In case you're wondering why you're being downvoted: the history is much more nuanced. While the Archimedes Palimpsest is a genuine and tragic example of lost text, the broader claim that Christianity engendered a period of scientific erasure is considered outdated (https://en.wikipedia.org/wiki/Conflict_thesis).
For example, monasteries were the primary centers of literacy and education in Europe during the early middle ages, and they acted as the primary bridge for the survival of Greco-Roman intellectual heritage in the West. Not always intentionally, but they were the only sanctuary for books during those times.
Besides, this is not how history works. Civilizations come and go and times of transition always take a toll. An eye-opening recent book on these questions I can recommend is Tom Holland's "Doninion: The Making of the Modern World".
For anybody interested in the book: https://www.goodreads.com/book/show/52259619-dominion I would highly recommend. Holland (historian, not the actor) makes a great case about why many of our thoughts nowadays are rooted in Christianity.
They are transient only in those rare domains that can be fully formalized/specified. Like chess. Anything that depends on the messy world of human - world interactions will require humans in the loop for translation and verification purposes.
>Anything that depends on the messy world of human - world interactions will require humans in the loop for translation and verification purposes.
I really don't see why that would necessarily be true. Any task that can be done by a human with a keyboard and a telephone is at risk of being done by an AI - and that includes the task of "translation and verification".
Sure, but at the risk of running into completely unforeseen and potentially catastrophic misunderstandings. We humans are wired to use human language to interact with other humans, who share our human experience, which AIs can only imperfectly model.
I have to say I don't feel this huge shared experience with many service industry workers. Especially over the phone. We barely speak the same language!
Mathematics is indeed one of those rare fields where intimate knowledge of human nature is not paramount. But even there, I don't expect LLMs to replace top-level researchers. The same evolutionary "baggage" which makes simulating and automating humans away impossible is also what enables (some of) us to have the deep insight into the most abstract regions of maths. In the end it all relies on the same skills developed through millions of years of tuning into the subtleties of 3D geometry, physics, psychology and so on.
I'm guessing that they were referring to the depth of the decision tree able to be computed in a given amount of time?
In essence, it used to be (I have not stayed current) that the "AI" was limited on how many moves into the future it could use to determine which move was most optimal.
That limit means that it is impossible to determine all the possible moves and which is guaranteed to lead to a win. (The "best" than can be done is to have a Machine Learning algorithm choose the most likely set of moves that a human would take from the current state, and which of that set would most likely lead to a win.
Actually, what you're listing above is just another set of beautiful (to you) abstractions. No, "banksters" are not "100% parasitical". The percent is definitely less than 100. But, you know, as they say: the devil is in the details.
Definitely less than 100? What do you know about it? How is what they've accomplished not monstrous crime so total, and we haven't even properly named it? It's systematized, bureaucratized, anti-human. "Clown World" is the meme, but that is not sufficient.
The same way you test any system - you find a sampling of test subjects, have them interact with the system and then evaluate those interactions. No system is guaranteed to never fail, it's all about degree of effectiveness and resilience.
The thing is (and maybe this is what parent meant by non-determinism, in which case I agree it's a problem), in this brave new technological use-case, the space of possible interactions dwarfs anything machines have dealt with before. And it seems inevitable that the space of possible misunderstandings which can arise during these interactions will balloon similarly. Simply because of the radically different nature of our AI interlocutor, compared to what (actually, who) we're used to interacting with in this world of representation and human life situations.
Does knowing the system architecture not help you with defining things like happy path vs edge case testing? I guess it's much less applicable for overall system testing, but in "normal" systems you test components separately before you test the whole thing, which is not the case with LLMs.
By "non-deterministic" I meant that it can give you different output for the same input. Ask the same question, get a different answer every time, some of which can be accurate, some... not so much. Especially if you ask the same question in the same dialog (so question is the same but the context is not so the answer will be different).
EDIT: More interestingly, I find an issue, what do I even DO? If it's not related to integrations or your underlying data, the black box just gave nonsensical output. What would I do to resolve it?
>EDIT: More interestingly, I find an issue, what do I even DO? If it's not related to integrations or your underlying data, the black box just gave nonsensical output. What would I do to resolve it?
Lots of stuff you could do. Adjust the system prompt, add guardrails/filters (catching mistakes and then asking the LLM loop again), improve the RAG (assuming they have one), fine tune (if necessary), etc.
> The same way you test any system - you find a sampling of test subjects, have them interact with the system and then evaluate those interactions.
That’s not strictly how I test my systems. I can release with confidence because of a litany of SWE best practices learned and borrowed from decades of my own and other people’s experiences.
> No system is guaranteed to never fail, it's all about degree of effectiveness and resilience.
It seems like the product space for services built on generative AI is diminishing by the day with respect to “effectiveness and resilience”. I was just laughing with some friends about how terrible most of the results are when using Apple’s new Genmoji feature. Apple, the company with one of the largest market caps in the world.
