📅 March 7th Webinar - Security Made Simple: Build AI with 1-Click Containerization
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🎉 Congratulations to Nik Dorndorf, Our February 2025 Ambassador of the Month! 🎉 Nik, Co-Founder & CTO of cre[ai]tion, is redefining the design process with AI. His startup is building a digital muse for designers, tackling the challenge of translating ideas into visuals more seamlessly. Instead of relying on tedious sketching, cre[ai]tion allows designers to “speak” in their own language—using visual attributes like shape, color, and material. With AI that adapts to each user’s unique style, the platform creates a more personalized, dynamic design experience.Beyond his work at cre[ai]tion, Nik is passionate about making AI more applicable to real-world workflows, shifting the focus from improving models to integrating AI seamlessly into specific tasks. His past research on uncertainty estimation in AI models explored ways to help users trust AI by understanding when a model is confident—or when it’s uncertain. His contributions to this field include co-authoring several papers,
Ready to experiment with state-of-the-art model families like DeepSeek, Llama and Mistral, but organizational hurdles keep slowing your down?HP AI Studio simplifies AI development with 1-click containerization, creating secure, reproducible environments in seconds. No more spending hours configuring infrastructure—just launch, develop, and deploy with confidence.Join Curtis Burkhalter, HP Product Manager, for a live webinar where we’ll walk through the entire process, from initial setup to advanced implementation.You’ll see how to:Quickly containerize AI projects without complex configurations Ensure data privacy compliance while experimenting with the latest models Save valuable development time with streamlined workflows Event Details:📅 Date: March 7, 2025⏰ Time: 9:00 - 9:30 AM PT🎤 Speaker: Curtis Burkhalter, HP Product Manager Register Here!
Now that I have your attention with that suspiciously simple title, let's be honest: evaluating AI systems is anything but a piece of cake. However, thinking about AI evaluations like a layered cake can help us understand the various levels of assessment needed for a robust evaluation framework.Just as a master baker carefully constructs a wedding cake with distinct layers - each contributing its own flavor and texture to the whole - AI evaluation requires multiple layers of assessment working in harmony. From the foundation layer of basic accuracy metrics to the delicate frosting of user experience evaluation, each layer serves a crucial purpose.Think of it this way: You wouldn't serve a cake without testing each component. The base needs to be sturdy, the filling must complement each layer, and the frosting should tie everything together. Similarly, your AI system needs evaluation at every level - from individual model responses to end-to-end system performance.By the end of this art
AI is evolving fast, and with all the hype, it’s easy to lose sight of what really matters. In a recent conversation, Thomas H. Davenport and Randy Bean broke down five key AI trends that will shape the year ahead—helping leaders cut through the noise and focus on what’s actually changing the game.n this discussion, they cover:Agentic AI: Hype vs. Reality – How to navigate the excitement and actual impact. AI ROI Matters – Why businesses need to track real value, not just trends. Data-Driven Culture Struggles – The persistent roadblocks to making data central to decision-making. Unstructured Data Challenges – Why tackling messy data is more important than ever. Evolving AI Leadership – How roles and reporting structures are shifting in the AI era.Drawing from their latest MIT Sloan Management Review article, these experts dive into how agentic AI and large language models are reshaping business in 2025. They explore how data-driven decision-making is changing the way companies operate
AI, rendering, and data-heavy workflows demand serious GPU power—but not every workstation needs a high-end setup 24/7. Z by HP Boost offers a smarter way to share GPUs across your network, so teams can tap into high-performance hardware whenever they need it.Whether you're an AI developer fine-tuning large language models, an architect using Stable Diffusion for design ideation, or a data scientist running complex simulations, this solution provides remote access to idle GPUs—turning a standard PC or laptop into a GPU-accelerated powerhouse.Scale resources efficiently—no need to equip every workstation with top-tier GPUs. Reduce cloud costs & keep data secure—run AI workloads locally without unpredictable expenses. Boost collaboration—multiple users can share available GPU power for faster processing.From NVIDIA RTX™ 2000 Ada to RTX 6000 Ada, Z by HP Boost helps you maximize performance without overhauling your entire setup.🔗 Check out the Develop3D 2025 Workstation Special Repor
Imagine teaching a robot to boil water. It might first search for a pot, fill it with water, then place it on the stove. But what if it mistakenly turns on the microwave instead of the stove? Traditional AI agents often struggle to recover from such errors, leading to cascading failures. This is the problem Agent-R—a novel framework for training self-reflective language model agents—aims to solve.In this article, we’ll unpack the theoretical backbone of Agent-R by focusing on how this framework redefines error recovery in AI.Why Error Recovery Matters in Interactive EnvironmentsMost AI agents learn by mimicking expert trajectories (e.g., perfect step-by-step guides). But real-world tasks are messy. Errors are inevitable, and waiting until the end of a task to correct them is like letting a typo in a sentence propagate into a garbled paragraph.The Core Challenge: Timely InterventionProblem: In multi-step tasks (e.g., crafting items in Minecraft or navigating a virtual lab), errors early
Introduction Organizations face a critical challenge when using AI: balancing the need for powerful AI capabilities with stringent data privacy requirements. As AI models become more sophisticated, the complexity of managing development environments while ensuring data security has become a significant hurdle for many teams. AI engineers and data scientists want to work with the latest cutting-edge models, whether they be open source or proprietary LLMs, but often find themselves spending valuable time configuring environments and wrestling with dependency conflicts, all while trying to maintain the security standards their organizations demand. Enter HP AI Studio and it’s 1-click containerization, a solution that transforms how developers work with sophisticated AI models like the recently released DeepSeek models in a secure, local environment. This innovation directly addresses the dual challenges of development environment management and data privacy, allowing teams to focus on wha
Multi-agent systems have become increasingly important in solving complex problems through distributed intelligence and collaboration. However, coordinating multiple agents effectively remains a significant challenge, particularly in the field of reinforcement learning. In this article, we'll explore a novel approach called Shared Recurrent Memory Transformer (SRMT) that aims to enhance coordination in decentralized multi-agent systems.The Challenge of Multi-Agent CoordinationImagine a group of robots trying to navigate through a crowded warehouse. Each robot has its own goal and can only see what's immediately around it. How can they work together efficiently without bumping into each other or getting stuck? This is the essence of the multi-agent pathfinding problem.Traditional approaches often struggle with this task, especially when the environment is complex or the number of agents is large. They may rely on centralized control, which doesn't scale well, or on explicit communicatio
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