News from the AI & ML world
DeeperML - #ai
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@blogs.nvidia.com
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NVIDIA Newsroom
, www.networkworld.com
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Cadence has unveiled the Millennium M2000 Supercomputer, a powerhouse featuring NVIDIA Blackwell systems, aimed at revolutionizing AI-driven engineering design and scientific simulations. This supercomputer integrates NVIDIA HGX B200 systems and NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, coupled with NVIDIA CUDA-X software libraries and Cadence's optimized software. The result is a system capable of delivering up to 80 times higher performance compared to its CPU-based predecessors, marking a significant leap forward in computational capability for electronic design automation, system design, and life sciences workloads.
This collaboration between Cadence and NVIDIA is set to enable engineers to conduct massive simulations, leading to breakthroughs in various fields, including the design and development of autonomous machines, drug molecules, semiconductors, and data centers. NVIDIA's founder and CEO, Jensen Huang, highlighted the transformative potential of AI, stating that it will infuse every aspect of business and product development. Huang also announced NVIDIA's plans to acquire ten Millennium Supercomputer systems based on the NVIDIA GB200 NVL72 platform to accelerate the company’s chip design workflows, emphasizing the importance of this technology for NVIDIA's future endeavors. In related news, the open-source OpenSearch software has launched its 3.0 version, which includes GPU acceleration to enhance AI workloads through its new OpenSearch Vector Engine. This update leverages NVIDIA GPUs to improve search performance with large-scale vector workloads and reduce index build times, aiming to address scalability issues common in vector databases. OpenSearch 3.0 also supports Anthropic PBC’s Model Context Protocol, facilitating the integration of large language models with external data. The Millennium M2000 Supercomputer harnesses accelerated software from NVIDIA and Cadence for applications including circuit simulation, computational fluid dynamics, data center design and molecular design. Recommended read:
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@siliconangle.com
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OpenSearch 3.0 has been released under the Linux Foundation, marking its first major release and positioning it as a strong competitor to ElasticSearch. This new version boasts significant improvements, including GPU acceleration, which promises to reduce costs and enhance the handling of billions of vectors for AI applications. Organizations leveraging OpenSearch for big data search, analytics, and AI are expected to experience a remarkable 9.5x performance increase due to the implementation of GPUs, specifically benefiting vector database workloads with a 9.3x boost through an experimental GPU-powered indexing mechanism.
The key highlight of OpenSearch 3.0 is the integration of GPU acceleration for vector search, which is particularly beneficial for AI workloads. This new feature utilizes Nvidia's cuVS library, enabling the power of Nvidia GPUs for creating vector indexes and powering vector searches against those indexes. The experimental GPU support aims to accelerate data-intensive workloads and index builds by up to 9.3x, while simultaneously reducing costs by 3.75x compared to CPU-only solutions. This advancement addresses scalability issues commonly encountered when dealing with billions of vectors in AI applications. OpenSearch 3.0 also introduces Model Context Protocol (MCP) support, facilitating communication between AI agents and the platform. The minimum Java version is now Java 21, which should remove any legacy code. The new gRPC protocol allows faster data transfer. According to Carl Meadows, Governing Board Chair at the OpenSearch Software Foundation and Director of Product Management at AWS, the enterprise search market is projected to reach $8.9 billion by 2030, positioning OpenSearch 3.0 as a pivotal step in supporting the community with an open, scalable platform for the future of search and analytics. Recommended read:
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@aithority.com
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AiThority
, Blocks and Files
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Nutanix is expanding its cloud capabilities with a focus on cloud-native technologies, external storage enhancements, and generative AI integrations, unveiled at its .NEXT 2025 conference. The company is introducing Cloud Native AOS, offering general availability of Dell PowerFlex support, integrating with Pure Storage FlashArray and FlashStack, and launching Nutanix Enterprise AI initiative with NVIDIA. These updates aim to create a generalized software platform where users can run applications anywhere, addressing the growing need for flexibility and scalability in modern IT environments.
