“I’m calling it now, the adoption of AI agents into software development will be one of the most costly mistakes in the field’s history. Agents cannot program…”
I’m calling it now, the adoption of AI agents into software development will be one of the most costly mistakes in the field’s history. Agents cannot program, and it’s taking longer and longer to realize that they can’t. They are a highly sophisticated statistical model designed to mimic the distribution of programming. The output is broken, but in a way that’s getting harder and harder to detect. Which is exactly what you’d expect from an increasingly accurate statistical model.
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This alarm's being rung for over a year now, so "calling it now" means finally reading the writing on the wall
The difference is it's geohot saying it this time. His opinion will likely be taken more seriously by silicone valley elites. For reference, he was the original PlayStation hacker and lead at Comma.ai (FOSS self-driving).
Let it be known that the first person to call it was actually Sam Altman when OpenAI's paper on AI Scaling Laws in 2020 subtly showed that the diminishing returns will stop showing improvement with infinite power, compute time, and data before 94% accuracy is reached.
yeah I was kinda like. calling it?
Now there's a name I haven't heard in a long time. George Hotz was the guy who first jailbroke iOS and the PlayStation 3 and made the towelroot exploit for early versions of Android, before legal threats drove him out of the scene.
And the self driving car company? Didn’t he also recently found another company for servers or something? I remember seeing him get like 1st place (or top 10) for one of the days on Advent of Code like a year or two ago. Thats very difficult, like Olympic swimmer of programmer type of thing.
This is very obvious unless you are in tech leadership, in which case your job is now to push this at all costs and suppress dissenting voices.
In tech leadership. I don't have to push it. My talented engineers took to it immediately.
They learned quickly that it is a tool. Instead of using a shovel and a wheelbarrow, they have a backhoe now. If you don't know how to dig a hole, the backhoe is just a way to make a mess faster. It doesn't replace intelligence.
They can use it to do the scutwork while they focus on the important stuff.
The duds are still typing shit into spreadsheets and emailing them as attachments while their coworkers are getting stuff done.
It is a tool. You can learn to use it or you can just be mad that it exists. In either case it isn't going away. Like the telephone, the car, the computer, the internet, it is here to stay.
What's fascinating about this conversation is ... how do people think software used to be made ? With talented and knowledgeable developers who would never "hallucinate" an API or a library function ? With cybersec experts who would never put their user's data in jeopardy ? With performance investigators checking the computational complexity of each function ? Bitch please...
Software engineering is not the kind of mystical cathedral building these people have in mind, it's more like a musty workshop in Pakistan where they make tractor tires with no safety equipment and a cigarette in their mouth. We've been throwing imperfect humans in various states of lucidity at every problem known to man for 30 years but suddenly people start believing that their bog standard CRUD software should be written by monks having attained cosmic godhood.
If you’re letting your engineers find uses for it instead of constantly demanding that they generate lengthy “user stories” and decision documents and deferring thinking to agents instead of quickly planning stuff out using their experience then you’re probably quite an outlier by now.
I am a very lazy man, micromanagement takes so much fucking energy. Hire talented people, give clear and unambiguous guidance and trust them to do the work. It is amazing how easy management can be when you don't get in the way.
Being in leadership is way easier than being an engineer and the pay is better too. Some people really overcomplicate shit.
Typing shit into spreadsheets isn't something software engineers typically do... And usually shit that's typed into spreadsheets has to be accurate. There are major consequences if not, and AI is not reliable enough for that kind of work.
