Comment by FfejL
3 days ago
> It's not that Dell doesn't care about AI or AI PCs anymore, it's just that over the past year or so it's come to realise that the consumer doesn't.
I wish every consumer product leader would figure this out.
3 days ago
> It's not that Dell doesn't care about AI or AI PCs anymore, it's just that over the past year or so it's come to realise that the consumer doesn't.
I wish every consumer product leader would figure this out.
People will want what LLMs can do they just don't want "AI". I think having it pervade products in a much more subtle way is the future though.
For example, if you close a youtube browser tab with a comment half written it will pop up an `alert("You will lose your comment if you close this window")`. It does this if the comment is a 2 page essay or "asdfasdf". Ideally the alert would only happen if the comment seemed important but it would readily discard short or nonsensical input. That is really difficult to do in traditional software but is something an LLM could do with low effort. The end result is I only have to deal with that annoying popup when I really am glad it is there.
That is a trivial example but you can imagine how a locally run LLM that was just part of the SDK/API developers could leverage would lead to better UI/UX. For now everyone is making the LLM the product, but once we start building products with an LLM as a background tool it will be great.
It is actually a really weird time, my whole career we wanted to obfuscate implementation and present a clean UI to end users, we want them peaking behind the curtain as little as possible. Now everything is like "This is built with AI! This uses AI!".
> if you close a youtube browser tab with a comment half written it will pop up an `alert("You will lose your comment if you close this window")`. It does this if the comment is a 2 page essay or "asdfasdf". Ideally the alert would only happen if the comment seemed important but it would readily discard short or nonsensical input. That is really difficult to do in traditional software but is something an LLM could do with low effort.
I don't think that's a great example, because you can evaluate the length of the content of a text box with a one-line "if" statement. You could even expand it to check for how long you've been writing, and cache the contents of the box with a couple more lines of code.
An LLM, by contrast, requires a significant amount of disk space and processing power for this task, and it would be unpredictable and difficult to debug, even if we could define a threshold for "important"!
I think it's an excellent example to be honest. Most of the time whenever someone proposes some use case for a large language model that's not just being a chat bot, it's either a bad idea, or a decent idea that you'd do much better with something much less fancy (like this, where you'd obviously prefer some length threshold) than with a large language model. It's wild how often I've heard people say "we should have an AI do X" when X is something that's very obviously either a terrible idea or best suited for traditional algorithms.
Sort of like how most of the time when people proposed a non-cryptocurrency use for "blockchain", they had either re-invented Git or re-invented the database. The similarity to how people treat "AI" is uncanny.
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The difference between "AI" and "linear regression" is whether you are talking to a VC or an engineer.
> Ideally the alert would only happen if the comment seemed important but it would readily discard short or nonsensical input. That is really difficult to do in traditional software but is something an LLM could do with low effort.
I read this post yesterday and this specific example kept coming back to me because something about it just didn't sit right. And I finally figured it out: Glancing at the alert box (or the browser-provided "do you want to navigate away from this page" modal) and considering the text that I had entered takes... less than 5 seconds.
Sure, 5 seconds here and there adds up over the course of a day, but I really feel like this example is grasping at straws.
It’s also trivially solvable with idk, a length check, or any number of other things which don’t need to 100b parameters to calculate.
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A rarer-ish chance to use this XKCD: https://xkcd.com/1205/
I'd put this in "save 5 seconds daily" to be generous. Remember that this is time saved over 5 years.
The problem isn't so much the five seconds, it is the muscle memory. You become accustomed to blindly hitting "Yes" every time you've accidentally typed something into the text box, and then that time when you actually put a lot of effort into something... Boom. Its gone. I have been bitten before. Something like the parent described would be a huge improvement.
Granted, it seems the even better UX is to save what the user inputs and let them recover if they lost something important. That would also help for other things, like crashes, which have also burned me in the past. But tradeoffs, as always.
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> Ideally the alert would only happen if the comment seemed important but it would readily discard short or nonsensical input.
That doesn't sound ideal at all. And in fact highlights what's wrong with AI product development nowadays.
AI as a tool is wildly popular. Almost everyone in the world uses ChatGPT or knows someone who does. Here's the thing about tools - you use them in a predictable way and they give you a predictable result. I ask a question, I get an answer. The thing doesn't randomly interject when I'm doing other things and I asked it nothing. I swing a hammer, it drives a nail. The hammer doesn't decide that the thing it's swinging at is vaguely thumb-shaped and self-destruct.
