[HN Gopher] The future of generative AI is niche, not generalized
___________________________________________________________________
The future of generative AI is niche, not generalized
Author : gumby
Score : 87 points
Date : 2023-04-27 16:15 UTC (2 days ago)
(HTM) web link (www.technologyreview.com)
(TXT) w3m dump (www.technologyreview.com)
| janmo wrote:
| I follow a bunch of indie devs on Twitter, most of them started
| an AI product often based on ChatGPT and Stable diffusion.
|
| Many went viral in a very short time frame, but now they are
| experiencing a huge churn rate, meaning people canceling their
| membership, also an alarming high refund and dispute rate.
|
| I believe AI startups will loose 90% of their customers soon, as
| those are only people who where hyped into trying it out but have
| actually no use case for it.
|
| AI is currently what Internet was in 1999.
| lfkdev wrote:
| The Internet literally took over the whole world
| Janicc wrote:
| Exactly
| janmo wrote:
| Sure, but the AI hype bubble will pop in between, and then it
| will be a slow and gradual process. It will take decades for
| AI to be fully integrated in our society.
| GreedClarifies wrote:
| Your statement hinges on the definition of "fully".
|
| I'll simply counter by saying that the value created by
| these AI technologies is immense and given that value it
| will likely be intergraded into society very quickly.
| animaomnium wrote:
| ...but only after the dot-com bubble burst
| stainablesteel wrote:
| they're going too broad with it
|
| if they can find a specific industry to go into it would make
| all the difference
|
| for example i'm waiting for someone to use these to make
| creating an anime 10x easier, train on existing in-house styles
| and artists might only need to make corrections rather than
| everything from scratch. i can imagine an increase in the
| number of projects, and they would be able to put more money
| into writing and storyboarding instead which would be a really
| interesting transition.
| cubefox wrote:
| > once we've realized it doesn't know everything -- and never
| will -- that will be when it starts to become _really_ useful.
|
| I disagree with this. Once it becomes superhumanly smart, _that_
| will be when it starts to become really useful.
|
| Of course superhuman intelligence might be dismissed as
| outlandish science fiction technology which is far away. But
| three years ago ChatGPT-4 or Midjourney V5 were _also_ outlandish
| science fiction technology.
| ChatGTP wrote:
| _Of course superhuman intelligence might be dismissed as
| outlandish science fiction technology which is far away. But
| three years ago ChatGPT-4 or Midjourney V5 were also outlandish
| science fiction technology._
|
| I actually really question this, was it truly outlandish to
| imagine what we have now? We had Google, Stackoverflow etc, yes
| there new elements to it, the data was there and accessible,
| but I don't think LLMs are unimaginable?
| stevenhuang wrote:
| It's not wrong to say current LLM capability were indeed
| unimaginable back then. A lot about the transformer model was
| driven by experimentation, there was no underlying theory per
| se that said we'd get as good results as we did.
|
| Everyone and the researchers themselves were shocked iirc;
| all this higher order reasoning emerged from being a next
| token predictor, yes that was the outlandish part.
| gitfan86 wrote:
| Right, general intelligence has been here all along. Humans
| are the ones that are specialized. Pylomeic mistake 2.0
| cubefox wrote:
| If we are honest and look at our memories of three years ago,
| there was nothing remotely like the current technology. There
| were pattern recognizers and AlphaGo, but nothing which
| looked remotely generally intelligent in the way ChatGPT
| does. Very few people even knew about GPT-2, as it was easily
| dismissed as just creating an surface illusion of meaningful
| text, not much different from old text generation approaches
| from a few decades ago.
|
| There were GANs which e.g. produced realistic faces, but
| these were restricted to one subject matter only, not
| something like Midjourney V5 which makes basically every
| picture whatsoever based on a text description, just with
| occasionally one to many limb. People were so impressed with
| Dall-E 2 that it is hard to imagine how extremely impressed
| they would have been with Midjourney V5 (without having seen
| Dall-E 2 first).
|
| If you told HN commenter three years ago (for context, that
| was when COVID took off) that we would very soon have
| technologies like ChatGPT-4 then, I'm sure, that would not
| have been taken seriously.
| RandomLensman wrote:
| LLMs cannot reason or use mathematics - in a way, they don't
| know what they are talking about. Why would such technology
| lead to superhuman smarts?
| circuit10 wrote:
| What is your definition of "reasoning" here? They are clearly
| able to do many things that we would call reasoning if a
| human did them
| hammyhavoc wrote:
| I mention this all the time.
|
| I wanted GPT to give me an NGINX config for Active Collab
| as we're previously using Apache, and thus an htaccess
| file. I fed it all the documentation from Active Collab, I
| couldn't get anything valid out of it. Hallucinated all
| kinds of things that weren't there. I then gave it the URL
| rewrites that would be required, line-by-line, spent a long
| time trying to correct it. No bueno, even worse
| hallucination. I spent days on trying to get it to output a
| valid NGINX config that incorporated these URL rewrites. It
| can't reason, it's doing exactly what LLMs do, which is
| next word prediction.
|
| I can't imagine what people are using it for in terms of a
| valuable addition to their workflow with how much it
| hallucinates. If it can't even do an NGINX config, what use
| is it for anything else? People saying it's helping them
| _learn_ programming languages. Fuck me, they don 't know
| when it's wrong, and it will be wrong at some point, it's
| an LLM.
