iOS 27.2 adds new setting to fix common HomePod annoyance - 9to5Mac

Apple is giving its smart speaker an artificial intelligence upgrade, a move that could eventually change how consumers discover and listen to music, radio and podcasts at home. The new HomePod mini 2 introduces a more conversational version of Siri that can draw on personal information, answer complex questions and make audio recommendations, potentially giving voice assistants a bigger role in deciding what people hear. The $149 speaker arrives in stores Oct. 30, although its biggest new feature, Siri AI, will come later through a free software update. It will initially support English in eight countries, including the U. S. The company has not specified when the update will be released. For the audio industry, the development promises to make voice-controlled listening more conversational and personalized. Rather than relying entirely on specific commands or knowing what to request, consumers could increasingly use AI assistants to help them find entertainment.

At the old pre-June prices, you could buy a 128 GB Apple TV and a pair of HomePod Minis for $350 ($150 + $100 + $100). Four short months later, with today’s new models, that combination would cost $600 ($300 + $150 + $150).

Apple’s new HomePod mini 2 costs $199 CAD, while the full-size HomePod 2 sells for $479 CAD. That’s a $280 difference, but is the larger speaker really worth paying more than twice as much for? The HomePod mini 2 was announced this week in Canada with an S10 chip, support for Siri AI and new audio features. It’s available for pre-order now, with deliveries beginning October 30. Apple also introduced a new Apple TV 4K with four-speaker surround sound and revealed Canadian pricing for its Apple Home Collection.

To build faster and more energy-efficient computers, researchers need to understand nanoscale magnetic materials. But looking inside these tiny magnets is surprisingly difficult: modern microscopes only measure the magnetic field outside the material, not the magnetic pattern inside. Because many different internal patterns can produce the exact same field, working backwards has long been a challenge. Physicists at the University of Vienna, together with colleagues in Dresden, Singapore, and Ulsan, have now solved this problem by building the laws of physics directly into modern machine learning methods. Their method reveals the realistic magnetic structure and even determines the precise distance between the sensor and the sample. The study is published in Physical Review Letters, and the software is freely available as open source. Magnetic materials only a few atomic layers thick could play a key role in future data storage and Energy efficient electronics. To develop them, scientists use advanced instruments like diamond Quantum sensors or magnetic force microscopes to scan their surfaces. Yet these tools cannot see the magnet directly.