Apple's AI isn't a letdown. AI is the letdown
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The first is that LLMs are bar none the absolute best natural language processing and producing systems we’ve ever made. They are absolutely fantastic at taking unstructured user inputs and producing natural-looking (if slightly stilted) output. The problem is that they’re not nearly as good at almost anything else we’ve ever needed a computer to do as other systems we’ve built to do those things. We invented a linguist and mistook it for an engineer.
The second is that there’s a maxim in media studies which is almost universally applicable, which is that the first use of a new media is to recapitulate the old. The first TV was radio shows, the first websites looked like print (I work in synthetic biology, and we’re in the “recapitulating industrial chemistry” phase). It’s only once people become familiar with the new medium (and, really, when you have “natives” to that medium) that we really become aware of what the new medium can do and start creating new things. It strikes me we’re in that recapitulating phase with the LLMs - I don’t think we actually know what these things are good for, so we’re just putting them everywhere and redoing stuff we already know how to do with them, and the results are pretty lackluster. It’s obvious there’s a “there” there with LLMs (in a way there wasn’t with, say, Web 3.0, or “the metaverse,” or some of the other weird fads recently), but we don’t really know how to actually wield these tools yet, and I can’t imagine the appropriate use of them will be chatbots when we do figure it out.
They may not be good at much else.
I'm not asking the model to translate from one language to another - I'm asking it to explain to me why a certain word combination means something specific.
it can also solve/explain a lot of things that aren't language. bag of tricks.
1. *"съименник" (suimennik)* – Es el término más cercano y significa literalmente "persona con el mismo nombre". 2. *"човек със същото име" (chovek sŭs sŭshtoto ime)* – Se traduce como "persona con el mismo nombre". 3. *"тезка" (tezka)* – Una forma coloquial más rara, influenciada por el ruso.
En el uso cotidiano, los búlgaros suelen decir algo como *"Имаме едно и също име"* (Imame edno i sashto ime), que significa *"Tenemos el mismo nombre"*.
Here the AI says there is no translation, not only there is one, I use it frequently: Адаш https://rechnik.chitanka.info/w/%D0%B0%D0%B4%D0%B0%D1%88
If the whole thing turns out to be a really nifty commodity component in other people's pipelines, the investors won't get a return on any kind of reasonable timetable. So OpenAI keeps pushing the AGI line even as it falls apart.
I work in medical insurance billing and there are hundreds of thousands (minimum) jobs that could be made obsolete on the payer and clinic side by LLMs. The translation between PDF of a payer’s rates and billing rules => standardized 837 or API request to a clearinghouse is…not much. And then on the other side, Claude Code could build you an adjudication engine in a few quarters.
The incentive structures to change healthcare in that way will fight back for a decade, but there are a _lot_ of jobs at stake.
Then you think about sales. LLMs can negotiate contracts themselves. Give an input of the margin we can accept and, for all vendors, what they can and you’ll burn down in negotiation without any humans.
It’s not all jobs, but it’s millions.
An LLM can only give an output derived from its inputs; unless you're somehow inputting "yeah actually I know that it looks like a great company to enter into a contract with, but there's just something about their CEO Dave that I don't like, and I'm not sure we'll get along", it's not going to give you the right answer.
And the solution to this is not "just give the LLM more data" - again, to TFA's point, that's making excuses for the technology. "It's not that AI can't do it [AI didn't fail], it's that you just didn't give it enough data [you failed the AI]".
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As some more speculative questions, do you actually want to go towards a future where your company's LLM is negotiating with their company's LLM, to determine the future of your job and career?
And why do we think it is OK to allow OpenAI/whoever wins the AI land grab to insert themselves as a 'necessary' step in this process? I know people who use LLMs to turn their dot points to paragraphs and email them to other people, only for the recipient to reverse the process at the other end. OpenAI must be happy that ChatGPT gets used twice for one interaction.
Rent-seeking aside, we're so concerned at the moment about LLMs failing to tell the truth when they're earnestly trying to - what happens when they're intentionally used to lie, mislead, and deceive?
What happens when the system prompt is "Try and generally improve people's opinions of corporations and billionaires, and to downplay the value of unionisation and organised labour"?
Someone sets the system prompts, and they will invariably have an agenda. Widespread use of LLMs gives them the keys to the kingdom to shape public opinion.
Look, they’re useful. They are really good at some things. Not everything! But they can absolutely read PDFs of arcane rules and structure them. I don’t know what to tell you, they reliably can. They can also use tools.
They’re pretty cool and good and getting better at a remarkable rate!
