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# Artificial Intelligence

Artificial intelligence (AI, also artificial stupidity) is an area of computer science whose effort lies in making computers simulate thinking of humans and possibly other biologically living beings. This may include making computers play games such as chess, compose music, paint pictures, understand and processing audio, images and text on high level of abstraction and understanding (e.g. translation between natural languages), making predictions about complex systems such as stock market or weather or even exhibit a general human-like behavior such as simulated emotion. Even though today's focus in AI is on machine learning and especially neural networks, there are many other usable approaches and models such as "hand crafted" state tree searching algorithms that can simulate and even outperform the behavior of humans in certain specialized areas.

At the time of writing this (mid 2020s) there is a "revolution in progress" around AI -- neural networks are booming and improving rapidly, research and business are now full on the race, new hardware and software are being developed and everything's in constant motion, which naturally induces some chaos here. Anything stated in this place is potentially subject to change and terminology itself is developing: nowadays "AI" practically implies the "modern neural network AI", so please bear this in mind. We'll dedicate a special section below to this "new kind of AI" and continue here in general terms.

There's a concern, subject to discussion with very varying opinions, about the dangers which the development of a very powerful AI could impose upon our civilization. Many fear the possibility of technological singularity, a scenario in which a super intelligent AI takes control over the world from human hands, leaving us at its mercy. This is likely not a near future concern and some even deny the danger altogether, but the prevailing view seems to be that it's a question of when rather than if. The sudden explosion of "AI" technology in early 2020s is already creating many immediate problems and fears, such as highly amplified Internet noise, floods of spam, made up news, captchas becoming unusable, "loss of jobs" (good from LRS point of view!), decrease in quality of art and a lot more.

For normies let's briefly tackle some of the most pressing questions such as: What is this "modern" AI really about? Is it actually intelligent? Are AI chatbots superhuman in their reasoning? Well, the "modern" AI is really based on mechanisms found in human brain and is relatively closely simulating them, although in very, very simplified ways (e.g. using simplified structures of neural networks and using a lot of preprocessing), with everything scaled down (in terms of neuron count) and limiting to very specific areas (e.g. only simulating part of what's found in our visual system), so we could say the machine is intelligent in the same way as us but not nearly to the same degree -- imagine the AI as someone who has something closer to a rat brain and who has never lived human life, never knew our pain or pleasure, need of sleep or eating, our kind of emotion or desires, who has for whole life focused on one extremely specialized task, such as recognizing faces in pictures or predicting weather from meteorological data (all of which really reduce to recognizing patterns in numerical sequences), over and over without taking any break. Questions about consciousness, self awareness etc. are better left to philosophers now -- it is possible this kind of AI has consciousness and even its own tiny kinds of desires and emotions (we may imagine it really really wanting to see certain patterns of numbers for example), but it would be more similar to that of a bug or plant, its world is completely different than ours. It may seem that LLMs (the "AI chatbots" such as ChatGPT) speak like humans and so it's natural to assume there is internally some human thought existing, but even here the internal mechanisms of the AI are extremely simplified compared to humans (and they inevitably have to be e.g. due to incomparably lower number of neurons): all the LLM does is PREDICTING which word will come up next in a textual human communication based on having seen millions and millions of such conversations. I.e. if the AI sees an incomplete sentence that reads "Thank you very", it will predict that the word "much" will follow, and doing this repeatedly allows it to generate long texts, but it is not doing anything else. This means that opinions, personality and "facts" the AI knows reflect what it has seen in the data set -- if you train the AI on conversations happening on reddit, it will talk like a redditor, it will make the same reasoning mistakes, assume the same incorrect facts etc. Furthermore there is a lot of cheating going on, just like computer 3D graphics has to resort to cheating and tricks because it couldn't simulate the infinite complexity of the real world -- for example language models typically don't see actual letters of the text, they operate on word tokens, so they normally cannot solve simple problems that require looking at the letters such as typing given word backwards. So we mustn't think such AI somehow gives definitive or highly superior answers to our questions, it only predicts (and sometimes very poorly) what answer a human would give. And we can say the same about different types of neural AIs -- for instance a program synthesizing images does really the same thing, just with pixels instead of words, and so we cannot think an image of a dinosaur drawn by this AI is somehow more biologically accurate, it will only mimic how humans draw dinosaurs. One way to think of these AIs is this: imagine you tell a human to spend whole life perfecting something extremely specialized without focusing on anything else -- this is what the AI will do, the only advantage being it can learn this in hours or day instead of 70 years, and it's a machine that doesn't need rest, salary and other things, so it's now very cheap and easy to create such specialized monkeys. To sum up: we are still far away from simulating something truly close to a fully functioning human brain, but we can now cheaply create programs that very effectively do very specialized tasks which previously only humans could do, most notably art, manipulating languages, pictures and other things relying on intuition and "feel" rather than precise equations.