Nutanix is also deepening its integration with NVIDIA AI Enterprise to accelerate the deployment of Agentic AI applications within enterprises. The latest version of Nutanix Enterprise AI (NAI) includes NVIDIA NIM microservices and the NVIDIA NeMo framework, simplifying the building, running, and managing of AI models and inferencing services across various environments, including edge, data centers, and public clouds. This integration aims to provide a streamlined foundation for building and running secure AI agents. The enhanced NAI solution features shared LLM endpoints, allowing customers to reuse existing deployed model endpoints for multiple applications, reducing hardware and storage costs. The platform incorporates NVIDIA's NeMo Guardrails to filter out non-approved content, ensuring compliance, privacy, and security within AI applications. Nutanix's Cloud Infrastructure solution, combined with NVIDIA's AI Data Platform, is designed to convert data into actionable insights, providing an optimized stack for GPU data processing and deployment across HCI, bare-metal, and cloud Infrastructure-as-a-Service. Recommended read:
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Alex Shipps@news.mit.edu
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LearnAI
, news.mit.edu
MIT and Adobe have jointly developed CausVid, a generative AI tool capable of crafting smooth, high-quality videos in mere seconds. This hybrid AI model utilizes a diffusion model to train an autoregressive system, enabling rapid and stable high-resolution video production. Unlike existing diffusion models like OpenAI's SORA and Google's VEO 2, which process entire sequences at once and can be slow and inflexible, CausVid adopts a unique frame-by-frame approach. This allows for quick generation and on-the-fly modifications, offering a significant advantage in interactive content creation.
The CausVid tool allows users to generate clips, modify them with new prompts in real-time, transform static photos into dynamic scenes, and even extend existing videos. Imagine turning a simple text prompt into a visually stunning clip of a paper airplane morphing into a swan or woolly mammoths trekking through a snowy landscape. Users can also build upon initial prompts, adding new elements and details to their scenes interactively. This dynamic capability significantly streamlines video creation, reducing a process that once involved up to 50 steps into just a few simple actions. According to researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), CausVid has a wide array of potential applications. It could be used in video editing to generate videos that synchronize with audio translations for live streams, helping viewers understand content in different languages. Furthermore, it could aid in rendering new content for video games or quickly producing training simulations for robots. Tianwei Yin, co-lead author of a new paper about the tool, highlights the model’s strength, attributing it to the combination of a pre-trained diffusion-based model with autoregressive architecture. Recommended read:
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@www.techmeme.com
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A recent report from Amazon Web Services (AWS) indicates a significant shift in IT spending priorities for 2025. Generative AI has overtaken cybersecurity as the primary focus for global IT leaders, with 45% now prioritizing AI investments. This change underscores the increasing emphasis on implementing AI strategies and acquiring the necessary talent, even amidst ongoing skills shortages. The AWS Generative AI Adoption Index surveyed 3,739 senior IT decision makers across nine countries, including the United States, Brazil, Canada, France, Germany, India, Japan, South Korea, and the United Kingdom.
This move to prioritize generative AI doesn't suggest a neglect of security, according to Rahul Pathak, Vice President of Generative AI and AI/ML Go-to-Market at AWS. Pathak stated that customers' security remains a massive priority, and the surge in AI investment reflects the widespread recognition of AI's diverse applications and the pressing need to accelerate its adoption. The survey revealed that 90% of organizations are already deploying generative AI in some capacity, with 44% moving beyond experimental phases to production deployment, indicating a critical inflection point in AI adoption. The survey also highlights the emergence of new leadership roles within organizations to manage AI initiatives. Sixty percent of companies have already appointed a Chief AI Officer (CAIO) or equivalent, and an additional 26% plan to do so by 2026. This executive-level commitment reflects the growing strategic importance of AI, although the study cautions that nearly a quarter of organizations may still lack formal AI transformation strategies by 2026. These companies are planning ways to bridge the gen AI talent gap this year by creating training plans to upskill their workforce for GenAI. Recommended read:
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Coen van@Techzine Global
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ServiceNow has announced the launch of AI Control Tower, a centralized control center designed to manage, secure, and optimize AI agents, models, and workflows across an organization. Unveiled at Knowledge 2025 in Las Vegas, this platform provides a holistic view of the entire AI ecosystem, enabling enterprises to monitor and manage both ServiceNow and third-party AI agents from a single location. The AI Control Tower aims to address the growing complexity of managing AI deployments, giving users a central point to see all AI systems, their deployment status, and ensuring governance and understanding of their activities.
