[HN Gopher] Generative AI is overrated, long live old-school AI
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Generative AI is overrated, long live old-school AI
Author : Buhljingo
Score : 155 points
Date : 2023-03-15 17:08 UTC (5 hours ago)
(HTM) web link (encord.com)
(TXT) w3m dump (encord.com)
| draxil wrote:
| We are all struck with the novelty of generative AI, it needs
| time to settle. People will throw the universe at the wall and
| see what really sticks.
|
| To my mind generative AI is great at finding needles in the
| haystack of stuff we already know. Of course it just as often
| gives you a fake needle right now, just to see if you notice.
|
| On the other hand "traditional"/predictive AI is often better at
| the things we don't already know or understand.
| version_five wrote:
| Seems like the person who wrote the blog works in "classical"
| deep learning. So do I, so here's the fairest take I can come up
| with: "AI" has for recent memory been a marketing term anyway.
| Deep learning and variations have had a good run at being what
| people mean when they refer to AI, probably overweighting towards
| big convolution based computer vision models.
|
| Now, "AI" in people's minds means generative models.
|
| That's it, it doesn't mean generative models are replacing CNNs,
| just like CNNs don't replace SVMs or regression or whatever. It's
| just that pop culture has fallen in love with something else.
| sterlind wrote:
| do people actually use SVMs anymore?
|
| like, regression, sure - because it's a tool to measure how
| well a hypothesis (polynomial function) matches the data
| (points.) and CNNs are still foundational in computer vision.
| but the first and last time I heard of SVMs was in college, by
| professors who were weirdly dismissive of these newfangled deep
| neural networks, and enamored by the "kernel trick."
|
| but aren't SVMs basically souped up regression models? are they
| used in anything ML-esque, i.e. besides validating a hypothesis
| about the behavior of a system?
| superdisk wrote:
| > but the first and last time I heard of SVMs was in college,
| by professors who were weirdly dismissive of these newfangled
| deep neural networks, and enamored by the "kernel trick."
|
| LOL. Exact same experience in my college courses. Glad to
| know it's universal.
| jacksnipe wrote:
| We loved them in medical testing. Very explainable models.
| teruakohatu wrote:
| > do people actually use SVMs anymore?
|
| Yes they are. They allow for non-linear decision boundaries
| and more dimensions than rows of data, which for many other
| ML methods is a problem.
|
| Linear regression, logistic regression, SVM and CART decision
| trees are all still very popular in the real world where data
| is hard to come by.
| rkhacker wrote:
| The Generative AI is the AI for the masses. While people were
| getting overhyped with all the possibilities and promises of AI
| and deep learning etc. it is for the first time that they can
| also tinker and get surprised by its results. People feel
| creative interacting with it.
| JohnFen wrote:
| Spot on. I work with deep learning systems in industrial
| control, and generative models are simply ill-suited for this
| sort of work. Wrong tool for the job.
|
| But neither the traditional nor generative models are "AI" in
| the sense that normal people think when they hear "AI".
| fakedang wrote:
| I'm curious about your work, because I worked on something
| similar during my grad school. What kind of applications in
| industry do you use deep learning systems for? Process
| control?
| JohnFen wrote:
| Yes, process control. It's used in coordination with vision
| systems to analyze work pieces, determine the best way of
| processing them, and direct other machinery how to do that
| processing.
| nico wrote:
| To me what's exciting about Chat/GPT type of tech, is that
| they can be the "coordinators" of other models.
|
| Imagine asking an AI assistant to perform a certain
| industrial control task. The assistant, instead of executing
| the task "itself", could figure out which model/system should
| perform the task and have it do it. Then even monitor the
| task and check it's completion.
| burnished wrote:
| What do you imagine this would do that existing automation
| does not?
| calf wrote:
| The central question is that a controller is assumed to be
| specifiable and thus formally verifiable through model
| checking in principle.
|
| With a neural network you have a black box and for example
| with ChatGPT it doesn't even have a specification. It turns
| the verification process upside down.
| JohnFen wrote:
| I'm not sure how the likes of ChatGPT could accomplish that
| even in theory, but I won't say it's not possible at some
| point in the future. Gpt itself, perhaps, someday.
| nico wrote:
| Already ChatSpot is doing it. Their system is essentially
| a ChatGPT-enhanced Hubspot management system using
| chatux.
|
| ChatSpot can understand your commands and then perform
| actions in the system for you, for example add a lead,
| change their contact info, write a blog post, publish it,
| add an image...
|
| Edit: but if you connected it with physical actions, it
| could control your house, maybe check your smart
| refrigerator, order food on Instacart, send you recipe,
| schedule the time to cook in your calendar, request an
| Uber to pick you up from work, invite someone over, play
| music...
|
| There's a discussion about this on another homepage
| thread here:
| https://news.ycombinator.com/item?id=35172362
| JohnFen wrote:
| Ah, ok. I thought you were talking about something a bit
| more profound than that.
| IanCal wrote:
| You can just tell the models to and tell them what tools
| they have available and how to call out to them.
| Langchain supports this iirc.
| version_five wrote:
| This is just wrong.
|
| Also, even if a LLM could do that, so could a shell script,
| without the risks involved in using "AI" for it, or for now
| the ridiculous external dependence that would involve.
