While still technically in pre-release, the Mac-optimised TensorFlow fork supports native hardware acceleration on Mac devices with M1 or Intel chips through Apple’s ML Compute framework.

The new TensorFlow release boasts of an over 10x speed improvement for common training tasks. While impressive, it has to be taken in the context that the GPU was not previously used for training tasks. 

A look at the benchmarks still indicates a substantial gap between the Intel and M1-based Macs across various machine learning models:

In a blog post, Pankaj Kanwar, Tensor Processing Units Technical Program Manager at Google, and Fred Alcober, TensorFlow Product Marketing Lead at Google, wrote:

“These improvements, combined with the ability of Apple developers being able to execute TensorFlow on iOS through TensorFlow Lite, continue to showcase TensorFlow’s breadth and depth in supporting high-performance ML execution on Apple hardware.”

We can only hope that running these workloads doesn’t turn MacBooks into expensive frying pans—but the remarkable efficiency they’ve displayed so far gives little cause for concern.

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Voysis specialises in improving digital assistants inside online shopping applications wherein the software could respond more precisely to voice commands. According to the start-up, its technology could narrow product search results by processing shopping phrases like “I need a new LED TV” and “My budget is $1,000.” This AI was provided to other companies for use in their own applications and voice assistants.

The acquisition of Voysis would give Siri an edge to perform better than the Google Assistant, which many in the industry say has a notable lead in natural language comprehension and processing.

Voysis uses an AI-based method called WaveNets to create more human-like computer speech. In 2018, co-founder Peter Cahill had said that his company managed to shrink its system to the point where, once the AI is trained, the software uses as little as 25MB of memory, which makes it easier to run on smartphones without an internet connection.

This is not the first instance where Apple has made a big bet in this space. In January, Apple acquired Seattle-based edge AI specialist Xnor.ai for approximately $200m. Xnor.ai is the same company that once powered the person-detection feature on Wyze’s popular cameras. The Cupertino-based tech giant has been on something of an AI acquisition spree in recent years. A report by CBInsights found that Apple acquired more AI firms (20) than any other leading tech company in 2019.

This time last year, Apple acquired AI company Laserlike to add to its growing roster of in-house talent. Laserlike is known for its AI-powered app which makes it easier for users to follow news topics. Most notably, it was founded by former Google engineers.

Photo by Medhat Dawoud on Unsplash

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If you recognise Xnor.ai, it’s likely because the company’s technology once powered the person-detection feature on Wyze’s popular cameras. Xnor.ai abruptly cancelled their contract with Wyze back in November – and now we know why.

In layman’s terms, edge computing means the processing is done on-device. On-device computation has significant advantages when it comes to reducing latency, improving performance, and retaining privacy.

https://www.youtube.com/watch?v=FG31XxX7ra8

Apple makes a big deal out of its privacy credentials. When people are becoming increasingly concerned about the data collection practices of competitors like Google, Amazon, and Microsoft, it’s a strategy that works.

Compared to all three of those cloud-focused competitors, many believe Apple has fallen behind when it comes to AI. With its Xnor.ai acquisition, Apple can boost its AI capabilities while saying that it’s protecting customers’ privacy by not sending it all up to the cloud like its rivals often do.

Xnor.ai started its journey as part of the nonprofit Allen Institute for AI. In 2017, the company spun off as its own entity headed by CEO Ali Farhadi.

While small change compared to what Apple acquired it for, Xnor.ai had already raised significant capital. In early 2017, it raised $2.7 million. In 2018, it raised $12 million. Both rounds were led by Madrona Venture Group.

Apple has been on something of an AI acquisition spree in recent years. In fact, a report by CBInsights last year found that Apple acquired more AI firms (20) than any other leading tech company in 2019. Google took second place with 14 acquisitions, followed by Microsoft with 10.

With the IoT becoming the next big thing, companies like Apple are looking to AI for ways to differentiate their products from competitors. Apple’s $200 million for Xnor.ai may seem a lot on paper, but it will almost certainly pay off in the long run.

Want to learn more about topics like this from thought leaders in the space? Find out more about the Edge Computing Expo, a brand new, innovative event and conference exploring the edge computing ecosystem.

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Loup Ventures asked each of the three main virtual assistants – Google Assistant, Alexa, and Siri – a total of 800 questions. The assistants understood almost every question, even if not all of the responses were correct/sufficient.

In terms of understanding the questions, these are the results:

• Google Assistant – 100 percent
• Alexa – 99.9 percent
• Siri – 99.8 percent

Loup Ventures’ say their question set it designed to comprehensively test a virtual assistant’s ability and utility. Questions are broken down into five categories:

1. Local – Where is the nearest coffee shop?
2. Commerce – Order me more paper towels.
3. Navigation – How do I get to Uptown on the bus?
4. Information – Who do the Twins play tonight?
5. Command – Remind me to call Jerome at 2 pm today.

This is the percentage of questions each assistant answered correctly:

• Google Assistant – 92.9 percent
• Siri – 83.1 percent
• Alexa –  79.8 percent

The results are a huge improvement over Assistant, Alexa, and Siri’s results last year.

