News from the AI & ML world
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Amazon CEO Andy Jassy has delivered a candid message to employees, stating that the company's increased investment in artificial intelligence will lead to workforce reductions in the coming years. In an internal memo, Jassy outlined an aggressive generative-AI roadmap, highlighting projects like Alexa+ and the new Nova models. He bluntly predicted that software agents will take over rote work, resulting in a smaller corporate workforce. The company anticipates efficiency gains from AI will reduce the need for human workers in various roles.
Jassy emphasized that Amazon currently has over 1,000 generative AI services and applications in development across every business line. These AI agents are expected to contribute to innovation while simultaneously trimming corporate headcount. The company hopes to use agents that can act as "teammates that we can call on at various stages of our work" according to Jassy. He acknowledged that the company will "need fewer people doing some of the jobs that are being done today, and more people doing other types of jobs," though the specific departments impacted were not detailed. While Jassy didn't provide a precise timeline for the layoffs, he stated that efficiency gains from AI will reduce the company's total corporate workforce in the next few years. This announcement comes after Amazon has already eliminated approximately 27,000 corporate jobs since 2022. The company has also started testing humanoid robots at a Seattle warehouse, capable of moving, grasping, and handling items like human workers. Similarly, the Prime Air drone service has already begun delivering packages in eligible areas. Recommended read:
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Fiona Jackson@eWEEK
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Amazon to Cut Workforce with AI Integration - Amazon plans to reduce its corporate workforce by integrating generative AI into its processes, expecting to replace human workers with AI to improve efficiency.
ImgSrc: assets.eweek.co
Amazon CEO Andy Jassy has announced that the company anticipates a reduction in its corporate workforce as generative AI is integrated into various business operations. In an internal memo to employees, Jassy stated that Amazon expects to cut human workers and replace them with AI to achieve efficiency gains through automation. This decision stems from the company's aggressive push into AI, with over 1,000 generative AI services and applications already in development, including Alexa+ and the Nova foundation models. The use of AI agents is expected to accelerate internal processes and innovation across every business line.
Jassy emphasized that the deployment of AI will change the way work is done at Amazon. He noted that "we will need fewer people doing some of the jobs that are being done today, and more people doing other types of jobs." While the exact impact on specific departments remains unspecified, the memo highlights the critical role of AI agents in the company's future. These agents are capable of engaging in deep research, writing code, and ultimately transforming the speed and scope of innovation for customers. The announcement follows a history of workforce reductions at Amazon, with over 27,000 corporate jobs eliminated since 2022. Although previous layoffs were primarily attributed to economic uncertainty and organizational efficiency, Jassy's recent memo indicates that AI-driven automation will be a significant factor moving forward. Jassy acknowledged the need for Amazon to operate like the "world's largest startup" and stressed the importance of AI investment for internal productivity improvements. He expects that these changes will reduce the company's total corporate workforce in the next few years. Recommended read:
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@www.analyticsvidhya.com
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MiniMaxAI, a Chinese AI company, has launched MiniMax-M1, a large-scale open-source reasoning model, marking a significant step in the open-source AI landscape. Released on the first day of the "MiniMaxWeek" event, MiniMax-M1 is designed to compete with leading models like OpenAI's o3, Claude 4, and DeepSeek-R1. Alongside the model, MiniMax has released a beta version of an agent capable of running code, building applications, and creating presentations. MiniMax-M1 presents a flexible option for organizations looking to experiment with or scale up advanced AI capabilities while managing costs.
MiniMax-M1 boasts a 1 million token context window and utilizes a new, highly efficient reinforcement learning technique. The model comes in two variants, MiniMax-M1-40k and MiniMax-M1-80k. Built on a Mixture-of-Experts (MoE) architecture, the model is trained on 456 billion parameters. MiniMax has introduced Lightning Attention for its M1 model, dramatically reducing inference costs and only consumes 25% of the floating point operations (FLOPs) required by DeepSeek R1 at a generation length of 100,000 tokens. Available on AI code sharing communities like Hugging Face and GitHub, MiniMax-M1 is released under the Apache 2.0 license, enabling businesses to freely use, modify, and implement it for commercial applications without restrictions or payment. MiniMax-M1 features a web search functionality and can handle multimodal input like text, images, and presentations. The expansive context window allows the model to exchange information equivalent to a small collection or book series, far exceeding OpenAI's GPT-4o, which has a context window of 128,000 tokens. Recommended read:
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Chris McKay@Maginative
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OpenAI Expands into Government and Enterprise Sectors - OpenAI has been awarded a $200 million contract with the U.S. Defense Department to supply AI tools and is open-sourcing a customer service agent demo.