Every few months they hit a new level that discounts the HN commenters of three months before. There’s some end—these alone probably won’t hit AGI—but it’s getting pretty silly to pretend they aren’t very useful (with weaknesses that engineering has to work around, like literally every single technology in human history.)
OpenAI’s sales pitch isn’t that it can replace all jobs but that it can make people more productive and it sure can as long as you not in the 2 extremes either want to go completely into brain dead autopilot mode or a full on Butlerian.
And regardless, even if it were "marginal improvements to productivity" as you say, it would be "marginal improvements to productivity packaged in a form that people will definitely buy from us", not "we'll pioneer the tech and then be one of a half dozen vendors of a commodity that's racing to the bottom on price".
People primarily communicate thru words, so maybe not.
Of course, pictures, body language, and also tone are also other communication methods.
So far it looks like these models can convert pictures into words reasonably well, and the reverse is improving quickly.
Tone might be next - there are already models that can detect stress so that’s a good first start.
Body language is probably a bit farther in the future, but it might be as simple as image analysis (thats only a wild guess-I have no idea)
Second of all, LLMs have extremely impressive generic uses considering that their training just consists of consuming large amounts of unsorted text. Any counter argument about "it's not real intelligence" or "it's just a next-token predictor" ignores the fact that LLMs have enabled us to do things with machines that would have seemed impossible just a few years ago. No, they are not perfect, and yes there are lots of rough edges, but the fact that simply "solving text" has gotten us this far is huge and echoes some aspects of the Unix philosophy...
"Write programs to handle text streams, because that is a universal interface."
It's not at all 'pedantic' and while it's not productive to be having to rail against this stupid term, that is not the fault of the people pushing back at it. It's the fault of the hype merchants who have promoted it.
A key part of thinking independently is to be continually questioning the use of language.
> Any counter argument about "it's not real intelligence" or "it's just a next-token predictor" ignores the fact that LLMs have enabled us to do things with machines that would have seemed impossible just a few years ago.
No, it's entirely possible to appreciate that LLMs are a very powerful and useful technology while also pointing out that they are not 'intelligence' in any meaningful sense of the word and that labeling them 'artificial intelligence' is unhelpful to users and, ultimately, to the industry.
I think you are misstating the problem here.
All of the things you name are still AI.
None of the things you name are, or have ever been, AI.
The problem is that there is AI, the computer science subfield of artificial intelligence, which includes things like expert systems, NPCs in games, and LLMs, and then there is AI, the "true" artificial intelligence, brought to us exclusively by science fiction, which includes things (or people!) like Commander Data, Skynet, Durandal, and HAL 9000.
The general public doesn't understand this distinction in a deep way—even those who recognize that things like Skynet are fiction get confused when they see an LLM apparently able to carry on a coherent conversation with a human—and too many of us, who came into this with a basic understanding of the distinction and who should know better, have bought the hype (and in some cases outright lies) of companies like OpenAI wholesale.
These facts (among others) have combined to allow the various AI grifters to continue operating without being called out on their bullshit.
And we are still just barely understanding the potential of multimodal transformers. Wait till we get to metamultimodal transformers, where the modalities themselves are assembled on the fly to best meet some goal. It's already fascinating scrolling through latent space [0] in diffusion models, now imagine scrolling through "modality space", with some arbitrary concept or message as a fixed point, being able to explore different novel expressions of the same idea, and sample at different points along the path between imagery and sound and text and whatever other useful modalities we discover. Acid trip as a service.
[0] https://keras.io/examples/generative/random_walks_with_stabl...
So, besides the complaints about accuracy, hallucinations (you said "acid trip") are dissed much more than would have been necessary.
This necessitates some kind of shared context, as language cannot be effectively compressed unless someone can decompress it at the other end by reintroducing the context that was removed from the communication.
For example, if I say, "this salad is good", you already know what "this", "salad", "is" and "good" mean. You also know how the order of the words affects their meaning, since they aren't strictly commutative. Each one of these utterances serves to transform its prior until you're left with sort of a highly-compressed four-word "key", which when processed by you, expands back into this high-dimensional concept of a good salad, and all the associations that come with it. If I had to explain any particular word to you, it might involve quite a long conversation.
As a side note, those words themselves, and their constituent phonemes/graphemes, also represent attractors within language/utterance space, where they each guide some high-entropy output into a lower-entropy, more predictable representation of something. This kind of hints at just why next-token prediction and other basic techniques work so well for creating highly generalized models. Each token is a transformation of the computed transformations of all prior tokens, and while some concepts are fixed points, meaning that, assuming a basic shared context, "I want to go fishing" and "Fishing is what I'd like to go do" both result in the same effective meaning, despite being a different series of graphemes. These fixed points under this transformation basically represent said shared context.