|
| I wonder if in 10 years people will be stuck debugging
| Rube-Goldberg machines composed of LLM api calls doing
| stuff that if-statements can do, probably cobbled together
| with actual if-statements
| Hermitian909 wrote:
| LLMs are already taking over these sorts of systems in
| industry.
|
| There are lots of systems where you're taking some
| information about a user and making a best guess at what
| action the system should take. Even without a need for
| super high accuracy these rule systems can get
| surprisingly complex and adding in new possible decisions
| can be tricky to maintain. In LLM world you just maintain
| a collection of possible actions and let the LLM map user
| inputs to those.
| JohnFen wrote:
| > doing stuff that if-statements can do, probably cobbled
| together with actual if-statements
|
| In other words, old-school expert systems.
| baq wrote:
| With the limit of 25k words it might actually be
| reasonable to test out a prompt for an expert system...
| but I'd still leave reasoning to something else, for now.
| Z3, prolog or some forward chaining tool like clips, but
| have the LLM hallucinate some of the rules?
| querez wrote:
| It's not wrong. It's how modern systems operate. E.g.
| look at Google's SayCan (https://say-can.github.io/)
| which operates exactly like this (an LLM ordering a Robot
| around).
| jrussino wrote:
| > I wonder if in 10 years people will be stuck debugging
| Rube-Goldberg machines composed of LLM api calls doing
| stuff that if-statements can do, probably cobbled
| together with actual if-statements
|
| Sounds like an extension of
| https://en.wikipedia.org/wiki/Wirth%27s_law. How many
| times have I done some simple arithmetic by typing it
| into my browser's bar and checking out the google
| calculator results? When a generation ago I would have
| plugged it into a calculator on my desk (or done it in my
| head, for that matter...). I would be entirely
| unsurprised to hear that in another generation we're
| using monstrously complicated "AI" systems to perform
| tasks that could be done way more simply/efficiently just
| because it's convenient.
| nico wrote:
| Sure, maybe you can use a shell script, but now the AI
| assistant can write it based on your verbal/text
| description, and then the assistant can also run it for
| you after you've checked it.
|
| What your are saying is: "why use the washing machine, if
| I my clothes are even cleaner when I wash them myself - I
| also spend less detergent and less water".
|
| You are free to keep doing your laundry by hand.
|
| But I bet most people prefer the washing machine.
| xwdv wrote:
| Spare me the shitty analogies. We write shell scripts
| because it's cheap, fast, and the behavior is very
| predictable.
|
| Like it or not, an AI's behavior is a black box and can't
| be "proven" to execute exactly the same every time for
| the scenarios you are targeting.
|
| A shell script will do exactly what it has been written
| to do every time, unless tampered with. And if changes
| need to be made, it can be done quickly without need for
| retraining, god knows how long that would take for an AI
| to learn something new. God help you if you need to
| maintain "versions" of your AI, trained for different
| things.
|
| Face it, AI are pointless and slow for certain classes of
| problems.
| qup wrote:
| > A shell script will do exactly what it has been written
| to do every time
|
| I wish I lived in that universe.
| com2kid wrote:
| > A shell script will do exactly what it has been written
| to do every time, unless tampered with.
|
| Or unless some magic environment variable changes, or one
| of the runtime dependencies changes, or it is run on a
| different operating system, or permissions aren't setup
| right, or one of its tasks errors out.
|
| Shell scripts are digital duct tape, the vast majority of
| shell scripts do not come close to being reliable
| software.
|
| > god knows how long that would take for an AI to learn
| something new
|
| Did you watch OpenAI's demo yesterday? They pasted in new
| versions of API docs and GPT4 updated its output code.
| When GPT forgot a parameter, the presenter fed back the
| error message and GPT added the parameter to the request.
| xwdv wrote:
| AI proponents are missing the point. _Anything_ you write
| to make an AI produce something is basically code. Docs
| are code.
|
| You don't have to feed a developer code or docs, you can
| give them a high level idea and they'll figure it out on
| their own if you want.
| echelon wrote:
| That code will eventually fall away.
|
| The big thing everyone in this single thread is missing
| is that AI is a metaheuristic.
|
| I wouldn't expect to use AI to run_script.py. That's
| easy. I'd expect it to look at the business signals and
| do the work of an intern. To look at metrics and adjust
| some parameters or notify some people. To quickly come up
| with and prototype novel ways to glue new things
| together. To solve brand new problems.
| xwdv wrote:
| To do the work of an intern an AI must go on Jira, read a
| ticket, then find the appropriate repositories where code
| needs to be modified, write tests for its modification,
| submit for code review, respond to feedback in code
| review, deploy its changes.
|
| It's not there yet.
| echelon wrote:
| This feels achievable in five years.
| JohnFen wrote:
| It _always_ feels achievable in five years. People were
| saying exactly this 30 years ago.
|
| Sooner or later it may (or may not) be a true statement,
| but it's awfully hard for me to say that it's any
| different right now than it has been before.
| suction wrote:
| [dead]
| njarboe wrote:
| Correct. So are humans.
| whatshisface wrote:
| I think you're fighting an uphill battle because of what
| you picked to defend here - shell scripts are very easy
| to write, and I have a hard time imagining a future where
| someone tells an LLM,
|
| "Write me a shell script that runs run_control.py with
| the speed argument set to one hundred."