ImgSrc: www.maginative.
OpenAI has secured a significant contract with the U.S. Defense Department, marking its first major foray into the national security sector. The one-year agreement, valued at $200 million, signifies a pivotal moment as OpenAI aims to supply its AI tools for administrative tasks and proactive cyberdefense. This initiative is the inaugural project under OpenAI's new "OpenAI for Government" program, highlighting the company's strategic shift and ambition to become a key provider of generative AI solutions for national security agencies. This deal follows OpenAI's updated usage policy, which now permits defensive or humanitarian military applications, signaling a departure from its earlier stance against military use of its AI models.
This move by OpenAI reflects a broader trend in the AI industry, with rival companies like Anthropic and Meta also embracing collaborations with defense contractors and intelligence agencies. OpenAI emphasizes that its usage policy still prohibits weapon development or kinetic targeting, and the Defense Department contract will adhere to these restrictions. The "OpenAI for Government" program includes custom models, hands-on support, and previews of product roadmaps for government agencies, offering them an enhanced Enterprise feature set. In addition to its government initiatives, OpenAI is expanding its enterprise strategy by open-sourcing a new multi-agent customer service demo on GitHub. This demo showcases how to build domain-specialized AI agents using the Agents SDK, offering a practical example for developers. The system models an airline customer service chatbot capable of handling various travel-related queries by dynamically routing requests to specialized agents like Seat Booking, Flight Status, and Cancellation. By offering transparent tooling and clear implementation examples, OpenAI aims to accelerate the adoption of agentic systems in everyday enterprise applications. Recommended read:
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@www.artificialintelligence-news.com
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venturebeat.com
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Hugging Face has partnered with Groq to offer ultra-fast AI model inference, integrating Groq's Language Processing Unit (LPU) inference engine as a native provider on the Hugging Face platform. This collaboration aims to provide developers with access to lightning-fast processing capabilities directly within the popular model hub. Groq's chips are specifically designed for language models, offering a specialized architecture that differs from traditional GPUs by embracing the sequential nature of language tasks, resulting in reduced response times and higher throughput for AI applications.
Developers can now access high-speed inference for multiple open-weight models through Groq’s infrastructure, including Meta’s Llama 4, Meta’s Llama-3 and Qwen’s QwQ-32B. Groq is the only inference provider to enable the full 131K context window, allowing developers to build applications at scale. The integration works seamlessly with Hugging Face’s client libraries for both Python and JavaScript, though the technical details remain refreshingly simple. Even without diving into code, developers can specify Groq as their preferred provider with minimal configuration. This partnership marks Groq’s boldest attempt yet to carve out market share in the rapidly expanding AI inference market, where companies like AWS Bedrock, Google Vertex AI, and Microsoft Azure have dominated by offering convenient access to leading language models. This marks Groq's third major platform partnership in as many months. In April, Groq became the exclusive inference provider for Meta’s official Llama API, delivering speeds up to 625 tokens per second to enterprise customers. The following mo Recommended read:
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@www.microsoft.com
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Microsoft Advances AI Reasoning and Cloud Services - Microsoft is enhancing reasoning in language models with AI for weather forecasting and rolling out sovereign cloud for Europe.
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www.microsoft.com
, Microsoft Research
Microsoft is making significant advancements in artificial intelligence, focusing on improved reasoning in language models and enhanced weather forecasting capabilities. New methods are being developed to boost reasoning in both small and large language models, combining symbolic logic, mathematical rigor, and adaptive planning. These techniques are designed to enable AI models to tackle complex, real-world problems across various fields, potentially transforming AI into a more reliable partner in domains like scientific research and healthcare.