They are not the automated from 0 to 100 coders that some people claim them to be. But they are quite capable, definitely much more capable than what anyone could have imagined ten years ago. Given well defined problems they can excel at even relatively complex tasks. I pointed Claude at a latex file of a somewhat complicated nonparametric statistical estimate calculation to look for any mistakes and it was actually able to find some. I then pointed it at a code that replicates the calculations and it was also able to correctly identify some issues with the code. I think this is the way one should use LLMs, not let it loose on coding tasks. In the former way you won't even be able to burn through your first tier account quota where as in the latter the LLM will likely end up getting in weird loops burning tokens like there is no tomorrow. Also this method of sane usage of LLMs is much more suitable for open local LLMs. I don't think there is any doubt anymore that LLMs can be very useful tools, not just for doing stuff but learning it too. People should move past the stage of invalid criticisms like "they are just stochastic parrots" and move to more serious matters like environmental impact, greedy fucking CEOs pretending LLMs are replacements for humans, degredation of skills, getting lazy at checking AI code, ethics of capitalizing on collective human knowledge and the unsustainable AI bubble that tech companies are pushing for.
That's not a criticism per se, it's a description of how they work.
Sure, and at that level of accuracy it's also a description of how humans work. I didn't invent these words myself, I'm just stringing them together based on a stochastic process my brain was trained into.
Like LLMs, some of my speech is semi-random initialization (dada wawa googoo), some of that is mimicry (some of that is mimicry), some of that is reinforcement learning (downvotes incoming), and some of that is the output of a subprocess that uses the same systems prompted at the meta-level and without verbalization (maybe they won't get the analogy between thinking and LLM scratchpads... how about I use this space to clarify).
Calling an LLM a stochastic parrot has the same social-emotional role as calling a human an animal. Yes, it is correct. But people can infer the connotation.
Humans are animals. LLMs randomly generate text based on the corpus they were trained on and the conversation so far, so stochastic parrot is an accurate description.
LLMs don't learn. Humans do. LLMs generate text randomly using a massive matrix. Humans don't; you lied. An LLM is incapable of lying because it has no understanding of truth. It just bullshits convincingly all the time. It's very very good at it, but it's all hallucinated for the LLM, true or false.
Expecting your random word generator to tell you truths is insane. The training measure is "sounds right" not "is right". It passes if it sounds like the other discourse it read. Just like the confident drunk guy at the pub who thinks he knows everything passes of he convinces the other drunk guys at the pub.
Whereas humans learn at school and on the job and the training measure is "your teacher or supervisor approves". LLMs were not trained on truth or accuracy. Trusting in them and treating them as equivalent to human intelligence, as you and a whole bunch of other folks do, is profoundly unsound, and soon the necessary price rises to pay for the processing costs (let alone the vast, vast, vast, vast, vast debts on the infrastructure) are going to make most slophouses which jettisoned their human talent go out of business. And very, very few people indeed will be sorry at that point.
Meanwhile LLM slop is shitting in github all day long, every day, and shitting on the internet, and it will eat it's own shit and produce crappier shit.
Your analogies don't change the truth, and that is that LLMs don't know the difference between sounds correct and is correct any more than MAGA voters know the difference between sounds good to me and is good for me.
What do you mean LLMs don't learn? How do you think they became capable of stringing a sentence together?
They don't learn during a deployment, but neither do humans; humans only learn during sleep. The behaviors a human exhibits while "learning" in the moment are just stochastic parrot behaviors based on their immediate context window, if the human doesn't sleep in time the event can slip out of their context window and they don't learn despite having acted as if they do.
You seem to be very naive about human learning in general. What makes the "truth" of school lessons greater than the "truth" of an LLM's curated dataset it is reinforcement learned on? Have you ever seen actual evidence that mitochondria exist, or are you just stochastically parroting your biology teacher?
I also oppose LLMs in almost all applications (live translation being an example of a good application). But please oppose it with arguments based in reality.
You're confusing constructing the LLM, which is done with an actual AI (neural network) and a massive corpus of text (stolen from millions of humans in the greatest intellectual theft in history) and running the LLM, which is done with a random number generator and a massive matrix of probable next words.
They don't learn. They don't change. They're as random next time as this time.
False and false. Soooo much pseudoscience.
Wrong again.
