Even as headline-grabbing AI ventures fail or fall short of expectations, one should not lose hope because 2023 will still see plenty of augmentation and gradual innovation going on quietly behind the scenes that will set the stage for even greater breakthroughs.

This second episode of the Delta Dialog deals with a very different perspective on open medical data than the first session, namely the difficulty of the challenges to be overcome. These are data quality, utility of AI within open medical data, policy, funding, and human capital.

Self-driving cars, or autonomous vehicles, have garnered a great deal of attention in recent years, and despite the huge amounts of money and resources pouring in, the industry continues to disappoint. Is a technological breakthrough imminent, or should one expect more underperformance?

Open access to data must focus on their quality, not just quantity. Opening data will not ensure quality, buy may even have the opposite effect, argues Prof Jeffery Lee Funk as part of The Yuan’s Open Data 2022 series.

Much hype has surrounded AI startups in recent years, but some of this may be overblown as such startups have tended to underperform compared with their peers in other fields. Such companies’ profitability, or lack thereof, along with decisions about whether to go ahead with IPOs or SPACs offer insights into the current state of the AI sector, and what the near future holds.

The promise of AI technology has often failed to live up to its hype. How to learn from past successes and failures to adjust the expectations for AI and change how new technologies are applied to do so more successfully is the issue The Yuan contributor Prof Funk grapples with in this his inaugural article.