Zoom has reversed course (again) and updated its terms of service after a backlash earlier this week. Following consumer blowback to a recently highlighted update to its terms which appeared to grant the platform the unlimited ability to use customer data to train AI models, it now says it will not use any consumer data to train AI models from Zoom or third parties. The previous wording said it wouldn't do so "without customer consent," which raised eyebrows since "consent" was (at best) a gray area for people joining a call (and acknowledging a pop-up) in which the meeting organizer enabled the feature and already agreed to the terms. Zoom's changes were listed in a preamble update to its previous blog post. "Following feedback received regarding Zoom's recently updated terms of service, particularly related to our new generative artificial intelligence features, Zoom has updated our terms of service and the below blog post to make it clear that Zoom does not use any of your audio, video, chat, screen-sharing, attachments, or other communications like customer content (such as poll results, whiteboard, and reactions) to train Zoom's or third-party artificial intelligence models," the notice reads.

Sen. Marsha Blackburn, R-Tenn., shares her takeaways from Tuesday's AI hearing with OpenAI CEO Sam Altman. She also reveals what next steps she and her colleagues are prepared to take to protect consumer data amid the AI boom. EXCLUSIVE: Nashville musicians are increasingly worried about complications with artificial intelligence's growing sophistication that could threaten their livelihood, Sen. Marsha Blackburn, R-Tenn., warned this week. "We met with the Nashville Technology Council a couple of weeks ago, and we have talked with so many of the musicians. They're concerned that using AI, they will do a copycat of their voice and take the lyrics of their song, which you can get on ChatGPT," Blackburn told Fox News Digital during an interview in her Senate office.

AI developments from generating videos, voices, pictures and human-like conversations are growing rapidly, lawmakers say they are trying to keep up. WASHINGTON, D.C. – AI has the potential to both benefit and harm the U.S. in unknown and unimagined ways but Congress has hardly any experts on the rapidly developing technology, lawmakers told Fox News. "AI is going to help us in many ways. It can also kill us," Rep. Ted Lieu, a California Democrat said. "As a recovering computer science major, my understanding of AI on a scale of one to 10 is about a five. There's a lot I don't know."

YouGov is an international online research data and analytics group. Our mission is to offer unparalleled insight into what the world thinks. Our innovative solutions help the world's most recognized brands, media owners and agencies to plan, activate and track their marketing activities better. At the core of the YouGov platform is an ever-growing source of consumer data that has been amassed over our twenty years of operation. We call it living data.

Opt-in doomsayers believe this legislation could cripple the entire advertising industry because consumers will have more meaningful control over their privacy, and authenticated audiences will shrink as a result. However, the trends that have shaped the market can be bucked, and publishers and advertisers have an exciting opportunity to create a new ecosystem in compliance with the opt-in marketplace that benefits everyone involved – including consumers. Further, the browser and device manufacturer changes that are already in-progress are already moving the industry towards a more logged-in environment, in which it becomes easier for consumers to opt in as they authenticate. As we look towards an opt-in era in the future, it's important that publishers and marketers consider how the industry arrived at this point, and the lessons they can take away from this journey. Under the opt-out default, it's easy to see that the consumer experience has been lacking, and a lot of that falls on technology. The opt-out default enabled the propagation of third-party cookies, and the collection of data – often in a way that was not as transparent as it could have been for consumers.

Did you know that a recent estimate suggests that 90% of a company's data consists of different types of dark data? As the interest in big data has increased rapidly, the amount of information businesses collect has also grown over the last few years. To make use of more and more data, companies are investing in talent and modern technologies to leverage the value of this data. But despite the efforts, nearly 60-73% of all enterprise data goes unused. For example, in the manufacturing industry, it has been estimated that around 90% of data generated by analog-to-digital conversions and sensors never really get used!

Various categories of fintech firms – Buy Now, Pay Later (BNPL), digital lending, payments and collections – are increasingly leveraging predictive models built using artificial intelligence and machine learning to support core business functions such as risk decisioning. According to a report by Grand View Research, Inc., the global AI in fintech market size is expected to reach US$41.16 billion by 2030, growing at a compound annual growth rate (CAGR) of 19.7% in Asia-Pacific alone from 2022 to 2030. The success of AI in fintech, or any business for that matter, hinges on an organisation's ability to make accurate predictions based on data. While internal data (first-party data) needs to be factored into AI models, this data often fails to capture critical predictive features, causing these models to underperform. In these situations, alternative data and feature enrichment can establish a powerful advantage.

A new study from the Georgia Institute of Technology School of Public Policy harnesses machine learning techniques to provide the best insight yet into the attitudes of electric vehicle (EV) drivers towards the existing charger network. The study findings could help policymakers focus their efforts. In the paper, which is featured on the cover of the June 2020 issue of Nature Sustainability, a team led by Assistant Professor Omar Isaac Asensio trained a machine learning algorithm to analyze unstructured consumer data from 12,270 electric vehicle charging stations across the United States. The study demonstrates how machine learning tools can be used to quickly analyze streaming data for policy evaluation in near real-time (see sidebar). Streaming data refers to data that comes in a feed, continuously, such as user reviews from an app.

Electric Vehicles (EVs) are a rapidly growing component of the automotive industry and are projected to have over 30 percent of the overall United States light duty vehicle market by 2030 (Wolinetz and Axsen, 2017). It's very different from traditional researches realated to transportation about road conditions (Huang et al., 2019), aviation (Bauranov et al., 2021) and manned driving (Chai et al., 2021). Furthermore, the US and other countries have bet big on Battery Electric Vehicles (BEVs), allotting funding for charging infrastructure, subsidies and tax credits and setting deadlines to phase out combustion engine vehicles. Correspondingly, the stock price of EV companies like Tesla have recently far exceeded those of traditional auto manufacturers, helping to illustrate the bullish outlook many consumers and investors have toward EVs in general. Despite this, there remain concerns among both consumers and experts about various aspects of electric cars, and despite the excitement surrounding them, EV adoption rates hovered around 1.8% in 2020 (energy.gov,

Security operations centers (SOCs) increasingly rely on network data flows as they collect telemetry from devices and monitor user behaviors. To make these massive data flows manageable, SOCs turn to rules, machine learning, and artificial (or augmented) intelligence to triage, de-duplicate, and add context to the alerts about potential dangerous or malicious activity. Pushing the boundaries of what machine learning can deliver when nourished by massive data has already led to significant invasions of privacy, especially when the efforts are driven by business demands. More often than not, ethics has taken a back seat when applying machine learning and AI. Companies such as ClearView AI and Cambridge Analytica have vastly overreached in their analysis of consumer data because they could, using consumer data without explicit permission and offering nothing in return.