Joining us today is the lead full stack AI engineer at Rogo as well as the lead guitarist for Des Confitures, Becks Simpson. Becks studied mechatronics in Australia and has a background in robotics. She moved into AI at a medical imaging startup before she came to Montreal to do a research project at the Montreal Institute of Learning Algorithms.
Tune in to hear more about Becks’ role as a lead full stack AI engineer at Rogo, how they determine what should and should not be added into the product tier for deep learning, the types of questions you should be asking along the investigation-to-product roadmap for AI and machine learning products, and so much more!Key Points From This Episode:
Tweetables:“People think that can do more than what it can and it has only been the last few years where we realized that actually, there’s a lot of work to put it in production successfully, there’s a lot of catastrophic ways it can fail, there are a lot of considerations that need to be put in.” — Becks Simpson “Make sure that if you ever want to put any kind of machine learning or AI or something into a product, have people who can look at a road map for doing that and who can evaluate whether it even makes sense from an ROI business standpoint, and then work with the teams.” — Becks Simpson “I think for the people who are in academia, a lot of them are doing it to push the needle, and to push the state of the art, and to build things that we didn’t have before and to see if they can answer questions that we couldn’t answer before. Having said that, there’s not always a link back to a practical use case.” — Becks Simpson “Academia will always produce really interesting things and then it’s industry that will look at whether or not they can be used for practical problems.” — Becks Simpson