The rise of Big Data I want to take you back to 2012 . In those early years, our focus was big data, and everyone else was looking at the cloud. We were pioneering, but in a world that was still beginning to understand the value of data.
Executive Summary The field of Artificial Intelligence is in a constant state of evolution. For years, the focus was on building a single, powerful model capable of tackling any task thrown its way. But as the complexity of our problems grows, it's becoming clear that a lone genius, no matter how brilliant, can be outmatched by a well-coordinated team of specialists. This is the paradigm shift that brings us to multi-agent AI systems. A Multi-Agent System (MAS) is a team of specialized AI agents that collaborate to tackle complex problems. Use a multi-agent system when your challenge involves multiple tasks, dynamic environments, or specialized expertise that require agent coordination and agent communication. For simpler or well-defined workflows, a single intelligent agent or structured agentic workflow is often a better, faster, and more cost-efficient choice. The golden rule is to use the simplest setup that effectively solves your problem.
Executive Summary In this report, the ML6 Robotics AI team presents our experience preparing, executing, and evaluating two imitation learning use cases with different levels of complexity on custom datasets using the LeRobot framework. These learnings inspired us to join the 2025 LeRobot Hackathon and create a podium-finishing submission (team 297).
Executive Summary The article explains how Artificial Intelligence (AI) is transforming procurement from a tactical, manual function into a strategic driver of value, innovation, and resilience. By leveraging technologies such as machine learning, natural language processing, and Generative AI, organizations can automate and optimize every stage of the procurement lifecycle—from intelligent sourcing and supplier risk management to contract analysis and the procure-to-pay (P2P) process. The rise of integrated “Agentic AI” systems enables end-to-end workflow automation, predictive risk detection, and data-driven decision-making, while maintaining a human-in-the-loop approach to ensure strategic oversight. Ultimately, AI empowers procurement teams to reduce costs, improve efficiency, and proactively manage risks, positioning the function as a key enabler of organizational competitiveness and agility.
Executive Summary ML6 went to the AI Engineer summit in Paris. We are confident that agents will remain a vital part of the industry, and MCP will be at the forefront of this trend. While MCP adoption is skyrocketing, its potential remains heavily underutilized. And while many agent projects are still explorative, those that find a suitable use case can radically transform their processes through iterative engineering.
The rise of Big Data I want to take you back to 2012 . In those early years, our focus was big data, and everyone else was looking at the cloud. We were pioneering, but in a world that was still beginning to understand the value of data.
Executive Summary AI is ushering in a “Visual Renaissance” that is transforming how consumer industries create and scale content. Beyond speeding up ideation and prototyping, AI-generated visuals enable hyper-personalization, dynamic content ecosystems, and streamlined global localization. Businesses face a trade-off between flexible off-the-shelf tools and precise but resource-intensive custom models. While the technology promises faster, more cost-effective, and tailored content, it also raises challenges around ethics, copyright, and brand control. The future lies in combining AI’s efficiency with human creativity and oversight, redefining how brands tell stories and engage consumers.
Executive Summary Join ML6 on our three-day journey at Elia’s “Hack The Grid 2025.” We confronted a critical challenge: an 8-year wait for new grid hardware, by developing a leaderboard-topping machine learning model that allows operators to safely push existing infrastructure to its absolute limits. Here’s how we did it.
EU Regulators are considering a Stop-the-Clock on the implementation of the European Union AI Act. Does this mean you should halt your AI governance efforts? Tl;dr: The EU AI Act’s obligations for General Purpose AI models and high-risk AI systems are impending, yet legal uncertainty arises as crucial compliance tools are delayed. Despite "Stop-the-Clock" talks, halting AI governance is ill-advised. Risks are here now, future compliance is vital, and ethical AI simply leads to better products. Proactive steps in AI literacy, AI governance, and keeping track of relevant guidelines and standards are crucial for businesses to navigate the evolving landscape.
Building a Creative Commons dataset for retraining Stable Diffusion using the open source dataset creation framework Fondant