If that were true, most people would learn very badly first thing in the morning and get better and better later in the day. I think you'll find that most school teachers would vehemently disagree with your nonsense conclusions.
Then again, perhaps by "doesn't sleep in time" you mean stays up all night, then admittedly they might function less well cognitively but (a) we tend not to regularly torture humans that way and (b) you're massively overstating the role of sleep in the learning process.
No, you seem to be very naive indeed, to extremes, about the intelligence and reliability of LLMs. When I ask them about general things that I know about, I tend to get the right answer about 60%-70% of the time. Why would I believe it when I didn't know the answer. To trust an LLM to tell you the truth about stuff you aren't checking when it clearly blags nonsense so frequently when you are is really really stupid.
Most teachers tend to consistently teach the content of the syllabus rather than randomise what they say to classes based on the preceding conversation. They reinforce and update their prior knowledge by also learning from the mark schemes of the tests and exams their students sit.
No. I trust my teachers. I am rational to do so. I don't trust LLMs. You are irrational to do so.
You are utterly deluded and have bought the hype. You seem unable to distinguish between distinct things and are dismissing a large amount of evidence that your "just as good as a human" is a crap-spewing shit machine, no more honest than donald J trump, and with no less sharting.
Not true. Inference is done by providing the context to the pre-trained neutral network (technically a transformer network not your daddy's old multilayer perceptron) to generate possible outcomes with logprobs that are then selected based on their likelihood. If it was just frequency-based RNG, they wouldn't have any semantics in the responses and would sound more like traditional Markov chains (like when you mash a button on predictive text and it spits out correct but meaningless gibberish).
If it were just selecting random words from a matrix of probabilities without the network and attentions, it would also be waaay faster and easier to run on a potato.
The stuff about human learning also isn't quite right. There are different types of "learning" and different kinds of memory.
Sleep is generally understood physiologically to be required to formulate long term memory (eg. as described in this paper).
The previous commentator was analogising human short and mid-term memory with LLM context windows (also things like vector databases etc.) and long term memory with retraining/merging/fine tuning of LLMs. It's not totally the same but the analogy is accurate. Brain behaviour is a big influence and inspiration on how machine learning techniques are designed.
Human memory is also notoriously inaccurate and unreliable and tasks done by humans often needs to be double checked and externally verified.
This isn't to say LLMs are trustworthy or reliable. They are not. More that humans think much more highly of themselves than is really warranted.
I repeat, the LLM is not doing machine learning while users are using it.
We agree here.
And we agree here too, but to trust an LLM to tell you the truth on your question that you don't know the answer is like trusting some random drunk at the pub, because you don't know whether the answer is from an LLM hallucination, a random lie/error on reddit or an expert's contribution to wikipedia.
And to trust an LLM when there's a trained programmer or professional journalist is stupid. Sure, an LLM might even sometimes write as good or better code than an intern, but again, the LLM is not learning from its mistakes as you correct it. The intern gradually becomes an expert. The LLM does not. Paying interns is an investment in future programmers, who get more expensive the more experienced they are.
The LLM is currently cheaper than the intern, but LLM pricing needs to go up by a factor of about ten to cover running costs let alone pay off the vastly more immense debts of buying all that hardware.
Like I said before, humans sleep every night, with rare exceptions. LLMs do not get retrained every night. The human brain adapts to feedback loops during everyday interactions, not just overnight. It's a silly analogy and this is a silly point to defend.
There are plenty of textbooks that say that volatile running RAM is like short term memory and hard disks and SSDs are like long term memory, but it would be silly to reverse the analogy as you are doing and claim that sleep is pressing the save button on the day's learning, or that this makes your word processor the same as your human intelligence because, and this is the central point you've been trying to argue around and about and against, they're doing fundamentally different things, and telling me one was inspired by the other doesn't change that. An LLM is fundamentally a stochastic regurgitator whose training is designed primarily to make it sound right. A human brain just doesn't work that way.