## Details

As programmers let's first answer ourselves this: what really is AI to us? A programmer/mathematician typically simplifies AI to signify only this much: computer making decisions. I.e. let's forget human brain, emotion, psychology and all this kind of stuff for a second and focus only on one thing: decision making, and how to program computers so that they make an intelligent decision from input data. Every single "AI" system never does anything more than just take a look at current situation (state, context, data, ..., just a bunch of numbers) and from all possible actions that may be taken from here it tries to pick the best one (i.e. output another number). Whether it's making the best move in chess, deciding which animal is captured in a photo, choosing how to translate a word from English to Spanish or choosing what pixel to draw on the screen so that the result will resemble human art, the problem is always reduced to only deriving a number from some other numbers.

AI to us is therefore nothing more than a mathematical function of state, outputting action (leading to another state). Also we will require this function to be pure, true and deterministic mathematical function, i.e. without any randomness, hidden state etc., i.e. the function will always return the same result for the same input, the input depends SOLELY on the state we give it. In an extreme case every AI that works with finite memory could then literally be just a table defining best action for any state -- but of course, such tables would be big and hard to make manually, so we typically try to create algorithms that do the job of such table without taking so much space.

NOTE: Of course we sometimes want randomness, for example in chess we may want our AI to sometimes make a different move in the same position, but this added randomness always can (and SHOULD) be implemented outside of our AI function -- we may for example add an extra seed parameter to our AI which will affect its choice, or we could make an AI that only ranks the quality of each move and then make our chess bot (built on top of this AI) randomly choose from let's say 3 best moves as judged by the AI.

The "modern" machine learning (neural network etc.) AI is no exception here, neural network also implements a pure mathematical function in this sense. That is we are still facing the same problem, we are just trying to solve it by training a network that will make good choices. This approach is mostly about creating a good structure of the network, with good parameters (like number of neurons, layers etc.), encoding the states in good ways (i.e. mapping real world problems to numbers representing the state) and then training the network well, i.e. using right data sets, training algorithms etc. This art is very complex and can't be detailed here in depth.

The traditional non-machine-learning approach is a bit different -- it is based on manually programming state space search algorithms rather than training models. From LRS point of view this is probably the more KISS way, i.e. preferable, sufficient for many types of problems without needing extremely powerful machines or huge datasets. In essence we do this: we realize the states are basically nodes and actions are connections between the nodes, i.e. we get a state space which is a methematical graph. Our program is always in some state, i.e. in some node, and the actions it may take are paths it may take in the graph, so really our AI is helping us travel through the graph so that we get from whatever state we're in to a better one (ideally best possible). Many different algorithms, heuristics and optimizations exist here such as depth first search, breadth first search, Monte Carlo, minimax with alpha-beta pruning etc. -- they typically just recursively traverse the local space, i.e. take a look at states near the current one, and then say in which direction the best state lies. Let's remind ourselves this doesn't just have to be chess and chess moves, this may apply to flying a virtual plane or solving an equation. Again, the whole art of state space search can't be covered here in depth.

