Google unveils privacy-friendly AI cookie killer

Third-party cookies might have a tasty name, but they can be pretty poisonous when they’re quietly tracking your online behavior. A new Google machine learning initiative aims to replace the rancid cookies with a privacy-first alternative. The search giant calls the system Federated Learning of Cohorts (FLoC). FLoC (pronounced “flock”) allows businesses to send ads to groups of potential customers rather than specific individuals. The system uses machine learning algorithms to create clusters of people with similar browsing habits. All the data analyzed by the algorithms — including your web history — is kept private on the browser and not uploaded anywhere else.…

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Is there a more environmentally friendly way to train AI?

Machine learning is changing the world, and it’s changing it fast. In just the last few years, it’s brought us virtual assistants that understand language, autonomous vehicles, new drug discoveries, AI-based triage for medical scans, handwriting recognition and more.  One thing that machine learning shouldn’t be changing is the climate.  The issue relates to how machine learning is developed. In order for machine learning (and deep learning) to be able to accurately make decisions and predictions, it needs to be ‘trained’.  Imagine an online marketplace for selling shoes, that’s been having a problem with people trying to sell other things on the site – bikes… This story continues at The Next Web

Zut alors! France spanks Google over ‘misleading’ hotel ranking algorithm

France’s competition watchdog has slapped Google with a €1.1 million ($1.3 million) fine over “misleading” hotel rankings generated by the tech giant’s algorithms. The Directorate-General for Competition, Consumer Affairs and Repression of Fraud (DGCCRF) began to probe Google’s classifications after hoteliers complained about their ratings. The investigation revealed that Google had replaced the rankings used by France’s tourism agency (Atout France) with the company’s own grading system. The DGCCRF found that the algorithmic classification had been applied to more than 7,500 establishments found in Google search results. [Read: How Polestar is using blockchain to increase transparency] The regulator said Google’s use of a five-star rating scale… This story continues at The Next WebOr just read more coverage about: Google

The Apple privacy offensive continues — and Google and Facebook are shook

One of my favorite longburn stories over the past couple of years is the Apple privacy drive. It’s a winding narrative, but the easiest way to sum it up is the company has made privacy one of its leading products. This has been bubbling away in the background for some time, but hit fever pitch recently with the launch of iOS 14. You can read more about the specifics of the Apple privacy offensive in its latest iPhone software here, but there’s one update that’s particularly relevant to the state of things today: app tracking and data. To put it… This story continues at The Next WebOr just read more coverage about: Apple,Facebook,Google

A closer look at the AI Incident Database of machine learning failures

The failures of artificial intelligent systems have become a recurring theme in technology news. Credit scoring algorithms that discriminate against women. Computer vision systems that misclassify dark-skinned people. Recommendation systems that promote violent content. Trending algorithms that amplify fake news. Most complex software systems fail at some point and need to be updated regularly. We have procedures and tools that help us find and fix these errors. But current AI systems, mostly dominated by machine learning algorithms, are different from traditional software. We are still exploring the implications of applying them to different applications, and protecting them against failure needs new… This story continues at The Next Web

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