|
| to get,
|
| "./run_control.py --speed 100"
| throw__away7391 wrote:
| Not to weigh in on any other aspect of this discussion,
| but when you say:
|
| > I have a hard time imagining a future where someone
| tells an LLM, "Write me a shell script that runs
| run_control.py with the speed argument set to one
| hundred."
|
| I'll point out that we already live in a world where
| single lines of pure function code are distributed as an
| NPM packages or API calls.
| furyofantares wrote:
| > shell scripts are very easy to write
|
| I've been a developer for a long-ass time, though I don't
| have super frequent occasion where I find it worthwhile
| to write a shell script. It comes up occasionally.
|
| In the past 2 weeks I've "written" 4 of them via ChatGPT
| for 1-off cases I'd have definitely found easier to just
| perform manually. It's been incredible how much easier it
| was to just get a working script from a description of
| the workflow I want.
|
| Usually I'd need to double check some basic things just
| for the scaffolding, and then, maybe double check some
| sed parameters too, and in one of these cases look up a
| whole bunch of stuff for ImageMagick parameters.
|
| Instead I just had a working thing almost instantly. I'm
| not always on the same type of system either, on my mac I
| asked for a zsh script but on my windows machine I asked
| for a powershell script (with which I'd had almost no
| familiarity). Actually I asked for a batch file first,
| which worked but I realized I might want to use the
| script again and I found it rather ugly to read, so I had
| it do it again as a powershell script which I now have
| saved.
|
| Sure though, someone won't tell an LLM to write a shell
| script that just calls a python script. They'd have it
| make the python script.
| bigfudge wrote:
| I think one effect of LLMs and their limited context will
| be the end of DRY. I've already found myself getting gpt
| to write stuff for me that could have been part of or
| leveraged existing code with a little more thinking. But
| the barrier to just starting from scratch to do exactly
| what I want, right now, just got a whole lot lower.
| satvikpendem wrote:
| What? There are a lot of non-coders out there, and they
| could absolutely use an LLM to ask it to create scripts
| to run. In fact I along with a few of my friends already
| do this, I recently asked ChatGPT to figure out how to
| integrate two libraries together after I copy-pasted to
| docs from each (now with the GPT-4 32k token limit).
| NobleLie wrote:
| Spoken word into microphone implementation:
|
| _Run run control with speed argument 100._
|
| AI: "Scheduling you for a speech therapist session to
| work on your stutter"
| jameshart wrote:
| It's not 'write me a shell script to run this python
| code', it's 'okay, the test part looks good, run the
| print again with the feed speed increased to 100, and
| make six copies. And Jarvis, throw a little hot-rod red
| on it.'
| suction wrote:
| [dead]
| pstorm wrote:
| That's a trivial example, sure. Think of saying "make
| this image more red, flip it vertically, then crop the
| bottom 25%, and finally output it with 80% compression as
| a jpg". That would take 15 minutes to figure out how to
| write that with imagick, or just have an LLM do it for
| you.
| tough wrote:
| I had to try, seems reasonable, haven't tested the code
|
| As an AI language model, I do not have direct access to
| image editing software, but I can provide you with a
| shell script that could achieve the described image
| manipulation using ImageMagick, assuming it is installed
| on your system.
|
| Here's an example shell script that you could use as a
| starting point:
|
| bash Copy code #!/bin/bash
|
| # Set the input image filename input_file="input.jpg"
|
| # Set the output image filename output_file="output.jpg"
|
| # Increase the redness of the image convert "$input_file"
| -modulate 100,150,100 -channel R -evaluate multiply 1.2
| +channel temp1.jpg
|
| # Flip the image vertically convert temp1.jpg -flip
| temp2.jpg
|
| # Crop the bottom 25% of the image convert temp2.jpg
| -gravity South -chop 0x25% temp3.jpg
|
| # Compress the image to 80% convert temp3.jpg -quality 80
| "$output_file"
|
| # Clean up temporary files rm temp*.jpg To use this
| script, save it to a file (e.g.,
| "image_manipulation.sh"), make it executable (e.g.,
| "chmod +x image_manipulation.sh"), and run it in the same
| directory as your input image. The output image will be
| saved in the same directory with the name "output.jpg".
|
| Please note that this is just an example script and may
| need to be modified depending on your specific image
| manipulation needs.
| fulafel wrote:
| This has lots of lossy encode/decode cycles that will
| accumulate artifacts. Good anecdote.
| sharemywin wrote:
| I've been doing similar things all the time lately.
|
| write me a function in python that ...
|
| I've always forgot the syntax for alot of
| functions/libraries etc.
|
| Also, I haven't really written lot of python until
| recently.
| pstorm wrote:
| You are getting a surprising amount of backlash from this,
| but I think you are right. There may be better tools for
| the job, but general tools tend to win out as they get
| "good enough"
| njarboe wrote:
| The term "AI" was corrupted as described. People now use the
| term "artificial general intelligence" (AGI) to refer to what
| used to be called AI.
| [deleted]
| gautamdivgi wrote:
| Isn't most of the mathematics of AI old, as in really old?
|
| Regression, both linear and logistic are from the mid 1800s to
| early 1900s. Neural networks, at least the basics are from
| around 1950.
|
| What has really changed is the engineering, the data volume and
| the number of fields we can apply the mathematics to. The math
| itself (or what is the basis of AI) is really old.
