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Interview
Backwards compatibility will likely be necessary, particularly for longer lived infrastructure – furthermore this will also play into interoperability between jurisdictions – this encompasses everything from data standards, to ensuring that AI models are free of drift( or at least measuring it), to compatibility of AI decisions with the cyberphysical systems that infrastructure is made of( e. g. ensuring sensors are still accurate, that any environmental markings such as road signs are well maintained).
Agility – both in process, in adoption, in people and in operations( e. g. MLOps) is key since the AI landscape moves quickly, and there are many moving parts to the transport ecosystem, whether that be supply chain, construction, operations, vehicles etc. Being able to update against an emergent threat or hazard or change quickly for efficiency purposes will make all the difference.
Monitoring systems that measure‘ dataset drift’ can be overlooked- these are key to understanding exactly when the real world has changed so much that an AI system is no longer operating within its training data – at which point the assurance argument needs to be re-established. These or similar design domain breach detection systems should be a part of any long-term deployment with safety implications
Not“ infrastructure” per se but effective training and adoption of tools by the key staff involved in managing the transportation routes and hubs is essential to build and maintain a trusted and effective infrastructure.
How can public and private sector organizations close the technical skills gap and build internal AI literacy to ensure longterm success in AI adoption? Building a responsible AI culture is key to supporting long-term success of AI adoption. Skills gap analysis and role redefinition( e. g. introducing AI aspects to existing job descriptions) can help bridge the divide. This plus technical upskilling helps avoid a siloed‘ AI team’ which doesn’ t necessarily have the buy-in from the rest of the organization and processes.
Another part of achieving a responsible AI culture is creating and implementing an organization-wide AI literacy or learning program that can ensure the workforce has the capabilities to leverage AI responsibly and effectively in their roles. This should be tied to organizational strategy and linked to organizational goals and challenges.
As leaders in the transportation sector, embracing AI is not just about innovation – it’ s about shaping the future of mobility responsibly and efficiently. In partnership with Cambridge Consultants, we developed the AI Implementation Guide to help public agencies cut through the noise and effectively implement AI within their organizations to better achieve their goals. This guide will help executives lead with vision, accelerate operational excellence, and build the resilient, trusted systems our industry – and society – depend on
Quote from ITS America, Laura Chace, President and CEO at Intelligent Transportation Society of America tlimagazine. com 13