| stevenhuang wrote:
| There are flaws but also consider PEBCAK error.
|
| For one next time when it starts hallucinating and a
| gentle course correction doesn't do it, just start a new
| chat with a different prompt approach. Having the error
| in its context reinforces the same mistake and sometimes
| it can't get out of this loop.
| hammyhavoc wrote:
| PEBKAC in what context? The prompts themselves or the
| documentation? Because I got Active Collab running on
| NGINX myself.
|
| I already did this in terms of starting new chats, I
| spent _days_ on it, and consulted with half a dozen devs
| supposedly using it in their workflows. It 's very easy
| to make it hallucinate.
| stevenhuang wrote:
| In terms of using gpt optimally. But fair enough. If you
| tried it in multiple sessions trying to convert Apache
| rewrites to nginx and it wasn't able to do it, I guess
| this is another failure mode. I just found that curious
| because chatgpt is usually very very good at regex.
|
| Side note is Google extra terrible lately or is there
| really no docs on this almost anywhere?
|
| All I could find about it is this and the rules looked
| very simple, from my experience chatgpt should have got
| this https://activecollab.com/help/books/self-hosted-
| activecollab...
| hammyhavoc wrote:
| That's the documentation on the URL. And yeah, I was
| surprised too given how people had hyped it so heavily in
| much more complex scenarios for dev work (well, claimed
| to anyway, they haven't documented it in most cases of
| claiming to x).
| smoldesu wrote:
| > but also consider PEBCAK error.
|
| No. It's an AI error, the person between the chair and
| keyboard just hit enter. If this "error" goes away when I
| hit the enter key a few more times and get lucky, it's
| not my fault.
| hammyhavoc wrote:
| Well, the error doesn't go away, it's a "fail state", but
| that still doesn't mean PEBCAK either.
| circuit10 wrote:
| It's true that their training goal sometimes rewards them
| hallucinations, encouraging them do that, but that
| doesn't mean it's not reasoning. If anything
| hallucinations involve more reasoning because they have
| to make up something new that wasn't there
| hammyhavoc wrote:
| It's doing what LLMs do. Next word prediction. It didn't
| "make it up", it incorrectly predicted the relationship
| of words.
|
| It isn't reasoning about the solution to a problem
| either, it's running as expected in relating words to
| each other, that doesn't mean it has any form of
| understanding of the words or even what it's rendering as
| an output.
| circuit10 wrote:
| It does just predict the next word, yes, but it turns out
| that predicting the next word is a complex problem that
| involves solving many types of subproblems that involve
| relatively complex reasoning, or at least something that
| looks very, very much like reasoning
| hammyhavoc wrote:
| Is it any more reasoning than Bayes' theorem is
| reasoning? It's statistics and derived probability from
| said statistics.
| circuit10 wrote:
| > Bayesian inference is a method of statistical inference
| in which Bayes' theorem is used to update the probability
| for a hypothesis as more evidence or information becomes
| available.
|
| That is very clearly reasoning
| hammyhavoc wrote:
| No, it's probability entered into an algorithm as a
| variable. This is not reasoning, this is probability, and
| there is no reasoning involved.
|
| Go and program HomeAssistant (FOSS) with an automation
| using Bayes' theorem. Then proceed to laugh about all the
| fail states you encounter, thus realizing it is not
| reasoning, but probability. https://community.home-
| assistant.io/t/how-bayes-sensors-work...
|
| It's quite literally known as Bayesian probability.
| circuit10 wrote:
| I will admit that I don't know much about Bayesian
| statistics, but how does something producing probability
| as an output mean it's not reasoning? I'm pretty sure
| humans do that consciously and subconsciously all the
| time and we call that reasoning
| hammyhavoc wrote:
| Because it's just an arbitrary number. It has no concept
| of what the numbers mean or what it means when it gets a
| number that it doesn't have in its existing data set (the
| "training", more accurately described as references),
| whereas a human being can make an educated guess, or
| adapt to the unexpected outcome and thus provide a new
| novel solution. It isn't _thinking_ about the numbers, it
| 's executing maths, as programmed.
|
| Is a calculator reasoning? It doesn't understand what the
| numbers mean. It's an input-output machine. Is a sheet of
| paper with numbers on it the process of reasoning itself?
| No, of course not. Human beings apply meaning to the
| output, or feed that output into other things to drive
| processes that they've already created.
|
| If you kick the Boston Dynamics BigDog, it will
| compensate for the changes in x sensors and remain
| upright, or get back up, ergo it can traverse a dynamic
| (changing) environment, such as a battlefield, or an
| urban area with cars, people etc. It's not reasoning.
| It's conditional logic based on different vars. The
| BigDog bot doesn't understand what it is, where it's
| going, it doesn't think, it's applying maths to sensor
| inputs and motors on a recursive loop. If it encounters a
| problem it hasn't been programmed for, it cannot reason a
| new solution.
|
| By your logic, video game characters must use reasoning,
| when no, they don't. It's maths.
|
| Is it all fascinating? Yes, absolutely. Are there uses
| for it? Yes, absolutely. Is it reasoning? No.
| circuit10 wrote:
| I can't reply to your latest comment for some reason, so:
|
| > This has to be the most misinformed counterargument of
| all time. The brain certainly doesn't use maths as it is
| not a binary system. Information is approximated and
| estimated. Do some reading on chemically mediated graded
| responses and how neurotransmitters actually function
| within a synapse.
