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.
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".
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.
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.
A much safer approach would be to have it produce a JSON object that is interpreted by the wrapping code. This allows you to inspect the interpreting code and provide guarantees about what can and cannot be done by users.
But neither execution model solves my main point: someone still has to write the interpreter (or the library functions if you go the dangerous route). Someone has to think ahead and guess what the user is going to try to do and provide APIs that allow that. And that work is most of the work: our existing intent recognition models are actually not bad at all at their job, the failings these voice assistants have are almost all to do with predicting the ways in which users will try to interact with the system.
The code generation and execution is trivially problematic. I don't need to try it out to know that if someone can coerce one of these bots to spit out its prompt, someone could coerce it to execute dangerous code if it's wired up to an interpreter.
As for the other part of my analysis, in order to empirically test it I would have to build a full-featured voice AI, release it to millions of users, and see if I was able to predict everything they tried to do. Given that my prediction is that even Apple can't do that well, I'm not sure why you think I would bother to try it out.
If an engineer told you that a bridge would collapse if built a certain way, you wouldn't insist on trying to build it anyway just to be sure. Most of the time in engineering you can't run a full-scale test, you have to make do with analyses. If you want to critique my analysis, I'm all ears, but "you haven't actually tested it" is not a critique.
All of my analysis is based on my usage of ChatGPT and my understanding of the underlying model. The hype is largely driven by people who don't understand how it actually works and think they're interacting with an artificial general intelligence.
It's a very impressive language model with a lot of applications, but with many fewer applications than the hype would suggest.
I'm hyped because I threw normal text (sentences) against it and it always did what I wanted and it didn't matter if it was bad English or bad German.
I didn't even try promt hacking or promt tuning because natural language already worked so good.
It's a tremendous good 'normal human' text interface.
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.
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.
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.
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)
In much the same way that Teslas are still literally killing people when they encounter vehicles parked across roadways in 2023, just like they did back in 2016, the difference between the appearance of understanding and actual understanding is a much wider chasm than it first appears.
The LLM will not "consider" anything. Even transcripts we've all seen in which LLMs seem to acknowledge errors (rather than doubling down and inventing false sources) are still just text-prediction, modeling what it would be like if someone acknowledged an error.
When a particular LLM seems to take the corrections offered in a widely-publicized transcript on board and not repeat them in future transcripts, that's because someone acted to modify the model, adding filters or weights to ensure that the text prediction path goes differently in the future.
It can reproduce the right output of 3 insert statements with a select *.
It can interpretate an SQL procedure too.
Whatever the architecture is behind this, is slightly more than just text prediction.
I don't think 'text prediction ' does this type of state management justice.
But hey let's see. Perhaps this is just an emerging feature from a tremendously huge text/language model.
To be able to code a program form a short description, combine different elements to make a solution, understanding is required.
The understanding is in the weights and pattern detection capabilities.
Sure, some part are missing, like the capacity we have to hold a few thought in our mind, correct ourselves, remember the mistakes we made, etc.
The same is true of NN that can differentiate cats and dogs, they detect patterns is a similar ways that humans do.
In the openAI api you can see the level of confidence the NN has on each words it output, it’s just not exposed on the chat interface.
I think the larger missings parts are by design, to give the system memories, agency, access to the internet, to programming would be careless at this point, but it’s happening anyway.
I feel it missing our capacity to examine our thought, to replay events in our head to reprogram itself when the results are not optimal.
Of course you want to do that with people too. Shouldn’t be too hard to bake in a self check module.
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.