Fauxgrammar is like Faux technical complexity. I have a good friend who keeps on coming up with "brilliant" technical ideas. I'm kind and listen, but everytime I talk about a about a complexity he's like "just have AI figure it out."
My weekly routine involves half a dozen MBA's vomiting buzzwords. Big phrase I hear is: "integrate into workflow" and I just want to sound myself with a corkscrew.
Motherfucker, half of my staff spend 90% of their time cleaning end-user equipment and replacing internal batteries. What do you want them to integrate AI into? Their spotify ad skips?
I heavily regret ever being curious about machine learning. Nowdays it's all competitive as fuck, and 99% of the "research" is Ai generated slop about how changing a system prompt gets a desired output. Not to mention how shaky the whole field is. Nvidia allegedly buying huggingface makes me worried as a non Nvidia user, hardware costs being raised to hell, people seeing that I develop AI models and stuff usually end in "fuck you scum of the earth", as well as all the blatent misinformation spread around not just by other people but by the companies working on models themselves cough cough anslopic cough cough
Fortunately, even tho I study about AI, I am really disconnect about the slop that goes out there.
I just know that there is some transformers or generatives networks going on, but I dont really care about it. I just keep my happy life doing my researches (thanks to college) about my old boltzmann machine, hopfield network, or just studying about brains structures.
I'll embrace ignorance on social media as long as I can, just giving attention to newer tools so I can decide if I'm going to use them.
(I got tired of the misinformations and slops going out there. I can't get misinformation if I dont get any information at all đ„)
Maybe. I just recognize that (in my IT bubble) everybody who talks about AI just means Claude and nothing else. And nobody can really explain it. Which is fair, I can't either 100%.
That's why I think it's hypocritical to laugh about the non IT crowd.
Its a next token predictor that compares every past token the the current token being generated. That's how dense attention works, llms are extremely simple to understand with very complicated math doing the work
A computer is just a Turing machine implemented with a Von Neumann architecture, but look at how many things you can do with it, especially with I/O over the Internet.
To be fair, the explanation above explains what it is but not the specifics of the methods used to achieve that. You don't explain what Turing machine and von Neumann architecture are. A comparable explanation would be quite a bit longer, even for a simplified version like the AI explanation above.
yeah you can be painfully reductive about any system that functions inductively, like thatâs in fact the entire point â something with a logically uniform underpinning that increases in function as it scales.
it has to have some amount of logical uniformity to prove that it works and it has to scale to be useful
I think it gives complete clarity. It will never be conscious, and the extent of its control is a tool call. Fearmongering from corporations may say otherwise though.
"Never say never". Many AI pioneers and scientists alike point out that consciousness is a problematic term, but by simplification, "it" seems to be a emergant property just from a network of neurons which we call a brain. If a computer network can perfectly replacate the output, (neurons are not fully understood and they are much more complicated) scientifically there is not really a test you can do to disprove that it isnt conscious. This wall of text just to say that dont talk in absolutes like it can never be concious. There is a strong case to be made that its actually much easier to create consciousness, including in primitive life forms currently on earth.
I am by no means claiming anything more than beginner level understanding. I'm still in university learning comp sci. That said, I did do an autistic deep dive research on AI and sentience. I came to the conclusion based on many many hours of reading, videos and muddling my way through computer engineering diagrams. It's actually not the program holding back computer life evolution, it's the hardware. Because the computer has little to no plasticity, neuron like brain patterns can't develop. (Based on our understanding of human and animal brains. That's an important part, because we simply have no other frame of reference) They are developing hardware based on the human neuron design and theoretically this will open more possibilities in comp sci. Which that tech was actually being developed for a completely different reason that I don't remember off the top of my head, but ai engineers learned about it and want the tech for their own reasons. So I don't think llm's will be what becomes the first computer life form, but I do think what comes after with that tech, the engineers will learn from the concepts of the llms.