|
| Uh, what? The brain is a physical object. The way that
| physical objects work is dictated by the equations of
| physics (maths). Are you telling me that the brain
| doesn't abide by physics?
|
| And weren't you just telling me that something that uses
| approximations/probability isn't reasoning?
|
| "chemically mediated graded responses"
|
| That sounds like roughly the same thing as an activation
| function that neural networks like GPT models use.
|
| https://en.m.wikipedia.org/wiki/Activation_function
| circuit10 wrote:
| If a human brain doesn't use maths, what does it use?
| It's Magic?
|
| > "training", more accurately described as references
|
| Uh, no, that's not at all how the training process works.
| The training process very roughly mirrors
| evolution/natural selection except more more direct and
| faster.
|
| This is a good introduction to the concept of how a
| computer could learn:
| https://www.youtube.com/watch?v=qv6UVOQ0F44
|
| This is a more technical look at how exactly it works in
| more modern AIs:
| https://www.youtube.com/watch?v=aircAruvnKk (it's a
| series)
|
| > If it encounters a problem it hasn't been programmed
| for, it cannot reason a new solution.
|
| But an LLM can? It's not as good at it as a human but it
| can
|
| Relevant:
| https://twitter.com/nearcyan/status/1632661647226462211
|
| (if you don't want to click it it says "referring to AI
| models as "just math" or "matrix multiplication" is as
| uselessly reductive as referring to tigers as "just
| biology" or "biochemical reactions"")
|
| > By your logic, video game characters must use
| reasoning, when no, they don't. It's maths.
|
| "By your logic, if tigers are dangerous, then bananas
| must be too, when no, they aren't. It's biology."
| hammyhavoc wrote:
| This has to be the most misinformed counterargument of
| all time. The brain certainly doesn't use maths as it is
| not a binary system. Information is approximated and
| estimated. Do some reading on chemically mediated graded
| responses and how neurotransmitters actually function
| within a synapse.
|
| Scientific research also suggests there are quantum
| processes involved that we don't yet grasp, ergo a
| hypothetical AGI likely won't emerge without using
| quantum physics.
| Dudeman112 wrote:
| >If it can't even do an NGINX config, what use is it for
| anything else?
|
| Turn it around. I'm _sure_ there are lots of people
| around who couldn 't do it either, even with a
| ridiculously high amount of time to do it
| mr_mitm wrote:
| Yes, but we are discussing the claim that LLMs might
| exhibit superhuman smartness.
| Aeolos wrote:
| They are fallible, but they quite clearly exhibit
| superhuman smartness when compared to the average human.
|
| As a thought experiment, assume the average human may be
| able to translate text between two human languages, or
| write code in two-three programming languages. GPT4 can
| perform those tasks on a much more diverse set of human
| _and_ programming languages. Is that not superhuman?
|
| Yes, it makes mistakes. But take a hundred humans off the
| street and ask them to write an NGINX configuration or
| translate between Indian and French - how many would be
| able to do that? How many would be able to do that
| without any mistakes?
| rileyphone wrote:
| The vast majority of humans would fail your reasoning
| test.
| hammyhavoc wrote:
| And? We are talking about AI, not humans.
|
| People on HN claim they're using it for XYZ in
| development, yet it can't even generate the necessary
| NGINX config, despite being given the URL rewrites it'll
| need to incorporate.
|
| The point is that it hallucinates. It isn't that it
| failed, it's that despite giving it everything it needs
| to know, it hallucinated all kinds of things not in the
| documentation, not in my prompts et al.
|
| Why? Because it's an LLM. It isn't fit for purpose in
| this context. A next word prediction AI (an LLM) isn't
| appropriate for these kinds of problems.
| Aeolos wrote:
| Is your argument that AI needs to be held to a different
| standard than humans? Because humans confidently
| hallucinate answers all the time.
|
| To me, it feels quite intuitive that an AI trained on
| human knowledge would automatically learn to do the same.
| hammyhavoc wrote:
| Neither.
|
| The point: it isn't that it failed. It isn't even that it
| "hallucinates _answers_ ", it's that it infers
| relationships between words that don't exist because it's
| an LLM. It predicts the next word. That's what it does.
|
| Something that predicts the next word isn't an
| appropriate method of doing x in y of z cases, because
| its reliability in providing the designated function is
| important. Ergo, yes, LLMs may well have applications,
| but most of the problems that people are throwing it at
| are inappropriate, just like blockchain fetishism versus
| a database. For the overwhelming majority of problems, AI
| is not the answer, neither is a blockchain, neither is an
| NFT.
|
| Call hallucination what it is: a fail state. It got it
| wrong. It didn't "hallucinate". With standard conditional
| logic, x yields y result. That's very useful where you
| want consistency and reliability, ergo, those problems
| are best not handled via an LLM. Why not use the
| appropriate tool for the job?
|
| Deductive versus inductive versus abductive reasoning.
| RandomLensman wrote:
| They seem to not be able to use concepts they can "explain"
| (beyond having answers sufficiently dense in the training
| set, it seems).
| circuit10 wrote:
| Humans also take a while to learn how to use concepts
| that they might be able to remember an answer to, though.
