Error: "Could not find a version that satisfies the requirement tensorflow-macos (from versions: none)." A tensorflow installation wheel that matches the current Python environment couldn't be found by the package manager. Check that the Python version used in the environment is supported (Python 3.8, Python 3.9, Python 3.10). Complex data type isn't supported by tensorflow-metal. Error: "Cannot assign a device for operation: Could not satisfy explicit device specification because the node was colocated with a group of nodes that required incompatible device."

Around this time two years ago, David Alston was running around the frigid streets of Chicago attending sneaker release pop-up parties during NBA All-Star Game weekend, trying to get some exposure for his upstart app, Kickstroid. His pre-pandemic objective: letting sneakerheads know how and where to get the latest reissues of various Air Jordans, the newest "Space Jam: A New Legacy"-inspired Nikes from LeBron James, and a relaunched Adidas D Rose 1 from Chicago hoop legend Derrick Rose. The positive feedback Alston got from users in person and on the app left him and his Kickstroid co-founder and college classmate, Nicco Adams, inspired. This weekend on their app, which launched in January 2020, they're tracking to see how the Nike "LeBron 9 Big Bang 2022" reissue will fare as this year's NBA All-Star Game is being held in Cleveland, James' former stomping ground. They say the shoe, with a $210 retail price, has a current "hype rating" of 7.2 (out of 10), a resell value of 4.6, which they also currently estimated at $284 and could price higher.

Take a journey through the creation of an educational game that brings together Core ML, ARKit, and other app frameworks. See it all come to life in an interactive coding session.

Apple used its strongest Attenborough voice to poke a little fun at its 2018 WWDC attendees on Monday. The keynote's opening video called back to last year's mega-hit BBC documentary series Planet Earth II and provides the viewing public, for perhaps the first time, a look into the migratory and social habits of the elusive Developer tritorapsis. Nature is a cruel and unforgiving mistress, to be sure, but not nearly as cruel as Apple's PR department is in this promotional video. According to Apple, D. tritorapsis is a unique species of the Developer genus. They're found on every continent worldwide, save for Antarctica, and exhibit a wide range of morphologies specifically adapted to their local programming environments.

Discover new ways to build intelligence into your iOS apps. With IBM Watson Services for Core ML, it's easy to build apps that access powerful Watson capabilities right from iPhone and iPad, so you can provide dynamic, intelligent insights that improve over time. Your apps can quickly analyze images, accurately classify visual content, and easily train models using Watson Services.

Earlier this week Apple unveiled Core ML, a software framework for letting developers deploy and work with trained machine learning models in apps on all of Apple's platforms--iOS, MacOS, TvOS, and WatchOS. Core ML is intended to spare developers from having to build all the platform-level plumbing themselves for deploying a model, serving predictions from it, and handling any extraordinary conditions that might arise. But it's also currently a beta product, and one with a highly constrained feature set. Core ML provides three basic frameworks for serving predictions: Foundation for providing common data types and functionality as used in Core ML apps, Vision for images, and GameplayKit for handling gameplay logic and behaviors. Each framework provides high-level objects, implemented as classes in Swift, that cover both specific use cases and more open-ended prediction serving.

Core ML lets you integrate a broad variety of machine learning model types into your app. In addition to supporting extensive deep learning with over 30 layer types, it also supports standard models such as tree ensembles, SVMs, and generalized linear models. Because it's built on top of low level technologies like Metal and Accelerate, Core ML seamlessly takes advantage of the CPU and GPU to provide maximum performance and efficiency. You can run machine learning models on the device so data doesn't need to leave the device to be analyzed. You can easily build computer vision machine learning features into your apps.

Discover how to enhance app intelligence by using machine learning and natural language processing (NLP). Learn how to use our performant on-device NLP APIs to break text into sentences and tokens, identify people and places mentioned in the text (typed, transcribed speech/handwriting). The NLP APIs can be used standalone or as a preprocessing framework for machine-learning based text modeling tasks. The APIs are available in many languages across all Apple platforms, thereby providing homogeneous text processing for consistent user experience. Open up your imagination as we walk you through hypothetical apps that harness the power of NLP to enhance the overall app experience.

If you ask Siri, the virtual personal assistant on the iPhone 4S, why it's so great, it answers with disarming humility: "I am what I am." But industry insiders say there's a little more to it than that. Siri goes well beyond voice recognition, they say, by applying powerful artificial intelligence and statistical analysis to decipher the meaning behind questioners' sometimes jumbled sentences. Add to that Siri's dry wit and you have the kind of breakout hit that will propel new uses of similar technology on your phone, tablet, and even your PC, experts say. Services like Siri are "natural language processing" apps that use statistical models to figure out what you probably meant to say when your pronunciation or word choice is garbled.

Google's TensorFlow is the open-source platform that underlies a lot of the machine-learning done on the internet. Gmail, Google Photos, Search, and even the program that bested the world's champion at the game of Go all rely on some version of TensorFlow to function, and now it's opening up to iOS developers. Google uploaded the newest version of TensorFlow, version 0.9, to GitHub on Thursday morning, fulfilling the long-promised goal of providing open-source iOS support to the advanced neural-based artificial intelligence network. The biggest beneficiaries of the move are Apple developers who now can harness one of the most powerful A.I. systems in the world for free in their apps and programs. TensorFlow has been available to Android developers open source since 2014, but since many apps begin as iOS projects exclusively, the number of projects that can include the technology just received a huge boost.