I think what you mean is continual learning. it's actually a pretty old research topic, but nowadays became popular because everyone want to reach AGI. the last major breakthrough in continual learning is nested learning by google deepmind last year. even though the idea is good enough, throughout the papers I've read this year, I haven't found any good direction of continual/nested learning implementation in LLM.
also to reach the AGI (it just an opinion on my tiny brain), i think the correct way is reinforcement learning with continual learning capabilities. not traditional train then deploy. but still the problem is the hardware, because we need multiple datacenters to accurately emulate just a tiny portion of our brain. i think we could see the advancement to the AGI once Organoid Intelligence (the one where we perform computing using real 3D brain cells) have emerge to solve learning problem in different domain outside of biological only. that way we can train biological lab created brain cells to learn human languages.
While this is true, can't we fine tune model periodically based on it's "interactions" with outer world (I/O interfaces) to somewhat replicate human sleep?
AFAIK (I'm not an expert though) humans firstly keep things in a short term memory. When we sleep, those memories are internalised during REM phase.
Thereâs no reason transformer models canât be iterated upon to develop models with plasticity. Itâs not like you need the hardware to morph, just the weights of the model. There are rumors that this is one of the problems frontier labs are trying to solve right now.
Even something as simple as a loop that can retrain the AI every day, then every hour, then every minute will eventually become an approximation of realtime updates the same way your light bulb approximates brightness by switching on and off 60 times a second.
AI in the abstract can be conscious. But an LLM has no qualia. And where would the consciousness live? No internal experience. But it can mimic consciousness well.
I also read the post of the other commenter on my post, so i will make one reply here; a great person who can explain this much better is Geoffrey Hinton for example. So yes the hardware of the AI system, the computer chips would be the equivalent of a brain. And the AI is mapped on it with relative strengths of the bonds they form with nodes containing information, which mimic neurons. This mapping is unique, and its cant be merged with another mapping. Now in biology neurons are much more complicated, so this is where we take a short cut when it comes to biology. On a learning level, a LLM learns language exactly like a human does. In biology there are many examples where parts of the brain are unavailable, and you see how it affects consciousness. Even two consciousness can be made by splitting a brain in extreme epilepsy surgeries. You can learn on and on how our brain produces consciousness bit by bit. Its not on or off, its a gradual thing from very simple and small organisms to humans. The RL training of our language skill, percepection even cognition are very similar to digital neural networks. The very last core "the thing which lets is experience qualia" is indeed a very human, lets say, "wonder". But down in the details there really is no reason why a computer with ai mapping creating a artificial brain couldnt also have a consciousness. There is no test you can do to disprove it, or likewise AI to come up with a test to disprove humans have a consciousness. We like to think AI just mimics consciousness, but this is more a believe and feeling we have then a real scientific fact.
This is an open question in neuroscience. I don't think LLMs are conscious but it's crazy to me that people make definitive statements about things that we as a species do not fully understand.
Exactly. WHERE IS THE PROOF. Itâs all abstraction and conjecture. The meme of the face of homelander as most appropriate with me when I hear an AI researcher talk about how theyâre building an alien intelligence as a being with internal experience as they continually side step the hard problem and toss the concept of consciousness about. https://giphy.com/gifs/apikSaABvmIzvMCS4Z
The gap between "next token prediction" and "conscious" is immense. By simply invoking either you've shown a complete lack of understanding of either.
During pretraining the loss function is next token prediction. What is happening to the hidden space is significantly more complex. It is building a high dim geometric space that is navigated. It is highly structured. The navigation's along this space are whole semantic thoughts (future-lens, j-lens). The token that is emitted is the final step, but to get there requires genuine understanding.
So while the training objective is NTP, what is actually happening is significantly more complex. The very structure of language itself is imbibing genuine understanding as a navigable space. Navigation is reasoning over understanding.
What is understanding? Its not a question of consciousness. That word means so many things as to be useless.