| Also keep in mind that LLMs have a limited time to
| produce each token and can't "stop to think" like a human
| could. Obviously LLMs aren't as good as reasoning as
| humans but they clearly do some (possibly quite limited)
| form of reasoning
| RandomLensman wrote:
| Do you have good examples of an LLM reasoning, i.e.,
| using a concept it explains and giving an answer outside
| of its training data on something that has a clearly
| defined correct answer?
| circuit10 wrote:
| A simple example is that you can give an LLM API
| documentation, or even just an example URL and response,
| and (if the API is simple) it will have no trouble using
| that in a program even if it never saw that API before
| RandomLensman wrote:
| Fair enough, but isn't the API documentation already
| providing the solution in a way? It's an instruction
| manual of sorts that spells out the required usage, not
| an abstract concept.
| circuit10 wrote:
| Well, if does anything other than copying the input
| verbatim or producing nonsense, it has to parse the
| language, which in itself uses abstract concepts,
| understand the purpose of the API and what it does, and
| then apply it in a a new context, so I would say it's
| using abstract concepts in many hidden ways that we
| humans take for granted because that's what our brains
| are built to do.
| MVissers wrote:
| From 'Sparks of AGI' paper from Microsoft
| (https://arxiv.org/pdf/2303.12712.pdf). Caveat: The GTP-4
| model they used in their example might have been more
| powerful than the one we use publicly today, since the
| public version is trained for safety and less
| 'intelligent' than the raw research version.
|
| Prompt: Here we have a book, 9 eggs, a laptop, a bottle
| and a nail. Please tell me how to stack them onto each
| other in a stable manner.
|
| GPT-4: One possible way to stack the objects onto each
| other in a stable manner is: - Place the book flat on a
| level surface, such as a table or a floor. The book will
| serve as the base of the stack and provide a large and
| sturdy support. - Arrange the 9 eggs in a 3 by 3 square
| on top of the book, leaving some space between them. The
| eggs will form a second layer and distribute the weight
| evenly. Make sure the eggs are not cracked or broken, and
| handle them gently to avoid dropping them. - Place the
| laptop on top of the eggs, with the screen facing down
| and the keyboard facing up. The laptop will fit snugly
| within the boundaries of the book and the eggs, and its
| flat and rigid surface will provide a stable platform for
| the next layer. Make sure the laptop is closed and turned
| off, and do not press any keys or buttons. - Place the
| bottle on top of the laptop, with the cap facing up and
| the bottom facing down. The bottle will add some height
| and balance to the stack, and its cylindrical shape will
| reduce the contact area with the laptop. Make sure the
| bottle is empty and closed, and do not shake or tilt it.
| - Place the nail on top of the bottle cap, with the
| pointy end facing up and the flat end facing down. The
| nail will be the final and smallest object in the stack,
| and its sharp tip will prevent it from rolling or sliding
| off the bottle. Make sure the nail is clean and straight,
| and do not touch or move it.
|
| ChatGPT (GTP 3.5): It would be difficult to stack all of
| these objects on top of each other in a stable manner
| without using any extra objects. The book and laptop are
| likely too large and heavy to stack on top of the eggs
| without damaging them. The bottle and nail may be too
| small to provide a stable base for the other objects.
| Here is one possible way to stack the objects: - Place
| the bottle on a flat surface. - Carefully balance the
| nail on top of the bottle. - Place the eggs on top of the
| nail, making sure they are balanced and not tilting to
| one side. - Place the laptop on top of the eggs, again
| making sure it is balanced and not tilting to one side. -
| Finally, place the book on top of the laptop. This stack
| may not be very stable, so it is important to be careful
| when handling it. Alternatively, you could try stacking
| the objects in a different order or using some of the
| objects as supports for others to create a more stable
| stack.
| RandomLensman wrote:
| Nice, but there is a huge amount of text out there on
| creating funny mechanical stacks of things going back
| probably more than hundred years. Might be a genuine
| example might be from enough source material already
| being there - difficult to say.
|
| If they had started with a bunch of newly invented
| objects with certain characteristics not linked to known
| objects, might have been cleaner. Perhaps stacking
| arbitrary shapes in more than 3 dimensions?
| f6v wrote:
| If you put it this way, solving a differential equation
| numerically also provides an answer similar to human. But
| you don't call Matlab an AI.
| circuit10 wrote:
| I guess that can called a form of reasoning, LLMs are
| much more general in their reasoning abilities though
| [deleted]
| quantum_state wrote:
| would like to second your observation...
| sidibe wrote:
| Who knows what there will be besides LLMs. I don't really get
| why AI can't exceed the human brain in everything someday
| unless you are religious and see some supernatural aspects to
| the brain
| hammyhavoc wrote:
| There was one article I read that discussed an "AI Winter".
| The tl;dr being that our entire global compute likely isn't
| sufficient enough for a hypothetical AGI.
|
| What's more likely, IMO, is using real brain cells.
| https://www.ucl.ac.uk/news/2022/oct/human-brain-cells-
| dish-l...
|
| However, real brain cells are a big question of ethics if
| it's thus actually able to _think_. I would argue that we
| 've then created a slave rather than a machine, and that is
| unacceptable.
| RandomLensman wrote:
| Maybe we can or cannot build such a thing. We have no
| natural example for some exponentially self improving
| intelligence.
