CreateML aims to simplify the creation of AI models. In fact, because it’s built in Swift, it’s possible to use drag-and-drop programming interfaces like Xcode Playgrounds to train models.

Core ML, Apple’s machine learning framework, was first introduced at WWDC last year. This year, the company has focused on making it leaner and meaner.

Apple claims Core ML 2 is 30 percent faster using a technique called batch prediction. Quantization has enabled the framework to shrink models by up to 75 percent.

This is how Apple describes Core ML:

“Core ML lets you integrate a broad variety of machine learning model types into your app. In addition to supporting extensive deep learning with over 30 layer types, it also supports standard models such as tree ensembles, SVMs, and generalised linear models.

Because it’s built on top of low-level technologies like Metal and Accelerate, Core ML seamlessly takes advantage of the CPU and GPU to provide maximum performance and efficiency.

You can run machine learning models on the device so data doesn’t need to leave the device to be analysed.”

An effort is evidently made to iterate that no information leaves the device as people become ever more wary about how their data is collected and used.

Google launched ML Kit last month at its I/O developer conference. Most of its abilities can run offline but are more limited than when connected to Google’s cloud. For example, the on-device version of the API could detect a dog is in a photo – but when connected to the internet – it could recognise the specific breed.

Apple says the developers of Memrise, a language-learning app, previously took 24 hours to train a model using 20,000 images. CreateML and Core ML 2 reduced it to 48 minutes on a MacBook Pro and 18 minutes on an iMac Pro. Furthermore, the size of the model was reduced from 90MB to just 3MB.

For developers who like Core ML, but use TensorFlow, Google released a tool in December 2017 which converts AI models into a compatible file type. You can find it on the now Microsoft-owned GitHub here.

What are your thoughts on Apple’s machine learning strategy?

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John Giannandrea stepped down on Monday but was expected to be staying at Google. He reportedly wanted to be in a position that allowed him to be “more hands-on with technology” than in his previous role. It seems that Apple offered him the opportunity he was looking for.

Apple is often regarded in tech circles as trailing Google when it comes to AI, so the knowledge Giannandrea is bringing to the company will be invaluable.

Giannandrea will run Apple’s “machine learning and A.I. strategy,” and become one of 16 executives who report directly to Apple’s chief executive, Tim Cook.

In his previous role, Giannandrea was appointed chief of Google’s AI and search businesses. The role he’s leaving is being split into two — Jeff Dean will be taking over heading AI at Google, while search will be led by Ben Gomes.

“Our technology must be infused with the values we all hold dear,” wrote Cook in an email to Apple staff. “John shares our commitment to privacy and our thoughtful approach as we make computers even smarter and more personal.”

TechCrunch observed that Giannandrea had a very pragmatic approach to AI — he considers ethics important to its development, but he does not believe in the end-of-the-world scenarios some have predicted.

“What I object to is this assumption that we will leap to some kind of superintelligent system that will then make humans obsolete,” he said. “I understand why people are concerned about it but I think it’s gotten way too much airtime. I just see no technological basis as to why this is imminent at all.”

Giannandrea’s ethos sounds very much aligned to Apple who has a long-standing commitment to practices which safeguard its users, including a strict approach to protecting their privacy.

Apple’s respect for user privacy is often seen as putting it at a disadvantage when it comes to AI which relies on large amounts of data. The company says it has developed methods of training machine learning algorithms without compromising privacy but is yet to detail how this works in practice.

What are your thoughts on Apple poaching Google’s AI chief?

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