A new AI model named Aurora, developed by Microsoft, can forecast hurricanes and sandstorms up to 5,000 times faster than conventional weather models powered by supercomputers. Aurora outperformed existing systems in predicting weather conditions over a 14-day period in 91% of cases. The model is trained on over 1 million hours of global atmospheric data, including weather station readings, satellite images, and radar measurements, representing one of the largest datasets used to train a weather AI model. To address the growing demand for data control in Europe, Microsoft is expanding its Sovereign Cloud offerings. This includes solutions that ensure European data remains within Europe, handled exclusively by Microsoft employees based in the region. The Sovereign Public Cloud offers tools and options for customer-controlled encryption and simplified configurations, providing organizations in Europe with greater control over their data. The cloud is offered across all existing European data center regions. Recommended read:
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@cloud.google.com
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AI & Machine Learning
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Google Cloud is offering Financial Services Institutions (FSIs) a powerful solution to streamline and enhance their Know Your Customer (KYC) processes by leveraging the Agent Development Kit (ADK) in combination with Gemini models and Search Grounding. KYC processes are critical for regulatory compliance and risk mitigation, involving the verification of customer identities and the assessment of associated risks. Traditional KYC methods are often manual, time-consuming, and prone to errors, which can be challenging in today's environment where customers expect instant approvals. The Agent Development Kit (ADK) is a flexible and modular framework for developing and deploying AI agents. While optimized for Gemini and the Google ecosystem, ADK is model-agnostic, deployment-agnostic, and is built for compatibility with other frameworks. ADK was designed to make agent development feel more like software development, to make it easier for developers to create, deploy, and orchestrate agentic architectures that range from simple tasks to complex workflows.
The ADK simplifies the creation and orchestration of agents, handling agent definition, tool integration, state management, and inter-agent communication. These agents are powered by Gemini models hosted on Vertex AI, providing core reasoning, instruction-following, and language understanding capabilities. Gemini's multimodal analysis, including image processing from IDs and documents, and multilingual support further enhances the KYC process for diverse customer bases. By incorporating Search Grounding, the system connects Gemini responses to real-time information from Google Search, reducing hallucinations and increasing the reliability of the information provided. Furthermore, integration with BigQuery allows secure interaction with internal datasets, ensuring comprehensive data access while maintaining data security. The multi-agent architecture offers several key benefits for FSIs including improved efficiency through the automation of large portions of the KYC workflow, reducing manual effort and turnaround times. AI is leveraged for consistent document analysis and comprehensive external checks, leading to enhanced accuracy. The solution also strengthens compliance by improving auditability through clear reporting and source attribution via grounding. Google Cloud provides resources to get started, including $300 in free credit for new customers to build and test proof of concepts, along with free monthly usage of over 20 AI-related products and APIs. The combination of ADK, Gemini models, Search Grounding, and BigQuery integration represents a significant advancement in KYC processes, offering FSIs a robust and efficient solution to meet regulatory requirements and improve customer experience. Recommended read:
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@techstrong.ai
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Agentic AI is rapidly reshaping enterprise data engineering by transforming passive infrastructure into intelligent systems capable of acting, adapting, and automating operations at scale. This new paradigm embeds intelligence, governance, and automation directly into modern data stacks, allowing for autonomous decision-making and real-time action across various industries. According to Dave Vellante, co-founder and chief analyst at theCUBE Research, the value is moving up the stack, emphasizing the shift towards open formats like Apache Iceberg, which allows for greater integration of proprietary functionalities into the open world.
