4 ms·

Create ML

144 points by gok 8y ago

oceanghost 8y ago
Did Apple just commoditize machine learning? If it wasn't before...
- mlthoughts2018 8y ago
  The big tech companies keep trying this (commoditizing ML models exposed via APIs), but I am extremely skeptical that they will succeed with this approach. The reason is simple. All of the work lies with validating that a model solves your particular problem well enough to be cost-effective for your customers or stakeholders to be satisfied. Machine learning is not a commodity the same way cloud infrastructure is, because the model development and validation aspects are inherently tailored to your extremely specific, one-off data generating processes and performance characteristic requirements. Even if you outsource the model itself, this still requires developing some notion of acceptance testing for the model's performance in your use case, on the distribution of data that matter to you. And such acceptance testing still requires high literacy in statistics and model evaluation (e.g., you can't skip on hiring that expensive machine learning engineer even if you outsource because then who, internally, is going to be able to tell you if the outsource solution is a bunch of junk, and why, and what to do about it.) Short of turning the big tech ML offerings into flat out consulting arrangements, where you trade-off having your own in-house and application-specific and data-specific machine learning staff in order to rent time from experts at the big co's, this model couldn't work. Don't get me wrong, just as IBM somehow still finds ways to leach from enterprise clients after all these years of not actually helping them, I am sure these lines of business will generate enterprise consulting money. I just think lots of people will be disappointed that it doesn't somehow mean you're getting Google ML engineer caliber attention or results spent on your company's bespoke needs, and that performance of generic and naive transfer learning from big cookie cutter models often ends up being way worse than you thought. I feel bad for people who end up suffering the amplified version of vendor lock-in this could create.
- Descon 8y ago
  This is nothing new: - https://aws.amazon.com/sagemaker/ https://aws.amazon.com/sagemaker/ - https://developer.ibm.com/ai/ https://developer.ibm.com/ai/
nso95 8y ago
How is this different from Core ML?
- rasmi 8y ago
  Core ML is bring-your-own-model (or use the built-in classifiers, which have generic labels). This is an easy way to train custom models (for example, on images from your own product catalog).
  - nso95 8y ago
    Thanks!
Someone1234 8y ago
Because I lack imagination and or don't understand ML enough, could someone give a couple of examples of what this could be used for? Particularly at these scales with presumably(?) a smaller initial data set.
- agitator 8y ago
  Easy example: A Hot Dog / Not Hotdog app More complex applications: apps that do clever audio processing, smart image filters, making some sort of intelligent predictions or recommendations based on a users behavior, communications, preferences, etc.
- rasmi 8y ago
  These are not revolutionary or even helpful ideas, but they can be convenient or fun. For example: Some medical applications (just for example, you would need quite a robust model): * Categorize moles on your skin as cancerous or not. * Categorize cuts on your skin as infected or not. * Have the user input certain characteristics (images, temperature data, etc) and give some preliminary diagnosis. Less interesting/helpful: * Detect photos of certain foods in your app (e.g. Twitter, Instagram, Yelp) and recommend relevant emoji or hashtags. * For a note-taking or to-do app, classify notes into categories automatically for the user. * Suggest actions in your app based on what the user types or does. * Recommend solutions or help articles from the feedback form in your app. Tag the feedback with a certain sentiment to determine how quickly you should follow up on it. * Detect your products in images/videos and tag them to make them searchable, and recommend relevant things to the user (e.g. in a beer tracking app [2], detect a certain kind of beer, suggest similar types). If any of this sounds like it's been done before, it probably has, but it's important to note this is done entirely on device (private!) and with custom labels. The current alternative is to use TensorFlow Lite [1], which is a bit more involved. I'm sure as the field develops we will see more creative (and useful/helpful) applications. [1] https://www.tensorflow.org/mobile/tflite/ https://www.tensorflow.org/mobile/tflite/ [2] https://untappd.com/ https://untappd.com/
rasmi 8y ago
This looks like a really easy way to integrate your own custom ML models into your app. I imagine this would appeal to a lot of hobbyist developers, especially given how simple it is. The docs [1] also seem to imply they're using transfer learning from more robust models for their image classifier: "Use at least 10 images per label for the training set, but more is always better." and "Create ML leverages the machine learning infrastructure built in to Apple products like Photos and Siri." [1] https://developer.apple.com/documentation/create_ml/creating_an_image_classifier_model https://developer.apple.com/documentation/create_ml/creating...
- tomnipotent 8y ago
  This will appeal to any developer that wants to deploy machine learning models on macOS or iOS and get the most out of the hardware (Metal w/Core ML). > The docs [1] also seem to imply they're using transfer learning from more robust models for their image classifier I read that as a recommendation for the minimum number of training samples per label (e.g. 10 photos w/leopard tagged). Apple's Turi Create [1] also also been getting some love these last few months to add support for exporting to Core ML. [1] https://github.com/apple/turicreate https://github.com/apple/turicreate
mromanuk 8y ago
Can I use this with Swift for Linux?
- dhritzkiv 8y ago
  No, as it is reliant on the Frameworks built into –and SDKs targeting– macOS 10.14
RosanaAnaDana 8y ago
Boy that 'ML' logo sure is reminiscent of the TensorFlow logo..
Slackattack 8y ago
Could not seem to find any mention of where the model is being trained...I would assume locally, but again, can't seem to find a mention of recommended hardware for this.
amelius 8y ago
Can we please replace "Machine Learning" by "Pattern Matching", as this is all it is ...
- reificator 8y ago
  Which sounds more impressive on a resume though?
  - thealfreds 8y ago
    * Machine Learned Pattern Matching
- lossolo 8y ago
  It's a known problem in ML community. Unfortunately saying "hey we do pattern matching in our product" dosen't sound so good to investors as "We are building thinking machines using artificial intelligence".
- erk__ 8y ago
  Well ML have had pattern matching for a very long time.
- singularity2001 8y ago
  Image Captioning, STT, DeepQN etc now have little to do with "Pattern Matching" anymore. [1] https://github.com/DeepRNN/image_captioning https://github.com/DeepRNN/image_captioning [∞]
  - singularity2001 8y ago
    Admittedly though the ridiculously primitive apple classifiers can be regarded as simple old "Pattern Matching".
therealmarv 8y ago
Not long time ago we got Swift for Tensorflow as first class citizen: https://medium.com/tensorflow/introducing-swift-for-tensorflow-b75722c58df0 https://medium.com/tensorflow/introducing-swift-for-tensorfl...
uptownfunk 8y ago
Things like this convince me that we will never get to AGI. I have a, perhaps untestable hypothesis, that an AGI could only be built by an AGI, or that if we create it, we won't entirely know how we created it in the first place. (Much as we don't completely understand how to create life force, even though we do create it, through the process of reproduction). We may be able to build components of it (like growing organs in a lab) but I am quite skeptical we will be able to create "life" or in this case "intelligence".
- seiferteric 8y ago
  In my uninformed opinion, ML is more akin to the subconscious parts of our brain, like our ability to recognize peoples faces. We don't learn how to do this, it's there when we are born. It is a hardwired part of our brain that was formed through evolution instead of learning and does not involve higher order thinking. It's obviously useful, and could/would form part of a AGI system but unlikely to be enough.
  - jsemrau 8y ago
    > to the subconscious parts of our brain Not too bad of a definition since the basis for ML is statistics on assumptions on what describe the world.
thomasjoulin 8y ago
Interestingly, it's available in Swift only, not Objective C: https://twitter.com/jckarter/status/1003768142407987200 https://twitter.com/jckarter/status/1003768142407987200

