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Optimizing Siri on HomePod in Far‑Field Settings - Apple
The typical audio environment for HomePod has many challenges — echo, reverberation, and noise. Unlike Siri on iPhone, which operates close to the user’s mouth, Siri on HomePod must work well in a far-field setting. Users want to invoke Siri from many locations, like the couch or the kitchen, without regard to where HomePod sits. A complete online system, which addresses all of the environmental issues that HomePod can experience, requires a tight integration of various multichannel signal processing technologies. Accordingly, the Audio Software Engineering and Siri Speech teams built a system that integrates both supervised deep learning models and unsupervised online learning algorithms and that leverages multiple microphone signals. The system selects the optimal audio stream for the speech recognizer by using top-down knowledge from the “Hey Siri” trigger phrase detectors. In this article, we discuss the machine learning techniques we use for online signal processing, as well as the challenges we faced and our solutions for achieving environmental and algorithmic robustness while ensuring energy efficiency.
apple  AI/ML  research  report  homepod  siri  audio 
7 days ago by rgl7194
Apple research paper outlines how Apple has optimized Siri to work on HomePod | iLounge News
Apple has published a new entry in its Machine Learning Journal providing in-depth technical information on how Apple designed Siri on the HomePod to deal with hearing and understanding a user’s voice in the larger spaces in which HomePod is intended to operate. Titled Optimizing Siri on HomePod in Far‑Field Settings, the paper explains how Siri on HomePod had to be designed to work in “challenging usage scenarios” such as dealing with users standing much farther away from the HomePod than they typically would be from their iPhone, as well as dealing with loud music playback from the HomePod itself, and making out the user speaking despite other sound sources in a room like a TV or household appliances. In the article, Apple goes on to outline how the HomePod’s six microphones and multichannel signal processing system built into its A8 chip work together to adapt to a variety of changing conditions while still making sure that Siri can hear the person speaking and respond appropriately. Machine learning algorithms are employed as part of the signal processing to create advanced algorithms for common features like echo cancellation and noise reduction, improving Siri’s reliability across a wide variety of frequently changing environments.
apple  AI/ML  research  report  homepod  siri  audio 
7 days ago by rgl7194
Apple published a surprising amount of detail about how the HomePod works | Ars Technica
Machine learning is a big focus at Apple right now—a blog post explains why.
Today, Apple published a long and informative blog post by its audio software engineering and speech teams about how they use machine learning to make Siri responsive on the HomePod, and it reveals a lot about why Apple has made machine learning such a focus of late.
The post discusses working in a far-field setting where users are calling on Siri from any number of locations around the room relative to the HomePod's location. The premise is essentially that making Siri work on the HomePod is harder than on the iPhone for that reason. The device must compete with loud music playback from itself.
Apple addresses these issues with multiple microphones along with machine learning methods—specifically:
Mask-based multichannel filtering using deep learning to remove echo and background noise
Unsupervised learning to separate simultaneous sound sources and trigger-phrase based stream selection to eliminate interfering speech
apple  AI/ML  research  report  homepod  siri  audio 
7 days ago by rgl7194
Apple's small Silk Labs purchase pushes AI to the edge | Computerworld
Apple’s AI push into on-device machine learning continues with news of its acquisition of Silk Labs breaking just as the U.S. heads into its annual holiday season.
The Information states Apple quietly acquired Silk Labs earlier this year.
Apple’s new purchase seems a good one.
The acquisition closely matches Apple’s feelings about the need to put AI/machine intelligence at the edge. Devices must be smart enough to function when they are offline and secure enough not to damage the privacy of customers.
apple  M&A  business  AI/ML  privacy 
16 days ago by rgl7194
How Driverless Cars Could Make Us Better Drivers - WhoWhatWhy
Algorithms to the Rescue!
Scientists are constantly striving to solve all the world’s problems — from global warming to oceans choking on plastics. Now robotics experts are turning their attention to traffic, using computer modeling to determine whether self-driving cars could ease the stressful, dangerous problem of congestion.
The results suggest that Artificial Intelligence (AI) could make driving safer and more pleasant, using autonomous vehicles as role models for other drivers on the roads.