| sterlind wrote:
| backpropagation didn't get solved until the '80s, weirdly.
| before then people were using genetic algorithms to train
| neural networks.
|
| and it was only in the last decade that the vanishing
| gradients problem was tamed.
|
| my impression is that ML researchers were stumbling along in
| the mathematical dark, until they hit a combination (deep
| neural nets trained via stochastic gradient descent with ReLU
| activation) that worked like magic and ended the AI winter.
| version_five wrote:
| Right, and the practice of neural networks has
| significantly overshot the mathematical theory. Most of the
| aspects we know work and result in good models have poorly
| understood theoretical underpinnings. The whole
| overparamiterized thing for example, or generalization
| generally. There's a lot that "just works" but we don't
| know why, thus the stumbling around and landing on stuff
| that works
| fnordpiglet wrote:
| AI has been marketing term since the day it was coined. It
| means literally nothing, which means it can mean anything.
| burbankio wrote:
| As the old joke goes, "AI" is anything that doesn't work yet.
|
| Once an "AI" system becomes reliable, we quickly take it for
| granted and it no longer seems impressive or interesting.
| It's just a database. Or an image classifier. Or a chatbot.
| fnordpiglet wrote:
| Magic is just science we don't understand yet.
| Jensson wrote:
| Science is just magic we do understand is a cooler take.
| fnordpiglet wrote:
| Yes that's the one I use for my daughter ;-)
| [deleted]
| patrulek wrote:
| Old-school, huh. The skynet is closer than we think i guess.
| kyleyeats wrote:
| I'm working on an old-school AI personal project right now. I
| don't know how long that lasts. The generative stuff is more and
| more tempting. It rewards the horrible micromanager in me like
| nothing else.
| pretendscholar wrote:
| I'm not sure I understand a definition of AI that doesn't include
| the ability to generate things.
| WoodenChair wrote:
| > I'm not sure I understand a definition of AI that doesn't
| include the ability to generate things.
|
| It depends how you define "generate." For example, is software
| that controls a robot arm generating anything? I guess it's
| generating the movements of the arm. But when people use the
| term "generative" with regards to machine learning models right
| now, they generally mean content--e.g. text or images for
| consumption.
| yunwal wrote:
| Generative has a more technical meaning than that.
|
| Generative AI is essentially the opposite of a classifier.
| You give it a prompt that could mean many different things,
| and it gives you one of those things. A robotic arm could use
| generative AI, because there are many different sets of
| electrical signals that would result in success for, say,
| catching a ball.
|
| Classification is an example of a non-generative AI in that
| there is only 1 correct answer, but it still requires machine
| learning to acquire the classification function.
| TuringTest wrote:
| You can use AI to validate things, i.e. to check that they
| conform to some specification.
|
| You may twist the language to say that they are generating a
| list of validations and errors, but even then it's definitely a
| different use case than merely creating new items.
| croes wrote:
| The point is that AI is more than just generating more of the
| same data it was trained on.
| uoaei wrote:
| Generative methods _per se_ are pretty sick and dope, and are
| still useful for many things beyond art generation.
| sposeray wrote:
| [dead]
| GuB-42 wrote:
| Is there a fundamental difference?
|
| I mean, the only thing GPT does is predict the next word, which
| makes it not so different from a compression algorithm. And
| diffusion models (the image generating stuff) are essentially
| fancy denoisers.
|
| Depending on how you assemble the big building blocks, you get
| generation or you get prediction.
| baq wrote:
| GPT-3.5 is not a Markov chain, this is trivially true. While
| 'predicts the next word' is true, the mechanism of it is of
| interest and that is most certainly not trivial.
| [deleted]
| sweezyjeezy wrote:
| Depends how far you take the word 'fundamental', on the one
| hand yeah most DL systems are trying to predict something, and
| they generally have some concept of compression built in. But
| in terms of the steps to curate a dataset, train, test, iterate
| and actually use the model for a given end goal - they are
| pretty fundamentally different.
| sharemywin wrote:
| I think the thing is though in Large multi models you give it
| all the data and test it against everything. And it generally
| does better across most of the benchmarks.
| sweezyjeezy wrote:
| That depends entirely on the use-case - for example if you
| wanted to build an AI to operate a self-driving car, just
| training on unlabelled data scraped from the internet is
| only going to get you so far. It doesn't learn how to do
| EVERYTHING (not yet at least).
| redox99 wrote:
| I wonder how good multimodal GPT4 is at ImageNet.
|
| (You give it the image and prompt it with the 1000 classes and
| ask it which one the image belongs to).
|
| I'm surprised ClosedAI didn't include this kind of benchmark. I
| guess it doesn't do too well?
| sharemywin wrote:
| Here's something on Clip
|
| https://www.pinecone.io/learn/zero-shot-image-classification...
| efitz wrote:
| I think that 100% of the actually useful use cases for generative
| AI could be described in two words: "supervised autocomplete".
| orangecat wrote:
| That's not wrong, but an ideal autocompleter is a near-
| omniscient superintelligence. "The optimal approach to curing
| Alzheimer's is ______". "The proof of the Riemann hypothesis is
| as follows: ______". "The best way for me to improve my life is
| _______".
