Data science and machine learning professionals now find out how to seek answers in data: that’s probably the central pillar of their work. Things get murkier after we take a look at a number of the thornier issues surrounding our data, from its built-in biases to the ways it might probably be leveraged for questionable ends.

As we enter the ultimate stretch of the yr, we invite our readers to explore a few of these big-picture issues which have sparked crucial discussions in recent times, and are all but guaranteed to proceed to shape the sphere in 2024 and beyond.

Our highlights this week dig right into a broad range of topics, from the character of data-backed knowledge itself to its application in specific fields like healthcare; we hope they encourage further reflection and draw latest participants into these essential conversations.

• What Role Should AI Play in Healthcare?
  The biases we’ve covered to date can wreak havoc on models, businesses, and bottom lines. As Stephanie Kirmer stresses, though, they grow to be much 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 consider a 6-year-old concept as essential and timeless. Transformers have been around since 2017 and have played a very important role within the mainstream adoption of AI tools; as Salvatore Raieli points out, though, they too likely have a shelf life, and it’s perhaps an excellent time to ask what comes next.