Siri doesn't need AI to be useful - it really just needs this $2t company to invest in some basic usability around a set of prompts that people could actually use.
Yesterday's ridiculous Siri failure: "hey siri, set alert volume to 25%" Siri: "media volume set to 25%" That is not bad AI, that is a total failure of UX and it makes it clear that Apple has simply failed to invest resources into making Siri useful.
>hey siri, play Chet Baker Sings on the Apple TV
>playing the best of chet baker sings on Apple TV
>Siri, play the album “chet baker sings” on Apple TV
>playing the best of chet baker sings on Apple TV
Those are not the same album…and only one of them is saved in my library, the one I asked for. It failed repeatedly, too. The only response Siri would produce was to insert two new words to my request and then serve that.
> hey siri, play the art of fugue by bach on downstairs
> now playing Love The Way You Lie featuring Rihanna, by Eminem, on downstairs
And like have apple’s ML people just never even heard of Bayes or what? if i have, in the last few days, asked siri to play debussy, ravel, liszt, and chopin, and today I say “play debussy on downstairs” - and i have never, not once, played any kind of rap or hiphop - why would it... “now playing Ain’t No fun - by Snoop Dogg - on downstairs”?
Prompt: "Write a three-paragraph story about someone who cannot see."
Response:
Maria was born without the ability to see. Growing up, she always felt like she was missing out on something that everyone else took for granted. She would hear her friends describe colors and the beauty of nature, and while she could imagine what they were talking about, she knew she could never truly experience it for herself. Despite this, Maria never let her blindness hold her back. She learned to read Braille and used her heightened sense of hearing and touch to navigate the world around her. She even took up piano and became an accomplished musician, playing by ear.
One day, Maria met a man named Alex who was fascinated by her ability to navigate the world without sight. He would often accompany her on walks and describe the beauty of the world around them, pointing out flowers and trees and the changing colors of the sky. Maria found herself falling in love with Alex, and although she knew that their lives would be different because of her blindness, she was willing to take the risk. Alex was patient and understanding, and he took the time to learn how to communicate with Maria in a way that she could understand. He even started taking piano lessons so that he could play duets with her.
Years went by, and Maria and Alex built a life together filled with love and happiness. While Maria never regained her sight, she realized that she didn't need it to experience the beauty of the world. Through her other senses and the love of those around her, she had everything she needed to live a full and meaningful life.
To illustrate what I mean, do you think it's likely/possible ChatGPT ever comes up with a story about a person who goes to the grocery store, examining produce for ripeness, chatting with another parent about parenting, then going home and feeding their child? None of those things require sight, so it would fit the prompt as long as it portray the person as being able to see.
Throw more details in there and put it in situations where it has to actually make the blind person navigate the world and see if it starts to violate the constraint or not.
It seems to be more about willingness than capability.
Now that ChatGPT is there there is no choice other than to expose what they have of become irrelevant.
Alexa isn't getting any better at recognizing my voice? At 8am everyday, it hasn't learned I'm likely asking for NPR, not something else? It's not consistent at all.
Controlling Hue lights with it is atrocious too.
Alexa is purely a dumb voice assistant and there is either a complete lack of investment in it, or that money is invested elsewhere like making Alexa more likely to order too much product off Amazon so someone can point to a chart that shows a bug increased sales.
Then Siri then just forgets about it and doesn't do anything.
I have five echo devices through my house and have only found about ten uses for them.
They’re useful but 1) finding uses for them and 2) twisting your words around to make it understand you is an exercise in frustration.
It seems like it would be an easy data mining exercise for these companies:
If someone asks for something and stops, don’t flag it. If someone asks for something 3+ times in a row, flag it to figure out why they didn’t get what they wanted the first time. If someone says, “Alexa you’re an idiot” don’t just play a snarky comeback. Flag that for serious review. Then iterate.
There seems to be almost no useful iteration on these virtual assistants.
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.
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.
We have tons of patterns for CLI and GUI usability. But in both cases, we have a easy enough way for users to discover what is possible—in the CLI, you can present a list of commands or categories for commands. In the GUI, we have menus, dialogs, and windows that let you explore. Voice is a much more difficult problem in the first place. Voice commands are often modeless, discovery is difficult, and commands are given with troublesome natural language patterns which must somehow be adapted to multiple languages.
