 The first special session of the VCCA 2021 will include a discussion on the global burden of hearing loss after a keynote talk by Professor Nicholas Lesiga on harnessing the power of artificial intelligence to reduce this burden. There's a link to his blog post in the video description below. The discussion will cover two big questions. How will hearing devices look and function in the future, and how will hearing diagnostics and services be delivered? In short, how can we improve the world's hearing via audiological interventions? First, some facts. 500 million people worldwide have a disabling hearing loss and most receive no treatment. As such, it is a top five contributor to the global disability burden and may be a leading modifiable risk factor for dementia. If you prefer to think in economic terms, it costs the global economy $1 trillion a year. To help kick off the discussions, we've come up with a few possible areas of focus and an example or two. In the area of diagnostics and services, how do we reach underserved populations and maintain care and monitoring after initial diagnosis and fitting? How do we detect and measure hearing loss with minimal equipment? The audiogram is 100 years old. Could self-tests, speech tests and behavioural pattern recognition provide similar or even better diagnostics? And can hearing be integrated more into general healthcare in order to better diagnose related health issues such as dementia and mental health? Despite the potential of AI, we may need to close the huge gap in trained professionals to provide care and help interventions and to reach the people who need them. For future hearing devices, the question is not only what they will look like, will they be confined to the ear or integrated into glasses or even implantable chips, but also what they will do? How much will they rely on built-in movement sensors, distributed ad hoc sensor networks, noise reduction and clever signal processing based on artificial intelligence and location-based settings? Sensors and noise reduction systems that function well in urban environments may be less useful or at worst detrimental in more rural settings. Issues like battery life and ease of repair vary greatly in importance across environments. If a device is not robust to the environment in which it is used, no amount of fancy algorithms in AI can help. And if there is a bad stigma, then that won't help either. Finally, how do we get there? What practical steps can be taken? The answer, as always, involves money. Where will this money come from? Will it be public or private? And not just money for services and devices, but also investment in and retention of the expertise required from electrical engineers to healthcare providers? This is a colossal challenge. Thus, do we need stronger international collaboration? Collaboration between industry and academia? Or sharing of resources and IP? Are we even asking the right questions or formulating the problem correctly? Maybe we're missing something crucial. We won't answer all of these questions in two 30-minute discussions, but we hope to push the conversation towards some tangible goals. We look forward to seeing you and hearing your thoughts in the breakout rooms. Thank you.