Engadget Podcast: Is Siri AI actually useful in iOS 27 and macOS Golden Gate? Engadget Podcast: Is Siri AI actually useful in iOS 27 and macOS Golden Gate? Also, we try to make sense of OpenAI's rumored AI smart speaker. Apple finally launched the public betas for iOS 27, macOS Golden Gate and all of its other new software this week. Siri AI seems genuinely useful, and potentially less annoying to consumers than Microsoft's Copilot.

Try the new Siri AI and system-wide performance improvements. Adventurous early adopters, your moment has arrived: the first public betas of Apple's 2027 software updates are here . Previews of iOS 27, iPadOS 27, macOS 27 Golden Gate and watchOS 27 are now available to be installed directly on your device. The headliner of iOS 27 is the long-awaited Siri AI . The new version transforms from a basic voice-command system into a modern AI assistant.

The new Siri AI is conversational, omnipresent, and actually helpful. I'm outside hiking and testing a developer beta of Siri AI, Apple's revamped voice assistant, when fog engulfs the Golden Gate Bridge behind me. So, I pull out my iPhone and ask this new Siri where I can grab some fluffy pancakes nearby. A translucent orb at the top of the smartphone screen spins around a few times, then the voice assistant responds with a recommendation: a spot called Eats in the Inner Richmond. This version of Siri--conversational, omnipresent, actually helpful--has been long delayed.

Apple has announced the latest version of macOS. It's all about the reintroduction of Siri, which is now accessible from anywhere on the Mac desktop. The official name of the Mac's operating system is macOS 27 Golden Gate, keeping the California naming scheme around. This year's update is focused on the relaunched Siri (now known as Siri AI), which really strives to transform into a proper AI chatbot along the lines of ChatGPT or Google Gemini--with a unique Apple twist. Is Your Mac Compatible With macOS Golden Gate?

Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts.

Understanding how the structure of language can be learned from sentences alone is a central question in both cognitive science and machine learning. Studies of the internal representations of Large Language Models (LLMs) support their ability to parse text when predicting the next word, while representing semantic notions independently of surface form. Yet, which data statistics make these feats possible, and how much data is required, remain largely unknown. Probabilistic context-free grammars (PCFGs) provide a tractable testbed for studying these questions. However, prior work has focused either on the post-hoc characterization of the parsing-like algorithms used by trained networks; or on the learnability of PCFGs with fixed syntax, where parsing is unnecessary. Here, we (i) introduce a tunable class of PCFGs in which both the degree of ambiguity and the correlation structure across scales can be controlled; (ii) provide a learning mechanism -- an inference algorithm inspired by the structure of deep convolutional networks -- that links learnability and sample complexity to specific language statistics; and (iii) validate our predictions empirically across deep convolutional and transformer-based architectures. Overall, we propose a unifying framework where correlations at different scales lift local ambiguities, enabling the emergence of hierarchical representations of the data.