Of course, driverless technology is still in its infancy, and there have been tragic consequences during trials of self-driving cars. But this new research was virtual — its results resemble a computer game more than a real-life experiment with actual traffic flow, as shown in the video below..
cars  driving  technology  self-driving  AI/ML 
16 days ago by rgl7194
Tech is coming for the weed industry at MJBizCon - The Verge
Now that cannabis is big business, entrepreneurs are eager to bring tech to the weed industry, which means that cannabis is on the blockchain and machine learning has come for marijuana cultivation.
Both technologies were on display at this year’s Marijuana Business Daily Conference, or MJBizCon, in Las Vegas. Six years ago, MJBizCon started with 17 exhibitors and 400 attendees in Denver. Since then, more states have legalized marijuana, which means more demand for cannabis and more opportunities to make money. For example, analysts expect that the cannabis market in Michigan, which recently became the first Midwestern state to approve recreational marijuana, will reach nearly $2 billion annually in a few years. Globally, the cannabis market is expected to expand from $13 billion this year to $32 billion in five years, experts say. That growth is why this year’s MJBizCon has over 1,000 companies exhibiting and 25,000 attendants from 63 countries.
marijuana  business  technology  blockchain  AI/ML 
23 days ago by rgl7194
How The Wall Street Journal is preparing its journalists to detect deepfakes
“We have seen this rapid rise in deep learning technology and the question is: Is that going to keep going, or is it plateauing? What’s going to happen next?”
Artificial intelligence is fueling the next phase of misinformation. The new type of synthetic media known as deepfakes poses major challenges for newsrooms when it comes to verification. This content is indeed difficult to track: Can you tell which of the images below is a fake?
(Check the bottom of this story for the answer.)
We at The Wall Street Journal are taking this threat seriously and have launched an internal deepfakes task force led by the Ethics & Standards and the Research & Development teams. This group, the WSJ Media Forensics Committee, is comprised of video, photo, visuals, research, platform, and news editors who have been trained in deepfake detection. Beyond this core effort, we’re hosting training seminars with reporters, developing newsroom guides, and collaborating with academic institutions such as Cornell Tech to identify ways technology can be used to combat this problem.
“Raising awareness in the newsroom about the latest technology is critical,” said Christine Glancey, a deputy editor on the Ethics & Standards team who spearheaded the forensics committee. “We don’t know where future deepfakes might surface so we want all eyes watching out for disinformation.”
Here’s an overview for journalists of the insights we’ve gained and the practices we’re using around deepfakes.
news  factcheck  AI/ML  fake 
23 days ago by rgl7194
NY Times Using Google AI to Digitize 5M+ Photos and Find 'Untold Stories'
The New York Times has teamed up with Google Cloud for digitizing five to seven million old photos in its archive. Google’s AI will also be tasked with unearthing “untold stories” in the massive trove of historical images.
“For over 100 years, The Times has archived approximately five to seven million of its old photos in hundreds of file cabinets three stories below street level near their Times Square offices in a location called the ‘morgue’,” Google writes. “Many of the photos have been stored in folders and not seen in years. Although a card catalog provides an overview of the archive’s contents, there are many details in the photos that are not captured in an indexed form.”
google  photography  nytimes  AI/ML  digital  scanning 
27 days ago by rgl7194
Finally… A Parental Monitoring App We Can Endorse | Above The Fray
After years of saying no to parental monitoring apps, we finally found one that works for parents, schools, and young people. Watch, then visit:
cellphones  iphone  parental_controls  AI/ML  apps  video 
6 weeks ago by rgl7194
What have tech companies done wrong with fake news? Google (yep) lists the ways
The growing stream of reporting on and data about fake news, misinformation, partisan content, and news literacy is hard to keep up with. This weekly roundup offers the highlights of what you might have missed.
“Who should be responsible for censoring ‘unwanted’ conversation, anyway? Governments? Users? Google?” Breitbart — yep, leading the column with a Breitbart story! — got leaked a Google presentation, “The Good Censor,” that shows how Google is grappling with the question of whether it’s possible to “have an open and inclusive internet while simultaneously limiting political oppression and despotism, hate, violence and harassment.”
The report is insightful and interesting, and I don’t really see why it should have been leaked instead of Google simply releasing it publicly — it’s good to see that at least some in the company are thinking about these issues in nuanced ways.
“This free speech ideal was instilled in the DNA of the Silicon Valley startups that now control the majority of our online conversations,” the Google report notes. But “recent global events have undermined this utopian narrative” of the internet as a place for free and uncensored discussion...
technology  business  google  fake_news  propaganda  social_media  AI/ML 
6 weeks ago by rgl7194

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