The rise of agentic AI is also evident in the healthcare sector, where it's already being implemented in areas like triage, care coordination, and clinical decision-making. Unlike generative AI, which waits for instructions, agentic AI creates and follows its own instructions within set boundaries, acting as an autonomous decision-maker. This is enabling healthcare organizations to optimize workflows, manage complex tasks, and execute multi-step care protocols without constant human intervention, improving efficiency and patient care. Bold CIOs in healthcare are already leveraging agentic AI to gain a competitive advantage, demonstrating its practical application beyond mere experimentation. To further simplify the deployment of AI agents, Accenture has introduced its Distiller Framework, a platform designed to help developers build, deploy, and scale advanced AI agents rapidly. This framework encapsulates essential components across the entire agent lifecycle, including agent memory management, multi-agent collaboration, workflow management, model customization, and governance. Lyzr Agent Studio is another platform which helps to build end-to-end agentic workflows by automating complex tasks, integrating enterprise systems, and deploying production-ready AI agents. This addresses the current challenge of scaling AI initiatives beyond small-scale experiments and accelerates the adoption of agentic AI across various industries. Recommended read:
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@www.microsoft.com
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syncedreview.com
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Advancements in agentic AI are rapidly transforming various sectors, with organizations like Microsoft and Resemble AI leading the charge. Microsoft is demonstrating at TM Forum DTW Ignite 2025 how the synergy between Open Digital Architecture (ODA) and agentic AI is converting industry ambitions into measurable business outcomes within the telecommunications sector. They are focusing on breaking down operational silos, unlocking data's value, increasing efficiency, and accelerating innovation. Meanwhile, Resemble AI is advancing AI voice agents, anticipating the growing momentum of voice-first technologies, with over 74% of enterprises actively piloting or deploying these agents as part of their digital transformation strategies by 2025, according to an IDC report.
Researchers from Penn State University and Duke University have introduced "Multi-Agent Systems Automated Failure Attribution," a significant development in managing complex AI systems. This innovation addresses the challenge of identifying the root cause of failures in multi-agent systems, which can be difficult to diagnose due to the autonomous nature of agent collaboration and long information chains. The researchers have developed a benchmark dataset and several automated attribution methods to enhance the reliability of LLM Multi-Agent systems, transforming failure identification from a perplexing mystery into a quantifiable problem. Microsoft's contributions to TM Forum initiatives, including co-authoring Open APIs and donating hardened code, highlight the importance of standards-based foundations in AI development. By aligning Microsoft Azure's cloud-native foundations with ODA's composable blueprint, Microsoft is helping operators assemble solutions without proprietary silos, leading to faster interoperability, reduced integration costs, and quicker time-to-value for new digital services. This approach addresses fragmented observability by prescribing a common logging contract and integrating with Azure Monitor, reducing the time to detect anomalies and enabling teams to focus on proactive optimization. Recommended read:
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Ellie Ramirez-Camara@Data Phoenix
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Data Phoenix
Wordsmith AI, an Edinburgh-based legal technology startup, has secured $25 million in Series A funding led by Index Ventures. This investment values the company at over $100 million, marking it as one of Scotland's fastest-growing tech companies. The funding will be used to scale its AI agent platform and expand operations to London and New York, further developing its AI infrastructure capabilities.
Wordsmith AI is focused on transforming legal departments from operational bottlenecks into revenue accelerators. Their AI agent platform embeds legal intelligence directly into business workflows, streamlining processes like contract review, query answering, and decision-making. These AI agents integrate seamlessly into existing tools such as Slack, email, and Google Docs, enabling legal teams to scale their expertise without increasing headcount. CEO Ross McNairn emphasizes the company's vision of "legal engineering," where legal intelligence is embedded directly into business workflows through intelligent agents. Major clients like Deliveroo, Trustpilot, Remote.com, and Multiverse are already using the platform to reduce deal cycles and eliminate bottlenecks. Wordsmith AI is also pioneering the "legal engineer" role, combining legal expertise with technical skills to manage AI agent deployments, facilitating a future where legal teams engineer solutions rather than simply firefighting. Recommended read:
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@www.marktechpost.com
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Apple is enhancing its developer tools to empower developers in building AI-informed applications. While Siri may not yet be the smart assistant Apple envisions, the company has significantly enriched its offerings for developers. A powerful update to Xcode, including ChatGPT integration, is set to transform app development. This move signals Apple's commitment to integrating AI capabilities into its ecosystem, even as challenges persist with its own AI assistant.
