With Global Accessibility Awareness Day just days away, Apple is previewing a raft of new iOS features for cognitive accessibility, along with Live Speech, Personal Voice and more. The company said it worked in "deep collaboration" with community groups representing users with disabilities, and drew on "advances in hardware and software, including on-device machine learning" to make them work. The biggest update is "Assistive Access" designed to support users with cognitive disabilities. Essentially, it provides a custom, simplified experience for the phone, FaceTime, Messages, Camera, Photos, and Music apps. That includes a "distinct interface with high contrast buttons and large text labels" along with tools that can be customized by trusted supporters for each individual.

Imagine if your smart speaker could be trained to recognize your accent, or if a pair of running shoes could alert you in real time if your gait changed, indicating fatigue. Or if, in the industrial world, sensors could parse vibration information from a machine that changed location and function often in real time, halting the machine if that information suggested there was a problem. We often write about the value of on-device machine learning (ML), but what we're generally discussing is running existing models on a device and matching incoming data against the established model. This is known as inference. So when you say the name "Alexa," your smart speaker matches the pattern and wakes up.

An on-device embedding based search package is been introduced by Tensorflow which could be run on android, ios and web applications. It runs with help of the Edge ML technique. This on-device package could help the user to search images, text or audio in just a snap of time. In this article, we would learn the implementation of on-device text-to-image search with TensorflowLite. Following are the topics to be covered.

Apple on Thursday said it's introducing new child safety features in iOS, iPadOS, watchOS, and macOS as part of its efforts to limit the spread of Child Sexual Abuse Material (CSAM) in the U.S. To that effect, the iPhone maker said it intends to begin client-side scanning of images shared via every Apple device for known child abuse content as they are being uploaded into iCloud Photos, in addition to leveraging on-device machine learning to vet all iMessage images sent or received by minor accounts (aged under 13) to warn parents of sexually explicit photos shared over the messaging platform. Furthermore, Apple also plans to update Siri and Search to stage an intervention when users try to perform searches for CSAM-related topics, alerting that the "interest in this topic is harmful and problematic." "Messages uses on-device machine learning to analyze image attachments and determine if a photo is sexually explicit," Apple noted. "The feature is designed so that Apple does not get access to the messages." The feature, called Communication Safety, is said to be an opt-in setting that must be enabled by parents through the Family Sharing feature. Detection of known CSAM images involves carrying out on-device matching using a database of known CSAM image hashes provided by the National Center for Missing and Exploited Children (NCMEC) and other child safety organizations before the photos are uploaded to the cloud.

Since the dawn of the iPhone, many of the smarts in smartphones have come from elsewhere: the corporate computers known as the cloud. Mobile apps sent user data cloudward for useful tasks like transcribing speech or suggesting message replies. Now Apple and Google say smartphones are smart enough to do some crucial and sensitive machine learning tasks like those on their own. At Apple's WWDC event this month, the company said its virtual assistant Siri will transcribe speech without tapping the cloud in some languages on recent and future iPhones and iPads. During its own I/O developer event last month, Google said the latest version of its Android operating system has a feature dedicated to secure, on-device processing of sensitive data, called the Private Compute Core.

Every decade witnesses a revolution, and ours did too – a digital revolution. One of the most defining moments of modern times has to be the rapidly evolving technologies around us. This includes machine learning and artificial intelligence. With every passing year, the market for machine learning has been rapidly increasing. According to Statista, the total funding of machine learning in 2019's first quarter alone is 28.5 billion USD. Now, over 97 percent of mobile users make use of AI voice assistants now.

Editor's Note: At Google Research, we're interested in exploring how technology can help improve people's daily lives and experiences. So it's been an incredible opportunity to work with Thomas Panek, avid runner and President & CEO of Guiding Eyes for the Blind, to apply computer vision for something important in his everyday life: independent exercise. Project Guideline is an early-stage research project that leverages on-device machine learning to allow Thomas to use a phone, headphones and a guideline painted on the ground to run independently. Below, Thomas shares why he collaborated with us on this research project, and what the journey has been like for him. I've always loved to run.

Posted by Hoi Lam , Android Machine Learning This blog post is part of a weekly series for #11WeeksOfAndroid. Each week we’re diving ...

Apple today introduced upgrades for its Core ML machine learning framework, including model encryption using Xcode and Core ML Model Deployment, a way to store and launch models and update AI independent of the app update cycle. AI within apps can power a range of features from classification of natural language or images to analysis of speech, sounds, and other media. "In the past, you would have to push more app updates just to get the newer models in your user's hands. Now with model deployment, you can quickly and easily update your models without updating the app itself," Apple engineer Anil Katti said in a WWDC session. Core ML Model Deployment also gives developers a way to group models into collections and offers targeted deployment for machine learning customized for operating system, device, region, app version, and other variables.

Google held its big annual hardware event Tuesday in New York to unveil the Pixel 4, Nest Mini, Pixelbook Go, Nest Wifi, and Pixel Buds. It was mostly predictable because details about virtually every piece of hardware the company revealed at the event were leaked months in advance, but if Google's biggest hardware event of the year had an overarching theme, it was the many applications of on-device machine learning. Most of the hardware Google introduced includes a dedicated chip for running AI, continuing an industry-wide trend to power services consumers will no doubt enjoy, but there can be privacy implications too. The new Nest Mini's on-device machine learning recognizes your most commonly used voice commands to quicken Google Assistant response time compared to the first-generation Home Mini. In Pixel Buds, due out next year, machine learning helps recognize ambient sound levels and increase or decrease sound the same way your smartphone dims or brightens when it's in sunlight or shade.