If you truly believe that the LLM is learning like a human or intelligent like a human, you are confusing analogies for reality.
You are arbitrarily deciding that the former is not part of the LLM.
No, it's not arbitrary, the learning is done by completely different software at a completely different time on completely different computers.
I'm pointing out that the LLM is the product of the machine learning, where you feed all of Wikipedia and reddit and stack overflow in and calculate the LLM from it.
It's like if you wrote a book about the wildlife of antarctica. First you would learn about the wildlife and then you would write the book. The book isn't learning anything and it isn't intelligent. The book represents knowledge but it doesn't itself know or understand anything.
Similarly, the LLM isn't learning anything and it isn't intelligent, it's just regurgitating randomly selected words from its training data that look like they usually occur after the other words in the conversation so far.
It genuinely doesn't understand a word you said, using its guess about what sort of conversation it's supposed to be having and it's always just guessing what word it's supposed to say next.
if it's broken in a way that can't be detected, is it actually broken?
all software is broken in some way. if the rate of bugs generated by llm and the severity of those bugs drops below the rate you would expect from a human programming team, then llm is offering something competitive.
It will eventually be detected, but it passes tests before hitting production, that is the problem.
Is not what anyone said and you're lying when you pretend they did.
No, humans make less mistakes. Less. That's the key here, statistical models are trained on human data so by pure logic can never, ever, under any circuimstance, reach 100% accuracy. With current understanding of LLMs with a focus on AI Scaling Laws, and more importantly of natural human language adaptation, they will never reach 94% accuracy with infinite power and infinite training. That's what the curve shows us in OpenAI's 2020 research paper on AI Scaling Laws and later Deepmind's paper correcting their math, that the diminishing returns will hit a limit far before convergence.
In addition to that, the AI also cannot detect subtle changes to established problems or any new unaccounted for variables, because they're a statistical model and not capable of actual thought. They also lack any sense of responsibility for their actions for the same reason.
You fucking sloppers always try to say "HuMAnS mAkE misTAKeS, TOO!" Yeah and the fucking slopbots are trained on those mistakes and make them again but worse.
But you're forgetting the key difference that makes it so much worse - we can fix human mistakes especially if we can talk to the human to figure out how. With an llm we have no external reference, only poorly designed code where the comments are there to guide the writing, not describe what was written. So it's much harder to debug an output, and the llm cannot be trusted to clean it up either.
A human can be held responsible. A machine cannot. If the machine writes bad code, and someone gets injured or killed because of it, who takes responsibility?
I state again: a machine cannot be held responsible.
It is never the coder that is responsible, it is the one who makes the code available to use. Often with humans, they are one and the same. With machines, they are not.
You can totally fix AI-written code with AI. You tell it something is wrong, it tries to fix it.
I did a recent experiment with AI writing a document format converter and that's exactly what I did. It wrote some code, I checked the output, found a formatting issue or similar, asked it to fix it, repeat. It works unreasonably well and with Fable the final code isn't even bad.
You can fix problems, if you know they are there and there is a model of that problem being fixed.
You can't fix problems you don't know are there, or do not have modeling.
That's true for humans too surely? How would anyone fix problems they don't know exist?
Humans generally don't hallucinate libraries or documentation. If there is a bug or error on a human maintaine repo the human in charge will generally know what went wrong and how to fix it, the AI will just gaslight your ass because the AI has no idea.
This is true. But it doesn't invalidate any of my points. Humans have unique failure modes too.
To add to the other response - it is much more difficult to work with Ai to debug inconsistent issues or similar unless you can understand the code and step through with a debugger to check for race conditions or similar.
Recently I was working with an Ai tool for some c code that depending on machine ran wildly differently. The Ai was unable to identify any issues, and kept recommending fixes for hardcoding values or similar that I had to revert. The fix ended up needing to use valgrind to create a different enough environment to see how a race condition was made to properly have one async call delay for the other.