So the way I see it: Interfaces often (always?) exhibit attractor dynamics, taking an input or hidden state with a relatively large amount of entropy, and reducing the entropy such that its output is predictable and can be used to synchronize other systems.
Understanding itself is an interface, you can imagine understanding to be the effective decontextualization and recontextualization of a concept, in other words, understanding is generalization. Generalization involves attractor dynamics, as that's all decontextualization and recontextualization are: structures which can be used to compress or decompress information by learning its associations, and the structure which guides these associations can be seen as an attractor.
Thus, understanding is an attractor which takes complex, noisy input and produces a stable, predictable hidden state or output. And successful, stable interfaces represent fixed points between two or more complex systems where the language is the transformation step that "aligns" some subset of attractors within each system such that states from system A can be received by system B with minimal loss of information. In order for two systems to be able to communicate effectively, they must have some structural similarities in their attractor landscapes, which represent the same relational context.
Transformers effectively learn how to be a "superattractor" of sorts, highly adept at creating interfaces on the fly by guiding incoming information through a series of transformations that quite literally are designed to maximize predictability/probability of the output. It's like a skeleton key, a missing map, that allows two systems to exchange information without necessarily having developed compatible interfaces, that is, some similar subset of their attractor space. For example, if I know English and my speaking partner only knows Chinese, the transformer represents a shared context that can be used to link our two interfaces.
It goes further too, because transformers are stellar for translating between concepts even if there is only one other party. They can take any two concepts and, depending on how well they were trained, create an ad hoc interface between them which allows information to become "unstuck" and be freely guided from one concept to another, unlocking a deeper understanding/generalization which can then be used elsewhere. We see this happening now with proteins, and as I liked how you put it, "protein language". You're absolutely right that language is just relationships and meanings, but hopefully it's clear now that all understanding boils down to the identification, extraction and transformation of relational structures, which can be thought of as "attractor spaces", guiding unpredictable input into predictable hidden state / output.
It's probably the same in AI — the world needs AI to be chat (or photos, or movies, or search, or an autopilot, or a service provider ...) before it can grow meaningfully beyond. Once people understand neural networks, we can broadly advance to new forms of mass-application machine learning. I am hopeful that that will be the next big leap. If McLuhan is correct, that next big leap will be something that is operable like machine learning, but essentially different.
Here's Marc Andreessen applying it to AI and search on Lex Fridman's podcast: https://youtu.be/-hxeDjAxvJ8?t=160
When it comes to AI, we're trying to replace existing technology with it. We want it to drive a car, write an email, fix a bug etc. That premise is what gives it economic value, since we have a bunch of cars/emails/bugs that need driving/writing/fixing.
Sure, it's interesting to think about other things it could potentially achieve when we think out of the box and find use cases that fit it more, but the "old things" we need to do won't magically go away. So I think we should be careful about such overgeneralizations, especially when they're covertly used to hype the technology and maintain investments.
I think the idea is a bit different than what you describe. New media contains in itself the essence of old media, but it does not necessarily supersede it. For example, we have theater and film.
This “rule” of media doesn’t help us predict how or whether AI will evolve, so it is difficult to relate it to hyping. It is an exclusionary heuristic for future predictions — it helps us exclude unlikely ones. But doesn’t help us come up with any.
I personally am hopeful that AI will evolve into something else that has more essence to it than mere function. But that’s just hope, which is rather less promising than hype.
There is a "there" with those other fads too. VRChat is a successful "metaverse" and Mastodon is a successful decentralized "web3" social media network. The reason these concepts are failures is because these small grains of success are suddenly expanded in scope to include a bunch of dumb ideas while the expectations are raised to astronomical levels.
That in turn causes investors throw stupid amounts of money at these concepts, which attracts all the grifters of the tech world. It smothers nacant new tech in the crib as it is suddenly assigned a valuation it can never realize while the grifters soak up all the investments that could've gone to competent startups.
No, that's not what "web3" means. Web3 is all about the blockchain (or you can call it "distributed ledger technology" if you want to distance it from cryptocurrency scams).
There's nothing blockchain-y about Mastodon or the ActivityPub protocol.
All that matters is that the moneyed class is able to choose a slightly related definition of it that generates speculative value.
Cryptocurrencies themselves could also serve as a example of a grain of success for web3.
