News from the AI & ML world
DeeperML - #googlecloud
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@docs.google.com
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AI & Machine Learning
, Kyle Wiggers ?
Google Cloud's Vertex AI is expanding its generative media capabilities, now boasting models across video, image, speech, and music. The platform is integrating Google's Lyria text-to-music model, allowing users to generate high-fidelity audio, and enhancing existing features in Veo 2, Chirp 3, and Imagen 3. These additions enable enterprises to create complete, production-ready assets from a single text prompt, encompassing images, videos with music, and speech elements. Vertex AI aims to provide a comprehensive solution for media creation across various modalities.
The enhancements to existing models include new editing and camera control features for Veo 2, providing creative control over video content. Chirp 3 now includes Instant Custom Voice, enabling users to create custom voices with only 10 seconds of audio input, as well as AI-powered narration and speech transcription with speaker distinction. Imagen 3 has improved image generation and inpainting capabilities for seamless object removal. These updates aim to help users refine and repurpose content with precision, reduce post-production time, and produce higher-quality assets. Google emphasizes the importance of safety and responsibility in the development and deployment of these models on Vertex AI. Built-in precautions include digital watermarking through SynthID, safety filters, and data governance measures. Additionally, Google offers IP indemnification, assuring users that they are protected from third-party intellectual property claims when using content generated with these tools. New customers can also start building with $300 in free credits to try Google Cloud AI and ML. Recommended read:
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@cloud.google.com
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Google Cloud is advancing its AI Hypercomputer with the introduction of Ironwood TPUs, the seventh generation of Tensor Processing Units, designed specifically for AI inference workloads. This integrated supercomputing system combines optimized hardware, open software, and flexible consumption models to deliver high intelligence per dollar for AI workloads. Google Cloud CEO Thomas Kurian highlights that AI has driven adoption of different parts of the platform, enabling companies to perform super-scaled training or inference of their own models. The AI Hypercomputer underpins nearly every AI workload running on Google Cloud, from Vertex AI to direct access for fine-grained control.
Advances in performance-optimized hardware are central to this innovation. Ironwood boasts 5x more peak compute capacity and 6x the high-bandwidth memory (HBM) capacity compared to the prior-generation, Trillium. It comes in two configurations: 256 chips or 9,216 chips, with the larger pod delivering 42.5 exaFLOPS of compute. Moreover, Ironwood is twice as power efficient compared to Trillium, offering significantly more value per watt. Alongside Ironwood, Google Cloud offers A4 and A4X VMs, featuring NVIDIA B200 and GB200 NVL72 GPUs, respectively. These advancements are supported by enhanced networking, including 400G Cloud Interconnect and Cross-Cloud Interconnect, providing up to 4x more bandwidth than the previous 100G offering. The new Ironwood TPUs are purpose-built for the age of inference, reflecting the increasing focus on deploying AI models. Ironwood incorporates an enhanced SparseCore, which accelerates sparse operations common in ranking and retrieval-based workloads, improving both latency and power consumption. As AI workloads shift from training to inference, Ironwood's design meets the demands of low-latency and high-throughput performance. This new TPU is integrated into Google's AI Hypercomputer, offering developers access through optimized stacks across PyTorch and JAX. Recommended read:
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@console.cloud.google.com
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Compute
, BigDATAwire
Google Cloud is empowering global scientific discovery and innovation by integrating Google DeepMind and Google Research technologies with its cloud infrastructure. This initiative aims to provide researchers with advanced, cloud-scale tools for scientific computing. The company is introducing supercomputing-class infrastructure, including H4D VMs powered by AMD CPUs and A4/A4X VMs powered by NVIDIA GPUs, which boast low-latency networking and high memory bandwidth. Additionally, Google Cloud Managed Lustre offers high-performance storage I/O, enabling scientists to tackle large-scale and complex scientific problems.
Google Cloud is also rolling out advanced scientific applications powered by AI models. These include AlphaFold 3 for predicting the structure and interactions of biomolecules, and WeatherNext models for weather forecasting. Moreover, the company is introducing AI agents designed to accelerate scientific discovery. As an example, Google Cloud and Ai2 are investing $20 million in the Cancer AI Alliance to accelerate cancer research using AI, advanced models, and cloud computing power. Google Cloud will provide the AI infrastructure and security, while Ai2 will deliver the training and development of cancer models. In addition to these advancements, Google unveiled its seventh-generation Tensor Processing Unit (TPU), Ironwood. The company claims Ironwood delivers 24 times the computing power of the world’s fastest supercomputer when deployed at scale. Ironwood is specifically designed for inference workloads, marking a shift in Google's AI chip development strategy. When scaled to 9,216 chips per pod, Ironwood delivers 42.5 exaflops of computing power, and each chip comes with 192GB of High Bandwidth Memory. Recommended read:
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@blogs.nvidia.com
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Google Cloud is making significant strides in the realm of multi-agent systems with new enhancements to its Vertex AI platform. This move is designed to empower enterprises to build and manage AI agents more effectively, recognizing the increasing importance of these agents in various business operations. The key highlight is the introduction of the Agent Development Kit (ADK), an open-source framework that streamlines the agent creation process, allowing developers to construct AI agents with minimal code. This approach fosters greater control over agent behavior and ensures seamless integration within the enterprise ecosystem.