AI can be powerful, and humans can be dumb. But if the code was human made, I would not have needed 3 hours to find a problem, and I wouldn't have tried to turn to AI for a simple fix because I'd know what I was looking for to start with.
Now keep doing this for months, with non-trivial software that other people use.
Probably fine if you review the code carefully. And if you're working in a domain that AI is decent at (e.g. web stuff). But even if it wasn't it doesn't mean AI cannot program.
Also, unlike say, writing articles, like 75% of coding is copy.pasting blindly from elsewhere anyway.
Not in my experience.
No one paying the bills cares
You know the feeling that you want to rewrite a project? But you know that most rewrites are a bad idea.
Be it your own, old code. Or code you inherited.
There is a small chance that the world realizes that they went in the wrong direction and nothing can get fixed. That will be the time of rewrites.
No, I don't expect this to be very likely. The agent code will remain, and human programmers get yelled at for not fixing it fast enough.
Rewriting all code after everyone has been using AI tools to break it doesn't sound any better than writing good code now, be it with or without LLMs.
It's so nice to see some people speaking reason. If only any of those people ran multibillion dollar companies.
Let them fail and then scramble to rehire.
It's going to be a wonderful time to be a Freelance Senior Developer and above in a few years.
remember, when you interview for a job and they ask you, "do you have any questions?", you ask;
But they do work, maybe not as a full replacement but my god the amount of boilerplate I can avoid in creating unit tests from scratch. Extracting and finding information in the code base is also useful, not everything is an easy text search of tracing a few code paths. It's an incredible tool for these kinds of work.
If it becomes harder to tell the difference then it also means it's closer to matching reality. And todays AI can do very impressive "reasoning", managing to debug complex issues I have had.
The most important part is that you as developer is fully responsible and can stand behind what they do and deliver using AI agents.
Right? The bottle has opened. It has taken so much mundane work out of programming. Also, I feel like a human is just as likely to create great looking code changes with a possible flaw. You just have to review the code. Whether it's a person or a bot, "lgtm" can only be used sparingly.
Unpopular prediction: AI agents are going to get better at coding. Not great, but halfway decent at cranking out basic features. Once everything levels out in like 3-5 years, AI agents will be a cherished part of the toolbox most software developers. It will be useful for skimming code, it will be useful for tedious parts of tasks that are just a degree off from boilerplate.
People are definitely gonna try to use it for things more complicated than that, and that'll be a mistake, and it will be costly, but the far side of it could be pretty cool actually. Admittedly I have an optimistic disposition.
I have yet to be impressed.I’m not very convinced*. I asked for format type mappings between Pipewire, WebGpu, and Vulcan and both ChatGPT and Gemini failed very badly only providing the most common type mappings. This should be a wildly easy task, something any programmer or even beginner programmers can complete. It’s just very boring, mundane, buffer shifting like work. It almost feels like pencil pushing.Why couldn’t they do it?
Are you using real agents or the free chats on the web, because the latter ones are really dumb. Even when you ask them to search the web for basis you don't get much success.
I haven’t paid
edit: Well I did for grok, but not on purpose. Grok still failed too.
The bigger paid models or potentially the local ones if given access to search the web can probably get you the right answers. The big ones have pretty much memorized half the internet, but can still be wrong so pushing them to verify their answers.
But the harder part is trusting what they say regardless. I can't just take an answer for truth, and unless I can verify the statement (fact checking myself, looking up the source, running/testing the code, etc) then it gets harder to do anything with AI. This is the thing I hate about AI in general, people just take whatever they say at face value. Lawyers with fake citations, random people asking chatgpt about random facts and such. Its a tool that people put too much faith in to do thinking for them.
Hence the "are going to get better" and "3-5 years".
If my job mandates me to use ai agents, idgaf, I'll use it. But my every oss contributions will be clanker-free
This is just factually incorrect. Difficult to get past a false assertion of this magnitude.
I thought we had got over the stochastic parrot nonsense by now.
