Machine Learning – Hype or Reality? Microsoft ML Experts Weigh In (3154 hits)
The recent Practice of Machine Learning Conference at Microsoft concluded with a lively panel discussion moderated by principal researcher Misha Bilenko on the topic of: "Are We at Peak ML, or at the Start of AI Takeover? Hype vs. Reality of Machine Learning.” Our panelists were:
Greg Buehrer, Partner Development Manager, Bing Ads
John Platt, Distinguished Scientist, Microsoft Research
Joseph Sirosh, Corporate Vice President of Machine Learning
This post recaps their conversation and the kinds of issues raised by our audience.
The first question played off the title of the panel: "As humans get replaced by machines, how will data scientists be useful and will they be replaced as well?"!
Greg commented that he didn't see that happening soon. Machines are good at mechanical and physical processes, but still not that great at automating decision-making especially in very complex environments. John and Joseph took the conversation further by discussing the challenges associated with employment and wealth distribution in an environment of seemingly ever-increasing automation, but the panelists admitted they couldn’t predict exactly how this trend might play out.
After discussing the Azure ML marketplace as a place where data scientists can publish their innovative ideas as web services, all panelists issued a call to action to the audience to be ever more data-driven in their work and build their ML skills, as there are many possibilities ahead of us to make products and services more intelligent.
Next, in response to an audience question, the panelists gave their opinions on whether there is a danger to using ML systems as "black boxes" inside systems such as drones, cars, etc. without fully understanding what's inside.
Joseph's opinion was that "I don't think of ML as being any different than any software algorithm collection, and there is a lot of software you can ask the same question about". He felt that there must be systems in place to ensure reliability. Greg agreed that mistakes can be made, and that experience and decision-making responsibilities have to be assigned appropriately. John then argued that "People are teaching ML incorrectly. They teach what's inside the black box, but before that you need to learn statistical hygiene: You need to have a test set, you need to not cheat, you have to do confidence intervals, and you need to worry about outliers. Learn statistical hygiene to avoid disasters". All three panelists agreed on this.