The AI Control Tower offers key benefits such as enterprise-wide AI visibility, built-in compliance and AI governance, end-to-end lifecycle management of agentic processes, real-time reporting, and improved alignment. It is designed to help AI systems administrators and other stakeholders monitor and manage every AI agent, model, or workflow within their system, providing real-time reporting for different metrics and embedded compliance and AI governance. The platform helps users understand the different systems by provider and type, improving risk and compliance management. In addition to the AI Control Tower, ServiceNow introduced AI Agent Fabric, facilitating communication between AI agents and partner integrations. ServiceNow has also partnered with NVIDIA to engineer an open-source model, Apriel Nemotron 15B, designed to drive advancements in enterprise large language models (LLMs) and power AI agents that support various enterprise workflows. The Apriel Nemotron 15B, developed using NVIDIA NeMo and ServiceNow domain-specific data, is engineered for reasoning, drawing inferences, weighing goals, and navigating rules in real time, making it efficient and scalable for concurrent enterprise workflows. Recommended read:
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@venturebeat.com
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Nvidia has launched Parakeet-TDT-0.6B-V2, a fully open-source transcription AI model, on Hugging Face. This represents a new standard for Automatic Speech Recognition (ASR). The model, boasting 600 million parameters, has quickly topped the Hugging Face Open ASR Leaderboard with a word error rate of just 6.05%. This level of accuracy positions it near proprietary transcription models, such as OpenAI’s GPT-4o-transcribe and ElevenLabs Scribe, making it a significant advancement in open-source speech AI. Parakeet operates under a commercially permissive CC-BY-4.0 license.
The speed of Parakeet-TDT-0.6B-V2 is a standout feature. According to Hugging Face’s Vaibhav Srivastav, it can "transcribe 60 minutes of audio in 1 second." Nvidia reports this is achieved with a real-time factor of 3386, meaning it processes audio 3386 times faster than real-time when running on Nvidia's GPU-accelerated hardware. This speed is attributed to its transformer-based architecture, fine-tuned with high-quality transcription data and optimized for inference on NVIDIA hardware using TensorRT and FP8 quantization. The model also supports punctuation, capitalization, and detailed word-level timestamping. Parakeet-TDT-0.6B-V2 is aimed at developers, researchers, and industry teams building various applications. This includes transcription services, voice assistants, subtitle generators, and conversational AI platforms. Its accessibility and performance make it an attractive option for commercial enterprises and indie developers looking to build speech recognition and transcription services into their applications. With its release on May 1, 2025, Parakeet is set to make a considerable impact on the field of speech AI. Recommended read:
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erichs211@gmail.com (Eric@techradar.com
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Google's powerful AI model, Gemini 2.5 Pro, has achieved a significant milestone by completing the classic Game Boy game Pokémon Blue. This accomplishment, spearheaded by software engineer Joel Z, demonstrates the AI's enhanced reasoning and problem-solving abilities. Google CEO Sundar Pichai celebrated the achievement online, highlighting it as a substantial win for AI development. The project showcases how AI can learn to handle complex tasks, requiring long-term planning, goal tracking, and visual navigation, which are vital components in the pursuit of general artificial intelligence.
Joel Z facilitated Gemini's gameplay over several months, livestreaming the AI's progress. While Joel is not affiliated with Google, his efforts were supported by the company's leadership. To enable Gemini to navigate the game, Joel used an emulator, mGBA, to feed screenshots and game data, like character position and map layout. He also incorporated smaller AI helpers, like a "Pathfinder" and a "Boulder Puzzle Solver," to tackle particularly challenging segments. These sub-agents, also versions of Gemini, were deployed strategically by the AI to manage complex situations, showcasing its ability to differentiate between routine and complicated tasks. Google is also experimenting with transforming its search engine into a Gemini-powered chatbot via an AI Mode. This new feature, currently being tested with a small percentage of U.S. users, delivers conversational answers generated from Google's vast index, effectively turning Search into an answer engine. Instead of a list of links, AI Mode provides rich, visual summaries and remembers prior queries, directly competing with the search features of Perplexity and ChatGPT. While this shift could potentially impact organic SEO tactics, it signifies Google's commitment to integrating AI more deeply into its core products, offering users a more intuitive and informative search experience. Recommended read:
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@the-decoder.com
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OpenAI is making significant strides in the enterprise AI and coding tool landscape. The company recently released a strategic guide, "AI in the Enterprise," offering practical strategies for organizations implementing AI at a large scale. This guide emphasizes real-world implementation rather than abstract theories, drawing from collaborations with major companies like Morgan Stanley and Klarna. It focuses on systematic evaluation, infrastructure readiness, and domain-specific integration, highlighting the importance of embedding AI directly into user-facing experiences, as demonstrated by Indeed's use of GPT-4o to personalize job matching.
