He came to give a lecture at UT Austin, where I did my undergrad. I had a chance to ask him a question: "what's the story behind inventing QuickSort?". He said something simple, like "first I thought of MergeSort, and then I thought of QuickSort" - as if it were just natural thought. He came across as a kind and humble person. Glad to have met one of the greats of the field!
Happy to meet you. I was there and I remember that question being asked. I think it was 2010.
If I remember correctly he had two immediate ideas, his first was bubble sort, the second turned out to be quicksort.
He was already very frail by then. Yet clarity of mind was undiminished. What came across in that talk, in addition to his technical material, was his humor and warmth.
Haha I was there too. I remember he made thinking clearly seem so simple. What a humble man.
If I remember correctly, his talk was about how the world of science-the pure pursuit of truth-and the world of engineering-the practical application of solutions under constraints-had to learn from each other.
The problem can be complex, which sometimes means the solution needs to be complex. Often, you can solve a complex problem in simple ways. There’s many ways to do that:
a) finding a new theoretical frame that simplifies the space or solutions, helps people think through it in a principled way
b) finding ways to use existing abstractions, that others may not have been able to find
c) using non-technical levers, like working at the org/business/UX level to cut scope, simplify requirements,
The way you can make complexity work for your career, is to make it clear why the problem is complex, and then what you did to simplify the problem. If you just present a simple solution, no one will get it. It’s like “showing your work”.
In some orgs, this is hopeless, as they really do reward complexity of implementation.
One of the odd things people do with tech is taking someone else's random projections at face value?
What does it mean to say "we were promised flying cars", or "every city would have micro-factories, that 3D printing would decentralize production"?
The people creating these narratives may a) truly believe it and tried to make it a reality, but failed b) never believed it at all, but failed anyway, c) or be somewhere else on this quadrant of belief vs actuality.
Why not just treat it as, "a prediction that went wrong". I suppose it's because a narrative of promise feels like a promise, and people don't like being lied to.
It's a strange narrative maneuver we keep doing with tech, which is more future-facing than most fields.
We do have flying cars, and we do have printers that print other printers, but both were some combination of really expensive/poor quality. Technically speaking, if you take it that most cities have 3D printers, most cities then do have micro factories, however that says nothing about general feasability...
Technology requires infrastructure and resources, and our infrastructure is strained and our resources are even more so... Until the costs become pocket change for the average person, technology will just remain generally unavailable.
> What does it mean to say "we were promised flying cars"...
I don't know about the other things you mentioned, but I think you have this in the wrong category. "We were promised flying cars" is one half of a construction contrasting utopian promises/hype with dystopian (or at lest underwhelming) outcomes. I think the most common version is:
> They promised us flying cars, instead we got 140 characters.
Translation: tech promised awesome things that would make our life better, but instead we actually got was stuff like the toxicity of social media.
IMHO, this insight is one of the reasons there's so much negativity around AI. People have been around the block enough to have good reason to question tech hype, and they're expecting the next thing to turn out as badly as social media did.
It's extremely painful that there's are free, OSS dictation tools that can run on-device, that are so much better than Apple's dictation, and yet it's quite difficult to use them on the iPhone. I'm referring to Whispr. Microphone access is a pain for custom keyboards -- for good reason, but still.
> When I was taken to the Tate Modern as a child I’d point at Mark Rothko pieces and say to my mother “I could do that”, and she would say “yes, but you didn’t.”
Actually, no you couldn't. The subtlety of the choice of colors, their shading, and their soft shaping, and the program of their creation over many years - you couldn't do that. They're lovely and sublime, and wonderful and an abyss. If you want to throw all that away and reduce it two boxes of paint, go ahead - but you'll be wasting a lifetime's engagement, of the joy of seeing with your intellect wide open.
> The value got extracted, but compensation isn't flowing back. That bothers me, and it deserves a broader policy conversation.
It bothers me, too. But, look at the history of the internet. There's no reason to expect we'll be able to fix this problem.
1. Search engines drove traffic to news/content sites, which monetized via ads. Humans barely tolerate these ad filled websites. And yet, local news went into steep decline, and the big national players got an ever-larger share of attention. The large, national sites were able to keep a subscriber-based paywall model. These were largely legacy media sites (ie: NYT).
2. News sites lost the local classifieds market, as the cost of advertising online went to zero (ie: Craigslist). This dynamic was a form of creative destruction - a better solution ate the business of an older solution.
3. Blog monetization was always tough, beyond ads. Unless you were a big blog, you couldn't make a living. What about getting a small amount of money per view from random visitors? The internet never developed a micro-payment or subscription model for the set of open sites - the blogosphere, etc. The best we got were closed platforms like Substack and Medium, which could control access via paywalls.
All this led to the internet being largely funded through the "attention economy": ads mostly, paywalls & subscriptions some.
The attention economy can't sustain itself when there are fewer eyeballs:
1. Tailwind docs have to be added just once to the training set for the AI to be proficient in that framework forever. So one HTTP request, more or less, to get the docs and docs are no longer required.
2. Tailwind does change, so an AI will want to access the docs for the version its working with. This will require access at inference time. This is more analogous to visiting a site.
