My guest for this episode of the Inner Circle podcast is Daniel Miessler, Director of Advisory Services for IOActive. Daniel presented a session at IOAsis a few weeks ago while we were in San Francisco attending the RSA Conference. I was not able to attend, but the session title–“Machine Learning Demystified: Separating the Magic from Marketing”–piqued my interest.
If you read through enough tech and security vendor websites and it will quickly feel like the whole world revolves around machine learning (or ML) and machine learning and machine learning algorithms are magic technologies that are going to transform the whole world. That is true to some extent, but there’s also a lot out there about ML that is either exaggerated or just plain false. There is also a fair amount of confusion and misperception about what machine learning is or isn’t, and how it is different from and/or works with artificial intelligence.
There are a number of things machine learning is great at, and a few that it is uniquely suited to fundamentally change the way we process and leverage data. If used improperly, though, machine learning can also provide results that simply tell you what you want to hear, or create issues if customers place too much trust in ML output.
The challenge for businesses and consumers, though, is how to see through buzzwords and marketing spin and figure out which companies or products are actually doing something worthwhile with ML. As I mentioned above, every company seems to talk about machine learning as if they’re all doing the same thing with it. If you don’t know any better and don’t understand what you’re looking for, it would be very easy to feel like ML is just a commodity technology that doesn’t provide a competitive advantage. Daniel provides a set of key questions you can use to quickly separate the snake oil from the real deal.