Joakim Hedenstedt
Head of Development (Commercialization), M.Sc. Eng., AICRIT Project,
Artificial Intelligence (AI), Decentralized Autonomous Organizations (DAO), Business Strategy, Go-to-market Strategy, Business Development, Operations, SW Development, Embedded Systems, Distributed Systems, Decentralized Systems
AICRIT Research-to-Business project. The combination of DAOs, distributed AI solutions, transparency and traceability are focus points. Business models are evaluated to ensure commercial viability.
Joakim has more than 30 years of industry experience from companies in the mobile communications and embedded systems industries, serving in various executive, senior sales, business development, marketing management, project management and engineering roles in organizations such as Texas Instruments, Mentor Graphics and companies in the mobile software industry working in the United States, France, Sweden and Finland. He is an entrepreneur and Co-Founder of a Start-up in financial and digital inclusion, an innovator and advocate of decentralized and distributed AI technologies as well as an advisor to start-ups. Joakim has local business experience from markets in Africa such as Rwanda, Nigeria, Zambia, Namibia, Kenya and Tanzania. Joakim is also a Blockchain, DAO and AI Pioneer.
Joakim holds an MSc from the Royal Institute of Technology in Stockholm, Sweden and has studied marketing at U.C. Berkeley.
My research interests include exploring the intersection of Artificial Intelligence (AI) and blockchain technology, with a strong focus on decentralization and distribution. The core aim is to develop and analyze methods for creating more resource-effective and independent AI solutions, both from a technological and a business perspective. Key points of interest:
- Resource-Efficiency in AI Models - impact of purpose built models and ensembles of expert models to increase resource efficiency
- Design for Decentralized and Distributed Deployment - reduce reliance on centralized infrastructure, leveraging blockchain principles for trust, data integrity, and decentralized governance
- Resilience and Robustness - effects of scenarios where access to centralized resources is limited or unreliable (e.g., during unrest, natural disasters)
- Economic and Social Impact - reducing cost of AI adoption, particularly in developing markets, and facilitatating broader access and innovation