Damian Smith has an unusual combination of interests and they all turn out to be connected. He studied physics, moved into FPGA engineering at SSTL (Surrey Satellite Technology Ltd, now part of Airbus), and has worked on the hardware that goes into actual satellites — geostationary orbits, Starlink, the unglamorous reality of space technology and the company culture that supports long-horizon engineering work.
His PhD research is on deep learning interpretability — the question of why an AI made a particular decision. He gives a striking example: an X-ray analysis model that appeared to correctly identify pathologies, but was actually picking up on the position of pacemakers in the images (because patients with pacemakers are more likely to have certain conditions). The model was right for the wrong reasons. That's the black-box problem in miniature, and Damian is working on attribution methods that make the reasoning visible. He draws on a Malcolm Gladwell analogy to explain why explainability matters even when performance is good.
The final segment is about improv comedy — how Natalie and Damian met — and the "yes, and" principle. The idea that you never block a scene partner's offer, you accept it and build on it, turns out to be a remarkably good model for psychological safety in technical teams. Both are better when people feel their ideas won't be dismissed before they've had a chance to develop.
Guest: Damian Smith, SSTL/Airbus FPGA engineer and PhD researcher in AI interpretability
Recorded for The Technology Show on Voice FM 103.9, Southampton — broadcast 19 September 2025. Listen live on Fridays at 1pm. More episodes: thetechnologyshow.uk