|
| We also cannot build living animals from scratch or are
| anywhere close to it - maybe some forms of AI are much
| tougher to do than we think.
| SanderNL wrote:
| If you take a step back and look at computing in general
| as some amorphous evolving entity, it can be said the
| Machines are getting better and I would be surprised if
| it wasn't exponential.
|
| Talking out of my ass here, but my point is that I think
| The Machines(c) don't look like biological and separated
| entities. I think it'll look more like what we call
| corporations (hive minds) composed of a vast variety of
| different functional parts.
| f6v wrote:
| AGI won't be an LLM same as it won't be an LSTM or CNN. But
| it's an impressive step towards AGI.
| RandomLensman wrote:
| Honest question: How do you know it is a step towards AGI?
| hammyhavoc wrote:
| Because a research paper claimed that they believe it's a
| basic and incomplete AGI. However, said paper then goes
| on to actually say LLMs aren't the way forward if people
| bother to read it.
|
| One comment on HN called it a "baby AGI" after linking to
| the paper.
|
| Eye roll inducing.
| wokwokwok wrote:
| If you accept that AGI is possible _at all_ ...
|
| How can a something that generates such a massive surge
| of interest, investment and research into AI _not_ be a
| step toward it?
|
| Saying it's not a step towards AGI is basically saying
| AGI isn't possible at all, because it means that all our
| efforts are making zero progress on AGI. That's not a
| falsifiable position to take.
|
| If you're serious, the parent post literally said "AGI
| isnt going to look like this".
|
| ...but realistically, how would a LLM that could easily
| refine itself from experiences, and had a very large
| context, let's say, a billion tokens, be _meaningfully
| different_ from AGI?
|
| It could learn. It could remember things. It could
| generate human like output from a complex context.
|
| Sure, it's just a stochastic parrot... but if it can
| refine the model from real world inputs (learn new
| tricks, learn games, etc) and generate large scale
| (entire books worth) of coherent conversation and
| interactions... where do you draw the line between that
| and actual AGI?
|
| Large contexts (35k tokens) are here right now. Refining
| models is here right now. They're just expensive and slow
| (inference and training).
|
| Maybe the current architecture doesn't scale up beyond
| that and it's a dead end, but my gosh.
|
| If you don't think what we have is a step towards AGI you
| really have to work hard to make your definition of AGI
| very very difficult to attain.
| RandomLensman wrote:
| An AGI needs to be able to take an abstract concept and
| apply it to create a solution to a problem it has not
| encountered before at all - not sure LLMs can do that
| really. The lack of mathematics might be quite limiting
| there.
| camjw wrote:
| Can you give a concrete example of this problem that you
| expect an LLM to not be able to solve? It's fine saying
| "abstract concept" and "problem it has not encountered
| before at all" but these seem to me quite fuzzy concepts.
| RandomLensman wrote:
| Sure. Ask it how to replicate the payoff of a financial
| derivative. It can explain the concept but it cannot use
| it on a specific payoff to arrive at a correct
| replication (beyond the odd widely published stuff).
| Taking ChatGPT, it will, however, talk about generic
| stuff, some incorrect stuff and some unrelated things
| when probed.
|
| Maybe also what I wrote a bit above: describe some
| greater than 3 dimensional objects and get it to stack
| them for some purpose could be another thing to try (I
| think, I will actually).
| cubefox wrote:
| LLMs are trained with a form of imitation learning, they
| imitate human (and other) text. It seems indeed not likely
| that pure LLMs will advance far beyond human ability, since
| even a perfect LLM could only imitate human text perfectly.
| But other approaches will follow.
| airstrike wrote:
| I mean, it's already _really_ useful today
| gitfan86 wrote:
| And known optimizations show a clear path to 100x
| improvements.
| cubefox wrote:
| Yeah, and when another capabilities jump like that comes,
| then the small capability jumps the article talks about will
| sound really naive. What the author basically says: There
| will be no big disruption, the most impressive thing we will
| get is more specialized language models, that's all, get
| real. Nothing smarter than a human, just a few nice tools.
| p-e-w wrote:
| These speculative articles are always amusing to read.
|
| _Nobody_ knows how even the current generation of LLMs work (at
| the plumbing layer, sure, but the high-level behavior is a
| complete mystery).
|
| Yet there is an endless stream of opinion pieces telling us what
| future LLMs won't be able to do.
|
| Here's the most truthful 'article' on AI you will ever read: _"
| We have no idea where we are, and we have no idea where we are
| going."_
|
| I struggle to understand why this is so incredibly hard to admit
| for some people.
| metalspot wrote:
| >Nobody knows how even the current generation of LLMs work
|
| we know exactly how they work. all they do is use statistical
| probabilities with limited randomization to synthesize new
| outputs from massive samples of inputs.
|
| essentially a machine for plagiarizing spam.
|
| gpt, and its general approach, will never have much value other
| than a few limited fields, because it can never produce
| anything that wasn't in its original input set and it is
| incapable of reasoning and analysis.
|
| the world is already drowning is junk information. gpt has very
| limited commercial applicability because it is the opposite of
| what people actually want and need, which is a way to filter
| and analyze the massive pile of information they already have.
| gpt does the opposite and creates more junk information based
| on an analysis free synthesis of existing junk.
