It’s been a week since we hosted the Expert Track at the AI Summit Brainport 2024! ๐ We look back on an inspiring day of keynotes and discussions, ranging from the interplay between AI and semiconductor innovation to practical strategies for responsible and efficient AI use. Here are a few key takeaways:
AI and Moore’s Law
AI both drives and benefits from advances in the semiconductor industry. The increasing demand for AI chips is fueling R&D and capital investments in this sector, while AI applications are enhancing productivity across the semiconductor lifecycle, from research and design to supply chain and sales.
Explainable AI in Both Technology and Business Cases
AI performance is only as good as the quality of data itโs built onโbiased data leads to biased outcomes, and limited data can lead to shortcut learning. Before beginning any AI project, itโs critical to ask foundational questions around system behavior, organizational values and ethical standards to ensure responsible AI practices. For instance: โHow should the AI system behave?โ And โWhat are our preferred norms, values and ideal behavior?โ
Creating Guardrails for Safe, Scalable AI Innovation
Implementing AI governance frameworks is essential to foster innovation while effectively managing risks. Clear guardrails around data access, ethics and regulatory compliance create the necessary conditions for innovation across all data-driven businesses, setting the foundation for sustainable AI and gen AI development. Next to working on AI technologies, it is of vital importance to build an AI-ready organization. With frameworks in place, businesses can ensure they are prepared to leverage AI responsibly and effectively.
Agentic Workflows: The Future of Generative AI and Specialized Models
Generative AI will drive new, agentic workflows, shifting how tasks are managed and completed. With specialized models handling specific tasks, businesses can achieve faster, cost-effective results with comparable quality to larger models. This trend toward specialized agents, also including personal AI agents tailored to individual users’ needs, is reshaping workflows and enabling businesses to delegate complex, multi-step projects more effectively.
Optimizing AIโs Energy Footprint
While the usage of AI can be energy-intensive, certain strategies can minimize its impact. Using high-tier models only for complex queries and smaller, efficient models for simpler tasks can help. Quantization, a method that reduces model size post-training while preserving performance, can also substantially cut energy use. Companies and individuals alike should be mindful of when and how they deploy AI, and the conversation around the energy usage and environmental impact of certain AI applications should become a bigger part of public discourse. Lastly, we should make informed choices about where and how to store data and perform calculations to maximize efficiency and reduce environmental impact.
A big thanks again to our speakers Guido D’hert (Accenture), Meike Nauta (Datacation), Patrick Attallah (NXP Semiconductors), Andrรกs Kลvรกri & Felipe Augusto Chies (Amazon Web Services (AWS), Susan Hommerson (Technische Universiteit Eindhoven), Sako Arts (Bright Cape) and Rudie Verweij (Incooling) for sharing their insights, and to Ymke de Jong for moderating! ๐
View videos of the Expert Track on our YouTube channel.
About the AI Innovation Center
The AI Innovation Center is an open innovation hub with the mission to support the adoption of Artificial Intelligence in the Brainport Eindhoven region.
Our Center is the heart of the applied AI community, where we host over 20 companies in the domain. We offer a wide variety of events and other activities focused on AI and related topics.
We invite you to connect with our growing ecosystem of companies and specialists, make use of our facilities and start or scale up your AI activities.
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