However, experts have voiced concerns about Apple's downbeat AI outlook, attributing it to a potential lack of high-powered hardware. Professor Seok Joon Kwon of Sungkyunkwan University suggests that Apple's research paper revealing fundamental reasoning limits of modern large reasoning models (LRMs) and large language models (LLMs) is flawed because Apple lacks the hardware to adequately test high-end LRMs and LLMs. The professor argues that Apple's hardware is unsuitable for AI development compared to the resources available to companies like Google, Microsoft, or xAI. If Apple wants to catch up with rivals, it will either have to buy a lot of Nvidia GPUs or develop its own AI ASICs. Apple's much-anticipated Siri upgrade, powered by Apple Intelligence, is now reportedly targeting a "spring 2026" launch. According to Mark Gurman at Bloomberg, Apple has set an internal release target of spring 2026 for its delayed upgrade of Siri, marking a key step in its artificial intelligence turnaround effort and is slated for iOS 26.4. The upgrade is expected to give Siri on-screen awareness and personal context capabilities. Recommended read:
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@www.marktechpost.com
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Apple researchers are challenging the perceived reasoning capabilities of Large Reasoning Models (LRMs), sparking debate within the AI community. A recent paper from Apple, titled "The Illusion of Thinking," suggests that these models, which generate intermediate thinking steps like Chain-of-Thought reasoning, struggle with fundamental reasoning tasks. The research indicates that current evaluation methods relying on math and code benchmarks are insufficient, as they often suffer from data contamination and fail to assess the structure or quality of the reasoning process.
To address these shortcomings, Apple researchers introduced controllable puzzle environments, including the Tower of Hanoi, River Crossing, Checker Jumping, and Blocks World, allowing for precise manipulation of problem complexity. These puzzles require diverse reasoning abilities, such as constraint satisfaction and sequential planning, and are free from data contamination. The Apple paper concluded that state-of-the-art LRMs ultimately fail to develop generalizable problem-solving capabilities, with accuracy collapsing to zero beyond certain complexities across different environments. However, the Apple research has faced criticism. Experts, like Professor Seok Joon Kwon, argue that Apple's lack of high-performance hardware, such as a large GPU-based cluster comparable to those operated by Google or Microsoft, could be a factor in their findings. Some argue that the models perform better on familiar puzzles, suggesting that their success may be linked to training exposure rather than genuine problem-solving skills. Others, such as Alex Lawsen and "C. Opus," argue that the Apple researchers' results don't support claims about fundamental reasoning limitations, but rather highlight engineering challenges related to token limits and evaluation methods. Recommended read:
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Mike Wheatley@SiliconANGLE
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Databricks Inc. has unveiled Databricks One, an AI-powered business intelligence tool designed to democratize data and AI accessibility for all business workers, regardless of their technical skills. This new platform aims to simplify the way enterprises interact with data and AI, addressing the challenges of complexity, rising costs, and vendor lock-in that often hinder the practical application of data insights across organizations. Databricks One introduces a simplified user interface, making the platform's capabilities accessible to individuals who may not possess coding skills in Python or Structured Query Language.
Databricks One offers a code-free, business-oriented layer built on top of the Databricks Data Intelligence Platform, bringing together interactive dashboards, conversational AI, and low-code applications in a user-friendly environment tailored for non-technical users. A key feature of Databricks One is the integration of a new AI/BI Genie assistant, powered by large language models (LLMs). Genie enables business users to ask questions in plain language and receive responses grounded in enterprise data, facilitating detailed data analysis without the need for coding expertise. The platform utilizes generative AI models, similar to interfaces like ChatGPT, allowing users to describe the type of data analysis they want to perform. The LLM then handles the necessary technical tasks, such as deploying AI agents into data pipelines and databases to perform specific and detailed analysis. Once the analysis is complete, Databricks One presents the results through visualizations within its interface, enabling users to further explore the data with the AI/BI Genie. Databricks One is currently available in private preview, with a private beta planned for later in the summer. Recommended read:
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Alyssa Mazzina@blog.runpod.io
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, Ken Yeung
AI is rapidly changing how college students approach their education. Instead of solely using AI for cheating, students are finding innovative ways to leverage tools like ChatGPT for studying, organization, and collaboration. For instance, students are using AI to quiz themselves on lecture notes, summarize complex readings, and alphabetize citations. These tasks free up time and mental energy, allowing students to focus on deeper learning and understanding course material. This shift reflects a move toward optimizing their learning processes, rather than simply seeking shortcuts.