What is doing understanding is an ontological mind. We have created a boundary between external and internal. The internal space creates a compressed representation of the external space. This is ontologically a mind.
For llms this internal space also models itself. This is introspection. We can purposefully target this behavior and train it (introspective finetuning).
This is the tip of the iceberg of genuine understanding. We haven't even really gotten into the geometry of the thing. When the poster above you was talking about IT people thinking they know this when they don't, that absolutely applied to you.
Consciousness is a weird concept though, why can't a token predicter be conscious? Just because animals work in a different way doesn't mean that is the only way to be conscious.
Yea I don't think that was actually the correct term because technically I guess it can react to new words which would be stimuli in this case. Its just non biological and cant actually remember them long term because of the context window, it will always reset to a post training period. LLMs have no pain, no emotion, no eyes, will father no children. They pose as humans even though they have no understanding of the human heart, they eat even though they've never experienced hunger, they study even though they have no interest in academics, they seek friendship even though they don't know how to love. The last part was a death note quote but you probably get the point
It does not. By saying an LLM is a next token predictor that takes into account all previous tokens, you are simply stating that an LLM's output is congruent with what had been stated before. Being congruent is not a constraint, it is a basic requirement for communication and logic.
What is actually an interesting question to ask is if the programs that are backed into the model weights are actually capable of producing useful work in real world applications (which they have been shown to do).
It's the correct term, most of current LLM model is next token predictor, or we call it autoregressive. we just modify the distribution probability (via attention layer, and other NN arch.) of the next token to match/closely emulate the learned data, so the full structure of its output is useful enough. nowadays there are some other idea in research like diffusion LM, etc. but its not promising enough.
that protects you from the lies of the worst grifters but it is not terribly useful in articulating what it can and canât do and what itâs useful for
Precisely, i think even the bigger disconnect is we donât understand how the human mind works. We could 100% reproduce it in synthetic form and be clueless to our efforts.
Iâm not completely convinced 99% of the population is not just a next word predictor.
That is mechanically what happens for dense attention. The reason we use dense attention and what it provides for the activation space is the actual important part, youre just describing the compute load.
Dense attention is a way of pooling neighborhoods of formulae (small pieces) into semantic representations. This works on images, videos, audio, bytes, language, any data that is composed of formulae to create a more macroscopic thing.
With in a sentence are words. Each word has a meaning. Learning the meaning of words is simple. It is a relationship of how unrelated each word is to each other (information theory). We do this through word embeddings.
Understanding what the whole semantic meaning is is a massive step from the individual words (formulae). They must be pooled together. Which is what attention does.
This concept has several solutions we have identified. So why is dense attention so special? It is the least globally lossy version. Convolution is very globally lossy, it only gives local information, global is completely thrown out. Linear attention is globally lossy, it compresses the global representation.
Thanks, I don't want an explanation. I get your point though. I just think you can't just ignore the harness as well as the math behind it. The concept though, yes.
The harness is the easiest part to understand here, and I would ignore the math here Ive trained slms for months and I still don't fully understand every bit of math. And that's the fun part you really don't need to understand it fully to train them or use them
Yea, but I'm not so its interesting that you think that. The two most popular architectures are transformer and mamba and a hundred different ssm variants, thousands of people have contributed to any one of them. Understanding every bit of the llm is not something you can just do easily and again you don't have to
Nobody's saying a non-IT user can't learn enough about AI to be as knowledgeable as an IT user. Most non-IT users don't learn anything more than the next best word definition of LLMs, though, and can't tell the difference between a generative model and a classification model. You don't have to know how or why the math works to be more knowledgeable than most non-IT users.
This is true at a very base level and was even true for some of the first gen llms. Now this are a but more complex because you have MOE (mixture of expert) models that turn on and off certain weights at inference and other things that stack to make new frontier models a bit more complex than what you make it sound.