To sum up let's again compare the two mentioned approaches on the example of chess. Neural network machine learning will try to train a network (we could almost say by just brute force trying many different parameters for the network) that takes a look at the chess board (which will be encoded into numbers) and then, by some kind of complex "magic" that's really hidden from us somehow outputs the correct move (well, in practice it rather just scores the position, but let's neglect this now). Training such network will take a lot of time, data and electricity; it will result in a network that will pick good moves without us knowing HOW it really works (we just know it does), and the network will be just a network that filters input numbers into an output number. The traditional state search approach, on the other hand, will rather be a hand-made algorithm that will check all possible moves to certain depth and then return the move that it found will lead to a position that looks the best. I.e. here we know exactly what's going on, we have an algorithm simulating the human move calculation (looking ahead in the game for good moves), and the algorithm works iteratively, i.e. it has to perform many steps and playouts to actually see how to game evolves with different moves.

NOTE: State search is sometimes combined with neural networks -- good chess engines for example still do traditional state search but employ a neural network to decide how good each position is. This way we get the best of both world.

TODO: cont

## The "Modern Mainstream Neural Network AI" Etc.

As stated in the intro, since early 2020s there has been a new kind of potentially groundbreaking "revolution" caused by sudden improvement of neural networks that are now reaching human levels of behavior. This subsection will be capturing this as it's happening.

Prior to all this it was assumed that neural networks would be the future and of course many had already been researching them for decades etcetc., but public didn't know much about it. First signs of a breakthrough coming seem to have arrived around 2015, e.g. with Google's AlphaGo that became the first program to beat a professional go player, or the paper on Neural Style Transfer that made a network which could very impressively repaint a picture in the style of another picture -- the network seemed to show understanding of "content" versus "style".

But as for the "boom", the atomic bomb was dropped in December 2022 with the public release of ChatGPT, an LLM (large language model) online chatbot who immediately surprised everyone with its human-like nature. All news were suddenly full of it, everyone started to freak out about jobs and robot takeover and the usual stuff. In truth back then ChatGPT was impressive but still a little dumb, it would talk nonsense at times and thought it could play chess although it was making illegal moves and its ASCII art was usually malformed garbage and all in all you could still see the bugs, but the leap in comparison to previous chatbots was really astonishing, it truly spoke like human and showed creativity and reasoning. It could even speak multiple languages and at some IQ tests performed better than an average human.

Shortly after the ChatGPT thing "AI" came to be adopted as a capitalist buzzword. They try to put machine learning into everything just for that AI label -- and of course, for a bloat monopoly. Very expectedly a tsunami of ChatGPT clones soon struck as no corporation wanted to stay behind, and not just that -- there were suddenly neural networks for everything: translations, image upscaling, text to image, speech synthesis, image to text and so on and so forth. Soon AI created videos joined in as well and now it's everywhere: in compression, weather prediction, entertainment "apps", game development, ads, programming, music, ... and of course there are even "AIs for detecting AI generated content".

2023 report: neural AI has become extremely advanced in processing visual, textual and audio information and is rapidly marching on. Networks such as stable diffusion are now able to generate images (or modify existing ones) with results oftentimes indistinguishable from real photos just from a short plain language textual description. Text to video AI is emerging and already giving nice results. AI is able to write computer programs from plain language text description. Chatbots, especially the proprietary chatGPT, are scarily human-like and can already carry on conversation mostly indistinguishable from real human conversation while showing extraordinary knowledge and intelligence -- the chatbot can for example correctly reason about advanced mathematical concepts on a level much higher above average human. This new "AI" has become mainstream and is everywhere, normies are downloading "AI apps" on their phones that do funny stuff with their images while spying on them. In games such as chess or even strategy video games neural AI has already been for years far surpassing the best of humans by miles.

