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TikTok agrees to pay $92 million to settle teen privacy class-action lawsuit

ZDNet

TikTok has agreed to pay a proposed $92 million to settle a class-action lawsuit alleging the company invaded user privacy. The settlement, if approved, would lay to rest claims that the video content-sharing app, owned by Beijing-headquartered ByteDance, wrongfully collected the private and biometric data of users including teenagers and minors. The class-action lawsuit originated from 21 separate class-action lawsuits filed in California and Illinois last year. If accepted, the settlement -- filed in the US District Court for the Northern District of Illinois -- would require the creation of a compensation fund for TikTok users. In addition, TikTok would be required to launch a new "privacy compliance" training program and would need to take further measures to protect user data.


5 Ways AI and ML will Drive Future Growth and Development

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Over the last decade, the pace of technological innovation has increased dramatically. In addition, breakthroughs have grown exponentially in the domain of information technology, particularly in artificial intelligence (AI) and machine learning (ML). These technologies are working to boost human efforts, to take on just about everything from quality of life to cybersecurity, and so on. AI assistants will assist senior citizens to remain free and live longer in their own homes. AI devices will keep healthy meals accessible, securely reach items on kitchen counters, and track movement in the home of an elderly person. The tools seemed to be able to do laundry, keep floors washed, and even assist with shower and cleanliness.


EETimes - Allowing Machines to Listen, and Understand

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As we move towards more ubiquitous, always-on sensing and computing, power becomes increasingly important. There's perhaps no better an example of where this is important than the voice-activated devices on our desks, in our pockets, and distributed around our homes. As we saw last year, keyword spotting in particular is currently a target for all kinds of neuromorphic technologies. The 2020 winner of the Misha Mahowald Prize for Neuromorphic Engineering is Prof. Shih-Chii Liu and her team, who have been working on low-latency, low-power sensors for detecting speech. The dynamic audio sensors that Shih-Chii Liu and her team at the Institute of Neuroinformatics (INI) have been developing could eventually address this market.


EU Artificial Intelligence regulation at risk in WTO e-commerce deal, study says

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The EU's attempts to regulate Artificial Intelligence could be met with future challenges resulting from an agreement on e-Commerce at the level of the World Trade Organisation (WTO), according to a new study published on Tuesday (26 January). Talks have been ongoing since January 2019 between members of the WTO in a bid to agree on global rules to facilitate worldwide e-commerce transactions. However, concerns have been highlighted that the text currently backed by the EU could result in a prohibition on signatories from adopting legislation that obliges firms to provide access to the source code of their software. In this vein, a report published by the Federation of German Consumer Organisations (vzbv) says that a number of EU objectives in the field of digital policy currently on the table could be stifled by the WTO agreement. "The EU's possibility to adopt rules that, for example, mandate external audits of AI systems will be confined to the policy space that is allowed under trade law," the study notes, adding that the European Council and the Commission are responsible for ensuring that trade deals it makes are compatible with internal policy initiatives.


How AI is Putting In-house Legal Teams in the Driver's Seat

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It's clear these newer entrants to the legal tech space are not afraid of embracing AI in order to drive efficiency and growth, particularly when that means that they can cut costs by bringing legal work in house.


Why machine learning strategies fail

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Most companies are struggling to develop working artificial intelligence strategies, according to a new survey by cloud services provider Rackspace Technology. The survey, which includes 1,870 organizations in a variety of industries, including manufacturing, finance, retail, government, and healthcare, shows that only 20 percent of companies have mature AI/machine learning initiatives. The rest are still trying to figure out how to make it work. Lower costs, improved precision, better customer experience, and new features are some of the benefits of applying machine learning models to real-world applications. But machine learning is not a magic wand.


A Comprehensive History of AI in the Call Center: From ACDs to Predictive Analytics and Beyond - CallMiner

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Voice continue to the most widely-utilized customer service channel by consumers, with 73% of consumers calling into the call center for customer service needs, according to Forrester. Other channels are gaining ground, however, with digital channels, such as chat and email, and web-based self-service becoming increasingly utilized by consumers. New technologies are providing consumers with more options for connecting with the companies they do business with, but technology advancements are also reshaping the way companies are meeting those needs. Once a pipe dream believed to be far off in the future, artificial intelligence (AI) is one innovation that's transforming the customer service landscape. We've put together this guide to provide a comprehensive history of AI in the call center, from the advent of artificial intelligence as a whole to its first use in the call center and the potential for future disruption.


Why most machine learning strategies fail

#artificialintelligence

Most companies are struggling to develop working artificial intelligence strategies, according to a new survey by cloud services provider Rackspace Technology. The survey, which includes 1,870 organizations in a variety of industries, including manufacturing, finance, retail, government, and healthcare, shows that only 20 percent of companies have mature AI/machine learning initiatives. The rest are still trying to figure out how to make it work. Lower costs, improved precision, better customer experience, and new features are some of the benefits of applying machine learning models to real-world applications. But machine learning is not a magic wand. And as many organizations and companies are learning, before you can apply the power of machine learning to your business and operations, you must overcome several barriers.


DeepCube's suite of products drives enterprise adoption of deep learning

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DeepCube announced the launch of a new suite of products and services to help drive enterprise adoption of deep learning, at scale, on intelligent edge devices and in data centers. The offerings build on DeepCube's patented platform, which is the industry's first software-based deep learning accelerator that drastically improves performance on any existing hardware. Now, DeepCube will offer solutions for neural network training and inference, allowing users to leverage DeepCube's technology to address challenges in … More The post DeepCube's suite of products drives enterprise adoption of deep learning appeared first on Help Net Security. Become a supporter of IT Security News and help us remove the ads.


7 ways AI could restore trust in public services

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Resource allocation is paramount in delivering effective public services, whether it is the management of intensive-care beds or the maintenance of the road and rail network. The ability to predict need before it occurs allows managers to make better decisions; giving them this capability will become increasingly important in the public sector. The maintenance of roads is a particularly interesting case, as AI can use many millions of high-definition photos to analyze their condition, and to give local councils the intelligence they need to direct maintenance efforts, improving resource allocation and public safety simultaneously.