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Martin has nine years of experience conducting user research with portable and novel technologies across multiple settings, including homes, health, and public settings. He has completed projects on how to improve the experience of using voice-based technologies for people with low-language proficiency in and has recently been working on the integration and evaluation of new Machine Learning technologies in various parts of the UK health service. Martin holds a PhD from the University of Nottingham, where he remains a Visiting Researcher.
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A quick follow-up to our blog posts about AI… The name of the game is no longer Moore's Law where we see processors getting exponentially faster. AI technology is driven not by computing processes of the past, but from an evolution beyond central processing unit (CPU)...
The first step when developing AI is to understand the user need; but just as critical, is knowing the context in which the data is being collected.
To truly deliver on the promise of AI, developers need to keep the end users in mind. By integrating three components of context, interaction, and trust, AI can be the runaway success that futurists predict it will be.
A stronger recruiting strategy that includes relationships with patient support groups and clinical treatment centers can provide better access to difficult-to-reach patient populations. Being intentional about how you plan the logistics of your human factors and user research can mitigate risks to validity introduced by biases.