ODSC events for 2019 are all wrapped up, and now it's time to start thinking and planning for the next decade. Our first major event of 2020 will be ODSC East April 13-17 in Boston, MA, and we're excited to announce the first fifty speakers out of 280 schedule. ODSC East 2020 is expected to host over 250 speakers. Tom Mitchell is known as the "Father of Machine Learning" having founded the Machine Learning Department at Carnegie Mellon University and led it as Department Head for its first 10 years, teaching many famous students including Andrew Ng. He is a world-renowned researcher in machine learning, artificial intelligence, and cognitive neuroscience.

Cassie Kozyrkov has served in various technical roles at Google over the past five years, but she now holds the somewhat curious position of "chief decision scientist." Decision science sits at the intersection of data and behavioral science and involves statistics, machine learning, psychology, economics, and more. In effect, this means Kozyrkov helps Google push a positive AI agenda -- or, at the very least, convince people that AI isn't as bad as the headlines claim. "Robots are stealing our jobs," "AI is humanity's greatest existential threat," and similar proclamations have abounded for a while, but over the past few years such fears have become more pronounced. Conversational AI assistants now live in our homes, cars and trucks are pretty much able to drive themselves, machines can beat humans at computer games, and even the creative arts are not immune to the AI onslaught. On the flip side, we're also told that boring and repetitive jobs could become a thing of the past.

Cassie Kozyrkov has served in various technical roles at Google over the past five years, but she now holds the somewhat curious position of "chief decision scientist." Decision science sits at the intersection of data and behavioral science and involves statistics, machine learning, psychology, economics, and more. In effect, this means Kozyrkov helps Google push a positive AI agenda -- or, at the very least, convince people that AI isn't as bad as the headlines claim. "Robots are stealing our jobs," "AI is humanity's greatest existential threat," and similar proclamations have abounded for a while, but over the past few years such fears have become more pronounced. Conversational AI assistants now live in our homes, cars and trucks are pretty much able to drive themselves, machines can beat humans at computer games, and even the creative arts are not immune to the AI onslaught. On the flip side, we're also told that boring and repetitive jobs could become a thing of the past.

What's really going on currently is a human understanding process where we're trying to work out what the machine brains we are building are really capable of. But to understand what AI software engines are capable of, we need to understand how they learn in the first place. AI has been called the process of automating the ineffable i.e. creating technology that can digitize those things that we humans find too great or too extreme to be expressed or described in words. So does accepting this core fundamental help to explain what contemporary AI really is and what it can do? Chief decision scientist for Google Cloud Cassie Kozyrkov explains that traditional software programming relies on a developer's ability to express instructions for a task explicitly.