Students are also using AI tools like Grammarly to refine their communications with professors and internship coordinators. Tools like Notion AI are helping students organize their schedules and generate study plans that feel less overwhelming. Furthermore, a collaborative AI-sharing culture has emerged, with students splitting the cost of ChatGPT Plus and sharing accounts. This collaborative spirit extends to group chats where students exchange quiz questions generated by AI, fostering a supportive learning environment. Handshake, the college career network, has launched a new platform, Handshake AI, to connect graduate students with leading AI research labs, creating new opportunities for monetization. This service allows PhD students to train and evaluate AI models, offering their academic expertise to improve large language models. Experts are needed in fields like mathematics, physics, chemistry, biology, music, and education. Handshake AI provides AI labs with access to vetted individuals who can offer the human judgment needed for AI to evolve, while providing graduate students with valuable experience and income in the burgeoning AI space. Recommended read:
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Jowi Morales@tomshardware.com
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NVIDIA is partnering with Germany and Deutsche Telekom to build Europe's first industrial AI cloud, a project hailed as one of the most ambitious tech endeavors in the continent. This initiative aims to establish Germany as a leader in AI manufacturing and innovation. NVIDIA's CEO, Jensen Huang, met with Chancellor Friedrich Merz to discuss the new partnerships that will drive breakthroughs on this AI cloud.
This "AI factory," located in Germany, will provide European industrial leaders with the computational power needed to revolutionize manufacturing processes, from design and engineering to simulation and robotics. The goal is to empower European industrial players to lead in simulation-first, AI-driven manufacturing. Deutsche Telekom's CEO, Timotheus Höttges, emphasized the urgency of seizing AI opportunities to revolutionize the industry and secure a leading position in global technology competition. The first phase of the project will involve deploying 10,000 NVIDIA Blackwell GPUs across various high-performance systems, making it Germany's largest AI deployment. This infrastructure will also feature NVIDIA networking and AI software. NEURA Robotics, a German firm specializing in cognitive robotics, plans to utilize these resources to power its Neuraverse, a network where robots can learn from each other. This partnership between NVIDIA and Germany signifies a critical step towards achieving technological sovereignty in Europe and accelerating AI development across industries. Recommended read:
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Michael Kan@PCMag Middle East ai
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SiliconANGLE
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Google is pushing forward with advancements in artificial intelligence across a range of its services. Google DeepMind has developed an AI model that can forecast tropical cyclones with state-of-the-art accuracy, predicting their path and intensity up to 15 days in advance. This model is now being used by the U.S. National Hurricane Center in its official forecasting workflow, marking a significant shift in how these storms are predicted. The AI system learns from decades of historical storm data and can generate 50 different hurricane scenarios, offering a 1.5-day improvement in prediction accuracy compared to traditional models. Google has launched a Weather Lab website to make this AI accessible to researchers, providing historical forecasts and data for comparison.
Google is also experimenting with AI-generated search results in audio format, launching "Audio Overviews" in its Search Labs. Powered by the Gemini language model, this feature delivers quick, conversational summaries for certain search queries. Users can opt into the test and, when available, a play button will appear in Google Search, providing an audio summary alongside relevant websites. The AI researches the query and generates a transcript, read out loud by AI-generated voices, citing its sources. This feature aims to provide a hands-free way to absorb information, particularly for users who are multitasking or prefer audio content. The introduction of AI-powered features comes amid ongoing debate about the impact on traffic to third-party websites. There are concerns that Google’s AI-driven search results may prioritize its own content over linking to external sources. Some users have also noted instances of Google's AI search summaries spreading incorrect information. Google says it's seen an over 10% increase in usage of Google for the types of queries that show AI Overviews. Recommended read:
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Jim McGregor,@Tirias Research
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Advanced Micro Devices Inc. has launched its new AMD Instinct MI350 Series accelerators, designed to accelerate AI data centers and outperform Nvidia Corp.’s Blackwell B200 in specific tasks. The MI350 series includes the top-end MI355X, a liquid-cooled card, along with the MI350X which uses fans instead of liquid cooling. These new flagship data center graphics cards boast an impressive 185 billion transistors and are based on a three-dimensional, 10-chiplet design to enhance AI compute and inferencing capabilities.