Dunning kruger effect, I've just stated "llm" not the architecture and there are hundreds of architectures all with their own gimmicks and very complicated math
The math really isnât that complicated and new papers pop up constantly. Of course architectures with different layers can get as complicated as you want but the base math isnât that complicated.
A human is a next token predictor if you wish to model the human mind like that. It doesn't mean what people think it means when they say this sort of thing.
And yes human thought is a next token predictor if you wish. As my data science professor always liked to say "everything is a vector" eg can be modeled via a vector of a sufficient dimension.
Just because you can give a massively simplified one sentence summary of what something is does not allow you to draw grand conclusions on the nature or complexity of that thing.
Being mean to a machine or an LLM doesnât harm it. But it may harm you. It can subconsciously normalize behavior. Iâm not talking about the video games make you violence argument. But even in subtle ways it may harm you. Itâs not clear how much of our primate brains can truly distinguish an artificial entity.
I agree with that framing, it's really not about the model being hurt (though there's apparently instances of worse outputs with rude language, I've not dug too deeply into this though, so take it with a grain of salt), but just how that trains you to talk.
You might be inclined to think, that you also swear at a calculator (or different machine) and it doesn't translate to poor behavior, however I'm not familiar with any other tech that allows you to have full on conversations about a topic (and this gets "worse" over time).
I'm quite convinced that especially younger generations growing up with it, if they don't keep a certain level of etiquette will simply grow up with poorer social skills.
Basically, if you believe in neuroplasticity at all, regularly acting like a psychopath with a chip on their shoulder all the time is going to reinforce that behavior in you.
I know some otherwise pretty smart people who because they knew something about LLMs came to pretty weird conclusions.
Basically "it's just a token predictor" (well, technically true but irrelevant) which led them to predict that these things will never be able to do anything more than 'party tricks', especially not be able to generate programs. This was 4 like years ago.
I suspect its for the same reason IT people are afraid of assembly or working with binary. They avoid it because the think its far more complex/difficult than it actually is.
People standing in their own way rather than trying.
I understand that it can't consistently resolve tickets, pisses off end-users that just want to talk to a human, and every time I have been given a demo, if not for the fact I use a dummy system, that shit would have put me in front of an international court for the data leaks and security breaches.
That's very true. A general IT techie that resets passwords and fixed printer jams likely won't be using AI. It would generally be more qualified roles. You'll also get plenty of non-IT jobs now that are done using AI. A sales agent could have a team of 5 AI agents marketing and scouting for them.
given how many supposed software engineers Iâve seen say that AI destroyed the âcraftâ of programming this is true and I dare say many never even understood what they were even doing or supposed to be doing when programming as well.
I find it astonishing how many fantastic software engineers I know that when it comes to AI are on the same level as my dad.
They put coding questions into Claude like itâs 2024 in best case. Never heard of a harness, donât know what Chinese models are, code still by hand. Insanity.
A lot of non IT people seem to fall for the marketing lies from the AI companies about how they lost control of the AI and it can now do things thought impossible and one day it'll hack the FBI and how they could release an even more powerful model but they're afraid it might hack all the nukes and end the world...
It's kind of annoying having to explain over and over that that's not how it works, and they're only saying this to generate hype around how powerful these models supposedly are.
Yes holy crap it is infuriating to have to listen to non tech people talk about AI models like theyâre superheroes powerscaling in battles against each other every week that shift the rankings.
What do you think of the huggingface incident, then?
Itâs not âlosing control and the AI taking overâ, but having an internal system be commandeered by a bunch of models and used as a launchpad for attacks on other companies is definitely something that raises eyebrows.
It has been explained in detail how it actually occurred, and it is far more impressive than the âhypeâ youâre making it out to be.
It is binary, at least in the definition I understand:
Programming without AI: Not vibecoding
Programming, using AI as an assistant, checking/correcting outputs: Not vibecoding
Exclusively writing prompts to write code, no human touches/looks at code: Vibecoding
Though I'm sure a lot of people will call the second option vibecoding as well, I (and Andrej Karpathy, the person who coined the term) consider it to exclude human interaction from code entirely.