| kneebonian wrote:
| I think the big difference is just being an Autocompleter is
| less concerned with generating something that is truthful, as
| in reflects the real world as we understand it described by
| physics, vs simply spitting out something that sounds good.
|
| Although we do have a litmus test in asking it "What is the
| meaning of life the universe and everything?"
| whiplash451 wrote:
| The author might be missing the fact that generative models can
| be used for "old-school" prediction tasks, with quite outstanding
| results.
|
| Their power does not only lie in their ability to _generate_ new
| data, but to _model_ existing data.
| jasonjmcghee wrote:
| The biggest issue with using them in this way is how alien the
| failure modes are.
|
| Interpretable models with transparent loss functions are easy
| to grok.
|
| How LLMs might fail on a classic task is (afaict right now)
| difficult to predict.
| whiplash451 wrote:
| What is not transparent in the cross-entropy loss used in a
| large number of deep nets?
| jasonjmcghee wrote:
| I think there was a breakdown in communication here.
|
| If I train a classic deep net as a classifier and there are
| 5 possible classes, it will only ever output those 5
| classes (unless there's a bug).
|
| With ChatGPT, for example, it could theoretically decide to
| introduce a 6th class - what I would call an alien failure
| mode, even if you explicitly told it not to.
|
| I think formally / provably constraining the output of LLM
| APIs will help mitigate these issues, rather than needing
| to use an embedding API / use the LLM as a featurizer and
| train another model on top of it.
| calf wrote:
| Formal proof is problematic because English has no formal
| specification. Some people are working on this, it's a
| nascent area bringing formal methods (model checking) to
| neural network models of computation. But it's an
| interesting fundamental issue that arises there, if you
| can't even specify the design intentions then how do you
| prove anything about it.
| [deleted]
| DeathArrow wrote:
| >investors have become only interested in companies building
| generative AI, relegating those working on predictive models to
| "old school" AI.
|
| If that is the definition of old school AI, I wonder how symbolic
| AI should be named.
| TuringTest wrote:
| _> If that is the definition of old school AI_
|
| It is not. Symbolic, deductive reasoning engines have the same
| claim to being old-school AI as predictive statistic models.
| snapcaster wrote:
| how about "useless with no successes of note" AI?
| [deleted]
| qorrect wrote:
| What ? We all use it everyday, it's just that as soon as the
| problem was solved with 'old AI', everyone forgot it was an
| AI problem.
| TuringTest wrote:
| I hope you've never used the power grid or parcel shipping,
| as those are heavily optimized using symbolic AI.
| peter_retief wrote:
| "So has generative AI been overhyped? Not exactly. Having
| generative models capable of delivering value is an exciting
| development. For the first time, people can interact with AI
| systems that don't just automate but create an activity of which
| only humans were previously capable."
|
| Good answer but I feel that most users/people do not understand
| the difference between generative and predictive machine learning
| and that will probably cause unpredictable failures and false
| flags. So yes it has been overhyped in my opinion
| Xelynega wrote:
| I think the issue is more with people marketing/talking about
| them as "AI". When I think AI I think of something like Skynet.
| I would assume something like Skynet would be good at chess,
| able to generate new text, and synthesize new images. I think
| when shown novel algorithms that can do those things and told
| by the people selling the algorithms that they are "AI", it's
| hard to disagree since they quack like an AI so it's easy to
| accept that these are the same "artificial intelligence"
| concept in our brains which we previously only had examples of
| from fiction.
|
| Basically I think it's overhyped by the use of the term "AI"
| and how easy we are to accept it generally. Some aspect of them
| being generative models could have been the term used to
| market/describe them, but instead a much broader term is used.
| kenjackson wrote:
| IMO, it has been underhyped. We're seeing things with LLMs that
| a decade ago I'd say was multiple decades out, if not more.
|
| We're just years into generative approaches. And I think we'll
| more combinations of methods used in the future.
|
| The goal of AI has never been to build an all knowing perfect
| system. It has also never been to replicate the way the human
| brain works. But its been to build an artificial system that
| can learn -- and AGI specifically to be able to give the
| appearance of human learning.
|
| I feel like we've turned this corner where the question now is,
| "Can we build something that knows everything that has been
| documented and can also synthesize and infer all of that data
| at a level of a very smart human". The fact that this has
| become the new bar is IMO one of the biggest tech changes in
| history. Not the biggest, but up there.
| peter_retief wrote:
| I am very excited about the possibilities of AI/ML but am
| concerned as to how it is been sold to the public.
| beepbooptheory wrote:
| Trying to imagine this stuff being even more hyped and I just
| don't think its possible. People around here are practically
| ready to sell their first born child to OpenAI/Microsoft at
| this point.
| PaulDavisThe1st wrote:
| > Can we build something that knows everything that has been
| documented and can also synthesize and infer all of that data
| at a level of a very smart human
|
| The word "know" is doing some heavy lifting there, as is
| "synthesize" and "infer".
| kenjackson wrote:
| By "know" I meant has access to. This is a very "database"
| sense of the word "know".
|
| Now "infer" and "synthesize" I meant the standard human
| definition of "synthesize" and "infer". In my interactions
| with relatively bright people, they really expect ChatGPT
| to be able to synthesize text at the level of a very sharp
| HS/college student. They don't want simple regurgitation of
| a text or a middel school analysis -- they want/expect
| ChatGPT to analyze nuance, and pull in its vast database to
| make connections to things that maybe aren't apparent at
| first glance.
