Andrew Barto said it at the Turing Award. He's still saying it now:
"To release things before you're satisfied with safety is not good engineering practice."
If you can't specify the reward function clearly, you can't predict the behavior. Full stop.
Full conversation with Andrew Barto on Eye on A.I. - https://t.co/UbXAlJT3uw
#AndrewBarto #AISafety #ReinforcementLearning
Designing the reward signal for a reinforcement learning system sounds simple, until it isn't.
Win/lose in a game? Easy. Real-world complexity?
That's where AI training gets genuinely hard. And this problem isn't new, it's been unsolved for decades.
Full conversation with Andrew Barto on Eye on A.I. - https://t.co/UbXAlJSvEY
#AndrewBarto #MachineLearning #ReinforcementLearning
Most people building with RL don't realize this: the system keeps learning after deployment. It doesn't stop when training does.
That's what makes it powerful. It's also what makes it unpredictable.
Full conversation with Andrew Barto on Eye on A.I. - https://t.co/UbXAlJT3uw
#AndrewBarto #MachineLearning #ReinforcementLearning
Real-time experiences convert at 30 to 200% higher rates than delayed ones.
That's been true since 2012.
An email sent 4 minutes after someone leaves your website? 6x the open rate of one sent
24 hours later.
The debate about whether you need real-time data is finally over.
Full episode with Jeff Lunsford of @Tealium on Eye on A.I. - https://t.co/7ZKTIm0Nkx
#RealTimeData #CustomerData #Tealium