Too many product managers nowadays want AI to not just be a tool, they want it to be magic. But magic is distracting, and unpredictable, and frequently gets things wrong because it doesn't understand the human's intent. That's why people mostly find AI integrations confusing and aggravating, despite the popularity of AI-as-a-tool.
> The hammer doesn't decide that the thing it's swinging at is vaguely thumb-shaped and self-destruct.
Sawstop literally patented this and made millions and seems to have genuinely improved the world.
I personally am a big fan of tools that make it hard to mangle my body parts.
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But... A lot of stuff you rely on now was probably once distracting and unpredictable. There are a ton of subtle UX behaviors a modern computer is doing that you don't notice, but if they all disappeared and you had to use windows 95 for a week you would miss.
That is more what I am advocating for, subtle background UX improvements based on an LLMs ability to interpret a users intent. We had limited abilities to look at an applications state and try to determine a users intent, but it is easier to do that with an LLM. Yeah like you point out some users don't want you to try and predict their intent, but if you can do it accurately a high percentage of the time it is "magic".
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I want magic that works. Sometimes I want a tool to interrupt me! I know my route to work so I'm not going to ask how I should get there today - but 1% of the time there is something wrong with my plan (accident, construction...) and I want the tool to say something. I know I need to turn right to get someplace, but sometimes as a human I'll say left instead: confusing me and the driver where they don't turn right, and AI that realizes who made the mistake would help.
The hard part is the AI needs to be correct when it doesn't something unexpected. I don't know if this is a solvable problem, but it is what I want.
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>For example, if you close a youtube browser tab with a comment half written it will pop up an `alert("You will lose your comment if you close this window")`. It does this if the comment is a 2 page essay or "asdfasdf". Ideally the alert would only happen if the comment seemed important but it would readily discard short or nonsensical input. That is really difficult to do in traditional software but is something an LLM could do with low effort. The end result is I only have to deal with that annoying popup when I really am glad it is there.
The funny thing is that this exact example could also be used by AI skeptics. It's forcing an LLM into a product with questionable utility, causing it to cost more to develop, be more resource intensive to run, and behave in a manner that isn't consistent or reliable. Meanwhile, if there was an incentive to tweak that alert based off likelihood of its usefulness, there could have always just been a check on the length of the text. Suggesting this should be done with an LLM as your specific example is evidence that LLMs are solutions looking for problems.
I've been totally AI-pilled because I don't see why that's of questionable utility. How is a regexp going to tell the difference between "asdffghjjk" and "So, she cheated on me". A mere byte count isn't going to do it either.
If the computer can tell the difference and be less annoying, it seems useful to me?
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> Ideally the alert would only happen if the comment seemed important but it would readily discard short or nonsensical input
No, ideally I would be able to predict and understand how my UI behaves, and train muscle memory.
If closing a tab would mean losing valuable data, the ideal UI would allow me to undo it, not try to guess if I cared.
Yeah. It's the Apple OS model (we know what's right for you, this is the right way) vs the many other customisable OSes where it conforms to you.
YouTube could use AI to not recommend videos I've already watched, which is apparently a really hard problem.
The problem is the people like me who DO rewatch youtube videos. There are a bunch of "Comfort food" videos I turn to sometimes. Like you would rewatch a movie you really enjoy.
But that's the real problem. You can't just average everyone and apply that result to anyone. The "average of everyone" fits exactly NO ONE.
The US Navy figured this out long ago in a famous anecdote in fact. They wanted to fit a cockpit to the "average" pilot, took a shitload of measurements of a lot of airmen, and it ended up nobody fit.
The actual solution was customization and accommodations.
It just might be that lot of users watch same videos multiple times. They must have some data on this and see that recommending same videos gets more views than recommending new ones.
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try disabling collecting the history about the videos you've watched in YouTube settings. There are still some recommendations after that but they are less cringe
My favorite is the new thing where they recommend a "members only" video, from a creator that covers current events, and the video is 2 years old.
You know what that reminds me very much of? That email client thing that asks you "did you forget to add an attachment?". That's been there for 3 decades (if not longer) before LLMs were a thing, so I'll pass on it and keep waiting for that truly amazing LLM-enabled capability that we couldn't dream of before. Any minute, now.
Using such an expensive technology to prevent someone from making a stupid mistake on a meaningless endeavor seems like a complete waste of time. Users should just be allowed to fail.
Amen! This is part of the overall societal decline of no failing for anyone. You gotta feel the pain to get the growth.
if somone from 1960 saw the quadrillions of cpu cycles we are wasting on absolutely nothing every second, they would have an aneurysm
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Expensive now is super cheap 10 years from now though.
