Currently the harvesting data for models look to be working better for Google / Amazon with current technology. The question is how will Apple progress long term and given time can they achieve AI with some level of privacy. I don’t know enough to have any comments but like Apple’s public stance on this.
I'd be excited if Apple found a way to trustlessly aggregate data and perform homomorphic training of their ML models, but they seem to be laying off that flavor of talent..
This appears to be slowing them down compared to their competitors, but it's not clear how much as against them being a bit behind for other reasons.
You can obtain training data from sources other than your customers. Apple have employees driving vans around with cameras and LIDAR.
Er.... that's not how it works. As a Valve employee put it (a talk about ML recognising cheaters), machine learning is just an equation:
y = f(x)
where you can actually derive the FUNCTION f if you have enough x/y pairs. Without those pairs then most of the successful techniques in machine learning fail to function.
Yes you can generate your own data but generating realistic stuff is a huge can of worms.
Data is a substitute for doing engineering by hand. If you do the latter, you require less data.
So that's wrong and untrue, as you recognize in this latest comment by saying you can partially define F. To make a concrete example, the convolution operator, the number of layers, their connectivity--all of that partially defines F. Finding a structure is what allows you to learn from the x/y pairs instead of just storing them in a look-up table.
It makes sense, then, that finding a better structure may allow you to learn from fewer x/y pairs. This is Apple's purported approach.
It also means that if you have a huge number of x/y pairs, you might benefit from (or get away with) using a simpler structure. This is Google's approach in the domains where they have a lot of data.
It is true that an important lesson has been "when designers build structure into machine learning systems, they often build in the wrong things, and they end up making commitments that the data cannot undo". But there's still a lot of structure that we put into machine learning system, and we're still learning about what good structures are for different applications, and different regimes of data.
That said, a dynamical system may exhibit chaos for one setting of the parameters, and may not exhibit chaos for other settings of the parameters. It may be true that the equations governing traffic flow have the potential to exhibit chaos, and do exhibit chaos when 1. humans drive, or 2. the drivers have only local sensing. But that doesn't mean it would still be true for driverless cars.
But again, that's not the issue at play. We wouldn't fault an driverless car for getting stuck in traffic. It's more like it has to work when it's snowing, when you can't see the lane markers, when a kid in a trashbag costume jumps into the street, etc. Those are the edge cases that we care about right now.
But in the more general case, the optimal solution for humans and the optimal solution for machines is not necessarily the same. So in driving, for instance, it may be the case that operating a vehicle in one way is safest for a human - yet not necessarily so for a machine. It's not hard to come up with examples. We are awesome at intuition and awful at microsecond reactions. Computers are awful at intuition and awesome at microsecond reactions. The optimal style of 'play' is likely going to differ. Yet if the machine is trained based on human data it would end up trying to shoehorn itself into replicating human behavior which may be suboptimal for it.
All that said, this sounds more like marketing talk. My guess is that Apple simply decided that self driving is extremely difficult and is going to come with some major growing pains which could negatively affected their crafted brand image, making it generally a poor investment of time and resources.
Presumably the anonymization needs to happen prior to collection for it to be effective?
It's more likely lacking infrastructure, which OP alluded to. ML requires infrastructure that companies like FB/Amazon/Google naturally possess. Apple is playing catch-up.
Other comment also mentioned that Google et al anonymize data, and have options to opt-out of data collection. We don't have evidence that they are necessarily worse at privacy than Apple (they could be, we just can't know for sure unless you work there and happen to know exactly how it all goes down).
Apple has data on you too. They're just worse at making use of it (and you can argue less incentivized but I don't know -- getting people to use apple-only products is a pretty strong incentive to make things work well, and Siri/Apple Maps don't work that well).
That shows they don't understand how AI work. That literally like code for traditional programming. It's literally impossible based on how modern ML works.
The other thing is, it's really tiring to see "Apple is a privacy first company" narrative on HN. Because, nobody knows exactly what Apple is doing behind the scenes with your data. There's literally no way to know what Apple is upto other than trusting their marketing material that claims to be privacy centric.
For all you know, they could still be doing stuff what other companies are doing secretly and nobody can prove nor disprove that. Next, iOS is closed source. Without access to source code, this privacy narrative is nothing more than just a marketing gimmick. We're a tech crowd and we should definitely take it a step ahead, investigate and then conclude, rather than blindly believing marketing posters that Apple puts out.
