5 ms·

Actually, just in three to five years, lots of "AI boxes" and those magical sparkling icons next to input fields summoning AI would be silently removed. LLMs a

by wg0 2y ago

Actually, just in three to five years, lots of "AI boxes" and those magical sparkling icons next to input fields summoning AI would be silently removed. LLMs are not accurate, they aren't subject matter experts that'll be maybe within 5% error margin. People will gradually learn and discover anf the cost of keeping a model updated and running won't drastically reduce so we'll most likely see dust settling down.

ben_w 2y ago
> LLMs are not accurate, they aren't subject matter experts that'll be maybe within 5% error margin. The Gell Mann amnesia effect suggests people will have a very hard time noticing the difference. Even if the models never improve, they're more accurate than a lot of newspaper reporting. > People will gradually learn and discover anf the cost of keeping a model updated and running won't drastically reduce so we'll most likely see dust settling down. So, you're betting on no significant cost reduction of compute hardware? Seems implausible to me.
- thesz 2y ago
  > So, you're betting on no significant cost reduction of compute hardware? Seems implausible to me. This is not about compute, but about data. https://arxiv.org/abs/2404.04125 https://arxiv.org/abs/2404.04125 "...our study reveals an exponential need for training data which implies that the key to "zero-shot" generalization capabilities under large-scale training paradigms remains to be found."
- nativeit 2y ago
  > …they’re more accurate than a lot of newspaper reporting. Is that when they’re cribbing straight out of the newspaper pages, or is this just a cynical snipe at the poor state of media that, not for nothing, tech companies have had a fair hand in kneecapping?
  - ben_w 2y ago
    The criticism of the performance of newspapers goes back well before Lovelace and Babbage: """I will add, that the man who never looks into a newspaper is better informed than he who reads them; inasmuch as he who knows nothing is nearer to truth than he whose mind is filled with falsehoods & errors. He who reads nothing will still learn the great facts, and the details are all false.""" - Thomas Jefferson (not Mark Twain), 1807, https://www.snopes.com/fact-check/mark-twain-read-newspaper-misinformed/ https://www.snopes.com/fact-check/mark-twain-read-newspaper-...
TeMPOraL 2y ago
> People will gradually learn and discover anf the cost of keeping a model updated and running won't drastically reduce so we'll most likely see dust settling down. As mentioned elsewhere, 3 to 5 years is some 3x to 5x as long as GPT-4 exists; some 2-3x as long as ChatGPT exists and LLMs suddenly graduated from being obscure research projects to general-purpose tools. Do you really believe the capability limit has already been hit? Not to mention, there's lots of money and reputation invested in searching for alternatives to current transformer architecture. Are you certain that within the next year or two, one or more of the alternatives won't pan out, bringing e.g. linear scaling in place of quadratic, without loss of capabilities?
- wg0 2y ago
  I'm pretty sure that statistical foundations of AI where a thing just been shy of 0.004 of the threshold value out of a million dimensional space can get miscategrized as something else will not deliver AGI or any useable and reliable AI for that matter other than that sequence of sequence mapping (voice to text, text to voice etc.) applications. As for money and reputation, that's a lot behind gold making too in medieval times and look where that lead too. Scientific optimism is a thinking distortion and a fallacy too.
- nativeit 2y ago
  Tool seems like a strong term for whatever ChatGPT is right now. Absurdly overhyped curiosity? Insanely overengineered autocorrect? Dystopian MadLibs? Wall Street Wank Sock? I’m not trying to downplay its potential, but I don’t know of anyone who trusts it enough for what I’d consider “tooling”.
cyanydeez 2y ago
Right now they're basically a improved search engine, but they aren't solving the hard problem of making money. Had Google become a utility and frozen it's search engine half a decade or more in the past, we would actually have something you could add AI on top of and come out with an improved product. As it stands, capitalism isn't going to fix GIGO with AI
bee_rider 2y ago
How do you define a percent error margin on the typical output of something like ChatGPT? IIRC the image generation folks have started using metrics like subjective users ratings because this stuff is really difficult to quantify objectively.
- airstrike 2y ago
  IMHO the terribly overlooked issue with generative AI is that the end users' views of the response generated by the LLM often differs greatly from the opinion of the person actually interacting with the model this is particularly evident with image generation, but I think it's true across the board. for example, you may think something I created on midjourney "looks amazing", whereas I may dislike it because it's so far from what I had in mind and was actually trying to accomplish when I was sending in my prompt
