The role of Synthetic Respondents in 'Human-centred' Research

In the growing buzz around generative AI, a new concept in research methodologies has arisen; "synthetic respondents". Instead of asking people the questions, a Large Language Model creates 'synthetic respondents' which you can ask as many questions as you like. And they will give you answers. And they will probably sound like real people. They will never get bored. They will never try to disguise their "true" thoughts and feelings (as David Ogilvy once said, “People don’t think what they feel, don’t say what they think, and don’t do what they say.”.) You can get answers from thousands of them, very quickly and at very little costs.

(Also - they never leave behind a bad smell, and won't eat all of your biscuits.)

But again - so obvious as to be barely worth mentioning - they aren't real people. They are synthetic - "made up." Just like the 'actors', pretending to be the sort of people we actually want to talk to.

They will do it faster. They will do it cheaper. Will they do it better - or at least, 'good enough'? Well... that's the real question.

AI and "Apple Intelligence"

(Title based on a Threads post from Daniel Jalkut.

What Is Apple Doing in AI? Summaries, Cloud and On-Device LLMs, OpenAI Deal - Bloomberg

But the company knows consumers will demand such a feature, and so it’s teaming up with OpenAI to add the startup’s technology to iOS 18, the next version of the iPhone’s software. The companies are preparing a major announcement of their partnership at WWDC, with Sam Altman-led OpenAI now racing to ensure it has the capacity to support the influx of users later this year.

Nah- I'm not buying it. For one - OpenAI and Microsoft are "true" partners; Microsoft's Azure compute + OpenAI's models + Microsoft's apps/OS 1 are getting deeply intertwined. An Apple + OpenAI partnership seems like a strategy to be permanently one step behind Microsoft.

But it seems inevitable that there's big Apple + AI news coming. Siri needs a significant upgrade. The new iPad Pro announcement made a big deal about having "AI compute" power2. "AI features" announcements at WWDC 2024 seems like the safest bet in tech.

So, what might be coming?

  • Siri was a pretty early bet on the future - but possibly too early, given the advancements in machine learning/deep learning since Siri was first released. But "Siri" is more of a brand than a technology - there are "Siri" features that don't seem to have anything to do with the voice assistant.
  • Meanwhile, while Siri might seem to be stuck in a rut, Apple's own proprietary ML/AI technology has been coming along. Apple Music has had a 'karaoke' feature where you can turn down the vocals - which I had wondered whether it was an AI-powered thing or just that they get separate stems; the latest Logic Pro which lets you split vocals, drums, bass and 'everything else' seems to suggest that its AI rather than a 'special access to the masters' situation. Given Apple's insistence on owning any technology that they rely on, this seems like the most likely approach. Whether Siri as a brand is dead or not… We'll see. (Worth noting that Siri came out before the film 'Her' - while OpenAI's latest release seems clearly... 'inspired' by the film, Siri looks more like the inspiration for the film.)
  • That said - it seems that Apple needs to catch up a lot, and fast. So a partnership with a technology leader does make sense. So... with who?
    • Meta? Meta's AI push has been for 'open' models (as opposed to - ironically - OpenAI's proprietary approach) that anyone can use. Apple's strength is in using on-device computing power (because they have a fair amount of it - it also fits with their 'privacy first' approach.) Maybe Apple would be licensing these models - but I suspect that the Apple/Meta relationship isn't strong enough to make it likely that Apple would want to put their future in Mark Zuckerberg's hands.
    • Google's AI journey has been... interesting. They seem to be leaders in terms of the underlying tech - but struggling to actually execute; OpenAI/ChatGPT/Copilots seem to be conclusively winning the PR/branding fight - perhaps they need a partner who can make a better product (away from the issues of competing with Search as a business model) that can better reflect the power of the underlying tech than Google's efforts to date. The Transformer architecture that led to the LLM explosion came out of Google, as did the Tensorflow software framework. While everyone else is fighting over nVidia's GPU chips, Google have been making their own TPUs for nearly a decade. Google is arguably the only GPU-rich business at the moment, and there is a general vibe of the AI industry being Google vs. Everyone Else - having a big partner might be exactly what they need right now. Maybe most importantly, Google pays Apple a lot of money for search engine prominence. The question is how much of the old Android/iOS friction still exists on Apple's side, while Google's opportunity is to make an AI-powered "Google Android" a distinct product from... lets call it the "Samsung Android" ecosystem - which doesn't make as much sense if Apple gets benefits that cheaper Android phones don't.
    • Microsoft? If Apple could get a more... lets say "grown-up" business partner without the volatility of OpenAI, then that could be interesting... And if the 'everyone vs Google' view of the market is right, then it seems to be in Microsoft's interests too. But it seems that Microsoft's clear positioning of 'Copilot+ PCs' as going head-to-head with Macbooks would be unlikely if that was in the pipeline.
    • There are other AI companies (eg. Anthropic) - but I'm not sure how many of them are scalable enough (read: access to the compute resources that would be needed to potentially switch on for every iPhone/iPad/Mac owner in the world overnight.)

If I had to make a bet, my money would be on a Google partnership, with something like the Gemma model running locally on iPhone/iPads etc. as 'Siri 2.0' and access to Gemini for the kind of tasks that need access to 'full fat' LLMs and more computing power.