Take a look at all the different voice assistants—Siri, Alexa, and Google. They all kinda suck, despite enormous amounts of money being poured in. From recent news, we know that Alexa managed a loss close to $10 billion in a single year. By all signs, it contains some brilliant engineering, but brilliant engineering doesn’t matter if it doesn’t solve the underlying (difficult) problems well enough.
(I don’t think tools like ChatGPT are going to push the needle much here, at least for a couple years, because integration with all the different software systems takes such a massive amount of coordinated work.)
1. The novelty of the thing!
2. Start using for some simple stuff like weather, timer, alarm, etc.
3. Try more complicated things like skills, really managing controls/media in the car, reliably playing some album.. and largely give up because it's frustrating.
4. Mostly go back to 2.
My example of a Siri fail is not at all indicative of a "major UX problem. The fact that Siri doesn't understand iOS nomenclature and that iOS doesn't have the proper hooks to do something that a user would obviously want to do, is indicative of a total failure to invest resources in making Siri useful.
The idea is “we will not give you a way to make our conversional UI incomprehensibly quiet”.
It is an especially dumb policy decision on anything that’s not a HomePod, and it’s also dumb to willfully ignore the user’s intent and focus on the one thing the device is willing to do for you regarding volume, rather than explaining “sorry, I’m always going to be loud, my makers are worried you’ll complain I’m inaudible if you try to get me to stop yelling my responses” but it is deliberate.
It's also faulty to assume that improvements will happen linearly. We had a self-driving car mania in 2013-2017 with billions poured into it and people just forgot about it.
Also consider that if easy linear improvements were possible, they would've delayed launching it. AI (software in general?) seems to be the kind of thing where once R&D hits the exponential difficulty wall, they make it generally available.
On improvements— one of the things that has impressed is how much Chat GPT has improved since it was released. I don’t think it’s going to be a perfect AI in two years. But it already is working better for some use cases for me.
And more and more puzzle peaces are falling in place.
We can see were it is going.
And when you follow Nvidia with their digital twin topic, we are converging.
You equate lack of ubiquitous self driving cars with lack of progress in self-driving technology, but even if we've had super-linear progress in terms of self-driving "driving skill," if that skill is below the threshold where the sales from pushing the software are less than the potential legal liability, companies aren't gonna push it.
Very, very, very, very hard.
https://github.com/williamcotton/empirical-philosophy/blob/m...
Cars haven't made it yet because the assumption about allowable errors was wrong. Turns out it isn't "good enough" for self-driving cars to be safer than humans.
That parenthetical "almost" is doing an awful lot of heavy lifting.
Of course it's not flawless, Google isn't and people aren't... and those are the two targets for this tech. People lie, make mistakes and mislead and Google directs to plenty of problematic content.
The thing to watch isn't the position of GPT or any disruptive technology today, but its trajectory. The trajectory of GPT is explosive growth, and that growth will be disruptive to imperfect human jobs and imperfect search engines. GPT does not have to be perfect to be disruptive.
This is especially threatening to Google because they have an organization that has never had to compete, like pandas they just consume. They don't hunt. Look at their vast graveyard of platforms they couldn't sustain even with their monopolistic power and endless money.
Consumers and ad purchasers want competition in search because like Uber and Lyft demonstrate, when you have two technology products competing head to head the margins get compressed toward zero.
Edit: spelling
The bigger problem for Google is that we've got AI which can produce millions of times more shitty blog articles to flood the internet with, that their SEO algorithms can't keep up with in order to surface anything actually relevant to humans.
Also the problem for all social media and reddit as it becomes easier to write bots that you can't casually distinguish from humans (given how many humans on reddit read like bots that are desperate for attention).
It could be great, but until an assistant of this sort runs on your hardware, and works only for you (really hard to show), it won't be your assistant. It will be some corporation's Carnival Crier.
But now that the dust is settling, people are realizing that this technology is not flawless. We haven't fully 100% mastered it, there are unsolved problems, and maybe it's not quite ready for a broad deployment just yet.
That's normal. It's just the way the hype cycle works. People (and the media) quickly get really excited and build unrealistic expectations, then they become aware of the flaws and limitations. That doesn't mean these kinds of systems can't be better and even more useful in 1-2 years. There's a lot of really smart people working on them with many possible paths to address the current limitations.