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From what I read Apple are taking a different approach? Google/Amazon are harvesting massive amounts of data and training against it and Apple are taking a priv

From what I read Apple are taking a different approach? Google/Amazon are harvesting massive amounts of data and training against it and Apple are taking a privacy first approach saying you shouldn’t have to harvest lots of data for intelligent AI. 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.

neuronic 8y ago
Thank you for this comment, long term is rarely even considered in consumer technology related to smartphones or "devices".
sterlind 8y ago
Are you suggesting Apple plans to do AI without harvesting data, or with transparent privacy controls on data? There's literally nothing for machine learning to learn besides harvested data. It's like suggesting that a power company is trying the strategy of using no wires for delivering electricity to houses.. 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..
- simonh 8y ago
  It's actually not that Apple isn't collecting data sets, but they're doing so at a more measured pace by thoroughly anonymising it using differential privacy. If the AI needs to use raw personal data in it's calculations, that is done on the user's device to keep that information private. 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.
  - mda 8y ago
    Others also anonimyzes data using differential privacy, k anonymity etc. It is a common practice and enforced when working with private / PII data.
- JimDabell 8y ago
  > There's literally nothing for machine learning to learn besides harvested data. You can obtain training data from sources other than your customers. Apple have employees driving vans around with cameras and LIDAR.
- mcintyre1994 8y ago
  Is this what their differential privacy stuff is about? It's not trustless (not least because 100% of the data comes from a closed source OS they control) but they do seem to be trying to figure out a privacy-friendly way to harvest data to train ML models.
- TheCoelacanth 8y ago
  You seem to be equating AI and statistics-based ML. Statistics-based ML is certainly producing the most results of any AI technique right now, but it is not necessarily the only one that ever will.
beambot 8y ago
To play devil's advocate: Perhaps Apple adopted this public stance as a strategy to hurt their competitors after failing to achieve parity.
digitalzombie 8y ago
You can harvest data and anonymize it. My friend at Google supposedly is doing this via research at Google. I'm not going to say Google care about our privacy as much as Apple but they are researching this probably for medical and for when opportunity arises. To be perfectly honest I think Apple's stance on privacy is all marketing, there are cases where they refused to unlock phones for the FBI but there are other cases where they ship recordings of your talk to SIRI to a third party company.
- saagarjha 8y ago
  > You can harvest data and anonymize it. Presumably the anonymization needs to happen prior to collection for it to be effective?
- DeonPenny 8y ago
  Yeh most company anonymize it because "data snooping" makes your algorithm worse. But try telling a layman that.
kajecounterhack 8y ago
Worse performance is definitely not from lacking data. They have more than enough data to make the kind of dense samples needed for ML; remember that ML is about sampling and fitting a generalization to some population, not collecting all the data from every person and overfitting to it. 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).
neya 8y ago
Machine learning is literally a numbers game. The bigger and higher quality your data set, the better your models. So, it's contradictory that a company like Apple assuming they've taken a "privacy first" stance, can also simultaneously claim to be a competent player in this machine learning arena. 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.
- saagarjha 8y ago
  > 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?
Quarrelsome 8y ago
> you shouldn’t have to harvest lots of data for intelligent AI. 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.
- paraschopra 8y ago
  Not true. You can code your prior knowledge into the model, and make do with way less x/y pairs. Data is a substitute for doing engineering by hand. If you do the latter, you require less data.
  - Quarrelsome 8y ago
    I don't see how it makes my statement untrue. The most effective ML techniques are more effective the more x/y pairs you have. Sure, you can get a leg up if you partially define F yourself but then you're no longer being as hands off as before. In practice the WHOLE of F() will likely be a blend of machine learning as well as heuristics as a product of various layers that are one or the other.
    - gugagore 8y ago
      Not the poster you're replying to, but it's just that you said "that's not how it works", and then suggested that the only way to get F is through data, even if it means generating synthetic 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.
DeonPenny 8y ago
>you shouldn’t have to harvest lots of data for intelligent AI 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.
- DSingularity 8y ago
  You are conflating AI and ML. You can have AI with no ML.

