Artificial intelligence (AI) has come a long way since its inception in the 1950s. From the early days of machine learning to the recent advancements in deep learning, AI has evolved significantly over the years. The 1980s saw the rise of expert systems, while the 1990s and 2000s witnessed the development of machine learning algorithms and the introduction of AI in various industries. The 2010s saw the emergence of cloud-based AI, which enabled scalable and centralized processing of data. However, this approach had limitations, such as latency, privacy concerns, and reliance on internet connectivity.

I see the world as a living network: interconnected, evolving, full of potential and paradox. It’s a system of systems, not unlike the platforms I’ve helped design or the cross-functional teams I’ve led across disciplines and continents. Where others might see boundaries between nations, technologies, or ideologies. I see interfaces waiting to be bridged. Designing meaningful artificial intelligence (AI), in many ways, begins from this same worldview.