You can totally have objections about the ability of AI to program - how good it is, poor failure modes, high cost, technical debt, knowledge debt, broken social contracts, etc. All valid.
But if you're still in the "It's just a next word predictor! It can't really think!" stage of denial, even now... Sorry you're an idiot.
Um... but it is just a sophisticated statistical model... that's literally what the math underpinning machine learning models is... and all it can do is make associations based on correlations within the field of the training data. That's what it does.
The mistake has been thinking this implies LLMs can never do X task, and using it as a catch-all argument for any value of X, but it isn't a good argument because it has been wrong for most of those.
As this article points out, an LLM can spit out chunks of regurgitated code that it scraped from the internet, but that does not make the LLM a programmer. The resulting output is an attempt to find an existing pattern in the database which fits with what the user has asked for, but it is not a product of actually understanding the use case for the code. It is just statistical correlation.
So, sure, an LLM can be set up to generate output related to X task. If you can collect and clean data that can be used to train the kind of output you want, it should be able to produce an approximate facsimile of the results you want. Is that valuable for your use case? Maybe.
We're still just talking about what is essentially a complex search function. The statistical model returns results from its database that correlate most closely to your input. That does not mean it returns the right answer. If there is no good correlation, it will still return a result.
As long as you understand that the result you get is just a correlation based on your input and may or may not be relevant to your specific problem, and you are not fooled into believing that the LLM actually understands what you're asking and produced a result by "thinking" about it, then you might be able to use an LLM as an effective tool - to search a large collection of information for something that is relevant(ish) to what you're asking for.
The real mistake has been broad misunderstanding of what LLMs actually do, and trying to use them as general-purpose problem solving tools (or worse, as accurate and reliable sources of information).
Language models are not databases and they are not markov bots (similar function but work directly using statistical word association maps). The big difference is that those things are algorithms someone wrote and can fully comprehend what they do, but machine learning models are large algorithms built by another algorithm processing training data. There is much more uncertainty about what is going on under the hood.
There is also great uncertainty about what concepts like understanding or thinking might mean in computer science terms. The main thing we can really know is that ultimately a human mind is a computer, which means that understanding and thinking have some yet unknown mathematical representation, and therefore a comparison can be made. We should eventually be able to quantify whether or to what extent a given algorithm thinks. But you said in another comment that you don't believe minds can be represented mathematically; this should mean that such comparisons would be apples to oranges, but you're making them anyway for some reason, and implying they have predictive power for the limitations of LLMs.
Certainly they do have limitations, at least individually and possibly as a technology. There are things given models are bad at, there are things they initially seem to be able to do well as humans but fail in different ways that suggest over-reliance on pattern matching. But these have been determined empirically through testing. The idea that they are "just statistical models" and this knowledge can be used to say what is impossible for them from philosophical first principles keeps getting repeated but has never worked in practice. The reality is that no one knows enough to say for sure where the line is.
Except that it's been demonstrated multiple times that original training data can be extracted from a language model, so it is completely valid to talk about the model as a database, because the training data is stored within it.
Here's a broad survey of more than 100 research papers demonstrating this: Training Data Extraction From Pre-trained Language Models: A Survey
So, this is a good anology in this case.
See, I know how an internal combustion engine works. I don't know, by looking at the hood of a particular vehicle, how exactly a specific car's engine operates (maybe it has 4 cylinders, or 6 or 8, maybe it has fuel injectors, maybe it has a carburetor, etc). However, I do know that the principles are the same for all internal combustion engines, and that just because I don't know the details of how a particular engine operates, that does not mean that its operation is beyond my understanding.
The same is true for machine learning models. There may be uncertainty as to how a particular model operates "under the hood", but the principles of operation are the same for all, and are not incomprehensible.
We actually don't know this. This is called computationalism. It is speculative, there are several alternative theories, and little in the way of experimental evidence supporting any particular theory.
