Posts Tagged ‘Keras’
[GoogleIO2024] What’s New in Google AI: Advancements in Models, Tools, and Edge Computing
The realm of artificial intelligence is advancing rapidly, as evidenced by insights from Josh Gordon, Laurence Moroney, and Joana Carrasqueira. Their discussion illuminated progress in Gemini APIs, open-source frameworks, and on-device capabilities, underscoring Google’s efforts to democratize AI for creators worldwide.
Breakthroughs in Gemini Models and Developer Interfaces
Josh highlighted Gemini 1.5 Pro’s multimodal prowess, handling extensive contexts like hours of video or thousands of images. Demonstrations included analyzing museum footage for exhibit details and extracting insights from lengthy PDFs, such as identifying themes in historical texts. Audio processing shone in examples like transcribing and querying lectures, revealing the model’s versatility.
Google AI Studio facilitates prototyping, with seamless transitions to code via SDKs in Python, JavaScript, and more. The Gemini API Cookbook offers practical guides, while features like context caching reduce costs for repetitive prompts. Developers can tune models swiftly, as shown in a book recommendation app refined with synthetic data.
Empowering Frameworks for Efficient AI Development
Joana explored Keras and JAX, pivotal for scalable AI. Keras 3.0 supports multiple backends, enabling seamless transitions between TensorFlow, PyTorch, and JAX, ideal for diverse workflows. Its streamlined APIs accelerate prototyping, as illustrated in a classification task using minimal code.
JAX’s strengths in high-performance computing were evident in examples like matrix operations and neural network training, leveraging just-in-time compilation for speed. PaliGemma, a vision-language model, exemplifies fine-tuning for tasks like captioning, with Kaggle Models providing accessible datasets. These tools lower barriers, fostering innovation across research and production.
On-Device AI and Responsible Innovation
Laurence introduced Google AI Edge, unifying on-device solutions to simplify adoption. MediaPipe abstractions ease complexities in preprocessing and model management, now supporting PyTorch conversions. The Model Explorer aids in tracing inferences, enhancing transparency.
Fine-tuned Gemma models run locally for privacy-sensitive applications, like personalized book experts using retrieval-augmented generation. Emphasis on agentic workflows hints at future self-correcting systems. Laurence stressed AI’s human-centric nature, urging ethical considerations through published principles, positioning it as an amplifier for global problem-solving.