People are missing the point. LLMs aren’t just fancy word parrots. They actually grasp something about how the world works. Sure, they’re still kind of stupid. Imagine a barely functional intern who somehow knows everything but can’t be trusted to file a document without accidentally launching a rocket.
Where I really disagree with the crowd is the whole “they have zero intelligence” take. Come on. These things are obviously smarter than some humans. I’m not saying they’re Einstein, but they could absolutely wipe the floor with someone who has Down syndrome in nearly every cognitive task. Memory, logic, problem-solving — you name it. And we don’t call people with developmental disorders letdowns, so why are we slapping that label on something that’s objectively outperforming them?
The issue is they got famous too quickly. Everyone wanted them to be Jarvis, but they’re more like a very weird guy on Reddit with a genius streak and a head injury. That doesn’t mean they’re useless. It just means we’re early. They’ve already cleared the low bar of human intelligence in more ways than people want to admit.
That's not entirely true, either. Because LLMs _can_ write code, sometimes even quite well. The problem isn't that they can't code, the problem is that they aren't reliable.
Something that can code well 80% of time is as useful as something that can't code at all, because you'd need to review everything it writes to catch that 20%. And any programmer will know that reviewing code is just as hard as writing it in the first place. (Well, that's unless you just blindly trust whatever it writes. I think kids these days call that "vibe coding"....)
The fantastically intoxicating valuations of many current stocks is due to breathing the fumes of LLMs as artificial intelligence.
TFA puts it this way:
"The real reason companies are doing this is because Wall Street wants them to. Investors have been salivating for an Apple “super cycle” — a tech upgrade so enticing that consumers will rush to get their hands on the new model. "
> The first ... natural language querying.
Natural-language inputs are structured: they are language. But in any case, we must not minimise the significant effort to collect [0] and label trustworthy data for training. Given untrustworthy, absurd, and/or outright ignorant and wrong training data, an LLM would spew nonsense. If we train an LLM on tribalistic fictions, Reddit codswallop, or politicians' partisan ravings, what do you think the result of any rational natural-language query would be? (Rhetorical question.)
In short, building and labelling the corpus of knowledge is the essential technical advancement. We already have been doing natural-language processing with computers for a long time.
> The second ... new media recaptiulates the old.
LLMs are a new application. There are some effective uses of the new application. But there are many unsuitable applications, particularly where correctness is critical. (TFA mentions this.) There are illegal uses too.
TFA itself says,
"What problems is it solving? Well, so far that’s not clear! Are customers demanding it? LOL, no."
How to monetise these and future implementations will determine whether LLMs devour anything serviceable the way Radio ate Theatre, the way TV ate Theatre, Radio and Print Journalism, the way the Internet ate TV, Radio, the Music Industry, and Print Journalism, and the way Social Media ate social discourse.
<edit: Note that the above devourings were mostly related to funding via advertising.>
If LLMs devour and replace the Village Idiot, we will have optimised and scaled the worst of humanity.
= = =
[0] _ major legal concerns to be unresolved
[1] _ https://en.wikipedia.org/wiki/Network_(1976_film) , https://www.npr.org/2020/09/29/917747123/you-literally-cant-...
To me the almost unsolvable problem Apple has is wanting to do as much as possible on device, but also have been historically very stingy with RAM (on iOS and Mac devices - iOS more understandably, given it doesn't really need huge amounts of RAM until LLMs came along). This gives them a real real problem, having to use very small models which hallucinate a lot more than giant cloud hosted ones.
Even if they did manage to get 16GB of RAM on their new iPhones that is still only going to be able to fit a 7b param model at a push (leaving 8GB for 'system' use).
In my experience even the best open source 7B local models are close to unusable. They'd have been mindblowning a few years ago but when you are used to "full size" cutting edge models it feels like an enormous downgrade. And I assume this to always be the case; while small models are always improving, so are the full size ones, so there will always be a big delta between them, and people are already used to the large ones.
So I think Apple probably needs to shift to using cloud services more like their Private Compute idea, but they have an issue there in so much that they have 1b+ users and it is not trivial at all to be able to handle that level of cloud usage for core iOS/Mac features (I suspect this is why virtually nothing uses Private Compute at the moment). Even if each iOS user only did 10 "cloud LLM" requests a day, that's over 10b/requests a day (10x the scale that OpenAI currently handles). And in reality it'd ideally be orders of magnitude more than that given how many possible integration options they are for mobile devices alone.
You assume hundreds of millions of users could identify serious mistakes when they see them.
But humans have demonstrated repeatedly that they can't.
