle travelling at light speed could get there, it will also be in state 1 (assuming they were entangled in such a way that the state will be the same).

This mistreatment of the mathematical notation as a description of the present state of the system also leads to confusing language like "it collapses into one of the states" as if the change in a probability distribution represents a physical change to the system. The mental picture people say this often have is that the particle literally physically becomes the probability distribution prior to measuring it---the particle "spreads out" like a wave according to the probability amplitudes of the state vector---and when you measure the particle, this allows you to update the probabilities, and so they must interpret this as the wave physically contracting into an eigenvalue---it "collapses" like a house of cards.

But this is, again, overcomplicating things. The particle never spreads out like a wave and it never "collapses" back into a particle. The mathematical notation is just a way of capturing the likelihoods of the particle showing up in one state or the other, and when you measure what state it actually shows up in, then you can update your probabilities accordingly. For example, if you the coin is 50%/50% heads/tails and you observe it land on tails, you can update the probabilities to 0%/100% heads/tails because you know it landed on tails and not heads. Nothing "collapsed": you're just observing the actual outcome of the event you were predicting and updating your statistics accordingly.

Any time you do something to the particles on Earth, the ones on the Moon are affected also

The no-communication theorem already proves that manipulating one particle in an entangled pair has no impact at al on another. The proof uses the reduced density matrices of the particles which capture both their probabilities of showing up in a particular state as well as their coherence terms which capture their ability to exhibit interference effects. No change you can make to one particle in an entangled pair can possibly lead to an alteration of the reduced density matrix of the other particle.

I don't think solving the Schrodinger equation really gives you a good idea of why quantum mechanics is even interesting. You also shouldstudy very specific applications of it where it yields counterintuitive outcomes to see why it is interesting, such as in the GHZ experiment.