AZEEM AZHAR: Welcome to The Exponential View podcast where multidisciplinary conversations about the near future happen every week. Now, as an entrepreneur, investor, and analyst I've been inside the technology industry for over 20 years. During that time, I've observed that exponentially developing technologies are changing the face of our economies, business models, and culture in unexpected ways. Now, I return to this question every week in my newsletter Exponential View, in this podcast, as well as in my recent book The Exponential Age. So, in today's edition I wanted to look back and forward on one of the key technologies of the exponential age, artificial intelligence. We're about a decade into the current industrial boom in AI and I thought it was time to take a scorecard, look at what we've achieved, and how and perhaps what we didn't on which milestones have surprised us. To help me I called on a great experts Murray Shanahan, a senior research scientist at London's DeepMind, as well as a professor of cognitive robotics at Imperial College in London. Murray works on machine learning, consciousness, the impacts of artificial intelligence. He and I have known each other for a few years and have indeed done a podcast together previously. We appeared as guests on a show hosted by a technology investor. So, my challenge to Murray today was not simply to access the last 10 years of development, but to look forward to the next 10. It's a bold challenge and we did our best to look forward as well as back. MURRAY SHANAHAN: It's very nice to be here.

We introduce a novel encoder-decoder architecture to embed functional processes into latent vector spaces. This embedding can then be decode d to sample the encoded functions over any arbitrary domain. This autoenco der generalizes the recently introduced Conditional Neural Process (CNP) model o f random processes. Our architecture employs the latest advances in graph neura l networks to process irregularly sampled functions. Thus, we refer to our model a s Conditional Graph Neural Process (CGNP). Graph neural networks can effective ly exploit "local" structures of the metric spaces over which the functions/pr ocessesare defined. The contributions of this paper are twofold: (i) a novel graph-b ased encoder-decoder architecture for functionaland process embeddings, and (i i) a demonstration of the importance of using the structure of metric spaces for this t ype of representations.

Artificial Intelligence (AI) is the form of intelligence that arises from machines, perceiving their environment and taking actions to maximize their chance of success for a given task. We live in a period where computer power is escalating quickly and where recent discoveries are bringing this power to the general public, often arising controversy. In this ecosystem, academia and industry define together a new paradigm to innovate and benefit humanity as a whole. Don't miss the opportunity to learn about AI in London, together with Imperial College London Innovation Forum (ICLIF). The event will be followed by a networking session with finger food and nibbles for the attendees.