Create ML

144 points by gok 8y ago

oceanghost 8y ago

Did Apple just commoditize machine learning? If it wasn't before...

mlthoughts2018 8y ago
The big tech companies keep trying this (commoditizing ML models exposed via APIs), but I am extremely skeptical that they will succeed with this approach. The reason is simple. All of the work lies with validating that a model solves your particular problem well enough to be cost-effective for your customers or stakeholders to be satisfied. Machine learning is not a commodity the same way cloud infrastructure is, because the model development and validation aspects are inherently tailored to your extremely specific, one-off data generating processes and performance characteristic requirements. Even if you outsource the model itself, this still requires developing some notion of acceptance testing for the model's performance in your use case, on the distribution of data that matter to you. And such acceptance testing still requires high literacy in statistics and model evaluation (e.g., you can't skip on hiring that expensive machine learning engineer even if you outsource because then who, internally, is going to be able to tell you if the outsource solution is a bunch of junk, and why, and what to do about it.) Short of turning the big tech ML offerings into flat out consulting arrangements, where you trade-off having your own in-house and application-specific and data-specific machine learning staff in order to rent time from experts at the big co's, this model couldn't work. Don't get me wrong, just as IBM somehow still finds ways to leach from enterprise clients after all these years of not actually helping them, I am sure these lines of business will generate enterprise consulting money. I just think lots of people will be disappointed that it doesn't somehow mean you're getting Google ML engineer caliber attention or results spent on your company's bespoke needs, and that performance of generic and naive transfer learning from big cookie cutter models often ends up being way worse than you thought. I feel bad for people who end up suffering the amplified version of vendor lock-in this could create.
Descon 8y ago
This is nothing new: - https://aws.amazon.com/sagemaker/ https://aws.amazon.com/sagemaker/ - https://developer.ibm.com/ai/ https://developer.ibm.com/ai/

nso95 8y ago

How is this different from Core ML?

rasmi 8y ago
Core ML is bring-your-own-model (or use the built-in classifiers, which have generic labels). This is an easy way to train custom models (for example, on images from your own product catalog).
- nso95 8y ago
  Thanks!

