Why is ai so hyped
Asked on 2024-07-30
1 search
The hype around AI, particularly in the context of Apple's WWDC, can be attributed to several key factors:
-
Integration with Apple Platforms: Apple has introduced "Apple Intelligence," a personal intelligence system that brings powerful generative models to iOS, iPadOS, and macOS. This integration allows for advanced capabilities in understanding and generating language and images, deeply embedded into the system with a focus on user privacy (Platforms State of the Union).
-
On-Device AI Capabilities: Apple emphasizes running AI tasks on-device to ensure low latency, better user experience, and enhanced privacy. This includes the use of Apple Silicon's hardware acceleration and unified memory for efficient model training and inference (Platforms State of the Union).
-
Advanced Machine Learning Frameworks: Apple's built-in machine learning frameworks offer a wide range of capabilities, including natural language processing, sound analysis, speech understanding, and vision intelligence. These frameworks can be extended using Create ML to incorporate additional data for training, allowing developers to improve model performance with unique datasets (Platforms State of the Union).
-
Cutting-Edge Research and Tools: Apple is actively involved in AI research, publishing hundreds of papers and providing open-source tools like MLX for researchers. This commitment to advancing AI technology and making research tools accessible helps drive innovation in the field (Explore machine learning on Apple platforms).
-
Generative AI and Developer Tools: Generative AI is transforming how developers write code, and Apple is integrating new intelligence capabilities into its developer tools, such as Xcode. This helps developers create more efficient and powerful applications (Platforms State of the Union).
These factors collectively contribute to the excitement and hype around AI, as they demonstrate significant advancements and practical applications of AI technology on Apple platforms.

Deploy machine learning and AI models on-device with Core ML
Learn new ways to optimize speed and memory performance when you convert and run machine learning and AI models through Core ML. We’ll cover new options for model representations, performance insights, execution, and model stitching which can be used together to create compelling and private on-device experiences.

Train your machine learning and AI models on Apple GPUs
Learn how to train your models on Apple Silicon with Metal for PyTorch, JAX and TensorFlow. Take advantage of new attention operations and quantization support for improved transformer model performance on your devices.

Platforms State of the Union
Discover the newest advancements on Apple platforms.