|
| The bar has raised so high so quickly -- it's crazy.
| kmeisthax wrote:
| People calling neural-net classifiers "old-school" AI confused
| me. For a second I thought they were talking about the really old
| "expert systems" with everything being a pile of hard-coded
| rules.
| 01100011 wrote:
| It still feels like there's a place for these rule based
| systems(Prolog?) to at least place some constraints on the
| output of non-deterministic, generative AI. If nothing else,
| have a generative AI generate the ruleset so you have some
| explicit rules you can audit from time to time.
| theLiminator wrote:
| Yeah, i think one potential way to use blackbox ai in newer
| systems is having guardrails that are validated as safe (but
| perhaps non-optimal) and ensuring that the ai takes action
| within that sample space. Obviously this is hard problem, but
| might open the doors for policies (in self-driving cars, for
| example) to be entirely ai driven.
| ElijahLynn wrote:
| This article could be improved by starting off stating what some
| examples of Predictive AI is, as they did with Generative AI.
| seydor wrote:
| I m not sure it's overrated, but the concerns are very real.
|
| We love the model because it speaks our language as if it's "one
| of us", but this may be deceiving, and the complete lack of model
| for truth is disturbing. Making silly poems is fun but the real
| uses are in medicine and biology, fields that are so complex that
| they are probably impenetrable to the human mind. Can
| Reinforcement learning alone create a model for the truth? The
| Transformer does not seem to have one, it only works with syntax
| and referencing. How much % of truthfulness can we achieve, and
| is it good enough for scientific applications? If a blocker is
| found in the interface between the model and reality, it will be
| a huge disappointment
| IIAOPSW wrote:
| Here's an alternative to a model for truth. There is no truth,
| only power. Suppose we completely abandon logical semantics and
| instead focused on social semantics. Instead of the usual
| boolean True/False variables and logic relations, we'll have
| people valued variables and like/dislike relations. I system
| entirely for reasoning about the amount of pull and persuasion
| is present without ever circuiting down to any ground truth
| reasons. In other words, a bullshit reasoning system. Can
| ordinary truth reasoning be jerryrigged out of this system?
| seydor wrote:
| Yes, it s called empiricism
| IIAOPSW wrote:
| This was rhetorical. My point was that a system or model
| which cares about something other than the truth can, upon
| reaching a certain level of sophistication, be able to
| handle reasoning about truth. Eg, an AI that cares entirely
| about approval for what it says rather than the actuality
| of what it says could still end up reasoning about truth,
| given that truth is most heavily correlated with approval.
| I reject the premise that there has to be an a priori truth
| model under the hood.
| nico wrote:
| > model for the truth?
|
| Without sensing/experiencing the world, there is no truth.
|
| The only truth we can ever truly know, is the present moment.
|
| Even our memories of things that we "know" that happened, we
| perceive them in the now.
|
| Language doesn't have a truth. You can make up anything you
| want with language.
|
| So the only "truth" you could teach an LLM, is your own
| description of it. But these LLMs are trained on thousands or
| even million different versions of "truth". Which is the
| correct one?
| stormfather wrote:
| I think for practical purposes you could hold that text from
| wikipedia or scientific papers if true, for example. The
| issue I think OP is referring to is if a LLM can refer back
| to these axiomatically true sources to ground and justify its
| outputs like a human would.
| nico wrote:
| Well in that case, maybe the debate is: do we want that?
| Why?
| valine wrote:
| If you can trust the model is at least as accurate as
| wikipedia then it becomes a drop in replacement for every
| task you do that requires wikipedia.
|
| There are a whole range of tasks that can't be done today
| with an LLM because of the hallucination issues. You
| can't rely on the information it gives you when writing a
| research paper, for example.
| Barrin92 wrote:
| For starters because one of the first products people
| decided to use these models for is a search engine, and I
| don't think it is a stretch to argue that search engines
| should have a positive relationship, rather than
| indifference, towards facts and the truth.
| glitchc wrote:
| Our senses lie to us all the time. What we perceive may have
| strong to almost no correlation to reality. Can you see in
| the ultraviolet? No human can. Flowers look completely
| different. Same goes for sounds and smells.
| seydor wrote:
| It can be exact and self-consistent, you can teach the rules
| of mathematics . There are some things that are provably
| unprovable but thats a known fact.
| nico wrote:
| You can still express contradiction in math.
|
| The rules don't determine the interpretation.
|
| An LLM will pretty much always respect the rules of
| language, but it can use them to tell you completely fake
| stuff.
| seydor wrote:
| math is language
| visarga wrote:
| There is a paper showing you can infer when the model is
| telling the truth by finding a direction in activation space
| that satisfies logical consistency properties, such as that a
| statement and its negation have opposite truth values.
| Apparently we can detect even when the model is being
| deceitful.
|
| https://arxiv.org/abs/2212.03827
|
| Another approach - a model can learn the distribution - is
| this fact known or not in the training set, how many times
| does it appear, is the distribution unimodal (agreement) or
| multi-modal (disagreement or just high variance). Knowing
| this a model can adjust its responses accordingly, for
| example by presenting multiple possibilities or avoiding to
| hallucinate when there is no information.