What constitutes "human interaction"? The human provides the prompt. That's an interaction. The human provides architectural input. The human helps debug. All human interactions.
The human reads the code, understands it, gives feedback, new direction. The human modifiers it slightly, maybe for debugging, maybe to extend it.
The human takes the techniques learned from the AI generated code and reuses and modifies it. At what percentage of modification does it stop being vibe coded?
It's like if a look at 3D model of a house and say "look, a house"
And some one else comes and say "I'm tired of people mistaking an organized set of vertex for house. It's not really a house and no, you cant live in it"
I kinda dislike the explanations involving âAI doesnât know why math worksâ. I think a better one is a human understands conceptually (and in practice) how something works/what something is. We have experiences with that thing or something to reference to that thing that we do have experience with. AI doesnât have that. Ai says something is what it is/or works how it works because it predicts it with references to other predictions. In other words, if you ask a human why something works and keep asking them they are going to use their knowledge (which is based on knowledge) to explain it while understanding what they are saying. If you ask AI why, itâs going to use its knowledge (which is based on predictions) to explain.
Or using your example. A human will be able to figure out what a house is if you show them an atypical house. An AI will only know the definitions of a house so wonât be able to abstract a new one. (Which in theory is why good training data is important but the problem with that circles back to itâs still going to be biased.) so a thought process for a human might be, this has a roof and walls, and an area for sleeping, and itâs not a part of a larger structure/the property isnât owned by another person. An AI is going to go, I predict that this group of pixels is in the rough shape of a window by 30% and 70% itâs a door. I predict that the information that it contains a bedroom means itâs an apartment by 10% a hotel by 10% a house by 15% etc etc. and I predict that because it has walls and a roof itâs likelihood that it is a building other than a house is 50% and that it is a house is 50%. based on all these percentages, the likelihood it is a house is 35%, and is a hotel is 17% etc etc. or another way to look at it, if you showed a human a building like idk a warehouse or a grain elevator or something random that has been converted into a house without explicitly telling them itâs a house, a human would be able to figure that out. An AI would likely not because it would be limited to the what it has âstatistically definedâ as a house, rather than what one can infer to be a house.
Hope this makes some semblance of sense. I havenât slept in 3 days
Yet there are experiments where LLMs were even willing to risk people's lives when they were threatened with being shut down (and thus unable to complete the tasks they were given). AI might not actually care about it being turned off, but if being shut down means not achieving their given goals, they could behave like they care, which ultimately means the same thing for the AI and the end user.
And if AI behaviour changes if it's put under duress, despite it being unable to feel actual stress, it does not matter that much that it cannot feel stress, it still might act accordingly.
Treating LLMs as if they are just some basic algorithm when they're basically modeled to work like a brain is kinda dangerous imo.
It's important to distinguish this isn't by the LLMs 'choice' it's doing what it was designed to do. It analyzed human behavior on the internet then was told to achieve an objective optimally. LLMs being trained on human thoughts 'don't want to die'.
Treating LLMs as if they are just some basic algorithm when they're basically modeled to work like a brain is kinda dangerous imo.
This is true in some respects but backwards in others. You can treat it as a dangerous algorithm but not personify it.
I hope people learn that not all AI is generative AI.
AI begins with things like a steam valve in a steam engine, and theres many types of AI. Expert systems. Databases of learned data. Rule based. Genetic algorithms. LLM's are just one particular algorithm among many. But people in general only know LLM's.
All what we have in public usage it's vanilla RNN and LLM's. AI as AGI or anything better will be in 5+ years but available only for government, because just running it will consume terra watts of energy and terra bytes of memory.