A common pattern I see is data-plane nodes receiving versioned metadata updates from the control-plane. As long as the version is higher than the node's previous one, it's correct to use. So, the metadata is a sort of monotonic counter with a bag of data attached to it. This pattern produces a monotonic counter, which I assume is a naive sort of CRDT - though the data itself doesn't benefit from CRDT merge semantics. In this world, as long as a node gets an update, its going to incorporate it into its state. In the article's terms, the system has Strong Convergence.
I'm trying to figure out under what practical circumstances updates would result in Eventual Convergence, not Strong Convergence. Wouldn't a node incorporate an update as soon as you receive it? What's causes the "eventual" behavior even after a node gets an update?
It seems to me the trouble is actually getting the update, not the data model as such. Yes, I realize partial orders are possible, making it impossible to merge certain sequences of updates for certain models. CRDTs solve that, as they're designed to do. (Though I hear that, for some CRDTs, merges might result in bad "human" results even if the merge operation follows all the CRDT rules.)
Apple needs on-device AI to do chores for me with the apps I have installed. Apple has everything it needs:
* Apps are already logged in, so no extra friction to grant access.
* Apps mostly use Apple-developed UI frameworks, so Apple could turn them into AI-readable representations, instead of raw pixels. In the same way a browser can give the AI the accessibility DOM, Apple could give AIs an easier representation to read and manipulate.
* iPhones already have specialized hardware for AI acceleration.
I want to be able to tell my phone to a) summarize my finances across all the apps I have b) give me a list of new articles of a certain topic from my magazine/news apps c) combine internet search with on-device files to generate personal reports.
All this is possible, but Apple doesn't care to do this. The path not taken is invisible, and no one will criticize them for squandering this opportunity. That's a more subtle drawback with only having two phone operating systems.
> iPhones already have specialized hardware for AI acceleration.
This really is the problem. Why do I spend hundreds of dollars more for specialized hardware that’s better than last years specialized hardware if all the AI features are going to be an API call to chatGPT? I am pretty sure I don’t need all of that hardware to watch YouTube videos or scroll Instagram/web, which is what 95% of the users do.
> Apple needs on-device AI to do chores for me with the apps I have installed
Nevermind that—iOS just needs to reliably be able to play the song I’m telling it to without complaining “sorry, something went wrong with the connection…”
I agree completely, it's really unfortunate how AI on apple devices has been going. The message summarization is borderline useless and widely mocked, meanwhile their giant billboard ads for it are largely stupid and uncompelling. Let me choose to give it access to my data if I want to do really useful stuff with on device processing. They've been leaning into the privacy thing, do the stuff that would be creepy if it left my device, generate push notification reminders for stuff I forgot to put in the calendar, or track my location and tell me I'm going to the wrong airport. Suggest birthday gifts for my friends and family, idk.
Edit: And add strong controls to limit what it can and cannot access, especially for the creepy stuff.
They're stuck on the privacy angle, because what it means is you can't call remote services. You'll always have access to more resources at a data-center than a phone. So, while the frontier of what's possible with purely-local models will keep advancing, it'll never exceed what's possible with remote models.
People care about extra privacy when the delta in capability is minimal. But people won't allow a massive discrepancy, like the difference between a 8B model and a 700B model.
I think on-device AI will show up more front and center but in a few more years.
A big issue to solve is battery life. Right now there's already a lot that goes on at night while the user sleeps with their phone plugged in. This helps to preserve battery life because you can run intensive tasks while hooked up to a power source.
If apps are doing a lot of AI stuff in the course of regular interaction, that could drain the battery fairly quickly.
Amazingly, I think the memory footprint of the phones will also need to get quite a bit larger to really support the big uses cases and workflows. (I do feel somewhat crazy that it is already possible to purchase an iPhone with 1TB of storage and 8GB of RAM).
2TB microsdxc cards have been available for a year or so, and 1TB cards have been available for several years and are even quite affordable. They work in many Android phones including my cheap Motorola. So it's Apple's sky-high premiums that has made their 1TB phones surprising.
> In the same way a browser can give the AI the accessibility DOM, Apple could give AIs an easier representation to read and manipulate.
Apps already have such an accessibility tree; it's used for VoiceOver and you can use it to write UI unit tests. (If you haven't tested your own app with VoiceOver, you should.)
I have used it actually! It's been years so the fact that it's an accessibility tree just like in a browser didn't come to mind immediately. Both Mac and Windows have such representations for native apps. The actual functionality apps and the accessibility clients support is something like a two-way negotiation. A lot of stuff that should be supported in apps, in theory, is not, just because no client supports it, etc.
This is all possible, but an absolutely terrible idea from a security point of view, while prompt injection attacks are still a thing, and there's little evidence they will stop being a thing soon.
Apple is generally anti market hype. It is a smart PR move to avoid mentioning AI after the Apple Intelligence fiasco, their researchers leaving, and the bubble sentiment at the moment.
It's not a smart move to avoid integrating the most important capability advance in computing in the past decade - LLMs. They do support small cases, like summarizing text. But there's scope to do more.
no, I placed this squarely on apple‘s shoulders. There are real use cases for new AI tools that are actually useful. Use cases that Apple is already invested into — Siri, text to speech & vice versa, etc. many of these have open source models that they could very easily be integrating into their product, even if they didn’t have a partnership with the premier AI research lab.
Instead, we got, what? An automated memeoji maker? Holy hell they dropped the ball on this.
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