  - epolanski 2y ago
    Your last paragraph is true regardless of how the image was generated. One can find anything YOU produce to have different qualities from you.
    - airstrike 2y ago
      True, but generally what art I produce IRL is objectively terrible, whereas I can come up with some pretty nice looking images on Midjourney.... which are still terrible to me when I wanted them to look like something else, but others may find them appealing because they don't know how I've failed at my objective In other words, there are two different objectives in a "drawing": (1) portraying that which I meant to portray and (2) making it aesthetically appealing People who only see the finished product may be impressed by #2 and never consider how bad I was at #1
RodgerTheGreat 2y ago
I truly hope the reckless enthusiasm for LLMs will cool down, but it seems plausible that discretized, compressed versions of today's cutting-edge models will eventually be able to run entirely locally, even on mobile devices; there are no guarantees that they'll get better, but many promising opportunities to get the same unreliable results faster and with less power consumption. Once the models run on-device, there's less of a financial motivation to pull the plug, so we could be stuck with them in one form or another for the long haul.
- berniedurfee 2y ago
  I just want to sit down in front of my TV, put on my Bluetooth headphones and have the headphones and TV connect automatically. Then, when I’m downstairs in my office and want to listen to music on my iPhone. I want my headphones to connect to my iPhone and not my TV upstairs! I don’t need Skynet, I just need my devices to be a little less stupid. I would consider that akin to magic at this point. Let’s start there and work our way up to handing over control of our nuclear arsenal.
  - nativeit 2y ago
    The University of Washington is studying an AI application where a pair of headphones will isolate a single voice in a crowd when one simply looks at them. Amazing stuff…until you try it anywhere near your car, and then it starts playing the voice over your car stereo (presumably).
- namaria 2y ago
  I don't believe this scenario to be very likely because a lot of the 'magic' in current LLMs (emphasis on 'large') is derived from the size of the training datasets and amount of compute they can throw at training and inference.
  - valine 2y ago
    Llama 3 8B captures that 'magic' fairly well and runs on a modest gaming PC. You can even run it on an iPhone 15 if you're willing to sacrifice floating point precision. Three years from now I full expect GPT4 quality models running locally on an iPhone.
bongodongobob 2y ago
I don't think that's really what Apple is going to do with it though, it's not going to be for factual question and answer stuff. It will be used more like a personal assistant, what's on my calendar this week, who is the last person who called me etc. I think it will more likely be an LLM in the background that uses tools to query iCloud and such, ie, making Siri actually useful.
onion2k 2y ago
LLMs are not accurate, they aren't subject matter experts that'll be maybe within 5% error margin. You're asserting that the AI features will be removed in 3 to 5 years because they're not accurate enough today, but you actually need them to remain inaccurate in 3 years time for your prediction to be correct. That seems unlikely. I agree that people will start to realize the cost, but the accuracy will improve, so people might be willing to pay.
- wg0 2y ago
  Anyone claiming that accuracy of AI models WILL improve is either unaware of how they really work or is a snake oil salesman. Forget about a model that knows EVERYTHING. Let's just train a model that only is expert in not all the law of United states just one state and not even that, just understands FULLY the tax law of just one state to the extent that whatever documents you throw at it, it beats a tax consultancy firm every single time. If even that were possible, OpenAI et.el would be playing this game differently.
  - nextaccountic 2y ago
    Why does a mobile app needs to beat a highly trained professional every single time in order to be useful? Is this standard applied to any other app?
    - wg0 2y ago
      Because it's taxation. Financial well being is at stack. We're even looking at a potential jail time for tax fraud, tax evasion and what not. My app is powered by GTPChatChat, the model beating all artificially curated benchmarks. Still wanna buy?
      - onion2k 2y ago
        This is one of those "perfect is the enemy of good" situations. Sure, for things where you have a legal responsibility to get things perfectly right using an LLM as the full solution is probably a bad idea (although lots of accountants are using them to speed up processes already, they just check outputs). That isn't the case for 99% of task though. Something that's mostly accurate is good. People are happy with that, and they will buy it.
deleted 2y ago
[deleted]