I don't think it can ever be understated how dangerous this is.
> I think Apple probably needs to shift to using cloud services more
You ignore lessons from the the recent spat between Apple and the UK.
same is true for humans whether they're interacting with LLMs or other humans. so I'm inclined to take statements like
> I don't think it can ever be understated how dangerous this is.
as hysteria
> as hysteria
It's not hysteria. Humans haven been trained, for better or worse, that computers are effectively infallible. Computed answers are going from near-100% correct to not-even-80% correct in an extremely short time.
It's not hysteria to say this is a dangerous combination.
a TI-82, sure. I doubt many public opinion surveys would reveal that most people think computers generally or LLMs specifically are infallible. If you are aware of data suggesting otherwise I'd be interested to see it.
I remain much more concerned about the damage done by humans unquestioningly believing the things other humans say.
Humans are still responsible for the content that is ultimately feed and drives a model. If anything, LLMs are another opportunity to amplify a particular viewpoint that would fall under your concerns.
far more often than being confidently wrong, the human will say, i’m not positive on the answer, let me double check and get back to you.
absolutely fair. I expect that LLMs will continue to get better at doing the same.
True, but it’s been years now since the debut of the chat-interface-AI to the general public and we have yet to figure out another interface that would work for generative AI for the general public. I’d say the only other example is Adobe and what they are doing with generative AI in their photo editing tools, but thats a far cry from a “general public” type thing. You have all the bumbling nonsense coming out of Microsoft and Google trying to shove AI into whatever tools they are selling while still getting 0 adoption. The copilot and Gemini corporate sales teams have been both “restructured“ this year because they managed to sign up so many clients in 2023/2024 and all those clients refused to renew.
When it comes to the general public, we have yet to find a better application of AI than a chat interface. Even outside of the general public, I oversee few teams that are building “agentic AI tools/workflows” and the amount of trouble they have to go through to make something slightly coherent is insane. I still believe that the right team with the right architecture and design can probably achieve things that are incredible with LLMs, but it’s not as easy as the term “AI” makes it sound.
for example Gemini forced itself on me on my SMS app, so I thought I ask it to search something simple inside the messages, and it just started generating some random text about searching and saying that it doesn't have access to the messages themselves.
When I use ChatGPT, of course I know they don't have access to my SMSs (it would be weird).
I can give ChatGPT the exact context I want to, and I know it will work with it as long as the context is not too big.
For example, one of my main use cases for ChatGPT is crafting SQL queries. I never liked the SQL language. I understand how powerful it is, but I was never good at it. I find it to be structured in such an awkward and confusing way. I’m not smart enough to express what I’m looking for in SQL format. But ChatGPT is perfect for that. I give it a brief explanation of tables schema then ask it for a query and it spits out a SQL query for me. 70% of the time it looks good, 30% I’m skeptical it solves what I want.
I have tried many “Database/postgres AI services” that promise much better interface. They ask you for creds to your database, then inject all context/schema into the LLM so you just ask questions directly. They have all been too underwhelming. I ask a question about joining and reducing tables A and B, and it insists to involves tables C and D. I have to say “don’t worry about C and D”. So it involves E and F. I say “fuck, only consider A and B” and it generated an invalid query that assumes A and B have some columns from C, D, E or F. I say “please don’t consider any schema other that A and B schema. I’m looking for a result of joining A and B, reduces by columns X/Y giving the average of those” then feel stupid having to argue with a machine about what I’m looking for.
It used to have no problem with simple phrases like “I’m walking home from the market” but now I’ll just as often have it transcribe “I’m walking home from the Mark Betts”, assuming Mark Betts was a name in my contacts, despite that sentence making much less structural sense
It’s bad enough that I’m using the feature much less because I have to spend as much time copyediting transcribed text before sending as I would if I just typed it out by hand. I can turn off stuff like the frequently confused notification summaries, but the keyboard has no such control as far as I know
Everything has limits - the only differences is where they are, and therefore how often you meet them.
If you are working with AI, using local models shows you where the problems can (and will) happen, which helps you write more robust code because you will be aware of these limits!
It's like how you write more efficient code if you have to use a resource constrained system.
Cargo culting of clueless managers which make long term usability of products much worse, everything requiring some stupid cloud, basic features locked up and you will be analyzed, this is just another shit on top.
You have any massive hype, you normally get this shit. Once big wave dies down with unavoidable sad moments for some, and tech progresses further (as it will) real added value for everybody may show up.
