Just as you read that title that the ML we need is everywhere now. Some of you just looked at your Apple watch. You don’t have to look very far to see some application of machine learning in your daily life. People have been making the argument that machine learning is everywhere for years.[1] Now if machine learning has become that obvious in your day to day existence you can only imagine how deeply it is starting to get ingrained in the business world.[2] Pretty much every vendor, every application, and every underlying technology is investing in some combination of machine learning and artificial intelligence.[3] This creates some really interesting situations where you have machine learning models working in unanticipated compounding and confounding ways. Yeah, I’m really proud of that last sentence. I’m invested in the combination of talking about how models can be compounding and confounding. That is probably just a loquacious way of saying the models either have working results or conflicts develop.
Is the ML we need everywhere now?
Is the ML we need everywhere now?
Is the ML we need everywhere now?
Just as you read that title that the ML we need is everywhere now. Some of you just looked at your Apple watch. You don’t have to look very far to see some application of machine learning in your daily life. People have been making the argument that machine learning is everywhere for years.[1] Now if machine learning has become that obvious in your day to day existence you can only imagine how deeply it is starting to get ingrained in the business world.[2] Pretty much every vendor, every application, and every underlying technology is investing in some combination of machine learning and artificial intelligence.[3] This creates some really interesting situations where you have machine learning models working in unanticipated compounding and confounding ways. Yeah, I’m really proud of that last sentence. I’m invested in the combination of talking about how models can be compounding and confounding. That is probably just a loquacious way of saying the models either have working results or conflicts develop.