neuronic 8y ago

Thank you for this comment, long term is rarely even considered in consumer technology related to smartphones or "devices".

sterlind 8y ago

Are you suggesting Apple plans to do AI without harvesting data, or with transparent privacy controls on data? There's literally nothing for machine learning to learn besides harvested data. It's like suggesting that a power company is trying the strategy of using no wires for delivering electricity to houses.. 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..

simonh 8y ago
It's actually not that Apple isn't collecting data sets, but they're doing so at a more measured pace by thoroughly anonymising it using differential privacy. If the AI needs to use raw personal data in it's calculations, that is done on the user's device to keep that information private. 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.
- mda 8y ago
  Others also anonimyzes data using differential privacy, k anonymity etc. It is a common practice and enforced when working with private / PII data.
JimDabell 8y ago
> There's literally nothing for machine learning to learn besides harvested data. You can obtain training data from sources other than your customers. Apple have employees driving vans around with cameras and LIDAR.
mcintyre1994 8y ago
Is this what their differential privacy stuff is about? It's not trustless (not least because 100% of the data comes from a closed source OS they control) but they do seem to be trying to figure out a privacy-friendly way to harvest data to train ML models.
TheCoelacanth 8y ago
You seem to be equating AI and statistics-based ML. Statistics-based ML is certainly producing the most results of any AI technique right now, but it is not necessarily the only one that ever will.

beambot 8y ago

To play devil's advocate: Perhaps Apple adopted this public stance as a strategy to hurt their competitors after failing to achieve parity.

digitalzombie 8y ago

You can harvest data and anonymize it. My friend at Google supposedly is doing this via research at Google. I'm not going to say Google care about our privacy as much as Apple but they are researching this probably for medical and for when opportunity arises. To be perfectly honest I think Apple's stance on privacy is all marketing, there are cases where they refused to unlock phones for the FBI but there are other cases where they ship recordings of your talk to SIRI to a third party company.

saagarjha 8y ago
> You can harvest data and anonymize it. Presumably the anonymization needs to happen prior to collection for it to be effective?
DeonPenny 8y ago
Yeh most company anonymize it because "data snooping" makes your algorithm worse. But try telling a layman that.

kajecounterhack 8y ago

Worse performance is definitely not from lacking data. They have more than enough data to make the kind of dense samples needed for ML; remember that ML is about sampling and fitting a generalization to some population, not collecting all the data from every person and overfitting to it. 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).

neya 8y ago

Machine learning is literally a numbers game. The bigger and higher quality your data set, the better your models. So, it's contradictory that a company like Apple assuming they've taken a "privacy first" stance, can also simultaneously claim to be a competent player in this machine learning arena. 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.

saagarjha 8y ago
> 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?

Quarrelsome 8y ago

> you shouldn’t have to harvest lots of data for intelligent AI. 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.

paraschopra 8y ago
Not true. You can code your prior knowledge into the model, and make do with way less x/y pairs. Data is a substitute for doing engineering by hand. If you do the latter, you require less data.
- Quarrelsome 8y ago
  I don't see how it makes my statement untrue. The most effective ML techniques are more effective the more x/y pairs you have. Sure, you can get a leg up if you partially define F yourself but then you're no longer being as hands off as before. In practice the WHOLE of F() will likely be a blend of machine learning as well as heuristics as a product of various layers that are one or the other.
  - gugagore 8y ago
    Not the poster you're replying to, but it's just that you said "that's not how it works", and then suggested that the only way to get F is through data, even if it means generating synthetic 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.

DeonPenny 8y ago

>you shouldn’t have to harvest lots of data for intelligent AI 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.

DSingularity 8y ago
You are conflating AI and ML. You can have AI with no ML.