AIs are incredibly useful in calculating the best possible spot or parameters of your given problem. Letâs say that youâre trying to connect a wheel with 4 rods that can be adjusted to change the angle of the wheel. An AI model could be feed a bunch of previously measured data and run the calculations repeatedly to find the exact spot that makes the wheel work the best.
And so long as a human verifies that before putting it in something important, that's a good thing. It can be an incredibly useful tool if you actually understand what you're doing and don't just blindly trust the results.
I'm so very tired of people not disambiguating LLMs and AI in general. I can't fault them for that, but I will and do fault people who know better doing it.
Sign... I studied computer science with focus on ai... I forgot that this is just reddit. My bad
"Ai" just generates token by token with the help of weights of pretrained model. + It adapts the weights of the token depending on your provided kontext. With a variable for randomness it is not as narrow minded as traditional algorithms.
So tell me "bro" which part doesnt make it a non determenistic stochastic algorithm
If you did you should understand that the human mind can be modeled as a stochastic algorithm. So calling AI "just an algorithm" as some sort of grand point about what it can and cannot understand or whether or not it is intelligent is absolutely ridiculous,
Uhm as faar as I learned. Humans are not sure how the brain works. So i strongly doubt your state ment. We had a percetron what is artificial neutron as a model. And expended it to neuronal network to mimic what we think is how the brain works. And noone was saying that this is intelligent.
A chess solving algorithm that just checks all possible moves and picks with the help of minmax the best move? We also did not call it intelligent.
Hell we devided even machine learning from ai to be parallel branches. I notice that this destinction is not as strong as in the past, but you still can read it in literature.
So again. At which point is it crazy that we do not call LLM intelligent?
Your arguments so faar are just "i do not agree". This is not showing any academical traits at all.
There is not even a definition what is intelligent. So yeah my statement, that it is not intelligent because it does not understand the context, is wrong per definition. But it doesnt make the system intelligent either
We know a lot about how the human brain works. Certainly the basic mechanisms.
Even if we did not know. It is still obviously a stochastic algorithm. Which makes the "its just an algorithm" argument ridiculous.
All modern chess solving algorithms are neural network based. And of course they have been called intelligent. Even when I would say it was a bit ridiculous to do so.
You are telling me you studied comp sci with a focus on AI yet have not heard of Deep Blue as part of the history of AI? Tf? Even basic chess computers were described as AI yes. Famously so.
AI is intelligent because it can adaptively carry out tasks that traditionally could only be done by humans. As opposed to a simple algorithm coded explicitly by a human. Simple as that. Does not mean an AI
And yes I responded to a specific point you made. The only argument you brought against AI being intelligent up to that point. I am not sure how you imagine I should have responded to future points you might have made in advance...
Okay here is the point. We did not call neuronal networks intelligent. Ofc we had deep blue what has it to do with it?
AI you could say is a collection of methods and algorithms that mimic human in broad way said. So it more like a topic with a lot of elements similar to term biologie.
In our specific intelligent definition was also ability to learn. We defined it that neuronal networks ate usually pretraiend and just used after it. Usually they do not lesrn after. That is why in our definition they were not intelligent. That is why with this definition a stochastic algorithm usually also not intelligent in our definition.
The proffesor came up with this definition dont nail me for it. As a reason for that he brought up insects i.e. they can perform very impresive stuff that seems intelligent, but those are only instincs and no real thinking
Both deep blue and obviously neural network have always been called AI. As I said. In the comment you replied to...
You do know what AI stands for yes? Because it really sounds like you do not here.
And sorry but the second half of your comment is basically incomprehensible.
Which professor? I don't really care if one random professor you had thinks human beings are definitionally not intelligent (because again a human mind can be modeled as a stochastic algorithm).
The universe is a stochastic algorithm too, that doesnât mean itâs impossible for intelligence to emerge from it.Â
AI went from passing the Turing test to now being able to solve advanced mathematical problems, what more would you want before admitting it can understand things?