by wg0 2y ago

ben_w 2y ago

> LLMs are not accurate, they aren't subject matter experts that'll be maybe within 5% error margin. The Gell Mann amnesia effect suggests people will have a very hard time noticing the difference. Even if the models never improve, they're more accurate than a lot of newspaper reporting. > People will gradually learn and discover anf the cost of keeping a model updated and running won't drastically reduce so we'll most likely see dust settling down. So, you're betting on no significant cost reduction of compute hardware? Seems implausible to me.

thesz 2y ago
> So, you're betting on no significant cost reduction of compute hardware? Seems implausible to me. This is not about compute, but about data. https://arxiv.org/abs/2404.04125 https://arxiv.org/abs/2404.04125 "...our study reveals an exponential need for training data which implies that the key to "zero-shot" generalization capabilities under large-scale training paradigms remains to be found."
nativeit 2y ago
> …they’re more accurate than a lot of newspaper reporting. Is that when they’re cribbing straight out of the newspaper pages, or is this just a cynical snipe at the poor state of media that, not for nothing, tech companies have had a fair hand in kneecapping?
- ben_w 2y ago
  The criticism of the performance of newspapers goes back well before Lovelace and Babbage: """I will add, that the man who never looks into a newspaper is better informed than he who reads them; inasmuch as he who knows nothing is nearer to truth than he whose mind is filled with falsehoods & errors. He who reads nothing will still learn the great facts, and the details are all false.""" - Thomas Jefferson (not Mark Twain), 1807, https://www.snopes.com/fact-check/mark-twain-read-newspaper-misinformed/ https://www.snopes.com/fact-check/mark-twain-read-newspaper-...

TeMPOraL 2y ago

> People will gradually learn and discover anf the cost of keeping a model updated and running won't drastically reduce so we'll most likely see dust settling down. As mentioned elsewhere, 3 to 5 years is some 3x to 5x as long as GPT-4 exists; some 2-3x as long as ChatGPT exists and LLMs suddenly graduated from being obscure research projects to general-purpose tools. Do you really believe the capability limit has already been hit? Not to mention, there's lots of money and reputation invested in searching for alternatives to current transformer architecture. Are you certain that within the next year or two, one or more of the alternatives won't pan out, bringing e.g. linear scaling in place of quadratic, without loss of capabilities?

wg0 2y ago
I'm pretty sure that statistical foundations of AI where a thing just been shy of 0.004 of the threshold value out of a million dimensional space can get miscategrized as something else will not deliver AGI or any useable and reliable AI for that matter other than that sequence of sequence mapping (voice to text, text to voice etc.) applications. As for money and reputation, that's a lot behind gold making too in medieval times and look where that lead too. Scientific optimism is a thinking distortion and a fallacy too.
nativeit 2y ago
Tool seems like a strong term for whatever ChatGPT is right now. Absurdly overhyped curiosity? Insanely overengineered autocorrect? Dystopian MadLibs? Wall Street Wank Sock? I’m not trying to downplay its potential, but I don’t know of anyone who trusts it enough for what I’d consider “tooling”.

cyanydeez 2y ago

Right now they're basically a improved search engine, but they aren't solving the hard problem of making money. Had Google become a utility and frozen it's search engine half a decade or more in the past, we would actually have something you could add AI on top of and come out with an improved product. As it stands, capitalism isn't going to fix GIGO with AI

bee_rider 2y ago

How do you define a percent error margin on the typical output of something like ChatGPT? IIRC the image generation folks have started using metrics like subjective users ratings because this stuff is really difficult to quantify objectively.