| visarga wrote:
| In exact domains you can often validate the model with
| numerical simulations, or use the simulations for reinforcement
| learning or evolution. The model can learn from outcomes, not
| only from humans. In biology it is necessary to validate
| experimentally, like any other drug or procedure.
| aaroninsf wrote:
| I am not so sure,
|
| there seems to be accumulating evidence that "finding the
| optimal solutions" means (requires) building a world model.
| Whether it's consistent with ground truth probably depends on
| what you mean by ground truth.
|
| Given the hypothesis that the optimal solution for deep
| learning presented with a given training set, is to represent
| (simulate) the formal systemic relationships that generated
| that set, by "modeling" such relationships (or discovering non-
| lossy optimized simplifications),
|
| I believe an implicit corollary, that the fidelity of
| simulation is only bounded by the information in the original
| data.
|
| Prediction: a big enough network, well enough trained, is
| capable of simulating with arbitrary fidelity, an arbitrarily
| complex system, to the point that lack of fidelity hits a noise
| floor.
|
| The testable bit of interest being whether such simulations
| predict novel states and outcomes (real world behavior) well
| enough.
|
| I don't see why they shouldn't, but the X-factor would seem to
| be the resolution and comprehensiveness of our training data.
|
| I can imagine toy domains like SHRDLU which are simple enough
| that we should be able to build large models well enough
| already to "model" them and tease this sort of speculation
| experimentally.
|
| I hope (assume) this is already being done...
| JohnFen wrote:
| > there seems to be accumulating evidence that "finding the
| optimal solutions" means (requires) building a world model.
|
| Was this ever in doubt? This has been the case forever (even
| before "AI"), and I thought it was well-established. The
| fidelity of the model is the core problem. What "AI" is
| really providing is a shortcut that allows the creation of
| better models.
|
| But no model can ever be perfect, because the value of them
| is that they're an abstraction. As the old truism goes, a
| perfect map of a terrain would necessarily be
| indistinguishable from the actual terrain.
| [deleted]
| ChatGTP wrote:
| _But no model can ever be perfect, because the value of
| them is that they 're an abstraction. As the old truism
| goes, a perfect map of a terrain would necessarily be
| indistinguishable from the actual terrain._
|
| Not sure why but I find this incredibly insightful...
| phonebucket wrote:
| There is much more to generative models than building out
| language models and image models.
|
| Generative models are about characterising probability
| distributions. If you ever predict more than just the average of
| something using data, then you are doing generative modelling.
|
| The difference between generative modelling and predictive
| modelling is similar to the difference between stochastic
| modelling and deterministic modelling in the traditional applied
| mathematical sciences. Both have their place. Neither is
| overrated.
|
| Grab the best tool for the job.
| tolciho wrote:
| As stated by John McCarthy--"I invented [AI] because we had to do
| something when we were trying to get money for a summer study"
| (the Lighthill debate)--this article passes the AI sniff test, or
| "please remember us predictive AI folks when you go to dole out
| your money" as all that is solid melts into PR.
| glitchc wrote:
| I see and I hear:
|
| "Don't be dazzled by AI computer vision's creative charm!
| Classical computer vision, though less flashy, remains crucial
| for solving real-world challenges and unleashing computer
| vision's true potential."
|
| Meant for those in classical computer vision before ML ate the
| field.
| wslh wrote:
| I would add that there are logic deductive and constraint systems
| that are more classical and work in some areas. It is not about a
| single method but we should he aware that AI is a superset of
| what we see.
| EGreg wrote:
| Yes! Just like HN is anti blockchain but super pro AI. It seems
| most applications of generative AI at scale will havd a huge
| negative for society, far worse than anything blockchain could
| have brought about.
| [deleted]
| jedberg wrote:
| The real innovation will come one someone uses a Generative AI to
| make something, and then use a predictive AI to rate it's
| accuracy, making it go again until it passes the predictive AI.
|
| Basically a form of adversarial training/generation.
| arrow7000 wrote:
| Isn't this exactly how GANs work already?
| jedberg wrote:
| Yes. But from I've seen no one has applied it to the latest
| Generative AIs.
| arrow7000 wrote:
| Maybe an adversarial approach was used in training these
| models in the first place?
| sharemywin wrote:
| It was they were' trained using reinforcement learning
| with human feedback to create the critic.
| jedberg wrote:
| I hadn't thought about human feedback being an
| adversarial system, but I guess that makes sense, since
| it's basically a classifier saying "you got this wrong".
| ChikkaChiChi wrote:
| Bilateral "thinking" makes sense, and you can even feed
| generative AI back into itself for simple error correction.
|
| I believe that we'll see the most success/accuracy once you
| have generative AI compare itself to itself, monitored by a
| GAN, which then spits out it's answer while retaining some
| knowledge as to how it came to the conclusion. A tricameral
| mind.
| goldenkey wrote:
| When the generative model is autoregressive (autocomplete), it
| can easily be used as a predictor. All of the state of the art
| language models are tested against multiple choice exams and
| other types of prediction tasks. In fact, it's how they are
| trained...masking - https://www.microsoft.com/en-
| us/research/blog/mpnet-combines...
|
| For example: "Multiple-choice questions in 57 subjects
| (professional & academic)" - https://openai.com/research/gpt-4
| croes wrote:
| Being good at standardized tests isn't really a good measure.