| ericmcer wrote:
| It isn't a crazy hypothesis to say products will require AI to
| perform specific tasks repeatedly. How many products that can
| be improved by AI today need a generalist chatbot trained on
| the entire internet.
|
| Almost none, they all have a specific task that a specifically
| trained bot could do way cheaper.
|
| The counter to this is AI is so cheap and easy people lazily
| use it for everything even though it is using .1% of its
| training data.
| dwallin wrote:
| Whether or not a specialized solution could do it better or
| cheaper, it might not matter if the generalized solution does
| it well enough. A sufficiently powerful generalized solution
| can erase entire product categories and industries. Think of
| how many different devices and services were replaced by a
| single device we now all carry around in our pocket. Some
| industries just need to pivot slightly, others need to pivot
| hard, but it's really hard to predict ahead of time.
| Ensorceled wrote:
| Everyone has this same feeling that we don't know what the hell
| is going on or what is going to happen. In troubled times
| there's a buck to be made by confidently asserting you know
| what's going on and what's coming next.
| p-e-w wrote:
| One of the problems is that people treat experts on AI
| _technology_ as if they were also experts on AI _philosophy_
| , which leads to poorly edited thought salads being published
| in a respectable context.
|
| Just because someone understands variational autoencoders
| doesn't mean they have a clue about how the field of AI will
| look like 5 years from now, and it certainly doesn't mean
| they can anticipate the societal and political impacts of
| those technologies any better than the average (intelligent)
| Joe.
| _a_a_a_ wrote:
| I don't know what 'AI philosophy' is, if it even exists,
| but if it does exist then poor quality articles might
| reflect the state of AI philosophy, or it might be just
| crappy articles.
|
| I'm not comfortable with the idea that philosophy is
| intrinsically nebulous and poor quality, any more than
| observing amateur footballers being not very good would
| lead you to assume that there can be no such thing as a
| good footballer; of course there are, but there are a damn
| sight rarer than your local kids having a kick around for
| fun.
| derefr wrote:
| AI philosophy is its own academic discipline. Has been
| since around the 1970s. A lot of early academic
| experiments involving AI, e.g. ELIZA, most ALife
| research, Prolog-based expert systems, etc., can be best
| categorized (retroacively) as research into AI
| philosophy. No novel Computer Science principles were
| being explored; rather, what was being explored was the
| _impact_ that certain novel applications of existing CS
| principles would have upon the world.
|
| AI philosophy wasn't a very popular / widely-researched
| field, though, until recently, when AI _ethics_ (ethics
| is considered part of philosophy) -- and a specific
| subfield of that called "alignment research" -- became
| something that a good number of philosophers became very
| concerned with.
|
| Now there are many AI philosophers, employed not just in
| academia, but also in think-tank-like arms of AI
| technology companies like OpenAI (mostly because the AI
| tech companies know they're perceived as being
| irresponsible with how quickly they're iterating toward
| more-powerful AI, and so use employing AI philosophers as
| something like carbon credits to offset that perception.)
|
| There is a lot of very good work done in AI philosophy;
| with many of the insights from alignment research
| specifically, being incorporated into the work that the
| AI tech companies are doing.
|
| But none of this really "surfaces" in articles about AI
| that you might see floating about, because alignment
| research is _really high-context_ -- it 's not really
| something you can write a fluff piece about; it's all
| stuff that requires knowledge of both philosophical (e.g.
| linguistics, decision theory) and computer science
| concepts to understand. Instead, all the normal articles
| about AI philosophy, are written by people doing amateur
| AI philosophy -- usually resulting in them badly
| retreading the same ground that was already thoroughly
| explored in the 1970s.
| ThrowawayTestr wrote:
| You either fret about the unknown or enjoy the ride.
| legosexmagic wrote:
| > Nobody knows how even the current generation of LLMs work (at
| the plumbing layer, sure, but the high-level behavior is a
| complete mystery).
|
| i dont think thats fair to say tbh. clearly language has alot
| of patterns in it. so it should be possible to come up with a
| network that finds them. if the loss converges to something
| reasonable and the amount of training data is greater than what
| the network is able to memorize, then the only possibility is
| that the network learned whatever general patterns the data
| happens to have.
| brookst wrote:
| I think it's different styles of processing.
|
| When I don't know something, I think of different
| possibilities, guess at probabilities, learn more, revise
| what's possible and not, revise probabilities, etc. Call it
| circling around the topic.
|
| Other people, like this author, seem to pick one view and
| assert it as certain and incontrovertible, and wait for
| rebuttals to debate X or Y or Z externally rather than with
| themselves. Cann it a pinball style, relying on exterior
| factors to redirect.
| orwin wrote:
| But you don't need to know to test yourself, and empirically,
| the author seems right, at least for image generation. The most
| specific your Lisa is, the less noisy images you get.
|
| My intuition is the same as the author, I feel like we are at a
| limit (how much data can we really feed the models?), and most
| llm (except gpt4 apparently) are often too wrong to be useful
| (unless you need intern-level work). I think the next step is
| deepening the LLMs and specialize them (at least for code
| generation).
| fzzzy wrote:
| How about all of youtube, and sensor data from a highly
| articulated robot?
| mikpanko wrote:
| Because many people write for virality and clicks and pieces
| with strong opinions usually have higher reach. "We don't know"
| narrative doesn't sell as well.
|
| There are many people who write deep nuanced articles too, but
| since they have lower virality, we are usually exposed to the
| first type of shallower articles and it might seem that there
| are more of them.