Simultaneously, OpenAI is reportedly in the process of acquiring Windsurf, an AI-powered developer platform, for approximately $3 billion. This acquisition aims to enhance OpenAI's AI coding capabilities and address increasing competition in the market for AI-driven coding assistants. Windsurf, previously known as Codeium, develops a tool that generates source code from natural language prompts and is used by over 800,000 developers. The deal, if finalized, would be OpenAI's largest acquisition to date, signaling a major move to compete with Microsoft's GitHub Copilot and Anthropic's Claude Code. Sam Altman, CEO of OpenAI, has also reaffirmed the company's commitment to its non-profit roots, transitioning the profit-seeking side of the business to a Public Benefit Corporation (PBC). This ensures that while OpenAI pursues commercial goals, it does so under the oversight of its original non-profit structure. Altman emphasized the importance of putting powerful tools in the hands of everyone and allowing users a great deal of freedom in how they use these tools, even if differing moral frameworks exist. This decision aims to build a "brain for the world" that is accessible and beneficial for a wide range of uses. Recommended read:
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@www.medianama.com
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Visa, Mastercard, and PayPal have recently unveiled agent-ready platforms, marking a significant shift in the landscape of online commerce. These platforms enable AI systems to autonomously shop, make decisions, and handle payments on behalf of users. Visa's Intelligent Commerce platform allows AI agents to securely make purchases using credit cards while adhering to consumer-defined spending limits. Similarly, Mastercard's Agent Pay facilitates the integration of tokenized card credentials directly into AI-driven workflows. PayPal has launched an Agent Toolkit, allowing developers to embed payment, invoicing, and shipping functionalities into AI assistants.
This development signals a move towards autonomous shopping and agentic commerce, potentially revolutionizing online checkout processes for both consumers and businesses. With Visa's Intelligent Commerce, AI agents can now go beyond simply recommending products to actually completing transactions. The system replaces traditional card details with secure, tokenized digital credentials accessible to authorized AI agents. Users maintain control by setting parameters such as spending limits and preferred merchant categories. The collaboration between Visa, Mastercard, and PayPal aims to establish secure frameworks for AI agents to conduct financial transactions. These platforms offer numerous potential benefits. For consumers, this could mean more efficient and personalized shopping experiences. For example, an AI assistant could be instructed to book a flight within a specific budget or order weekly groceries, completing the purchase without manual input. Businesses stand to gain from new opportunities for customer interaction and increased sales through hyper-personalized offers and real-time transaction data. As these platforms mature, retailers may need to adapt by creating "agent-readable" catalogs and promotions, as brand equity shifts towards the agents consumers choose to use. Recommended read:
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@the-decoder.com
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Google is integrating its Gemini AI model deeper into its search engine with the introduction of 'AI Mode'. This new feature, currently in a limited testing phase in the US, aims to transform the search experience into a conversational one. Instead of the traditional list of links, AI Mode delivers answers generated directly from Google’s index, functioning much like a Gemini-powered chatbot. The search giant is also dropping the Labs waitlist, allowing any U.S. user who opts in to try the new search function.
The AI Mode includes visual place and product cards, enhanced multimedia features, and a left-side panel for managing past searches. This provides more organized results for destinations, products, and services. Users can ask contextual follow-up questions, and the AI Mode will populate a sidebar with cards referring to the sources it's using to formulate its answers. It can also access Google's Shopping Graph and localized data from Maps. This move is seen as Google's direct response to AI-native upstarts that are recasting the search bar as a natural-language front end to the internet. Google CEO Sundar Pichai is hopeful to have an agreement with Apple to have Gemini as an option as part of Apple Intelligence by middle of this year. The rise of AI in search raises concerns for marketers. Organic SEO tactics built on blue links will erode and there will be a need to prepare content for zero‑click, AI‑generated summaries. Recommended read:
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@the-decoder.com
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Google is enhancing its AI capabilities across several platforms. NotebookLM, the AI-powered research tool, is expanding its "Audio Overviews" feature to approximately 75 languages, including less common ones such as Icelandic, Basque, and Latin. This enhancement will enable users worldwide to listen to AI-generated summaries of documents, web pages, and YouTube transcripts, making research more accessible. The audio for each language is generated by AI agents using metaprompting, with the Gemini 2.5 Pro language model as the underlying system, moving towards audio production technology based entirely on Gemini’s multimodality.
These Audio Overviews are designed to distill a mix of documents into a scripted conversation between two synthetic hosts. Users can direct the tone and depth through prompts, and then download an MP3 or keep playback within the notebook. This expansion rebuilds the speech stack and language detection while maintaining a one-click flow. Early testers have reported that multilingual voices make long reading lists easier to digest and provide an alternative channel for blind or low-vision audiences. In addition to NotebookLM enhancements, Google Gemini is receiving AI-assisted image editing capabilities. Users will be able to modify backgrounds, swap objects, and make other adjustments to both AI-generated and personal photos directly within the chat interface. These editing tools are being introduced gradually for users on web and mobile devices, supporting over 45 languages in most countries. To access the new features on your phone, users will need the latest version of the Gemini app. Recommended read:
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