Yes but not evenly distributed, BeNeLux, Germany, Austria, and Nordic countries have a lot of iPhone users, while moving further east (or south) you see lower market share. Maybe it’s “two handfuls” of wealthy western countries rather than just one, but I think OPs point holds true.
Thanks, this is a helpful comment. It isn’t clear to me that these models destroy incentive to create. I mean, ChatGPT can generate comments in my style all day, and yet I’m still incentivized to comment. I fancy myself a photographer. I still want to take photos even if DALL-E 4 will generate better ones. What even is the point of creating art? I think there are two purposes: personal expression and enjoyment for ot…
> I think there are two purposes: personal expression and enjoyment for others. This is exactly what non-artists assume artists do art for. The reality is that most professional visual artists work in publishing, marketing, entertainment and the like. It’s a regular job. The incentive is money. Similarly for theatre, music, video, dance, etc etc. Artists can’t feed their families off exposure and expressing themselve…
It is absolutely wild trying to make this point on HN. Art is for funsies while writing code for targeting ads is a Serious Job.
Has anybody here improved their day-to-day workflow with any kind of "implicit" generative AI rather than explicitly talking to an LLM?
So far all attempts seem to be building an universal Clippy. In my experience, all kinds of forced autocomplete and other suggestions have been worse than useless.
Has anybody here improved their day-to-day workflow with any kind of "implicit" generative AI rather than explicitly talking to an LLM? So far all attempts seem to be building an universal Clippy. In my experience, all kinds of forced autocomplete and other suggestions have been worse than useless.
GitHub Copilot works well in my experience. It does bad suggestions at times, but also really spot-on ones.
Other than that, AI for me is meme/image generation and a semi-useful chatbot.
It probably will change. Note that, so far, a 16GB apple device has much better usability than the equivalent on windows. This may sound biased, but the memory compression and foreground/background actions by macOS tight integration with the hardware is really good. I've never felt like I couldn't do things on smaller hardware, except (larges) LLMs. Also when I compare with my co-workers the memory pressure is a lot…
> This may sound biased, It certainly does, close to irrational even. IIRC memory compression is enabled by default on Windows as well.
Biased and irrational are both things HN readers say to avoid using the word "subjective".
Oh missed that! But kinda as expected: only works on 2 android phones (pixel 8 pro, S24). Pretty typical: Apple isn’t first, but also typically will scale faster with HW+platform integration.
On Apple’s side, Apple Intelligence will only be enabled on A17 Pro and M-series chips, so only the iPhone 15 Pro and Pro Max will be supported in terms of phones.
2 phones, ~4 tablets, ~12 PCs.
Looking at sales, looks like about 10x the phone volume of s24 (and pixel 8 doesn’t register on the chats).
For people interested in AI research, there's nothing new here. IMO they should do a better job of referencing existing papers and techniques. The way they wrote about "adaptors" can make it seem like it's something novel, but it's actually just re-iterating vanilla LoRA. It was enough to convince one of the top-voted HackerNews comments that this was a "huge development". Benchmarks are nice though.
Very little of the “AI” boom has been novel, most has been iterative elaborations (though innovative nonetheless). Academics have been using neural network statistical models for decades. What’s new is the combination of compute capability and data volume available for training. It’s iterative all the way down though, that’s how all technologies are developed.
For people interested in AI research, there's nothing new here. IMO they should do a better job of referencing existing papers and techniques. The way they wrote about "adaptors" can make it seem like it's something novel, but it's actually just re-iterating vanilla LoRA. It was enough to convince one of the top-voted HackerNews comments that this was a "huge development". Benchmarks are nice though.
Very little of the “AI” boom has been novel, most has been iterative elaborations (though innovative nonetheless). Academics have been using neural network statistical models for decades. What’s new is the combination of compute capability and data volume available for training. It’s iterative all the way down though, that’s how all technologies are developed.
Most people don't realize this, but almost all research works that way. Only the media spins research as breakthrough-based, because that way it is easier to sell stories. But almost everything is incremental/iterative. Even the transformer architecture, which in some way can be seen as the most significant architectural advancement in AI in the past years, was a pretty small, incremental step when it came out. Only with a lot of further work building on top of that did it become what we see today. The problem is that science-journalists vastly outnumber scientists producing these incremental steps, so instead of reporting on topics when improvements actually accumulated to a big advancement, every step along the way gets its own article with tons of unnecessary commentary heralding its features.
> We train our foundation models on licensed data, including data selected to enhance specific features, as well as publicly available data collected by our web-crawler, AppleBot. Web publishers have the option to opt out of the use of their web content for Apple Intelligence training with a data usage control. And, of course, nobody has known to opt-out by blocking AppleBot-Extended until after the announcement wher…
I hate to tell you, but I've been training a neural network on the internet for over a decade now. Specifically the one between my ears. Unfortunately, it seems to be gradually going insane.
Yes, and if you recreate parts of what you learned you might run into copyright issues. Too much inspiration from something you've studied, and it becomes a derivative work subject to all the regulations. And there are no clear and strict rules, it is always a judgement call.
For people interested in AI research, there's nothing new here. IMO they should do a better job of referencing existing papers and techniques. The way they wrote about "adaptors" can make it seem like it's something novel, but it's actually just re-iterating vanilla LoRA. It was enough to convince one of the top-voted HackerNews comments that this was a "huge development". Benchmarks are nice though.
> For people interested in AI research, there's nothing new here. Was anyone expecting anything new? Apple has never been big on living at the cutting edge of technology exploring spaces that no one has explored before—from laptops to the iPhone to iPads to watches, every success they've had has come from taking tech that was already prototyped by many other companies and smoothing out the usability kinks to get it r…
I think you misinterpreted OP's comment. Apple makes it sound like there's smth new, but there isn't. They don't have to innovate, but it's good practice to credit who've done what they're taking and using. Also to use the names everyone else is already using.