airstrike 2y ago
IMHO the terribly overlooked issue with generative AI is that the end users' views of the response generated by the LLM often differs greatly from the opinion of the person actually interacting with the model this is particularly evident with image generation, but I think it's true across the board. for example, you may think something I created on midjourney "looks amazing", whereas I may dislike it because it's so far from what I had in mind and was actually trying to accomplish when I was sending in my prompt
- epolanski 2y ago
  Your last paragraph is true regardless of how the image was generated. One can find anything YOU produce to have different qualities from you.
  - airstrike 2y ago
    True, but generally what art I produce IRL is objectively terrible, whereas I can come up with some pretty nice looking images on Midjourney.... which are still terrible to me when I wanted them to look like something else, but others may find them appealing because they don't know how I've failed at my objective In other words, there are two different objectives in a "drawing": (1) portraying that which I meant to portray and (2) making it aesthetically appealing People who only see the finished product may be impressed by #2 and never consider how bad I was at #1

RodgerTheGreat 2y ago

I truly hope the reckless enthusiasm for LLMs will cool down, but it seems plausible that discretized, compressed versions of today's cutting-edge models will eventually be able to run entirely locally, even on mobile devices; there are no guarantees that they'll get better, but many promising opportunities to get the same unreliable results faster and with less power consumption. Once the models run on-device, there's less of a financial motivation to pull the plug, so we could be stuck with them in one form or another for the long haul.

berniedurfee 2y ago
I just want to sit down in front of my TV, put on my Bluetooth headphones and have the headphones and TV connect automatically. Then, when I’m downstairs in my office and want to listen to music on my iPhone. I want my headphones to connect to my iPhone and not my TV upstairs! I don’t need Skynet, I just need my devices to be a little less stupid. I would consider that akin to magic at this point. Let’s start there and work our way up to handing over control of our nuclear arsenal.
- nativeit 2y ago
  The University of Washington is studying an AI application where a pair of headphones will isolate a single voice in a crowd when one simply looks at them. Amazing stuff…until you try it anywhere near your car, and then it starts playing the voice over your car stereo (presumably).
namaria 2y ago
I don't believe this scenario to be very likely because a lot of the 'magic' in current LLMs (emphasis on 'large') is derived from the size of the training datasets and amount of compute they can throw at training and inference.
- valine 2y ago
  Llama 3 8B captures that 'magic' fairly well and runs on a modest gaming PC. You can even run it on an iPhone 15 if you're willing to sacrifice floating point precision. Three years from now I full expect GPT4 quality models running locally on an iPhone.

bongodongobob 2y ago

I don't think that's really what Apple is going to do with it though, it's not going to be for factual question and answer stuff. It will be used more like a personal assistant, what's on my calendar this week, who is the last person who called me etc. I think it will more likely be an LLM in the background that uses tools to query iCloud and such, ie, making Siri actually useful.

onion2k 2y ago

LLMs are not accurate, they aren't subject matter experts that'll be maybe within 5% error margin. You're asserting that the AI features will be removed in 3 to 5 years because they're not accurate enough today, but you actually need them to remain inaccurate in 3 years time for your prediction to be correct. That seems unlikely. I agree that people will start to realize the cost, but the accuracy will improve, so people might be willing to pay.

wg0 2y ago
Anyone claiming that accuracy of AI models WILL improve is either unaware of how they really work or is a snake oil salesman. Forget about a model that knows EVERYTHING. Let's just train a model that only is expert in not all the law of United states just one state and not even that, just understands FULLY the tax law of just one state to the extent that whatever documents you throw at it, it beats a tax consultancy firm every single time. If even that were possible, OpenAI et.el would be playing this game differently.
- nextaccountic 2y ago
  Why does a mobile app needs to beat a highly trained professional every single time in order to be useful? Is this standard applied to any other app?
  - wg0 2y ago
    Because it's taxation. Financial well being is at stack. We're even looking at a potential jail time for tax fraud, tax evasion and what not. My app is powered by GTPChatChat, the model beating all artificially curated benchmarks. Still wanna buy?
    - onion2k 2y ago
      This is one of those "perfect is the enemy of good" situations. Sure, for things where you have a legal responsibility to get things perfectly right using an LLM as the full solution is probably a bad idea (although lots of accountants are using them to speed up processes already, they just check outputs). That isn't the case for 99% of task though. Something that's mostly accurate is good. People are happy with that, and they will buy it.

deleted 2y ago

[deleted]