Welcome!

@ThingsExpo Authors: Elizabeth White, Yeshim Deniz, Liz McMillan, Zakia Bouachraoui, Pat Romanski

Related Topics: @ThingsExpo, Machine Learning , Artificial Intelligence

@ThingsExpo: Article

How Is Apple Using Machine Learning? | @ThingsExpo #AI #ML #DL #DX #IoT

Today, machine learning is found in almost every product and service by Apple

Today, machine learning is found in almost every product and service by Apple. They use deep learning to extend battery life between charges on their devices and detect fraud on the Apple store, recognize the locations and faces in your photos, and help Apple choose news stories for you.

The concept of AI (Artificial Intelligence) has been the subject of many discussions lately. According to some predictions, AI will have the ability to learn by itself, outclassing the capabilities of the human brain, and even manage to fight for equal rights by the year 2100. Even though these are (still) just speculations and predictions, companies like Apple are developing and implementing machine learning technology, which is still in its infancy. How is Apple using machine learning?

Apple's beginnings with deep learning technologies
Let's start with Apple's beginnings with using AI. It was during the 1990s, when the company was using certain machine learning techniques in its products with handwriting recognition. This machine learning techniques were, of course, much more primitive.

Today, machine learning is found in almost every product and service by Apple. They use deep learning to extend battery life between charges on their devices and detect fraud on the Apple store, recognize the locations and faces in your photos, and help Apple choose news stories for you. Machine learning determines whether the owners of Apple Watch cloud are really exercising or just perambulating. It figures out whether you'd be better off switching to the cell network due to a weak Wi-Fi signal.

Apple's smart assistant
In 2011, Apple integrated a smart assistant into its operating system, and was the first tech giant to pull it off. The name of that smart assistant is Siri, and it was an adaptation of a standalone app that Apple had purchased (along with the app's developing team). Siri had ‘exploded', with ecstatic initial reviews. However, over the next few years, users wanted to see Apple deal with Siri's shortcomings. Thus, Siri got a ‘brain transplant' in 2014.

Siri's voice recognition was moved to a neural-net based system. The system began leveraging machine learning techniques, including DNN (deep neural networks), long short-term memory units, convolutional neural networks, n-grams, and gate recurrent units. Siri was operational with deep learning, while it still looked the same.

Every iPhone user has come across Apple's AI, for example, when you swipe on your device screen to get a shortlist of all the apps that you're most likely to open next, or when it identifies a caller who's not memorized in your contact list. Whenever a map location pops out for the accommodation you've reserved, or when you get reminded of an appointment that you forgot to put into your calendar. Apple's neural-network trained system watches as you type, detecting items and key events like appointments, contacts, and flight information. The information is not collected by the company, but stays on your iPhone and in cloud-based storage backups - the information is filtered so it can't be inferred. All this is made possible by Apple's adoption of neural nets and deep learning.

During this year's WWDC, Apple presented how machine learning is used by a new Siri-powered watch face to customize its content in real-time, including news, traffic information, reminders, upcoming meetings, etc., when they are supposed to be most relevant.

Making mobile AI faster with new machine learning API
Apple wants to make the AI on your iPhone as powerful and fast as possible. A week ago, the company unveiled a new machine learning API, named Core ML. The most important benefit of Core ML will be faster responsiveness of the AI when executing on the Apple Watch, iPad, and iPhone. What would this cover? Well, everything from face recognition to text analysis, with an effect of a wide range of apps.

The essential machine learning tools that the new Core ML will support include neural networks (deep, convolutional, and recurrent), tree ensembles, and linear models. As for privacy, the data that's used for improving user experience won't leave the users' tablets and phones.

The announcement of making AI work better on mobile devices became an industry-wide trend, meaning that other companies might be trying that as well. As for Apple, it's clear that deep learning technology has changed their products. However, it's not clear whether it's changing the company itself. Apple carefully controls the user experience, with everything being precisely coded and pre-designed. However, engineers must take a step back (when using machine learning) and let the software discover solutions by itself. Will machine learning systems have a hand in product design, if Apple manages to adjust to the modern reality?

More Stories By Nate Vickery

Nate M. Vickery is a business consultant from Sydney, Australia. He has a degree in marketing and almost a decade of experience in company management through latest technology trends. Nate is also the editor-in-chief at bizzmarkblog.com.

IoT & Smart Cities Stories
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
Early Bird Registration Discount Expires on August 31, 2018 Conference Registration Link ▸ HERE. Pick from all 200 sessions in all 10 tracks, plus 22 Keynotes & General Sessions! Lunch is served two days. EXPIRES AUGUST 31, 2018. Ticket prices: ($1,295-Aug 31) ($1,495-Oct 31) ($1,995-Nov 12) ($2,500-Walk-in)
According to Forrester Research, every business will become either a digital predator or digital prey by 2020. To avoid demise, organizations must rapidly create new sources of value in their end-to-end customer experiences. True digital predators also must break down information and process silos and extend digital transformation initiatives to empower employees with the digital resources needed to win, serve, and retain customers.
IoT is rapidly becoming mainstream as more and more investments are made into the platforms and technology. As this movement continues to expand and gain momentum it creates a massive wall of noise that can be difficult to sift through. Unfortunately, this inevitably makes IoT less approachable for people to get started with and can hamper efforts to integrate this key technology into your own portfolio. There are so many connected products already in place today with many hundreds more on the h...
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
Charles Araujo is an industry analyst, internationally recognized authority on the Digital Enterprise and author of The Quantum Age of IT: Why Everything You Know About IT is About to Change. As Principal Analyst with Intellyx, he writes, speaks and advises organizations on how to navigate through this time of disruption. He is also the founder of The Institute for Digital Transformation and a sought after keynote speaker. He has been a regular contributor to both InformationWeek and CIO Insight...
Digital Transformation is much more than a buzzword. The radical shift to digital mechanisms for almost every process is evident across all industries and verticals. This is often especially true in financial services, where the legacy environment is many times unable to keep up with the rapidly shifting demands of the consumer. The constant pressure to provide complete, omnichannel delivery of customer-facing solutions to meet both regulatory and customer demands is putting enormous pressure on...
Andrew Keys is Co-Founder of ConsenSys Enterprise. He comes to ConsenSys Enterprise with capital markets, technology and entrepreneurial experience. Previously, he worked for UBS investment bank in equities analysis. Later, he was responsible for the creation and distribution of life settlement products to hedge funds and investment banks. After, he co-founded a revenue cycle management company where he learned about Bitcoin and eventually Ethereal. Andrew's role at ConsenSys Enterprise is a mul...
Business professionals no longer wonder if they'll migrate to the cloud; it's now a matter of when. The cloud environment has proved to be a major force in transitioning to an agile business model that enables quick decisions and fast implementation that solidify customer relationships. And when the cloud is combined with the power of cognitive computing, it drives innovation and transformation that achieves astounding competitive advantage.
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by ...