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Have you tried chatgpt? It's tremendously coherent. The chance that chatgpt just hears your example and than does the right thing is nearly a no brainer. It

Have you tried chatgpt? It's tremendously coherent. The chance that chatgpt just hears your example and than does the right thing is nearly a no brainer. It already understood much more complicated prompts from my tests with ease. Siri and other agents struggle tremendously and the implication after such a long time has to be a reflection of either a tremendous Missmanagement or that those classical approaches are just too hard. Or that the chatgpt people are genius. But that would be even worse: it would show how much impact chatgpt and co will have sooner than later.

klodolph 4y ago
Coherent is nice, but correct would be better.
- nerdponx 4y ago
  It's mostly correct most of the time. That's pretty good.
  - more_corn 4y ago
    It’s about as trustworthy as the average person. You’d want to verify any fact it fed you. Of course you want to do that with people too. Shouldn’t be too hard to bake in a self check module.
- Hyption 4y ago
  It's correct often enough already to be useful. And it doesn't need to be more correct to disrupt industries already it just needs to be more correct than humans. The most crazy thing is that it's already so good and coherent that it makes totally sense to train a ml model like chatgpt instead of training humans! Because it scales. We never had this. And.i already mentor a few junior people I don't mind training an ai instead.
  - SamoyedFurFluff 4y ago
    I struggle to believe we can train a LLM the same was as a junior eng, mostly in that I’ve tried to keep a LLM as a journal of sorts and it forgets things way more than a junior does.
    - Hyption 4y ago
      We are not limited to LLM. And just an hour ago I asked chatgpt how to do something specific with docusaurus (Facebook static page generator) and it just told me the answer. You know how often I explain things in my current position? How slow some developers are? How often they forget things I told and explained them? How often they still get things wrong? It's a slow process and doesn't scale very well at all. If the table turns and we all teach one system instead ooohh boy. It will make experts faster and better and potentially removes a certain amount of people in every industry faster than we can imagine. You know the people who are adding some value but not that really but it's still better to have them than not having them? Cloud probably got rid of plenty of basic sysadmins. These new systems break through tasks were no one had an idea how we will break through.
  - pwinnski 4y ago
    This is the same dangerous fallacy of thinking that causes "self-driving" cars to kill people. If it is "correct enough" to lull people into a false sense of security, the inevitable failures will be worse than if it people remained on guard. The particular way in which these LLMs fabricate information makes them incorrect enough to be dangerous, not "correct enough to be useful." People keep mistaking text completion for intelligence and understanding.
    - alfor 4y ago
      Humans are not perfect either at driving, programming or writing. At some point it is a question of rate of error. The ML model will keep improving as the net get bigger and the dataset larger. But I agree they are good at having a very good surface understanding while having little of dept at the moment. Ex: a human making a mistake of visual interpretation while driving will examine in his mind the error he made and thing of the consequences in a more dangerous situation. The NN will not bother (at the moment)
lolinder 4y ago
> The chance that chatgpt just hears your example and than does the right thing is nearly a no brainer. ChatGPT can't do anything. Someone has to, somehow, wire it up to actual physical or digital controls. Intent recognition and slot filling still have to happen somewhere, and that means defining discrete intents and slots. GPT may be better at mapping from speech to intents, but it can't magically interface with APIs that haven't been defined. EDIT: To elaborate a bit, the problem in OP's story isn't caused by a bad language model, it's caused by no one at Apple thinking to define different "alert volume" and "media volume" intents. Current language models are plenty good enough to recognize the distinction, so simply adding ChatGPT won't be enough to make any feature work unless someone at Apple predicts the need and writes the interface.
- hgsgm 4y ago
  If I tell my computer to "froblate" it says "command not found" not "command shutting down". If I tell it to "shtudown", it said "did you mean 'shutdown'? There is an AI failure in distinguish "mispronounced command I know" from "command I don't know".
  - lolinder 4y ago
    I'm not convinced that ChatGPT would do better than existing models in that regard. Using it for intent recognition is already a bit of a square peg round hole situation. Basic intent recognition models are trained to produce a single neuron per intent as the output, which makes it pretty easy to use the activation levels of the output to decide whether to perform an action, confirm an action, or ask for clarification. You just need to check if the certainty is below a certain threshold. With ChatGPT you'd have to encode the intents as text of some kind (JSON?) and hope that it doesn't just hallucinate an intent that your APIs don't have when it's faced with ambiguous input. You could probably have hallucinated intents map to a decent-sounding error message, but that feels more brittle to me than the existing approaches.
- Hyption 4y ago
  It can interface with apis it only read about. Ask it to write an SQL query and it will. Give it upfront context and it will be able to produce API commands. The voice recognition can trigger chatgpt API 'create API command for the following text's. The practical problem is cost and hardware. But this is closer than we ever were.