As for work - in my corporation, despite having pure dev senior role, coding is 10-20% of the work, and its part I can handle just fine on my own, I don't need babysitting from almost-correct statistical models. In fact I learn and keep fresh much better when still doing it on my own. You don't become or stay senior when solutions are handed down to you. Same reason I use git in command line and not clicking around. For code sweatshops I can imagine much more added value, but not here in this massive banking corporation. Politics, relationships, knowing processes and their quirks and limitations is what progresses stuff and gets it done. AI won't help here, if anybody thinks differently they have f_cking no idea what I talk about. In 10 years it may be different, lets open the discussion again then.
They literally have data centers worth of devices running inferences anonymously
So does OG Siri or Alexa, Letdown does not mean completely useless, it just means what the users are getting is far less than what they were promised, not that they get nothing.
In this context AI will be a letdown regardless of improvements in offline or even cloud models. It is not only because of additional complexity of offline model Apple will not deliver, their product vision just does not look achievable in the current state of tech in LLMs [1].
Apple itself while more grounded compared to peers who regularly talk about building AGI, or God etc, has been still showing public concept demos akin to what gaming studios or early stage founders do. Reality usually fall short when you run ahead of product development in marketing, it will be no different for Apple.
This is a golden rule of brand and product development - never show what have not built fully to the public if you want them to trust your brand.
To be clear, it is not bad for the company per se to do this, top tier AAA gaming studios do just fine as businesses despite letting down fans game after game with oversell and under deliver, but suffer as brands nobody will have good thing to say about Blizzard or EA or any other major studio.
Apple monetizes its brand very well by being able to price their products at premium compared to peers that will be at risk if users feel letdown.
[1] Perhaps new innovations will make radical improvements even in the near future, regardless that will not change Apple can ship in 2025 or even 2026 so still a letdown for users being promised things for last 2 years already.
Idk about that, wouldn't pay for it.
Nope
*except for backup.
oops.
How certain are you that the US has not issued a secret order, eg, via the FISA court, to turn off data protection for someone's account?
And if that is part of your risk profile, you should not be backing up your stuff in the cloud using google or apple PERIOD.
*We are not allowed to tell you this part. In fact, this line and the star were edited out prior to publication.
You’re welcome.
The human brain consumes around 20 watts, while of course there are substantial differences with implementation I think it's reasonable to draw a line and say that eventually we should expect models to hit similar levels of performance per watt. We see some evidence now that small models can achieve high levels of performance with better training techniques, and it's perfectly conceivable that acceptable levels of performance for general use will eventually be baked into models small enough to run on end hardware. And at the speed of development here, "eventually" could mean 1-2 years.
Instead of loudly jumping on this depressing bandwagon, Apple should have quietly improved Siri and then announced it when it was WORKING.
Not a big fan of Apple's monopoly, but I like their privacy on-device handling. I don't care for Apple but on-device models are definitely the way to go from a consumer point of view.
I don't think most people are capable of doing a cost/benefit ratio calculation on how what they do affects the rest of the world, and the wealthy are far and away the worst abusers of this sadass truth.
Or clearly thinking they might get value from it. I personally agree they're likely getting value, but it's pretty easy to dupe otherwise smart people when handing them something with cabilities far outside their realm of expertise, so I'd caution against using a large user base as anything more than a suggestive signal when determining whether people are "clearly getting value."
For an example from a different domain, consider a lot of generic market-timing stock investment advice. It's pretty easy to sell predictions where you're right a significant fraction of the time, but the usual tradeoff is that the magnitude of your errors is much greater than the magnitude of your successes. Users can be easily persuaded that your advice is worth it because of your high success rate, but it's not possible for them to actually get any net value from the product.
Even beginning data scientists get caught in that sort of trap in their first forays into the markets [0], and people always have a hard time computing net value from products with a high proportion of small upsides and a small proportion of huge downsides [1].
It's kind of like the various philosophical arguments about micro murders. 10 murders per year is huge in a town of 40k people, but nobody bats an eye at 10 extra pedestrian deaths per year from routinely driving 35+ in a 25. Interestingly, even if that level of speeding actually saves you the maximal amount of time (rarely the case for most commutes, where light cycles and whatnot drastically reduce the average speedup from "ordinary" reckless driving), you'll on average cause more minutes of lost life from the average number of deaths you'll cause than you'll save from the speeding. It's a net negative behavior for society as a whole, but almost nobody is inclined to even try to think about it that way, and the immediate benefit of seemingly saving a few minutes outweighs the small risk of catastrophic harm. Similarly with rolling through stop signs (both from the immediate danger, and from the habit you're developing that makes you less likely to be able to successfully stop in the instances you actually intend to).
