Posts Tagged ‘GemmaModels’
[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.
Links:
[GoogleIO2024] Developer Keynote: Innovations in AI and Development Tools at Google I/O 2024
The Developer Keynote at Google I/O 2024 showcased a transformative vision for software creation, emphasizing how generative artificial intelligence is reshaping the landscape for creators worldwide. Delivered by a team of Google experts, the session highlighted accessible AI models, enhanced productivity across platforms, and new tools designed to simplify complex workflows. This presentation underscored Google’s commitment to empowering millions of developers through an ecosystem that spans billions of devices, fostering innovation without the burden of underlying infrastructure challenges.
Advancing AI Accessibility and Model Integration
A core theme of the keynote revolved around making advanced AI capabilities available to every programmer. The speakers introduced Gemini 1.5 Flash, a lightweight yet powerful model optimized for speed and cost-effectiveness, now accessible globally via the Gemini API in Google AI Studio. This tool balances quality, efficiency, and affordability, enabling developers to experiment with multimodal applications that incorporate audio, video, and extensive context windows. For instance, Jacqueline demonstrated a personal workflow where voice memos and prior blog posts were synthesized into a draft article, illustrating how large context windows—up to two million tokens—unlock novel interactions while reducing computational expenses through features like context caching.
This approach extends beyond simple API calls, as the team emphasized techniques such as model tuning and system instructions to personalize outputs. Real-world examples included Loc.AI’s use of Gemini for renaming elements in frontend designs from Figma, enhancing code readability by interpreting nondescript labels. Similarly, Invision leverages the model’s speed for real-time environmental descriptions aiding low-vision users, while Zapier automates podcast editing by removing filler words from audio uploads. These cases highlight how Gemini empowers practical transformations, from efficiency gains to user delight, encouraging participation in the Gemini API developer competition for innovative applications.
Enhancing Mobile Development with Android and Gemini
Shifting focus to mobile ecosystems, the keynote delved into Android’s evolution as an AI-centric operating system. With over three billion devices, Android now integrates Gemini to enable on-device experiences that prioritize privacy and low latency. Gemini Nano, the most efficient model for edge computing, powers features like smart replies in messaging without data leaving the device, available on select hardware like the Pixel 8 Pro and Samsung Galaxy S24 series, with broader rollout planned.
Early adopters such as Patreon and Grammarly showcased its potential: Patreon for summarizing community chats, and Grammarly for intelligent suggestions. Maru elaborated on Kotlin Multiplatform support in Jetpack libraries, allowing shared business logic across Android, iOS, and web, as seen in Google Docs migrations. Compose advancements, including performance boosts and adaptive layouts, were highlighted, with examples from SoundCloud demonstrating faster UI development and cross-form-factor compatibility. Testing improvements, like Android Device Streaming via Firebase and resizable emulators, ensure robust validation for diverse hardware.
Jamal illustrated Gemini’s role in Android Studio, evolving from Studio Bot to provide code optimizations, translations, and multimodal inputs for rapid prototyping. A demo converted a wireframe image into functional Jetpack Compose code, underscoring how AI accelerates from ideation to implementation.
Revolutionizing Web and Cross-Platform Experiences
The web’s potential was amplified through AI integrations, marking its 35th anniversary with tools like WebGPU and WebAssembly for on-device inference. John discussed how these enable efficient model execution across devices, with examples like Bilibili’s 30% session duration increase via MediaPipe’s image recognition. Chrome’s enhancements, including AI-powered dev tools for error explanations and code suggestions, streamline debugging, as shown in a Boba tea app troubleshooting CORS issues.
Aaron introduced Project IDX, now in public beta, as an integrated workspace for full-stack, multiplatform development, incorporating Google Maps, DevTools, and soon Checks for privacy compliance. Flutter’s updates, including WebAssembly support for up to 2x performance gains, were exemplified by Bricket’s cross-platform expansion. Firebase’s evolution, with Data Connect for SQL integration, App Hosting for scalable web apps, and Genkit for seamless AI workflows, further simplifies backend connections.
Customizing AI Models and Future Prospects
Shabani and Lawrence explored open models like Gemma, with new variants such as PaliGemma for vision-language tasks and the upcoming Gemma 2 for enhanced performance on optimized hardware. A demo in Colab illustrated fine-tuning Gemma for personalized book recommendations, using synthetic data from Gemini and on-device inference via MediaPipe. Project Gameface’s Android expansion demonstrated accessibility advancements, while an early data science agent concept showcased multi-step reasoning with long context.
The keynote concluded with resources like accelerators and the Google Developer Program, emphasizing community-driven innovation. Eugene AI’s emissions reduction via DeepMind research exemplified real-world impact, reinforcing Google’s ecosystem for reaching global audiences.