Once your data moves to a private cloud like iCloud, there's no way to guarantee anyone's privacy or safety for that matter other than blindly trusting the vendor. Don't believe me? Ask Jennifer Lawrence.
You are aware that the attack was a spear phishing attempt, rather than an inherent weakness in iCloud?
IMO they need to be investing a LOT more on the software side of things if they intend to stay competitive, especially since iPhone sales are already drying up.
Apple has always had problems with their online services, but iOS security is a problem? Compared to what, the dumpster fire over in Android?
In other words, people are buying phones less often, because phones for 3-4 years ago are still quite capable of meeting the needs of most users, so people are naturally going to start buying phones less often.
I suspect Apple was very aware of this trend, and the price increase was an effort to increase margins to make up for an expected decrease in unit sales. After all, Samsung also indicated lower guidance on their Smartphone numbers for this year, so Apple is not the only company affected by this trend.
Maybe they missed the mark a bit, and a significant number of potential customers chose not to upgrade this year, but I seriously doubt those customers turned around and bought an Android instead. I suspect they will just get plan to get another year out of their 6S and wait for what comes out this year.
This is the situation I'm in. Have a 6s that I'm not in a rush to upgrade until something appealing appears. It's plenty fast enough for everything I do. I actually started looking at Android alternatives thinking I could get a high-end phone with a removable battery but learned that's not a real thing anymore. Plus I don't like the idea of using too many Google services.
Still not sure what my upgrade path with be yet. Maybe an iPhone 8 but it feels like I'm just getting the same phone as I've got but without a headphone jack.
Android still has a horrible permission system after all of these years and privacy is laughable.
And as far as “iPhones sells are drying up”. They are still predicted to report the second highest revenues this quarter of any company in history - only surpassed by their previous record.
Profits, perhaps, but not revenues.
I had a temp iphone and it took over a lot of my texts by redirecting it toward apple's server first. When I got my android back I receive no text msgs from my friends who have iphone.
It also seem like they're making cheaper stuff so they can increase their margin via tech support and peripherals.
I agree they should invest more in software so they don't have to resort to these crappy tactic.
Also they should make a cheaper iphone region lock it to china and india only if they want to increase their margin. Their current strategy for these markets is stupid.
Siri has gotten a lot better especially at natural language recognition.[1] The HomePod for example is exceptionally good at detecting Hey Siri commands while loud music is playing. That’s all local machine learning processing coupled with a great mic array. It’s more the breadth of services that are the issue, but that’s not machine learning.
The music recs.. well you don’t cite anything. I can say the weekly For You new music mix works good for me.
[1] https://www.cnet.com/news/siri-vs-alexa-vs-google-assistant/
For example, how can it screw up something as simple as enabling alarms: https://ibb.co/vkYpGcq (this has happened multiple times to me).
Why most of the questions are answered with a google search link? Why it keeps randomly forgetting context (example: I tell her to wake me up at 1pm, then repeat just 1:01pm, and so on to add more alarms. Most of the times, it works, but sometimes it stops understanding what I want, even though the speech recognition is right and I’m saying the request in the same format as my previous requests, just with an incremented time).
I’m not saying other Apple products can’t use AI, just that Siri seems to be very low on their priority list (and I don’t blame engineering, this is something that comes from management).
Having said that, I think Siri is better at NLR than it's often given credit for, and Alexa is much worse than it's often given credit for; Alexa's NLR seems less akin to its competitors than that of a 1980s text adventure, i.e., "use exactly this precise phrasing or you won't be able to [get the treasure | turn on your living room lights]". Amazon's real strength is in making it so damn easy to write Alexa skills and to forge deals with hardware/appliance vendors, which goes back to your "breadth of services" observation.
Training set for driving is more readily available.
Or, take a look at Apple Music's recommendation system in comparison to Spotify's discover weekly.
Now, take a look at Amazon and Google. They both have their own cloud infrastructure (which ironically Apple uses) with machine learning built right into it. Both have state of the art image recognition systems (proprietary) for various applications (Eg. Google Image search, Amazon shopping, etc.)
I am not saying Apple can't do it, I just think they're lagging behind and this move totally makes sense.