[0] Not a source, those are a dime a dozen if you want to see a DS lose a lot of money, but XKCD is always enjoyable: https://xkcd.com/1570/
[1] Also not a source, just another great XKCD: https://xkcd.com/937/
With the current state of LLMs they should stay within the bounds of writing out random, but statistically likely, words. However, I think we are already at a point where will be paying price later down the road for all the hallucinations we have unleashed to the world in the past few years.
For all the hype LLM generation gets, I think the rise of LLM-backed “semantic” embedding search does not get enough attention. It’s used in RAG (which inherits the hallucinatory problems), but seems underutilized elsewhere.
The worst (and coincidentally/paradoxically I use the most) searches I’ve seen is Gmail and Dropbox, both of which cannot find emails or files that I know exist, even if using the exact email subject and file name keywords.
Apple could arguably solve this with a universal search SDK, and I’d value this far more than yet-another-summarize-this-paragraph tool.
Siri on the other hand should have access to definitely non-noise data like: your calendar. The message your mom sent to say ‘see you at _____ airport and your entire chat history with her.
I am 100% certain that if you gave GPT4 this info, it could EASILY get this right 100% of the time.
Apple’s ability to make Siri do anything useful with AI is totally incomprehensible and it is definitely not a problem with AI.
It could well be a problems with running a very tiny AI on device. I would not trust even GPT 3.5 with this task and it is a lot more capable than anything an iPhone could run.
90% of the mass consumer AI tech demos in the past 2-3 years are the exact same demos that voice assistants used to do with just speech-to-text + search functions. And these older tech demos are already things only 10% of users probably did regularly. So they are adding AI features to halo features that look good in marketing but people never use.
Keep the OS secure and let me use an Apple AI app in 2-3 years when they have rolled their own LLM.
I'm not sure anyone is going to buy it, but it doesn't cost them anything to get a few of their PR hacks to give it a try.
It's about as convincing as "we didn't build a bad phone, you're just holding it wrong!".
Auto. Vision Pro. AI.
Is there a pattern emerging here?
Search in Mail is abysmal since forever. Everyone knows it. Apple knows it. No change still. So, no surprise here.
https://mjtsai.com/blog/2019/10/11/mail-data-loss-in-macos-1...
Remember when Google launched a social network?
Remember when Facebook made a phone?
Remember when intel tried to make mobile chips?
Apple is the best in the world at making expensive computers in various sizes. From pocket size to desktop. And some peripherals. That’s their core competency. AI is not on the list.
And google bought android.
Also I’d say google’s core competency is ‘organising information nicely’
Or how Watch has the full power of all my location info, my driving status, my motion, all opted in, but can't figure out squat.
My watch thinks last week I did a 23 mile hike in 1 hour, 20 miles of it accomplished while in do-not-disturb-while-driving mode.
Fall detection? Nope. False negative, verified enabled and does not work.
Autocorrect… it's actually gotten worse.
If your explanation boils down to a bunch of "it should do..." or "most of the time it does..." then I still don't want it.
Outside tech users it’s a novelty that lasts about a week or disappears in a puff of smoke the moment money is asked for it.
The whole industry is blind to the fact the market doesn’t need it and it it doesn’t really solve any problems. It’s not even a means to an end.
What consumers want is to be left the fuck alone and their stuff to last longer. But this doesn’t make numbers go up.
Instead they're failing to build a bunch of stuff that nobody asked for under the banner, "Apple Intelligence".
Please Apple, just make Siri better.
Apple is behind the curve like Google was prior to Gemini 2.5 Pro, but unlike Google, I cannot see Apple having the talent to catch up unless they make some expensive acquisitions and even then they will still be behind. I was shocked at how good Gemini 2.5 Pro is. The cost and value for money difference is so big that I'm considering switching away from my API usage of Claude Sonnet 3.7 to Gemini 2.5 Pro.
On device AI model which handles light tasks like notifications. Apple Compute Cloud which runs a more powerful version of the same model and handles more complex operations. And integration with OpenAI, Gemini etc for LLM tasks.
Eg. The other day I had a birthday invitation for a kids party sent to me as a jpg. I figure, I’ll ask Siri to add it to my calendar. After a lot of persuasion I manage to get it to send it to ChatGPT to process it (which makes no sense since even the photos app can read text in an image).
But anyway, it sends it to ChatGPT. Which replies by reading out the contents of the image including all the day and time and location. And that’s it. No action from Siri.
So I follow up with ‘so can you add it to my calendar now?’ Siri replies “what would you like to me add to your calendar?”
