Shivon Zilis

Project Director, Office of the CEO, Neuralink & Tesla; Co-chair, Machine Learning and the Market for Intelligence Conference @ Rotman

ORGANIZATIONS like Google, Facebook, Apple, Microsoft, Amazon, Uber and Bloomberg bet heavily on machine intelligence, and its capabilities are pervasive throughout their suite of products. But for most organizations, the successful use of machine intelligence is surprisingly binary — like flipping a stubborn light switch. Why is it so difficult for companies to wrap their heads around it? Because machine intelligence is different from traditional software. Unlike with big data — where you could ‘buy’ a new capability — machine intelligence depends on deeper organizational and process changes. Companies need to decide whether they will trust machine intelligence analysis for one-off decisions or if they will embed machine intelligence models in their core processes; teams need to figure out how to test newfound capabilities; and employees need to be coached to learn from the data they enter.

Unlike traditional hard-coded software, machine intelligence gives only probabilistic outputs. We want to be able to ask machine intelligence to make subjective decisions based on imperfect information. As a result, machine intelligence software will make mistakes, just like people do, and we’ll need to be thoughtful about when to trust it and when not to.

The concept of this new ‘machine trust’ is daunting and makes machine intelligence harder to adopt than traditional software. I’ve had a few people tell me that the biggest predictor of whether a company will successfully adopt machine intelligence is whether they have a C-suite executive with an advanced math degree. These executives understand that this isn’t magic — it is just (really hard) math.

I see all of this activity only continuing to accelerate. The world will give us more open sourced and commercially available machine intelligence building blocks; there will be more data; there will be more people interested in learning these methods — and there will always be plenty of problems worth solving.