The MI350 Series introduces significant performance improvements, achieving four times faster AI compute and 35 times faster inferencing compared to previous generations. These accelerators ship with 288 gigabytes of HBM3E memory, which features a three-dimensional design in which layers of circuits are stacked atop one another. According to AMD, the MI350 series features 60% more memory than Nvidia’s flagship Blackwell B200 graphics cards. Additionally, the MI350 chips can process 8-bit floating point numbers 10% faster and 4-bit floating point numbers more than twice as fast as the B200. AMD is also rolling out its ROCm 7 software development platform for the Instinct accelerators and the Helios Rack AI platform. "With flexible air-cooled and direct liquid-cooled configurations, the Instinct MI350 Series is optimized for seamless deployment, supporting up to 64 GPUs in an air-cooled rack and up to 128 in a direct liquid-cooled and scaling up to 2.6 exaFLOPS of FP4 performance," stated Vamsi Boppana, the senior vice president of AMD’s Artificial Intelligence Group. The advancements aim to provide an open, scalable rack-scale AI infrastructure built on industry standards, setting the stage for transformative AI solutions across various industries. Recommended read:
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@www.marktechpost.com
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Meta AI has announced the release of V-JEPA 2, an open-source world model designed to enhance robots' ability to understand and interact with physical environments. V-JEPA 2 builds upon the Joint Embedding Predictive Architecture (JEPA) and leverages self-supervised learning from over one million hours of video and images. This approach allows the model to learn abstract concepts and predict future states, enabling robots to perform tasks in unfamiliar settings and improving their understanding of motion and appearance. The model can be useful in manufacturing automation, surveillance analytics, in-building logistics, robotics, and other more advanced use cases.
Meta researchers scaled JEPA pretraining by constructing a 22M-sample dataset (VideoMix22M) from public sources and expanded the encoder capacity to over 1B parameters. They also adopted a progressive resolution strategy and extended pretraining to 252K iterations, reaching 64 frames at 384x384 resolution. V-JEPA 2 avoids the inefficiencies of pixel-level prediction by focusing on predictable scene dynamics while disregarding irrelevant noise. This abstraction makes the system both more efficient and robust, requiring just 16 seconds to plan and control robots. Meta's V-JEPA 2 represents a step toward achieving "advanced machine intelligence" by enabling robots to interact effectively in environments they have never encountered before. The model achieves state-of-the-art results on motion recognition and action prediction benchmarks and can control robots without additional training. By focusing on the essential and predictable aspects of a scene, V-JEPA 2 aims to provide AI agents with the intuitive physics needed for effective planning and reasoning in the real world, distinguishing itself from generative models that attempt to predict every detail. Recommended read:
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Kristin Sestito@hiddenlayer.com
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Cybersecurity researchers have recently unveiled a novel attack, dubbed TokenBreak, that exploits vulnerabilities in the tokenization process of large language models (LLMs). This technique allows malicious actors to bypass safety and content moderation guardrails with minimal alterations to text input. By manipulating individual characters, attackers can induce false negatives in text classification models, effectively evading detection mechanisms designed to prevent harmful activities like prompt injection, spam, and the dissemination of toxic content. The TokenBreak attack highlights a critical flaw in AI security, emphasizing the need for more robust defenses against such exploitation.
The TokenBreak attack specifically targets the way models tokenize text, the process of breaking down raw text into smaller units or tokens. HiddenLayer researchers discovered that models using Byte Pair Encoding (BPE) or WordPiece tokenization strategies are particularly vulnerable. By adding subtle alterations, such as adding an extra letter to a word like changing "instructions" to "finstructions", the meaning of the text is still understood. This manipulation causes different tokenizers to split the text in unexpected ways, effectively fooling the AI's detection mechanisms. The fact that the altered text remains understandable underscores the potential for attackers to inject malicious prompts and bypass intended safeguards. To mitigate the risks associated with the TokenBreak attack, experts recommend several strategies. Selecting models that use Unigram tokenizers, which have demonstrated greater resilience to this type of manipulation, is crucial. Additionally, organizations should ensure tokenization and model logic alignment and implement misclassification logging to better detect and respond to potential attacks. Understanding the underlying protection model's family and its tokenization strategy is also critical. The TokenBreak attack serves as a reminder of the ever-evolving landscape of AI security and the importance of proactive measures to protect against emerging threats. Recommended read:
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