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Google Shopping Introduces AI-Driven Recommendations
- Written by Kiara Fabbri Former Tech News Writer
- Fact-Checked by Justyn Newman Former Lead Cybersecurity Editor
Google introduced today a personalized feed to its shopping platform, offering users tailored product suggestions based on their interests. The new feature, accessible on both mobile and desktop and is being rolled out across the U.S, as noted by The Verge.
At the top of the page, users will see a carousel displaying products they have recently searched for. As they scroll, the feed will present recommended items and in-line videos, curated based on recent Google searches and YouTube viewing history, notes The Verge.
“Imagine a store that’s tailored just to your current interests, and that’s really what we set out to create here,” Sean Scott, Google’s vice president and general manager of consumer shopping, explained to The Verge.
Additionally, a personalized deals tab will highlight products on sale that match user preferences, although personalization is only available for those logged into their Google accounts, adds The Verge.
The update also introduces AI-generated summaries for product searches. For example, a search for “Men’s winter jacket for Seattle” will provide an AI-generated brief outlining key factors to consider for that climate, along with product recommendations.
These summaries are intended to help users make more informed purchasing decisions by offering relevant information and suggestions sourced from across the web.
Other features include dynamic filters that enable users to refine search results by criteria such as size or local availability.
The platform also integrates virtual try-on tools using augmented reality (AR) to further assist users in their purchase decisions. Tools for price comparison, insights, and tracking are also included to help users navigate pricing and discounts.
The AI-generated summaries are marked as “experimental,” with Google encouraging users to provide feedback to improve the system.
The new features are gradually being introduced, starting this week, and can be accessed via the “Shopping” tab on Google Search or directly through the Google Shopping website.
Image by Wiki Blind, from Flickr
New Tool Detects Malware Exploiting Smartphone Accessibility Features
- Written by Kiara Fabbri Former Tech News Writer
- Fact-Checked by Justyn Newman Former Lead Cybersecurity Editor
In a Rush? Here are the Quick Facts!
- Georgia Tech developed DVa to combat malware exploiting accessibility features.
- Accessibility tools help users but also create vulnerabilities for hackers.
- DVa scans smartphones and reports harmful apps and potential damages to users.
Researchers at Georgia Tech recently announced that they have created a new tool called Detector of Victim-specific Accessibility (DVa) to fight malware that takes advantage of smartphone accessibility features.
While tools like screen readers and voice-to-text help people with disabilities, they also make phones easier targets for hackers.
Malware can exploit these features to read screens, access important apps, and block removal attempts. This can result in severe consequences, such as unauthorized bank transfers or the exposure of sensitive information in cryptocurrency apps.
DVa works in the cloud to scan smartphones for malware and sends users a report of its findings. It tells users which apps are harmful, how to delete them, and alerts them about potential damages.
The tool also reports its findings to Google, helping the company remove harmful apps from the Play Store.
Brendan Saltaformaggio, an associate professor at Georgia Tech, stressed the importance of having security experts involved when designing accessible technology. He warned that without their input, these tools could be misused by hackers.
To test DVa, the research team worked with Netskope, a cloud security company, and installed malware samples on Google Pixel phones to see how it affected the system.
While DVa can identify many current malware threats, researchers noted that balancing security with accessibility remains tricky. DVa has some limitations.
It relies on specific functions defined by accessibility tools, which means it might miss malware that uses clever tricks to exploit those functions.
Additionally, while DVa tries to adapt to known threats, it can struggle when malware uses complicated behaviors or new evasion tactics. These challenges could prevent DVa from detecting certain types of malware.
Ken Xu, a Ph.D. student of the project, highlighted the need to differentiate between safe and harmful uses of accessibility services. Despite these challenges, researchers are hopeful that DVa can improve and help keep accessible technology safe in the future.
