In October 2018, months after a brief reveal, Amazon brought Whisper Mode to select third- and first-party Alexa devices. It expanded the feature to all locales in November 2019, such that all smart home appliances powered by Alexa -- the company's virtual assistant -- now respond to whispered speech by whispering back. Amazon was a bit light on the technical details initially, save that Whisper Mode uses a neural network -- layers of mathematical functions loosely modeled after the human brain's neurons -- to distinguish among normal and whispered words. But in an academic paper appearing in the January 2020 issue of the journal IEEE Signal Processing Letters and an accompanying blog post, it detailed the research that led to the expansion. The principal challenge was converting normal speech into whispered speech while maintaining naturalness and speaker identity, explained Marius Cotescu, an applied scientist in Amazon's text-to-speech research group.

China is once more tightening its grip on internet content, and this time algorithms are in the spotlight. The Cyberspace Administration of China has published upcoming rules that dictate how internet companies manage content, including a push for recommendation algorithms that promote "positive" ideas (read: government policies) while excluding "bad" material. The measure explicitly forbids content that "endangers national security, leaks state secrets, subverts state power [and] undermines national unity." The new rules are due to take effect on March 1st, and also call for tighter management of accounts, sign-ups, moderation and "rumors." Governments have lately stepped up attempts to regulate algorithms, although China's approach is very different than that from other countries.

Every day, roughly 60 percent of all market trades are made by computer. When the market turns volatile, this can climb to as high as 90 percent. Robo-advisors are increasingly making this process available to the consumer, saving them time and money as a result. With humans no longer in the transaction chain, fees are slashed. Undercutting the typical 2 percent cut of profits (not to mention 20 percent incentives) charged by a wealth manager, most robo-advisors take around 0.25 percent.

Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. The editors of spoke with six innovators who are making the planet a more sustainable and accessible place for future generations. From Saieshan Govinder's work tackling the youth unemployment crisis in South Africa to Srilekha Chakraborty's initiatives that help girls in rural India learn and talk about their health, these game-changers are leading the way to a better world.

Dating apps like Tinder, OkCupid and Grindr are sharing users' "highly personal" data like sexual preferences and location with advertising partners, according to a European data protection agency. The Norwegian Consumer Council released findings on Tuesday suggesting the information you enter on dating apps is being used to create comprehensive profiles, which are then sold and used for targeted advertising and other practices. "These practices are out of control and are rife with privacy violations and breaches of European law," including the General Data Protection Regulation, said Finn Myrstad, director of digital policy in the Norwegian Consumer Council in a statement. The consumer advocacy group filed three GDPR complaints against the queer dating app Grindr and five advertising divisions of tech companies that reportedly receive the personal data including Twitter's MoPub and AT&T's AppNexus. "Every time you open an app like Grindr, advertisement networks get your GPS location, device identifiers and even the fact that you use a gay dating app," said Max Schrems, founder of the European privacy non-profit noyb.

When it comes to emerging technology, most consumers are often quite fearful of this kind of change. Many are concerned that automation will eliminate thousands of jobs in the near future. Others fear that their private data can be bought and sold, putting them in danger or removing any sense of privacy from the internet. A great deal of consumers are also incredibly skeptical of Machine Learning, Big Data, and Artificial Intelligence specifically. Only 35% of customers state that they are comfortable interacting with businesses that utilize AI – even though 84% of us use AI-based devices and services frequently, such as Siri and Google Home.

Hattie, nicknamed'Tinder Granny' by the press, explains why she's quitting the popular dating app on WETV's'Extreme Love.' At age 83, Hattie is no longer on the prowl for one-night stands. The grandmother of three, famously nicknamed "Tinder Granny" by the press for her voracious appetite of younger men and love of swiping right to prospective suitors, is the subject of WETV's reality show "Extreme Love," which explores how traditional ideas of love are reimagined across the country. She previously appeared in the 2012 documentary "Extreme Cougar Wives." "I never considered what I do to be extreme," Hattie told Fox News.

The second generation of Google's smallest smart speaker gets a new name, more eco-friendly, a little smarter and more bass. The £49 Nest Mini replaces the Google Home Mini as part of a revamped and renamed line of Google smart home products under the Nest brand, pushing its predecessor to a clearance price of only £19. From the outside you would be hard pushed to see what has changed. The Nest Mini sticks with the same pincushion design with a fabric top and nonslip rubber pad on the bottom. The top contains three far-field microphones and is touch sensitive.

People are becoming increasingly comfortable using Digital Assistants (DAs) to interact with services or connected objects. However, for non-programming users, the available possibilities for customizing their DA are limited and do not include the possibility of teaching the assistant new tasks. To make the most of the potential of DAs, users should be able to customize assistants by instructing them through Natural Language (NL). To provide such functionalities, NL interpretation in traditional assistants should be improved: (1) The intent identification system should be able to recognize new forms of known intents, and to acquire new intents as they are expressed by the user. (2) In order to be adaptive to novel intents, the Natural Language Understanding module should be sample efficient, and should not rely on a pretrained model. Rather, the system should continuously collect the training data as it learns new intents from the user. In this work, we propose AidMe (Adaptive Intent Detection in Multi-Domain Environments), a user-in-the-loop adaptive intent detection framework that allows the assistant to adapt to its user by learning his intents as their interaction progresses. AidMe builds its repertoire of intents and collects data to train a model of semantic similarity evaluation that can discriminate between the learned intents and autonomously discover new forms of known intents. AidMe addresses two major issues - intent learning and user adaptation - for instructable digital assistants. We demonstrate the capabilities of AidMe as a standalone system by comparing it with a one-shot learning system and a pretrained NLU module through simulations of interactions with a user. We also show how AidMe can smoothly integrate to an existing instructable digital assistant.

Recently, a report was released regarding the misuse from companies claiming to use artificial intelligence [29] [30] on their products and services. According to the Verge [29], 40% of European startups that claimed to use AI don't actually use the technology. Last year, TechTalks, also stumbled upon such misuse by companies claiming to use machine learning and advanced artificial intelligence to gather and examine thousands of users' data to enhance user experience in their products and services [2] [33]. Unfortunately, there's still a lot of confusion within the public and the media regarding what truly is artificial intelligence [44], and what truly is machine learning [18]. Often the terms are being used as synonyms, in other cases, these are being used as discrete, parallel advancements, while others are taking advantage of the trend to create hype and excitement, as to increase sales and revenue [2] [31] [32] [45].