  1. Also- GitHub CoPilot

  2. Yes, iPads/iPhones/Macs have had 'neural cores' for a few years - but the new iPad seems to be stepping this up significantly, but with no news on what its actually going to power. Worth noting - if you're developing AI/ML/LLM-type software on a Mac, you're using the GPU - not NPU chips. So far, they seem to be locked away for Apple's use (which includes Apple's APIs if you're building apps for the app store - but not if you're running something like TensorFlow in Python.)

The Device is the Boring Bit

The Apple Vision Pro is now on sale. People are getting their hands on them, and sharing their opinions. People who haven't got their hands on them are sharing their opinions. There are a lot of opinions flying around.

First thing - sure, I'm interested in the headset, and the device actually getting in 'normal' people's hands (or on their faces) is this week's news; I'm not going to buy one, because it's ridiculously expensive and if I had that sort of money to throw around, I probably wouldn't be driving a car that's approaching either its 18th birthday or its last trip to the scrapyard and has done the equivalent milage of 5 times around the circumference of the earth.


But what I'm really interested in is the Vision platform; the bits in the software that are going to be the same when the next headset device is launched. And once there are a bunch of different ‘Vision’ devices - where they will fit, in the spaces in people's lives.

Who owns Taylor Swift's voice?

Ben Evans on Threads;

It's a lot easier to understand the IP issues in 'give me this song but in Taylor Swift's voice' than 'make me a song in the style of the top ten hits of the last decade.' If a human did that, they wouldn't necessarily have to pay anyone, so why would an LLM?

There's an interesting twist with the "Taylor Swift's voice" example; Scooter Braun owns all of Taylor Swift's recordings (at least, I think all the ones released before any ChatGPT-era training dataset were compiled) - he bought the record company, so he owns the master recordings (and all the copies of the master recordings, and the rights relating to them) - but not the songs themselves. Taylor Swift still owns them - which is why she can make her "Taylor's Version" re-recordings (which Scooter Braun doesn't get a penny out of.)

So there's a key difference here; a human would copy the songs (that is, they would be working off the version of the songs that are in their heads - the idea of the songs), so Swift would get paid as the owner of the songs.

But the kind of generative AI we're talking about would be copying 100% from the recordings (ie. the training data would be the sounds, digitised and converted into a stream of numbers) - which Swift doesn't own. The AI doesn't "see" the idea of the songs - it wouldn’t “know” what the lyrics were, what key the songs were in, what chords were being played on what instrument - any more than a Large Language Model “knows” what the words in its (tokenised) training dataset or output mean.

She still owns her songs, but she’s sold her voice.

(Pre) WWDC 2023

WWDC usually isn’t one to look forward to - unless you’re the sort of person who cares about things like Xcode features - because it isn’t the venue where they talk about the new iPhones. Maybe there will be clues about new iPhone features in some new APIs or something, but the focus is generally on developers.

This year is different…

Somebody, make it stop

Well, my son is learning to play the guitar - at 13, he's about the age I was when I started learning, but he's been playing for a good few years and it won't be long until he's better than me at 45 (if he isn't already...)

One of the songs he's learning with his guitar teacher at the moment is Smells Like Teen Spirit - which came out in 1991, when I was about the same age that he is now and also learning how to play it on the guitar.

Which got me wondering - if I was playing something when I was 13 that is as old as that song he's playing is now, what would I have been playing? I know that I was playing 'old stuff' at the time - learning a lot of Jimi Hendrix riffs, some Beatles, some Cream etc. After all, the '60s were a hell of a decade for guitar heroes…

Well, I did the maths, and the equivalent for me in 1991 would have to have been a song from 1959. That wasn't just before all the "old" stuff from the (late) 1960s that I would have been playing- that was a time when Jimi Hendrix was still "Jimmy Hendrix" and learning how to play on his first $5 acoustic guitar. Keith Richards was still studying at technical college, and The Beatles hadn't even started playing with Pete Best yet - let alone Ringo. Elvis Presley was in the army, discovering amphetamines and meeting the 14 year old girl who he would marry seven years later. Tamala Motown had only just been formed.

Oddly though, it was also the year that the most valuable Gibson Les Pauls were made - even though it was still years until they would become famous, thanks to the likes of Keith Richards, Eric Clapton, Jimmy Page and Peter Green.

Anyway, now I feel old.

Robot War part 3: The Race

This is the tech war of the moment; a race to be the first to develop an AI/Machine Learning/Deep Learning product that will be a commercial success. Google have a head start - Microsoft+OpenAI look like they could be set to catch up, and maybe even overtake Google. But if this is a race then where is the finish line? What is the ultimate goal? Is it all about the $175 billion Search advertising market - or is it bigger than that?

The Next Big Thing (2023)

Nine years ago (Jan 2014), I wrote a post about "the next big thing". I think its fair to say that in a history of technological innovations and revolutions, there isn't really much in the last decade or so that would warrant much more than a footnote; the theme has been 'evolution, not revolution'.

Well, I think the Next Big Thing is - finally - here. And it isn't a thing consumers will go out and buy. Its an abstract, intangible thing; software not hardware, service not product.

For the first time in years, tech has got genuinely interesting again.