Posts Tagged ‘Keras’
[GoogleIO2025] Google’s AI stack for developers
Keynote Speakers
Joana Carrasqueira holds the position of Head of Developer Relations at Google DeepMind, where she leads efforts to empower developers with AI tools and resources. With an MBA from IE Business School and a background transitioning from pharmaceutical science to technology, she focuses on bridging research and practical applications to foster innovation.
Josh Gordon serves as the lead for AI Developer Relations at Google, guiding the adoption of machine learning technologies. Holding a degree from Columbia University, he brings over 15 years of experience in AI, emphasizing accessible tools for developers across various domains.
Abstract
This scholarly review examines Google’s comprehensive AI ecosystem, spanning infrastructure, frameworks, and developer tools designed to facilitate robust AI applications. It analyzes foundational models like Gemini and Gemma, alongside frameworks such as JAX and Keras, elucidating their architectural designs, integration strategies, and contributions to fields like robotics and healthcare. By evaluating demonstrations and strategic alignments, the discussion highlights implications for collaborative innovation, ethical AI deployment, and accelerated research-to-reality transitions in a developer-centric landscape.
Infrastructure and Model Foundations
Joana Carrasqueira and Josh Gordon open by outlining Google’s AI stack, rooted in decades of leadership from TensorFlow’s open-sourcing in 2015 to transformative research like Transformers in 2017, culminating in the Gemini era. Carrasqueira emphasizes the stack’s flexibility, combining infrastructure with cutting-edge research to enable real-world impacts across industries.
Central are foundation models, with Gemini’s multimodal native design processing text, images, video, audio, and code seamlessly. Gordon details Gemini’s families: Pro for balanced performance, Flash for efficiency, and Ultra for complex tasks. Innovations like 2.5 Pro’s long-context reasoning and audio understanding advance agentic capabilities, while Gemma’s lightweight variants—3N at 3B parameters—run on edge devices with audio features.
Methodologies involve pre-training on diverse datasets, yielding state-of-the-art benchmarks. Contexts include democratizing AI, with implications for inclusive access, though necessitating safeguards against biases.
Domain-specific models like Med-Gemma analyze medical images, while robotics variants incorporate physical actions. These extend multimodal reasoning to practical domains, implying transformative applications in healthcare and automation.
Frameworks for Research and Application
Gordon transitions to frameworks, with JAX suiting researchers via NumPy-like APIs and just-in-time compilation for high-performance computations. Its composability—via transformations like grad and vmap—facilitates gradient computations and vectorization.
Code sample for JAX gradient:
import jax
import jax.numpy as jnp
def f(x):
return jnp.sin(x) * x
grad_f = jax.grad(f)
print(grad_f(3.0))
Keras, for applied AI, offers intuitive layers, with Keras 3 supporting backends like JAX, TensorFlow, and PyTorch. Its multi-backend nature implies cross-framework portability.
PyTorch collaborations enhance interoperability, with implications for unified ecosystems reducing vendor lock-in.
Developer Tools and Community Engagement
Carrasqueira highlights tools like AI Studio for no-code prototyping and Gemini API for multimodal integrations. Features like system instructions and caching optimize interactions.
Vertex AI provides enterprise-grade capabilities, with agents orchestrating tasks via tools. Implications include scalable production deployments.
Community resources—cookbooks, forums—foster collaboration, implying accelerated innovation through shared knowledge.
Breakthroughs and Future Directions
Gordon showcases AlphaFold 3’s molecular predictions and Alpha Evolve’s material discoveries, demonstrating AI’s scientific acceleration. Robotics models enable dexterous actions, implying industrial transformations.
The stack’s end-to-end nature—from models to tools—implies seamless pipelines, with ethical considerations paramount for societal benefits.
Links:
[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.