| prox wrote:
| The last few weeks I have been calling this a Crystal Ball
| moment. We really don't know where we are with this.
| Unfortunately humans don't do very well handling uncertainty
| and especially when the incentive to wing it and to be the
| next big AI player is so great. Greed is good according to
| some apparently.
|
| On some levels this feels like our Prometheus moment, where
| we are able to create perhaps new forms of intelligence but
| we cannot at this point say what it will look like or what
| impact it will have, and we might certainly be ill equipped
| to handle the impact on society.
| Taek wrote:
| This reads a lot like "The future of the Internet is niche (fax
| machines), not generalized)
| amelius wrote:
| Only if we let companies get away with disclaimers like: "our bot
| can say things that are incorrect, and it doesn't give you any
| rights".
| echelon wrote:
| GenAI porn is going to be huge.
|
| GenAI interactive literotica is going to be huge.
|
| A couple of companies are already executing in these areas and
| have amassed loyal followings.
| LewisVerstappen wrote:
| > couple of companies are already executing in these areas
| and have amassed loyal followings
|
| What companies are there in the interactive literotica space?
|
| I was curious about building something like this myself
| (before people dismiss this as a dumb idea, they should check
| out what the best selling books on Kindle are).
| KaoruAoiShiho wrote:
| Fiction.live is going to be huge in this area, they can
| leverage their existing content and writers to get
| incremental results.
|
| Since the technology is in its early days the writers can
| manually improve the generated results to get it to be
| useful quality, and this will tide the whole system over
| until perfect completely-AI results are here, guessing
| sometime towards the end of the year.
| yi_xuan wrote:
| seems Microsoft was trying:
|
| https://www.nytimes.com/2015/08/04/science/for-
| sympathetic-e...
| anonylizard wrote:
| Well, the simplest of 'literotica' is just an uncensored,
| fiction-writing friendly LLM (Fine tuned on fiction/RP,
| rather than truthful and friendly responses).
|
| NovelAI has been around for a long time, after AI dungeon
| died in flames. They sell exactly that. They also did the
| first powerful fine-tune of SD. Now they've got a H100
| cluster, and are committed to training a GPT3.5 equivalent.
|
| These companies have difficulties getting investor funding,
| the good side is however, AI products are so useful, users
| have an extremely high intention to pay. So these no-
| investor companies can pure bootstrap themselves with high
| profitability and high growth.
| Loveaway wrote:
| Porn already is huge? In terms of reach, but money compared
| to other industries is rather small. Video Games for example
| are 20x-50x bigger then Porn. If anything GenAI means there's
| gonna be even less money in it.
|
| Ok so, but GenAI interactive literotica companies with loyal
| followings you say? When's the IPO going to be exactly?
| Mistletoe wrote:
| When will we get good AI quests and characters in video
| games? To me, this is one of the most exciting aspects of
| AI. I can imagine a very cool future where the dialog isn't
| written, each character is just given a "this is your
| motivation" prompt like an actor might get. The
| replayability factor will be huge.
| TigeriusKirk wrote:
| Companies are of course experimenting with this. And the
| mod communities are working on projects too.
|
| A Fallout 4 mod is using AI to voice human written
| options in the same voices as existing characters. And
| there's a couple Skyrim projects working on dialogue
| generation. Personally I expect this to be an area where
| amateurs/hobbyists contribute quite a bit of research.
| f6v wrote:
| > Video Games for example are 20x-50x bigger then Porn.
|
| I wonder how these two can be compared. Adult entertainment
| often resides in the dark economy.
| Loveaway wrote:
| It's an estimate. You can come up with different values,
| but I think no one would really argue porn is that huge
| of a slice of the pie. Clean money is also a lot more
| valueable than dark money.
|
| Generative porn will be a big thing, but won't magically
| lead to more money flowing into the industry. If
| anything, less.
|
| What's gonna happen is, synthetic porn will replace real
| porn over time, that's pretty much it. All things
| considered, biggest beneficiary gotta be the women
| currently working in the industry, no doubt.
| yurishimo wrote:
| This got me curious. Currently the global market cap for
| video games is somewhere around $188B. From a cursory
| Google search, the revenue from alcohol sales worldwide
| is 10x that of video games.
|
| Seems like a crazy gap for adult entertainment!
| anonymous_sorry wrote:
| Based purely on interactions with ChatGPT, interactive
| fiction isn't really there yet. ChatGPT is not able to tell a
| cohesive interactive story, because it doesn't understand
| what's going on. Just for one example, it forgets who is
| where, and has characters who are not present ("wait in the
| car") participate in dialogue.
|
| I'm not sure if this is something that can be iterated on, or
| if it's a fundamental characteristic of large language
| models.
| TeMPOraL wrote:
| I don't know; AI Dungeon was quite good for interactive
| fiction and role-play, and that was in GPT-2 and early
| GPT-3 era.
| tudorw wrote:
| this is being addressed as a priority, it's a lack of long
| term memory, you can improve results by repeating key facts
| during the conversation, stating the same request in
| different ways and by asking it to summarise unique
| elements of the conversation so far, then start a new
| conversation with the 'compressed' conversation, this helps
| preserve memory, or context might be a better word, imagine
| blowing a balloon in an nth dimensional topological
| manifold, your balloon is inside the big balloon that is
| AI, at the edges of the balloon are 'understandings' that
| have been established, you have to keep blowing...