Someone1234 8y ago

Because I lack imagination and or don't understand ML enough, could someone give a couple of examples of what this could be used for? Particularly at these scales with presumably(?) a smaller initial data set.

agitator 8y ago
Easy example: A Hot Dog / Not Hotdog app More complex applications: apps that do clever audio processing, smart image filters, making some sort of intelligent predictions or recommendations based on a users behavior, communications, preferences, etc.
rasmi 8y ago
These are not revolutionary or even helpful ideas, but they can be convenient or fun. For example: Some medical applications (just for example, you would need quite a robust model): * Categorize moles on your skin as cancerous or not. * Categorize cuts on your skin as infected or not. * Have the user input certain characteristics (images, temperature data, etc) and give some preliminary diagnosis. Less interesting/helpful: * Detect photos of certain foods in your app (e.g. Twitter, Instagram, Yelp) and recommend relevant emoji or hashtags. * For a note-taking or to-do app, classify notes into categories automatically for the user. * Suggest actions in your app based on what the user types or does. * Recommend solutions or help articles from the feedback form in your app. Tag the feedback with a certain sentiment to determine how quickly you should follow up on it. * Detect your products in images/videos and tag them to make them searchable, and recommend relevant things to the user (e.g. in a beer tracking app [2], detect a certain kind of beer, suggest similar types). If any of this sounds like it's been done before, it probably has, but it's important to note this is done entirely on device (private!) and with custom labels. The current alternative is to use TensorFlow Lite [1], which is a bit more involved. I'm sure as the field develops we will see more creative (and useful/helpful) applications. [1] https://www.tensorflow.org/mobile/tflite/ https://www.tensorflow.org/mobile/tflite/ [2] https://untappd.com/ https://untappd.com/

rasmi 8y ago

This looks like a really easy way to integrate your own custom ML models into your app. I imagine this would appeal to a lot of hobbyist developers, especially given how simple it is. The docs [1] also seem to imply they're using transfer learning from more robust models for their image classifier: "Use at least 10 images per label for the training set, but more is always better." and "Create ML leverages the machine learning infrastructure built in to Apple products like Photos and Siri." [1] https://developer.apple.com/documentation/create_ml/creating_an_image_classifier_model https://developer.apple.com/documentation/create_ml/creating...

tomnipotent 8y ago
This will appeal to any developer that wants to deploy machine learning models on macOS or iOS and get the most out of the hardware (Metal w/Core ML). > The docs [1] also seem to imply they're using transfer learning from more robust models for their image classifier I read that as a recommendation for the minimum number of training samples per label (e.g. 10 photos w/leopard tagged). Apple's Turi Create [1] also also been getting some love these last few months to add support for exporting to Core ML. [1] https://github.com/apple/turicreate https://github.com/apple/turicreate

mromanuk 8y ago

Can I use this with Swift for Linux?

dhritzkiv 8y ago
No, as it is reliant on the Frameworks built into –and SDKs targeting– macOS 10.14

RosanaAnaDana 8y ago

Boy that 'ML' logo sure is reminiscent of the TensorFlow logo..

Slackattack 8y ago

Could not seem to find any mention of where the model is being trained...I would assume locally, but again, can't seem to find a mention of recommended hardware for this.

amelius 8y ago

Can we please replace "Machine Learning" by "Pattern Matching", as this is all it is ...

reificator 8y ago
Which sounds more impressive on a resume though?
- thealfreds 8y ago
  * Machine Learned Pattern Matching
lossolo 8y ago
It's a known problem in ML community. Unfortunately saying "hey we do pattern matching in our product" dosen't sound so good to investors as "We are building thinking machines using artificial intelligence".
erk__ 8y ago
Well ML have had pattern matching for a very long time.
singularity2001 8y ago
Image Captioning, STT, DeepQN etc now have little to do with "Pattern Matching" anymore. [1] https://github.com/DeepRNN/image_captioning https://github.com/DeepRNN/image_captioning [∞]
- singularity2001 8y ago
  Admittedly though the ridiculously primitive apple classifiers can be regarded as simple old "Pattern Matching".

therealmarv 8y ago

Not long time ago we got Swift for Tensorflow as first class citizen: https://medium.com/tensorflow/introducing-swift-for-tensorflow-b75722c58df0 https://medium.com/tensorflow/introducing-swift-for-tensorfl...

uptownfunk 8y ago

Things like this convince me that we will never get to AGI. I have a, perhaps untestable hypothesis, that an AGI could only be built by an AGI, or that if we create it, we won't entirely know how we created it in the first place. (Much as we don't completely understand how to create life force, even though we do create it, through the process of reproduction). We may be able to build components of it (like growing organs in a lab) but I am quite skeptical we will be able to create "life" or in this case "intelligence".

seiferteric 8y ago
In my uninformed opinion, ML is more akin to the subconscious parts of our brain, like our ability to recognize peoples faces. We don't learn how to do this, it's there when we are born. It is a hardwired part of our brain that was formed through evolution instead of learning and does not involve higher order thinking. It's obviously useful, and could/would form part of a AGI system but unlikely to be enough.
- jsemrau 8y ago
  > to the subconscious parts of our brain Not too bad of a definition since the basis for ML is statistics on assumptions on what describe the world.

thomasjoulin 8y ago

Interestingly, it's available in Swift only, not Objective C: https://twitter.com/jckarter/status/1003768142407987200 https://twitter.com/jckarter/status/1003768142407987200