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Related to the prior post on reflection, reflection requires space. Space is needed to explore and investigate, test, probe, and question. And adapt.

What is the value of reflection, and how can it be incorporated into AI?

This week I’ll be out of town on a retreat. More later, but posts may be shorter/off the usual schedule.

Assume for argument’s sake that we have no idea how AI will transform the future.

Ensuring high floors for performance is arguably more important than slightly raising the performance ceiling (SOTA chasing). The worst case examples are the ones that get shared, so ensure that they aren’t that bad.

Google search made it easier to access relevant and useful information via a clean design and very simple interface.

Building on yesterday’s post, there can be a pull towards digitalization as access to compute, technology, prediction, etc. gets easier and cheaper. But don’t blindly follow that pull at the expense of the physical.

If AI replaces architects, who will the contractors yell at?

I’m in Washington, DC today to present our FAIR-Frame paper at SIGIR.

With prediction getting cheaper and cheaper, there is more RPG-ification of day-to-day life. Decisions being outsourced to AI means that decisions are being outsourced to dice. These models are estimating probabilities and acting upon them. In a way, we are too when we make our own decisions, but it’s good to keep this in mind explicitly.