Welcome!

Wearables Authors: Liz McMillan, Elizabeth White, Yeshim Deniz, Pat Romanski, Rostyslav Demush

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
Cell networks have the advantage of long-range communications, reaching an estimated 90% of the world. But cell networks such as 2G, 3G and LTE consume lots of power and were designed for connecting people. They are not optimized for low- or battery-powered devices or for IoT applications with infrequently transmitted data. Cell IoT modules that support narrow-band IoT and 4G cell networks will enable cell connectivity, device management, and app enablement for low-power wide-area network IoT. B...
In his session at 21st Cloud Expo, Raju Shreewastava, founder of Big Data Trunk, provided a fun and simple way to introduce Machine Leaning to anyone and everyone. He solved a machine learning problem and demonstrated an easy way to be able to do machine learning without even coding. Raju Shreewastava is the founder of Big Data Trunk (www.BigDataTrunk.com), a Big Data Training and consulting firm with offices in the United States. He previously led the data warehouse/business intelligence and Bi...
Contextual Analytics of various threat data provides a deeper understanding of a given threat and enables identification of unknown threat vectors. In his session at @ThingsExpo, David Dufour, Head of Security Architecture, IoT, Webroot, Inc., discussed how through the use of Big Data analytics and deep data correlation across different threat types, it is possible to gain a better understanding of where, how and to what level of danger a malicious actor poses to an organization, and to determin...
Nicolas Fierro is CEO of MIMIR Blockchain Solutions. He is a programmer, technologist, and operations dev who has worked with Ethereum and blockchain since 2014. His knowledge in blockchain dates to when he performed dev ops services to the Ethereum Foundation as one the privileged few developers to work with the original core team in Switzerland.
Cloud-enabled transformation has evolved from cost saving measure to business innovation strategy -- one that combines the cloud with cognitive capabilities to drive market disruption. Learn how you can achieve the insight and agility you need to gain a competitive advantage. Industry-acclaimed CTO and cloud expert, Shankar Kalyana presents. Only the most exceptional IBMers are appointed with the rare distinction of IBM Fellow, the highest technical honor in the company. Shankar has also receive...
Digital Transformation and Disruption, Amazon Style - What You Can Learn. Chris Kocher is a co-founder of Grey Heron, a management and strategic marketing consulting firm. He has 25+ years in both strategic and hands-on operating experience helping executives and investors build revenues and shareholder value. He has consulted with over 130 companies on innovating with new business models, product strategies and monetization. Chris has held management positions at HP and Symantec in addition to ...
"MobiDev is a Ukraine-based software development company. We do mobile development, and we're specialists in that. But we do full stack software development for entrepreneurs, for emerging companies, and for enterprise ventures," explained Alan Winters, U.S. Head of Business Development at MobiDev, in this SYS-CON.tv interview at 20th Cloud Expo, held June 6-8, 2017, at the Javits Center in New York City, NY.
Cloud computing delivers on-demand resources that provide businesses with flexibility and cost-savings. The challenge in moving workloads to the cloud has been the cost and complexity of ensuring the initial and ongoing security and regulatory (PCI, HIPAA, FFIEC) compliance across private and public clouds. Manual security compliance is slow, prone to human error, and represents over 50% of the cost of managing cloud applications. Determining how to automate cloud security compliance is critical...
Enterprises have taken advantage of IoT to achieve important revenue and cost advantages. What is less apparent is how incumbent enterprises operating at scale have, following success with IoT, built analytic, operations management and software development capabilities - ranging from autonomous vehicles to manageable robotics installations. They have embraced these capabilities as if they were Silicon Valley startups.
Recently, REAN Cloud built a digital concierge for a North Carolina hospital that had observed that most patient call button questions were repetitive. In addition, the paper-based process used to measure patient health metrics was laborious, not in real-time and sometimes error-prone. In their session at 21st Cloud Expo, Sean Finnerty, Executive Director, Practice Lead, Health Care & Life Science at REAN Cloud, and Dr. S.P.T. Krishnan, Principal Architect at REAN Cloud, discussed how they built...