Your approach to training an AI agent is just as important as your approach to training any new hire. While AI (obviously) learns differently than people do, setting it up for success with a solid foundation is crucial to making it an effective part of your team.

To sum it up briefly: agentic AI systems are trained on specialized data sets, and capable of independently solving multistep problems in service of a particular goal.

The NCAA isn’t done evolving – it’s expected that there will be even more shifts in compensation models, and that NIL laws will continue to broaden. This means there are a lot of new opportunities for brands and other companies to find entry into college sports.

Now is the time to shift from thinking about all the big dramatic things that it could someday be capable of, to thinking about the ways it could concretely help us during our day to day work.

Synthetic data is, in theory, a useful way to fill in the gaps and get an understanding of trends and patterns when the picture is incomplete.

You need to create experiences that feel genuine and encourage excitement around fans’ favorite teams and players.

Sometimes, there are diamonds in the rough – good performers, or successful initiatives, that do well enough to be worth noticing, but aren’t massive anomalies that would really stand out on a graph.

The biggest takeaway from the second half of the year – movies play a much bigger role in Netflix viewing than the first report showed.

What is Meridian? Is it actually new? Are there some features that Meridian has that are really interesting? How is it different? Just like you, we wanted to know, so we got our very best data nerds on the case.

High-traffic events like the Super Bowl can easily break a system. Here are some ways to handle massive influxes of data efficiently, and effectively.