Research, planning, reasoning, synthesis, selection

My family follows Major League Baseball (MLB) a little more than casually. It all started when we moved back to the San Francisco Bay Area in 2010 and within months, the local Giants won the first of three World Series in alternating years - 2010, 2012 and 2014.

Since then I've sought to see a home-team game for every MLB team and am up to 19 of 30.

But I digress. I've been issuing the following test to various AI models for two years and it's done a decent job in the past, but never with perfect accuracy:

Construct a schedule that lets me drive between multiple MLB stadiums this summer to see a home game in the shortest amount of time and without backtracking.

With yesterday's release of Open AI o3, it definitively nailed it. Check out how well it researched, considered, optimized and presented the trip.

There's a lot going on here. It had to look up the schedules of each team for this season, find dates where they lined up, sequence them geographically, consider alternatives, and present the optimal solution. Yes, I confirmed the schedule is correct for all of the games it recommended.

This whimsical example demonstrates incredible capabilities for far more serious research and topics.

AI's capabilities are advancing at a breathtaking pace. How are you using it in your professional and personal life?