You have to understand, the current branch of machine learning models grew out of algorithms whose purpose was processing large data sets with thousands or millions of variables and optimizing for areas in the data set where many of those variables were maximized (or minimized). Here's a better explanation:
Hill Climbing Algorithm & Artificial Intelligence - Computerphile
How these tools perform their optimization, and what they optimize for, has been recombined in different ways to produce different types of models, and the search space of variables has been expanded with increased computing power, but the underlying operating principles are still the same. This is not a tool that can comprehend what it is doing, it can't be self-aware. It can only process large amounts of input data and attempt to maximize for particular dimensions. This seems vague to humans because the amount of variables being handled at any given time is far more than a human mind can focus on, but that doesn't make the optimization routine intelligent or conscious. It's just doing a lot of number crunching really fast, optimizing for specific aspects as directed by its developers.
It's been demonstrated that some more prominent pieces of training data can be reproduced, the majority of it cannot. This shows that those particular pieces of data are represented in some form within the model, it does not show that the way it works is equivalent to database lookups. If I can write down the lyrics of a song from memory, it shows that those lyrics are encoded as data in some form in my brain, but that doesn't mean it's valid to talk about my brain as a literal database, especially not in the sense that the limitations in the capabilities of a database can be ascribed to me (or its strengths, I cannot remember the exact lyrics of most songs I've heard, even if I can remember some).
This video literally starts out by describing evolution as a similar optimization algorithm. If you know the basic mechanism of evolution, does that mean you can use that to then say with certainty and specificity what biological life in its vast diversity of techniques is not capable of? The "underlying operating principles" of evolution don't "understand" chemistry or deception, but they still produce organisms capable of photosynthesis and camouflage. It's an algorithm that produces other algorithms, which is what puts those resulting algorithms in a different category of comprehensibility than fixed algorithms that were explicitly written by someone. We are very far from having a comprehensive understanding of biological systems, despite knowing how evolution works.
This is like saying evolution is only a simple mechanism taking in the world as data, which, yeah, obviously, but that property doesn't carry forward to what it produces. The bigger problem here though is, again, concepts like comprehension, consciousness, and intelligence are not well defined in computational terms, and it is unclear what statements involving them mean in any practical sense. These sorts of claims are non-falsifiable and don't make testable predictions about the boundaries of AI capability.
This is like saying "but it is just a sophisticated network of neurons. All it can do is transform input signals into output signals. That's what it does."
Not really.
A machine learning model is a computer program. It is fundamentally a math equation, which we understand completely.
A living brain is not fundamentally a math equation, and is not purely a statistical model, at least not in any empirically demonstrable way. We don't understand completely how it works, but we do know that it's more complex than what you're trying to imply.
The comparison is not valid. Machine learning models are not an equivalent to a biological brain.
Tbf, LLMs may be comparable in complexity to their specific brain.
Lol you couldn't be more wrong about this. One of the most widely commented things about DNNs is that we don't really understand them completely. I don't know how you would miss that if you knew anything about AI at all.
It is. A very complicated one, sure. Which part of the brain do you think is impossible to simulate with maths?
No one who actually works on digital neural networks thinks this. We may not be able to predict the behavior of a particular neural network with certainty (because there are a lot, like millions, of variables), but that does not mean that we don't understand how they work.
simulation ≠ reality
Yes it is, if your simulation is sufficiently accurate. Let me rephrase. Which part of the brain do you think has behaviour that is required for intelligence/consciousness and that behaviour cannot be replicated with sufficient accuracy on a computer (of arbitrary capacity)?
Thank you for letting us know where you stand. Those who tag users can act accordingly to this public declaration of robo-handjob-giving.
Our apologies. Let us issue a correction:
"Agents should never be allowed to program"
There.
Calling them a statistical model is misrepresenting their functional understanding of concepts.
This guy does not get it.
He's bang on. LLMs have no understanding of anything: they are literally just statistical models.