Data science and machine learning professionals now how to seek answers in data: that’s probably the central pillar of their work. Things get murkier when we look at some of the thornier issues surrounding our data, from its built-in biases to the ways it can be leveraged for questionable ends.
As we enter the final stretch of the year, we invite our readers to explore some of these big-picture issues that have sparked crucial discussions in recent years, and are all but guaranteed to continue to shape the field in 2024 and beyond.
Our highlights this week dig into a broad range of topics, from the nature of data-backed knowledge itself to its application in specific fields like healthcare; we hope they inspire further reflection and draw new participants into these essential conversations.
- What Role Should AI Play in Healthcare?
The biases we’ve covered thus far can wreak havoc on models, businesses, and bottom lines. As Stephanie Kirmer stresses, though, they become even more acute in fields like healthcare, where life-and-death situations are common and “the risks of failure are so catastrophic.” - A Requiem for the Transformer?
In a rapidly changing field, it’s tempting to think of a 6-year-old concept as essential and timeless. Transformers have been around since 2017 and have played an important role in the mainstream adoption of AI tools; as Salvatore Raieli points out, though, they too likely have a shelf life, and it’s perhaps a good time to ask what comes next.
