THALES IN THE UK
“ Frugal AI goes beyond low-shot learning, where you have minimal data and you need to reach the same level of performance,” he adds.“ It’ s also about thinking about what resources you have in terms of hardware, in terms of connectivity. Can you operate these AI systems under harsh weather conditions with limited data, on low-cost hardware, with minimal power resources, in a disconnected environment?”
Building teams beyond code Ajay spent a decade in the Civil Service before joining Thales. At Dstl, Ajay ran teams that combined AI developers with data scientists, human factors specialists and psychologists.“ That’ s when I started implementing AI for operational environments,” he says.“ It’ s not just the AI coders. You need data scientists, you need human factors people, you need psychologists.”
His time at Counter Terrorism Policing showed the value of diverse thinking. He created the University Innovation Concept, which connected academic institutions with operational problems through an initiative called the Problem Book. Rather than specifying solutions, his team identified challenges and let universities develop novel approaches.
One project with an academic institution examined blast protection for buildings. Traditional approaches involve multi-million pound projects using engineered materials to clad buildings, before the university discovered that a common UK bush growing along walls provides 40 % blast protection naturally.
“Moving from minimum viable product or proof of concept through to taking it to the frontline is a completely different process”
Ajay Chakravarthy, Chief AI Officer, Thales
“ That’ s the kind of disruptive thinking I’ m talking about,” Ajay says.“ There’ s no AI here. AI is not always the solution.”
His most recent government role involved directing digital, data and technology at the Department for Science, Innovation and Technology, where he oversaw the digital function of the £ 6 billion in broadband deployment programmes. The move to Thales brought him back to applied AI work, but with a focus on industrial deployment rather than government operations. At Thales, deployment timelines for mission-critical AI systems typically span one to six years. Ajay’ s remit involves executing the strategy for what the company calls TrUE AI – transparent, understandable and ethical AI – while matching the pace set by major technology firms. He needs to maintain assurance standards whilst accelerating delivery.
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