To further enhance multi-agent collaboration, Google Cloud is championing the Agent2Agent protocol, an open language that enables agents built on different frameworks or by various vendors to communicate and work together seamlessly. This interoperability is crucial for creating comprehensive AI solutions that span different systems and data sources. Google Cloud is actively partnering with over 50 industry leaders to drive the adoption of this open standard, fostering a shared vision for the future of multi-agent systems. Vertex AI offers a comprehensive platform that integrates models, data, and agents, enabling enterprises to orchestrate the three pillars of production AI. This combination ensures agents perform reliably, mitigating the need for fragmented solutions. With the addition of Lyria, Google’s text-to-music model, Vertex AI now stands as the only platform with generative media models across all modalities – video, image, speech, and music. The platform allows enterprises to build and test concepts with free credits for new customers and offers free monthly usage of over 20 products, including AI APIs. Recommended read:
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staff@insideAI News
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Google has unveiled its seventh-generation Tensor Processing Unit (TPU), named "Ironwood," marking a significant advancement in AI accelerator technology. Designed specifically for AI inference workloads, Ironwood is Google's most performant and scalable custom AI accelerator to date. This launch is part of Google Cloud's strategy to lead in supplying AI models, applications, and infrastructure, capitalizing on its own substantial AI needs to drive homegrown infrastructure development. Google Cloud is positioning itself for the "agentic AI era," with Ironwood playing a pivotal role in enabling multiple AI systems to work together across platforms.
Google's Ironwood TPU comes in configurations of up to 9,216 liquid-cooled chips interconnected via Inter-Chip Interconnect (ICI) networking, spanning nearly 10 MW. The architecture of the Ironwood TPU is designed to optimize hardware and software for AI workloads, allowing developers to leverage Google's Pathways software stack to harness tens of thousands of Ironwood TPUs. The chip design philosophy shift is from models that provide real-time information for interpretation, to models that proactively generate insights and interpretations, which Google calls the "age of inference." Ironwood is designed to manage the computational and communication demands of "thinking models" like large language models and advanced reasoning tasks. A configuration with 9,216 chips per pod can support more than 24 times the compute power of the world’s no. 1 supercomputer, El Capitan. NVIDIA is collaborating with Google Cloud to bring agentic AI to enterprises seeking to locally harness the Google Gemini family of AI models using the NVIDIA Blackwell HGX and DGX platforms and NVIDIA Confidential Computing for data safety. This collaboration enables enterprises to innovate securely while maintaining data privacy. Recommended read:
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Alex Woodie@BigDATAwire
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Google Cloud is making significant strides into the agentic AI era, showcasing new technologies at its annual NEXT conference. The company is introducing its seventh-generation Tensor Processing Unit (TPU), named "Ironwood," designed specifically for inference workloads. Google is also pushing agent interoperability with the Agent2Agent standard and a new developer kit. The company is focused on transitioning from model training to inference, aiming to solve real-world problems using AI systems.
The newly unveiled Ironwood TPU is a game-changer, with each pod scaling up to over 9,000 chips and delivering a staggering 42.5 exaflops of compute. This power surpasses the world's fastest supercomputer, El Capitan, by more than 24 times. Ironwood is built for the demands of complex AI models like Gemini 2.5, boasting improvements in power efficiency and liquid cooling. Innovations like optical switching have led to 100 times improvements in sustained performance. Google is also focused on AI agent technologies, unveiling an open Agent2Agent (A2A) protocol to enable AI agent interoperability. Over 50 technology partners are supporting this initiative, aimed at facilitating the development of AI agents trained to automate tasks across various cloud portfolios. Additionally, Google is providing tools for building and deploying agents, along with broader access to AgentSpace, its framework for building AI agents. This move underscores Google's commitment to making AI more accessible and versatile for enterprises. Recommended read:
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Jon Swartz@Techstrong.ai
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Salesforce and Google Cloud have signed a significant seven-year agreement worth $2.5 billion to deepen their integration of AI and CRM technologies. This collaboration aims to empower Salesforce customers by enabling them to run Agentforce AI assistants, customer-management software, and data cloud products on Google Cloud's infrastructure. The partnership reflects a mutual effort to enhance AI and CRM capabilities, potentially drawing corporate clients away from competitors like Microsoft.
As part of this expanded partnership, Agentforce will leverage Google's Gemini models. This integration will allow agents to handle more complex tasks using Gemini's multi-modal capabilities, including working with images, audio, and video. Furthermore, agents will gain access to real-time insights and answers grounded in Google Search through Vertex AI, enhancing their ability to provide informed and efficient customer support. Recommended read:
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