> readily discard short or nonsensical input
When "asdfasdf" is actually a package name, and it's in reply to a request for an NPM package, and the question is formulated in a way that makes it hard for LLMs to make that connection, you will get a false positive.
I imagine this will happen more than not.
So, like, machine learning. Remember when people used to call it AI/ML? Definitely wasn't as much money being spent on it back then.
> The end result is I only have to deal with that annoying popup when I really am glad it is there.
Are you sure about that? It will trigger only for what the LLM declares important, not what you care about.
Is anyone delivering local LLMs that can actually be trained on your data? Or just pre made models for the lowest common denominator?
> For example, if you close a youtube browser tab with a comment half written it will pop up an `alert("You will lose your comment if you close this window")`. It does this if the comment is a 2 page essay or "asdfasdf". Ideally the alert would only happen if the comment seemed important but it would readily discard short or nonsensical input. That is really difficult to do in traditional software but is something an LLM could do with low effort.
I agree this would be a great use of LLMs! However, it would have to be really low latency, like on the order of milliseconds. I don't think the tech is there yet, although maybe it will be soon-ish.
It’s because “AI” isn’t a feature. “AI” without context is meaningless.
Google isn’t running ads on TV for Google Docs touting that it uses conflict-free replicated data types, or whatever, because (almost entirely) no one cares. Most people care the same amount about “AI” too.
Would that be ideal though? Adding enormous complexity to solve a trivial problem which would work I'm sure 99.999% of the time, but not 100% of the time.
Ideally, in my view, is that the browser asks you if you are sure regardless of content.
I use LLMs, but that browser "are you sure" type of integration is adding a massive amount of work to do something that ultimately isn't useful in any real way.
I want AI to do useful stuff. Like comb through eBay auctions or Cars.com. Find the exact thing I want. Look at things in photos, descriptions, etc
I don't think an NPU has that capability.
> you can imagine how a locally run LLM that was just part of the SDK/API developers could leverage would lead to better UI/UX
It’s already there for Apple developers: https://developer.apple.com/documentation/foundationmodels
I saw some presentations about it last year. It’s extremely easy to use.
At my current work much of our software stack is based on GOFAI techniques. Except no one calls them AI anymore, they call it a "rules engine". Rules engines, like LLMs, used to be sold standalone and promoted as miracle workers in and of themselves. We called them "expert systems" then; this term has largely faded from use.
This AI summer is really kind of a replay of the last AI summer. In a recent story about expert systems seen here on Hackernews, there was even a description of Gary Kildall from The Computer Chronicles expressing skepticism about AI that parallels modern-day AI skepticism. LLMs and CNNs will, as you describe, settle into certain applications where they'll be profoundly useful, become embedded in other software as techniques rather than an application in and of themselves... and then we won't call them AI. Winter is coming.
Yeah, the problem with the term "AI" is that it's far too general to be useful.
I've seen people argue that the goalposts keep moving with respect to whether or not something is considered AI, but that's because you can argue that a lot of things computers do are artificial intelligence. Once something becomes commonplace and well understood, it's not useful to communicate about it as AI.
I don't think the term AI will "stick" to a given technology until AGI (or something close to it).
You don't need a LLM for that, a simple Markov Chain can solve that with a much smaller footprint.
No. No-no-no-no-no. I want predictability. I don't want a black box with no tuning handles and no awareness of the context to randomly change the behavior of my environment.
I’ve seen some thoroughly unhinged suggestions floating around the web for a UI/UX that is wholly generated and continuously adjusted by an LLM and I struggle to imagine a more nightmarish computing experience.
Honestly some of the recommendations to watch next I get on Netflix are pretty good.
No idea if they are AI Netflix doesn't tell and I don't ask.
AI is just a toxic brand at this point IMO.
This was a really innovative and big deal back in the day.
https://en.wikipedia.org/wiki/Netflix_Prize
It doesn’t fix the content problem these days though.
Bingo. Nobody uses ChatGPT because it's AI. They use it because it does their homework, or it helps them write emails, or whatever else. The story can't just be "AI PC." It has to be "hey look, it's ChatGPT but you don't have to pay a subscription fee."
Hopefully, you could make a browser extension to detect if a HTML form has unsaved changes; it should not require AI and LLM. (This will work better without the document including JavaScripts, but it is possible to work with JavaScripts too.)
I want a functioning search engine. Keep your goofy opinionated mostly wrong LLM out of my way, please.