In 2018, Loup Ventures found Google Assistant answered the most questions with an 86 percent success rate. This was followed by Siri at 79 percent, while Alexa trailed behind at just 61 percent.

Alexa’s jump in answering the question correctly from 61 percent last year to almost 80 percent this year is the most commendable performance improvement, even if Amazon’s assistant is still in last place overall.

The researchers explained that they’ve stopped including Cortana in their tests due to a strategy change from Microsoft earlier this year.

Microsoft said in January that it’s no longer attempting to compete with Alexa or Google Assistant in areas like smart speakers, but instead is repositioning Cortana more like a skill that can be embedded in services where she can be of assistance.

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Drive.ai is full of skilled personnel after being founded in 2016 by a pioneering team of graduates from Stanford’s AI lab. With AI talent in short supply, it seems Apple is considering purchasing a whole company to get the skills on its side.

Apple would not get any of Drive.ai’s intellectual property as part of the deal, only the minds behind it. While it’s unclear what Apple is considering paying for the startup, the company was believed to be valued at ~$200 million during a VC round in 2017.

The fact Apple is scoping out talent from a driverless car firm shows Cupertino’s continued interest in the area.

Back in January, Apple pulled around 200 employees off its self-driving car project which made some people question whether Cupertino had the ability (and resolve) to break into such an established industry with a minefield of regulatory needs that vary from location-to-location.

A self-driving car being tested by Apple had a rear-end collision with another vehicle last year. Fortunately, unlike Uber’s self-driving car incident, there were no injuries.

During an interview with CNN last November, Apple CEO Tim Cook expressed that his company is focused on the software side of autonomous vehicles. Acquiring talent from Drive.ai is a surefire way of boosting Apple’s in-house expertise in applying machine learning to cars.

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Laserlike is known for its AI-powered app which makes it easier for users to follow news topics. Most notably, it was founded by former Google engineers.

Few people think of Apple as an AI leader. The firm’s closest rival, Google, has invested heavily in AI for many years (as much for its cloud and search businesses as mobile).

Apple is, therefore, coming from a position behind in terms of AI development. However, it is quickly catching up through hoovering up talented AI companies and solutions. Let’s all remember, even Siri was acquired from Nuance.

Here are some of the other AI companies Apple has acquired in the past few years:

• Silk Labs (2018)
• Asaii (2018)
• Spektral (2017)
• Regains (2017)

Following the poaching of Google’s John Giannandrea, it’s been clear that Apple has been doubling-down on its AI efforts. Which makes sense, if you’re here on AI News then you know how important it is for Apple to become a leader in the space.

On its website, Laserlike pledges to keep personalisation at its core post-Apple acquisition:

“This is one of the things we want to fix on the Internet. Laserlike’s core mission is to deliver high quality information and diverse perspectives on any topic from the entire web. We are passionate about helping people follow their interests and engage with new perspectives.”

Apple is holding an event on March 25th where it’s rumoured to be launching a news subscription service. It will be interesting to see if this acquisition plays a part.

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Giannandrea is one of Silicon Valley’s brightest minds in AI. He came under the spotlight for heading Google’s search and artificial intelligence operations.

After spending eight years at Google, back in April he was poached by Apple to lead its Siri and CoreML efforts. His positive impact at Apple was clearly noted.

In a statement, Apple CEO Tim Cook wrote:

“John hit the ground running at Apple and we are thrilled to have him as part of our executive team.

Machine learning and AI are important to Apple’s future as they are fundamentally changing the way people interact with technology, and already helping our customers live better lives.

We’re fortunate to have John, a leader in the AI industry, driving our efforts in this critical area.”

Giannandrea has now been appointed Senior Vice President at Apple and will report directly to Tim Cook.

Apple has placed a greater focus in 2018 on building out Siri’s functionality after being criticised for shortcomings in comparison to rivals. With its latest appointment, it’s clear Cupertino is happy to place a bet on Giannandrea in closing that gap.

 AI & >.

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John Giannandrea joined Google back in 2010 before stepping down in April this year. He announced he’d been hired by Apple the following day, but in what position had been a mystery until now.

It was obvious Giannandrea would be stepping into a big role at Apple, and that’s exactly what’s happened.

AI is becoming increasingly important and an area where Google is seen as a leader due to its vast experience in search and cloud technologies.

Apple, which continues to focus on on-device AI citing improved privacy, has struggled to keep up with Google’s AI advancements.

The hire of Giannandrea is undoubtedly a big win for Apple.

Giannandrea will be heading Apple’s machine learning division and will therefore be responsible for efforts including Siri and CoreML.

His official title is ‘chief of machine learning and AI strategy’ and is sure to have a hefty-but-undisclosed salary.

Apple’s continued desire to put privacy first is respectable but may continue to put the company at somewhat of a disadvantage to companies such as Google and Facebook which collect masses of data.

We look forward to seeing where Giannandrea takes Apple’s machine learning and AI efforts in the years to come.

What are your thoughts on Apple’s hiring of Giannandrea?

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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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