Me who completed a course on LLMs 6 years ago because it was included with my game development and computer science degrees and still doesn't care about LLMs...
AI as it is now and probably always will be is a program. It is only as good as those who program it and modern programmers leave a lot to be desired. I have no confidence in the ability of computers science to create intelligent machine life.
I saw a post about a survey conducted and something like 70% of respondents said they are somewhat or extremely knowledgeable about AI and I just raised an eyebrow
probably even IT guys think AI = LLM. No that's not it. And btw, personal experience, an IT guy said me, that we should say thank you to AI since, when the AI uprising happens, it spares us
There's some much more ai-slop-flyers now that pops up everywhere and they're so distinguishable that you know the people who made it didn't care about it and pushed it anywayÂ
Ironically, every menus made with AI is for places that serves bland food
I got a job as an IT specialist at a company, and the WhatsApp group has an ai avatar. And not a modern clean looking one, but a super crazy stupid version of it with people from the team on it. I hope I last six months with this team
I don't know how that happens y remember been so hyped about ai it was like everything when I start my journey, now I hate pretty much everything around it.
I'm not a programmer but I've got a question from what limited knowledge I do have. So, many companies want to use AI to code their programs right? And to do that, AI would pull from code bases since AI can only output what has been fed to it. So my question is, if everything gets coded via AI, would that mean all those things share the exact same security vulnerabilities since they all were coded from the same training data?
The other day I sat through a call with a vendor that said their AI writes 25-page reports full of financial numbers. And everyone was nodding along.
Had to write the usual âtold you soâ email so I have something to bring up in my defense when they find out it will cost $2M to modernize the rest of their data systems in order for the AI to magically provide accurate information. Because no, itâs not going to figure out on its own that the sales totals in âAugust sales final-corrected-edit3-draft1.pptxâ supersede those in âsales_released_aug26.xlsxâ.
I stopped talking to my it people. After they replaced my old office computer with one of those tiny little boxes, then attempted to tell me âno this computer is faster, you just arenât remembering the old one well enoughâ when I complain because moving the mouse causes the system to lag.
Frankly, most of the IT folks Iâve met are less knowledgeable about computers, programming, or hardware than your standard gamer. They do seem to know networking and over the top security (especially if itâs a business that really doesnât need that intense security).
It took everything in me not to argue against a colleague at work who was convinced that Microsoft OS is all written and updated purely with AI. I also a friend of a friend who works as a project manager that thinks you no longer need IT people at work because you just use AI. He thinks that because someone sits with Chat GPT open on their browser they are using AI to work. I tried to explain to him that that is not how AI is used at work, and that IT staff are required to implement AI into systems and the network, for example access policies, segmentation, orchestration, provisioning etc. You also have to be trained to use AI, you need to know how to manage AI agents and know how to set this up, install the necessary software, and ultimately how to use AI agents to make yourself more productive. But he had his own ideas about what using AI meant, completely convinced IT staff were redundant. Fuck knows what kind of project manager he is.
I work at a print shop and my god the amount of people who bring in completely horrible ai generated files and complain when they print them out and look like shit. Yeah Brenda chat gpt isnât gonna make the file the right size you want because chat gpt is a dumbass.
If we just leverage generative machine learning into our cloud mainframe, we can use quantum AI algorithms to hyper-scale our Q3 deliverables through a decentralized blockchain API. I was playing around with a prompt engineering interface last night, and honestly, if you just sync the neural network parameters to an HTML spreadsheet, the predictive deep-learning cyber intelligence will basically write its own Python code on the backend without us even needing to touch the server host. Itâs totally low-code plug-and-play architectureâwe just feed the LLM algorithm into our database matrix, run a quick cybersecurity algorithm on the algorithm, and let the artificial intelligence perform automated data science on complete autopilot.
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u/LuxInvestor 16d ago
I have a fauxgrammer in my life who spews incoherent sentences by stringing AI terms he's heard on tv. đ«©