klodolph 4y ago

Coherent is nice, but correct would be better.

nerdponx 4y ago
It's mostly correct most of the time. That's pretty good.
- more_corn 4y ago
  It’s about as trustworthy as the average person. You’d want to verify any fact it fed you. Of course you want to do that with people too. Shouldn’t be too hard to bake in a self check module.
Hyption 4y ago
It's correct often enough already to be useful. And it doesn't need to be more correct to disrupt industries already it just needs to be more correct than humans. The most crazy thing is that it's already so good and coherent that it makes totally sense to train a ml model like chatgpt instead of training humans! Because it scales. We never had this. And.i already mentor a few junior people I don't mind training an ai instead.
- SamoyedFurFluff 4y ago
  I struggle to believe we can train a LLM the same was as a junior eng, mostly in that I’ve tried to keep a LLM as a journal of sorts and it forgets things way more than a junior does.
  - Hyption 4y ago
    We are not limited to LLM. And just an hour ago I asked chatgpt how to do something specific with docusaurus (Facebook static page generator) and it just told me the answer. You know how often I explain things in my current position? How slow some developers are? How often they forget things I told and explained them? How often they still get things wrong? It's a slow process and doesn't scale very well at all. If the table turns and we all teach one system instead ooohh boy. It will make experts faster and better and potentially removes a certain amount of people in every industry faster than we can imagine. You know the people who are adding some value but not that really but it's still better to have them than not having them? Cloud probably got rid of plenty of basic sysadmins. These new systems break through tasks were no one had an idea how we will break through.
- pwinnski 4y ago
  This is the same dangerous fallacy of thinking that causes "self-driving" cars to kill people. If it is "correct enough" to lull people into a false sense of security, the inevitable failures will be worse than if it people remained on guard. The particular way in which these LLMs fabricate information makes them incorrect enough to be dangerous, not "correct enough to be useful." People keep mistaking text completion for intelligence and understanding.
  - alfor 4y ago
    Humans are not perfect either at driving, programming or writing. At some point it is a question of rate of error. The ML model will keep improving as the net get bigger and the dataset larger. But I agree they are good at having a very good surface understanding while having little of dept at the moment. Ex: a human making a mistake of visual interpretation while driving will examine in his mind the error he made and thing of the consequences in a more dangerous situation. The NN will not bother (at the moment)

lolinder 4y ago

> The chance that chatgpt just hears your example and than does the right thing is nearly a no brainer. ChatGPT can't do anything. Someone has to, somehow, wire it up to actual physical or digital controls. Intent recognition and slot filling still have to happen somewhere, and that means defining discrete intents and slots. GPT may be better at mapping from speech to intents, but it can't magically interface with APIs that haven't been defined. EDIT: To elaborate a bit, the problem in OP's story isn't caused by a bad language model, it's caused by no one at Apple thinking to define different "alert volume" and "media volume" intents. Current language models are plenty good enough to recognize the distinction, so simply adding ChatGPT won't be enough to make any feature work unless someone at Apple predicts the need and writes the interface.

hgsgm 4y ago
If I tell my computer to "froblate" it says "command not found" not "command shutting down". If I tell it to "shtudown", it said "did you mean 'shutdown'? There is an AI failure in distinguish "mispronounced command I know" from "command I don't know".
- lolinder 4y ago
  I'm not convinced that ChatGPT would do better than existing models in that regard. Using it for intent recognition is already a bit of a square peg round hole situation. Basic intent recognition models are trained to produce a single neuron per intent as the output, which makes it pretty easy to use the activation levels of the output to decide whether to perform an action, confirm an action, or ask for clarification. You just need to check if the certainty is below a certain threshold. With ChatGPT you'd have to encode the intents as text of some kind (JSON?) and hope that it doesn't just hallucinate an intent that your APIs don't have when it's faced with ambiguous input. You could probably have hallucinated intents map to a decent-sounding error message, but that feels more brittle to me than the existing approaches.
Hyption 4y ago
It can interface with apis it only read about. Ask it to write an SQL query and it will. Give it upfront context and it will be able to produce API commands. The voice recognition can trigger chatgpt API 'create API command for the following text's. The practical problem is cost and hardware. But this is closer than we ever were.