That was 5 minutes of my life I won’t get back…
It's an algorithm; it's just an algorithm. It's useful for a few things. It isn't useful for most things. Like MVC, or relational databases, or finite state machines, or OOP, it's not something you should have to (or want to) tell the end user that you are using in the internals. The reason most "AI" products brag about using "AI", is there isn't anything else interesting about them.
“I found some web results. I can show them if you ask again from your iPhone”
Nah, Apple is the letdown, and has been since before ChatGPT.
It sounds ridiculous now but Web 1.0 was mostly about putting companies paper brochures onto websites.
It sounds doubly ridiculous that Web 1.0 came to an end when the market crashed because no one could figure out how to make money from the internet.
Web 1.0 started in 1994 and it would be ten years until Facebook arrived.
So AI has some really really big surprises in store that no one has thought of yet and when they do, fortunes will be made.
I never once had anything to do with blockchain, NFTs. Crypto just lost me money.
It not being lucrative was a *feature*, not a defect.
We keep getting shut down by simpler, purpose built tools that work predictably.
LLM is just good for synthesizing vague inputs.
But also, both realtime and non-realtime translations are real problems. They might not be your problems, but I for one use LLM as translators pretty much daily.
Just because the 'AI' services we use today cause certain levels of error doesn't mean we can disregard their value as zero. Are Apple’s services flawless? (Vision Pro... I won’t say more.) Since humans are not perfect, everything created by humans can never be perfect. Between 0 and 1, there are countless values like 0.1, 0.0002, etc. Ignoring this concept is something I personally find truly repulsive.
When the so-called 'AI' industry was booming, Apple was completely fumbling. Why? Because Apple is not perfect either. After all, Apple is run by people.
I say this as a massive Apple fanboy. AI was heavily advertised as a selling point of iPhone 15 Pro and is completely MIA 6 months later. It’s a major letdown. It’s not the end of the world, but let’s just call it what it is.
For those saying Apple doesn’t release imperfect products, may I introduce to you Siri? It was average when they bought it and it’s become a punch line.
And there are so many uses of AI that don’t have to be at the risk level of, “Oops, AI left grandma at LeGuardia.” Apple should go back to its roots and provide high quality LLM/MCP and other API sdks to developers and let them go nuts. Then just clone or buy the apps that work like they always do.
People do that because it's very useful, not because it's safe.
Siri didn’t need to suck all these years. Even before the LLM craze.
Most people still have no idea how useful it can be.
I'm a firm beleiver it will be an absolute godsend to older folks who struggle to learn new interfaces and technologies.
Still sits there despite having long plateaued in revenue and is still priced for some impressive revenue growth.
Go figure.
I will say Apple AI completely sucks for a company with all the resources available to them.
We have programs, actual programs that you can run on your laptop, that will understand images and describe them to you, understand your voice, talk to you, explain things to you. We have experts that will answer your every question, that will research things for you, and all we keep saying is how disappointing it is that they aren't better than humans.
To me, this is very much the old joke of "wow, your dog can sing?!" "Eh, it's not that impressive, he's pitchy". To go from "AI that can converse fluently is impossible, basically science fiction" to "AI is a letdown" just shows me the infinite capability humans have to find anything disappointing, no matter how jaw-droppingly amazing it is.
Yeah transformers doing NLP well is pretty impressive. No, it is not worth burning hundreds of billions of dollars on GPU data centers. And please, stop the hype. Non-technical decision makers are really spoiling everything with magical thinking about "artificial intelligence". We still have to learn domains and engineer products. There is no silver bullet.
Grok has already overloaded their 200k GPU cluster and is struggling to keep up with the demand.
> jaw-droppingly amazing
Did I imagine you said these things?
Uh do you have any freaking idea of what happens with your location data? bank account information is a matter of security. So is face ID data.
The insane levels of hypocrisy hearing this come from a mainstream media source. The damage that has been done to all of society by misrepresenting and half-truthing about events to appease audiences is unrivaled, yet here they are on the high horse of "anything less than 100% accurate is useless"
Take note CNN, take fucking note.
AI is not useful if you want curated fact, if you want consistent output, if you want repeated quality.
How about training an AI on 1990s style encyclopedias, with their low error rate.
Even wikipedia has random yahoos coming in and changing pages about the moon landing, to say it was filmed in a studio.
AI is being trained on random, it outputs random.
Using curated data means Widgets Inc outputs help to a customer the same always. 99% means AI tells the customer "you stink!" or "our products suck" randomly.
It's useless.