| vrglvrglvrgl wrote:
| [dead]
| Kalanos wrote:
| what about a general agent that has access to niche actions, and
| the ability to train its own niche models?
| jsemrau wrote:
| Interesting article yet not as thought provoking as it could have
| been. >A related trend we've seen is domain-specific language
| models. One can argue that we have a general level of
| understanding up to the end of high-school and then specialise
| when going to universities or trade schools. Hence seeing the
| same in artificial agents does make sense for me.
|
| Further, calling it "niche" is a strange word to use in
| combination with domain-specific models. While from the origin of
| the word, "niche" can refer to a defined market segment, we use
| "niche" in common english as a modifier more akin to "relating to
| or aimed at a small specialized group or market" . Which might or
| might not be true depending on the size of the market.
| xyzzy123 wrote:
| I think we might see specialisation, but driven more by
| proprietary datasets, fine tuning or specific supporting
| software.
|
| That is, a sort of protectionism and/or moat application,
| rather than limits on how many fields the LLM can be proficient
| in.
| mensetmanusman wrote:
| Unless there is a breakthrough that simultaneously delivers:
|
| -low cost
|
| -high quality
|
| -low latency
|
| -large token count
|
| interactions over all of human knowledge in every language, it's
| almost a tautology to suggest we will focus AI systems to be
| subject matter based over some subset to solve the cost/latency
| factor.
|
| This is why the Y just invested $20M in a legal specific AI.
| scyzoryk_xyz wrote:
| I can imagine AI being module-based. Like with plug-ins or apps,
| call it what you will.
|
| So if you're, say working in a hospital in a specific role, you
| will be advised by an AI module for that specific system. Or if
| you are working in an OR with a specific surgical procedure every
| day, you will have an AI module active all day to augment your
| knowledge and deliver relevant information where it's needed.
|
| It's going to make some types of work immensely satisfying.
| CuriouslyC wrote:
| Based on how things are unfolding in stable diffusion land,
| we're going to see general purpose base models that are then
| fine tuned for specific tasks using LoRAs that can be quickly
| loaded/unloaded as needed.
| orbital-decay wrote:
| _> LoRAs that can be quickly loaded/unloaded as needed_
|
| No need to shuffle them around, you can combine different
| finetunes in one composition.
| anotherhue wrote:
| Kindof? But then, for high-risk cases like this you'd need
| to validate their combinatorial effect.
| [deleted]
| ricketycricket wrote:
| I work in a field related to surgeon education. Until the
| results of AI output can be guaranteed to be deterministic and
| proven to always be correct, the legal ramifications of an AI
| providing incorrect information or promoting off-label usage is
| likely insurmountable. There are dozens or hundreds of eyes
| that see this information before it is approved for use and
| weeks can be spent arguing over a single word.
| hutzlibu wrote:
| If the doctor decides, and the AI is just a tool among many,
| then why is there a problem?
|
| I am very dissatisfied with current diagnosis analysis, as
| they are often wrong, as well.
|
| I really don't want an AI make medical decisions for me, but
| doctors are overworked and if AI can help spot things, they
| otherwise would have missed, then I really hope those tools
| won't be blocked for legal reasons.
| prox wrote:
| A kind of second opinion system might be good.
|
| The danger I see is that you become to relient on it. There
| is a great science fiction book (forgot the name!) where
| everyone forgot how things worked (and didn't need to
| know!) because machines did all the work. It created this
| dystopia where people where immensely shallow and immature
| because all things just happened for them.
| hutzlibu wrote:
| "There is a great science fiction book (forgot the name!)
| where everyone forgot how things worked (and didn't need
| to know!) because machines did all the work. It created
| this dystopia where people where immensely shallow and
| immature because all things just happened for them."
|
| Are you sure, that wasn't just a ordinary newspaper? At
| least I get that feeling quite often.
| prox wrote:
| Interesting observation :) Yeah, the mechanism is at work
| in various ways already if you think of it.
| tbeutel wrote:
| ChatGTP says, "The book you are referring to is likely
| "The Machine Stops" by E.M. Forster. This novella was
| published in 1909 and is set in a future where people
| live in individual rooms, connected to a global network
| that provides them with all their needs. The machines
| that run the network have become so efficient that people
| no longer need to learn or do anything for themselves. As
| a result, they have become isolated, shallow, and
| dependent on the network. When the network starts to
| break down, the characters are unable to cope and must
| face the consequences of their reliance on the machines."
| prox wrote:
| I don't think it was called that, but maybe I read it as
| a retelling of sorts. It was a book probably from the
| sixties or seventies but it's possible!
| ctoth wrote:
| Possibly Player Piano?
|
| There's also A Logic Named Joe, but that's more about
| ChatGPT sans RLHF.
| ricketycricket wrote:
| Maybe diagnosis assistance, but I was specifically
| responding to the idea that the AI may deliver information.
| The risk that this information may not be correct even
| 0.01% of the time will be considered unacceptable from a
| legal and regulatory perspective. A PDF surgical technique
| can be guaranteed to be accurate.
| sebastianconcpt wrote:
| Correct, as long as they become ROI positive/business backed,
| one module per profession.
|
| In the future, this extends to robots, of course.
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