|
| What happens with completely new questions from totally
| different subject. The generative model will produce nonsense.
| [deleted]
| k8si wrote:
| For GPT4: "Pricing is $0.03 per 1,000 "prompt" tokens (about
| 750 words) and $0.06 per 1,000 "completion" tokens (again,
| about 750 words)."
|
| Meanwhile, there are off-shelf models that you can train very
| efficiently, on relevant data, privately, and you can run these
| on your own infrastructure.
|
| Yes, GPT4 is probably great at all the benchmark tasks, but
| models have been great at all the open benchmark tasks for a
| long time. That's why they have to keep making harder tasks.
|
| Depending on what you actually want to do with LMs, GPT4 might
| lose to a BERTish model in a cost-benefit analysis--especially
| given that (in my experience), the hard part of ML is still
| getting data/QA/infrastructure aligned with whatever it is you
| want to do with the ML. (At least at larger companies, maybe
| it's different at startups.)
| all2 wrote:
| From TFA: TLDR; Don't be dazzled by generative
| AI's creative charm! Predictive AI, though less flashy, remains
| crucial for solving real-world challenges and unleashing AI's
| true potential. By merging the powers of both AI types and
| closing the prototype-to-production gap, we'll accelerate the AI
| revolution and transform our world. Keep an eye on both these AI
| stars to witness the future unfold.
| nathias wrote:
| after the era of low hanging fruits of generative AI will be over
| I'm sure there will be a return to other approaches
| jasfi wrote:
| It's not overrated. People are excited because there's so much
| room to improve, these are still early days. I'm working on a
| SaaS to help people create new things with this tech myself
| (https://inventai.xyz).
| rvz wrote:
| > People are excited because there's so much room to improve
|
| That is hype due to OpenAI's excellent marketing and it is
| clearly overrated. Microsoft essentially has acquired OpenAI
| and is using AI safety and competition excuses to close source
| everything and sell their AI snake-oil.
|
| > these are still early days.
|
| Neural networks is not an early concept and LLMs still share
| the same eternal problems as neural networks. Neither is the
| way that they have been trained on which still hasn't changed
| for a decade. Even so, that explains the lack of transparent
| reasoning and more sophistry that it generates all for more
| data, more GPUs to incinerate the planet to produce a black box
| 'AI' model that can easily get confused due to adversarial
| attacks.
| qorrect wrote:
| > Neural networks is not an early concept
|
| No , but the first MLPs from the 1960's famously couldn't
| solve the XOR problem , they threw a hidden layer in there
| and fixed it, and now we're in the 'how many layers can we
| jam in there' phase.
|
| My point being although neural networks are not new, they
| keep adding fun new things to it to create novel new
| features.
| ulrikhansen54 wrote:
| There's about ~10% point improvement left (i.e, from 80% to
| 90%) before it starts to stagnate. We've seen the same with
| predictive models benchmarked on ImageNet et. al.
| whiplash451 wrote:
| By stagnate, you mean beating humankind at the task, right?
| :)
| taylorius wrote:
| We mean beating humankind at the task, swiftly followed by
| humankind declaring _that_ task wasn 't a sign of proper
| intelligence anyway, and moving it's goalposts to a
| different field.
| ulrikhansen54 wrote:
| Ha, touche...
| dimitrios1 wrote:
| It's funny to me we look at GPT4 scoring high on all these
| tests and think it's worth anything when educators and a
| lot of us here have been lamenting the standardized tests
| since Bush made it a preeminent feature of our country's
| education system. They are not a good measure of
| intelligence. They measure how well you can take a test.
| dahdum wrote:
| > They are not a good measure of intelligence. They
| measure how well you can take a test.
|
| The tests aren't trying to measure intelligence, but
| rather whether you've learned the material.
| dimitrios1 wrote:
| Again, they are horrible at that.
| kenjackson wrote:
| Funny -- I literally had someone tell me this same thing
| this morning... but the exact same guy last week was
| arguing with me against the reduced importance of these
| same tests for college admissions. Last week he was
| arguing how critical these tests were for the college
| admissions process, but this morning the same tests are
| basically worthless.
|
| Not saying you hold the same opinions -- but I wouldn't
| be surprised if people's take on these tests is more
| about what is convenient for their psyche than any actual
| principled position.
| dimitrios1 wrote:
| In principle I agree. On one hand, we can positively
| conclude that IQ is indeed important, but at the same
| time are horrible at measuring it. That being said, there
| is a country mile difference between most of these tests
| suitability for the purposes they are being used.
| jasfi wrote:
| There's no way there's only 10% left to improve in those
| models. New versions are coming out regularly that are
| clearly improved. Midjourney v5 and GPT-4 were just released
| showing huge improvements, for example.
|
| Not only that, but the innovation around this tech is also
| just getting started. It's immediately applicable for
| business use. The classical techniques still have their uses,
| of course.
| ulrikhansen54 wrote:
| It's not that there's only 10% left to improve. It's that
| the data needed, compute requirements, and model size are
| as intensive, getting from 0 to 80 as they are getting from
| 80 to ~85 or ~90. See
| https://paperswithcode.com/sota/image-classification-on-
| imag...
| qorrect wrote:
| > Not only that, but the innovation around this tech is
| also just getting started.
|
| You mean since the 2010's ?
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