I think they will eventually. It’s always been a very incoherent sales pitch that your expensive PCs are packed full of expensive hardware that’s supposed to do AI things, but your cheap PCs that have none of that are still capable of doing 100% of the AI tasks that customers actually care about: accessing chatGPT.
Also, what kind of AI tasks is the average person doing? The people thinking about this stuff are detached from reality. For most people a computer is a gateway to talking to friends and family, sharing pictures, browsing social media, and looking up recipes and how-to guides. Maybe they do some tracking of things as well in something like Excel or Google Sheets.
Consumer AI has never really made any sense. It's going to end up in the same category of things as 3D TV's, smart appliances, etc.
I don't remember any other time in the tech industry's history when "what companies and CEOs want to push" was less connected to "what customers want." Nobody transformed their business around 3D TVs like current companies are transforming themselves to deliver "AI-everything".
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Just off the top of my head of some "consumer" areas that I personally encounter...
I don't want AI involved in my laundry machines. The only possible exception I could see would be some sort of emergency-off system, but I don't think that even needs to be "AI". But I don't want AI determining when my laundry is adequately washed or dried; I know what I'm doing, and I neither need nor want help from AI.
I don't want AI involved in my cooking. Admittedly, I have asked ChatGPT for some cooking information (sometimes easier than finding it on slop-and-ad-ridden Google), but I don't want AI in the oven or in the refrigerator or in the stove.
I don't want AI controlling my thermostat. I don't want AI controlling my water heater. I don't want AI controlling my garage door. I don't want AI balancing my checkbook.
I am totally fine with involving computers and technology in these things, but I don't want it to be "AI". I have way less trust in nondeterministic neural network systems than I do in basic well-tested sensors, microcontrollers, and tiny low-level C programs.
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I do think it makes some sense in limited capacity.
Have some half decent model integrated with OS's builtin image editing app so average user can do basic fixing of their vacation photos by some prompts
Have some local model with access to files automatically tag your photos, maybe even ask some questions and add tags based on that and then use that for search ("give me photo of that person from last year's vacation"
Similarly with chat records
But once you start throwing it in cloud... people get anxious about their data getting lost, or might not exactly see the value in subscription
You and I live in different bubbles. ChatGPT is the go-to for my non-techie friends to ask for advice on basically everything. Women asking it for relationship advice and medical questions, to guys with business ideas and lawsuit stuff.
Consumer local AI? Maybe.
On the other hand everyone non-technical I know under 40 uses LLMs and my 74 year old dad just started using ChatGPT.
You could use a search engine and hope someone answered a close enough question (and wade through the SEO slop), or just get an AI to actually help you.
“Do my homework assignment for me.”
Dell are less beholden to shareholder pressure than others, Michael Dell owns 50% of the company since it went public again.
Meanwhile we got Copilot in Notepad.
I think part of the issue is that it's hard to be "exciting" in a lot of spaces, like desktop computers.
People have more or less converged on what they want on a desktop computers in the last ~30 years. I'm not saying that there isn't room for improvement, but I am saying that I think we're largely at the state of "boring", and improvements are generally going to be more incremental. The problem is that "slightly better than last year" really isn't a super sexy thing to tell your shareholders. Since the US economy has basically become a giant ponzi scheme based more on vibes than actual solid business, everything sort of depends on everything being super sexy and revolutionary and disruptive at all times.
As such, there are going to be many attempts from companies to "revolutionize" the boring thing that they're selling. This isn't inherently "bad", we do need to inject entropy into things or we wouldn't make progress, but a lazy and/or uninspired executive can try and "revolutionize" their product by hopping on the next tech bandwagon.
We saw this nine years ago with "Long Blockchain Ice Tea" [1], and probably way farther back all the way to antiquity.
[1] https://en.wikipedia.org/wiki/Long_Blockchain_Corp.
Companies don’t really exist to make products for consumers, they live to create stock value for investors. And the stock market loves AI
The stock market as always been about whatever is the fad in the short term, and whatever produces value in the long term. Today AI is the fad, but investors who care about fundamentals have always cared about pleasing customers because that is where the real value has always come from. (though be careful - not all customers are worth having, some wannabe customers should not be pleased)
As someone pointed out, Dell is 50% owned by Michael Dell. So it's less influenced by this paradigm.
The will of the stock market doesn't influence Dell, they're a privately held corporation. They're no longer listed on any public stock market.
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Treating consumers as customers, good.
There is place for it but it is insanely overrated. AI overlords are trying to sell incremental (if in places pretty big) improvement in tools as revolution.