I can definitely use LLMs and other generative AI directly, and understand the caveats, and even get great results from them. But so far every service I’ve interacted with that was a “white label” repackaging of generative AI has been absolute dogwater.
Yet it won't be easy not to anthropomorphize it, expecting it to just know what we mean, as any human would. And most of the time it will, but once in a while it will betray its unthinking nature, taking the user by surprise.
Most AI Chatbots do not rely on their training data, but on the data that is passed to them through RAG. In that sense they are not compressing the data, just searching and rewording it for you.
It feels like you're being pedantic, to defend your original claim which was inaccurate.
User input: Does NYC provide disability benefits? if so, for how long?
RAG pipeline: 1 result found in Postgres, here's the relevant fragment: "In New York City, disability benefits provide cash assistance to employees who are unable to work due to off-the-job injuries or illnesses, including disabilities from pregnancies. These benefits are typically equal to 50% of the employee's average weekly wage, with a maximum of $170 per week, and are available for up to 26 weeks within a 52-week period."
LLM scaffolding: "You are a helpful chatbot. Given the question above and the data provided, reply to the user in a kind helpful way".
the LLM here is only "using the probability encoded in the training data" to know that after "Yes, it does" it should output the token "!"
However, it is not "decompressing" its "training data" to write
the maximum duration, however, is 26 weeks within a 52-week period!
It is just getting this from the data provided at run-time in the prompt, not from training data.
Didn't read the whole wall of text/slop, but noticed how the first note (referred from "the intuition I developed of the years of thinking deeply about these problems[0]") is nonsensical in the context. If this is reply is indeed AI-generated, it hilariously self-disproves itself this way. I would congratulate you for the irony, but I have a feeling this is not intentional.
I'd like to congratulate you on writing a wall of text that gave off all the signals of being written by a conspiracy theorist or crank or someone off their meds, yet also such that when I bothered to read it, I found it to be completely level-headed. Nothing you claimed felt the least bit outrageous to me. I actually only read it because it looked like it was going to be deliciously unhinged ravings.
“The meaning of words and concepts is derived entirely from relationships between concepts” would be a pretty outrageous statement to me.
The meaning of words is derived from our experience of reality.
Words is how the experiencing self classifies experienced reality into a lossy shared map for the purposes of communication with other similarly experiencing selves, and without that shared experience words are meaningless, no matter what graph you put them in.
> The meaning of words is derived from our experience of reality.
I didn't say "words". I said "concepts"[0].
> Words is how the experiencing self classifies experienced reality into a lossy shared map for the purposes of communication with other similarly experiencing selves, and without that shared experience words are meaningless, no matter what graph you put them in.
Sure, ultimately everything is grounded in some experiences. But I'm not talking about grounding, I'm talking about the mental structures we build on top of those. The kind of higher-level, more abstract thinking (logical or otherwise) we do, is done in terms of those structures, not underlying experiences.
Also: you can see what I mean by "meaning being defined in terms of relationships" if you pick anything, any concept - "a tree", "blue sky", "a chair", "eigenvector", "love", anything - and try to fully define what it means. You'll find the only way you can do it is by relating it to some other concepts, which themselves can only be defined by relating them to other concepts. It's not an infinite regression, eventually you'll reach some kind of empirical experience that can be used as anchor - but still, most of your effort will be spent drawing boundaries in concept space.
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[0] - And WRT. LLMs, tokens are not words either; if that wasn't obvious 2 years ago, it should be today, now that multimodal LLMs are commonplace. The fact that this - tokenizing video and audio and other modalities into the same class of tokens as text, and embedding them in the same latent space - worked spectacularly well - is pretty informative to me. For one, it's a much better framework to discuss the paradox of Sapir-Whorf hypotheses than whatever was mentioned on Wikipedia to date
You wrote “meaning of words and concepts”, which was already a pretty wild phrase mixing up completely different ideas…
A word is a lexical unit, whereas a concept consists of 1) a number of short designations (terms, usually words, possibly various symbols) that stand for 2) a longer definition (created traditionally through the use of other terms, a.k.a. words).
> I'm talking about the mental structures we build on top of those
Which are always backed by experience of reality, even the most “abstract” things we talk about.
> You'll find the only way you can do it is by relating it to some other concepts
Not really. There is no way to fully communicate anything you experience to another person without direct access to their mind, which we never gain. Defining things is a subset of communication, and just as well it is impossible to fully define anything that involves experience, which is everything.
So you are reiterating the idea of organising concepts into graphs. You can do that, but note that any such graph:
1) is a lossy map/model, possibly useful (e.g., for communicating something to humans or providing instructions to an automated system) but always wrong with infinite maps possible to describe the same reality from different angles;
2) does not acquire meaning just because you made it a graph. Symbols acquire meanings in the mind of an experiencing self, and the meaning they acquire depends on recipient’s prior experience and does not map 1:1 to whatever meaning there was in the mind of the sender.
You can feel that I am using a specific narrow definition of “meaning” but I am doing that to communicate a point.