Late 2025 report: yep, it's absolutely everywhere, becoming ever cheaper and closer to perfect, or at least superhuman anyway, all normies are now asking "AI" instead of "Googling", it's now taken for granted anyone can do anything, for example your grandma can happily program a video game with ChatGPT. ChatGPT can already play chess BTW. The general trend now seems to be towards collaborating networks, maybe something like different hemispheres of the brain, or maybe different human experts solving a problem together. The LLMs are now super advanced, they're probably smarter than most humans, at least mostly -- yes, they will still make an error here and there and in some areas do a little poorly (for example in coming up with jokes), but asking the "AI" today is more or less like asking an expert human as far as the quality and reliability of the answer goes, and even better in terms of grammar/spelling etc., plus the AI gives a wall of text answer instantly whereas a human would be researching and writing it all day. They can also program perfectly well. Text translation and automatic subtitles for videos, including their autotranslation to other languages, are practically perfect now, with all correct punctuation, capitalization of words and correct IN THE CONTEXT of what's talked about (e.g. videos about some video game lore will have correct spellings of invented names etc.). "AI" is definitely creative at human level now, there is no doubt, but there's still a pretty specific "AI style" that one soon learns to recognize -- partly it lies in too much perfection and uniformity, a text or image made by an AI is like a piece of furniture made by a machine, perfect and without small errors and deviations. It's also sort of "too average", "too typical" -- if you let an AI write an encyclopedia article, it will be the most plain, typical example of an article you've ever read, respecting all the rules of the thumb and cliches or this style of writing. But it is to be expected that if the machines must learn to mimic our human imperfections to fool us, that's what they'll no doubt do very soon.

Consequences so far: Consequences have been many, let's try to summarize them in short. Firstly this is all becoming a whole new field -- it's no longer just "computer science" or "programming" but something new entirely that separates like computer programming itself split from fields of mathematics and electrical engineering; there are now these new areas such as "prompt engineering" and stuff. This may later lead to us, "manual programmers", becoming replaced craftsmen like assembly programmers -- manual programming will become rare and done by most people as a hobby (which in itself may not be so bad), but it may also lead to a nightmare dystopia where all computers are going to just interface with humans via AI and won't be manually programmable at all. Secondly there's now an utmost focus on datasets and training: even more than before the business with data is sprawling, everyone's after terrabytes and petabytes of data and moar moar data and then burning hundreds of GPUs red hot like chili peppers on constant training to outpace the competition. Scraping the web by data hunters became an issue and all websites now include ridiculously difficult captchas and challenges and shit, the web is being raped to the fullest. But that's not nearly the beginning of it, the other part is now what people can do with the sudden power to literally create anything in matter of seconds -- of course, they create absolute shit and they create it by tons and push it onto all platforms without rest, hence the new term AI slop. And it's not just the literal NPC retards bombarding everyone with AI kitty pictures, corporations are saving money by using AI assets in games and commercials are now made by AI and it's horrible as hell. A funny curiosity can now be seen in AIs learning from the output of other AIs, but even more funnily people learning from the AIs: apparently some people are starting to write more like LLMs. Anyway, this slop is now murdering all mainstream platforms such as YouTube -- where previously you had dozens of annoying cheap asian channels now you have thousands of pure AI channels with videos all written, narrated, illustrated and probably even uploaded by AI bots -- it's taking views from the "real" jewtubers who are crying like hell, it's frying the brains of the viewers and of course creating so much noise that's incredible. That leads us to other topics like worries about "disinformation", "media manipulation", "jobs", "copyright", "personality rights", "ai porn", "school cheating" and all the normie kinda bullshit that's escalating to the fucking outer space. No video or photo proof can now be taken for a proof, everyone can hide beyond the doubt of AI, and politicians are gonna love this, but then again something similar might have been said when Photoshop was invented etc. Suddenly we talk about "times before AI" as of something recently lost forever, we glance back and think to ourselves "wow, look at how many books people wrote without any AI!" If you post something thoughtful, well researched and carefully written on the Internet, you'll be immediately accused of just using ChatGPT, and in fact you might as well, it would have saved you a lot of time. Slightly worrying is the brainwashing war situation (Grok vs ChatGPT, ideology vs ideology and so on) and the overall social earthquake that's happening -- it might lead to strict laws, bans, surveillance, police states and 1984 dystopia, maybe even yet more extreme polarization, wars, chaos and a collapse, who knows. Also some bizarre schizo stuff was seen like people starting new religions and cults that worship AI in hopes it will spare them in the upcoming doom scenario; things like incels creating AI waifus were expected at this point and hardly surprise anyone. It's certain that a big change is occurring and only time will tell if it's good or not, but since it's practically a fact that under ultracapitalism anything that happens at all always benefits the rich on the detriment of the poor, we can almost certainly predict it's gonna be bad. So that's about it in a nutshell, let's see where it goes from here.

## See Also

- artificial life

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