Cameron Willden, W. L. Gore, touches on the differences between data science and statistics, and the differences between machine learning and AI. He responds to some of the learning objectives covered in a keynote by author David J. Hand and provides his own perspective based on years in industry. You can watch the keynote and the panel discussion at any time.
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“Yes, I believe there are some really important differences between, in particular, data science and statistics. I think that David explained the differences pretty well...
“As a statistician, we tend to have more of an emphasis on quantifying the uncertainty in our estimates, and data science tends to emphasize more the point predictions that come out of a model. And because of that, we tend to gravitate toward a different set of tools.
“Statisticians are looking at hypothesis testing and different types of statistical intervals – things like bootstrapping – where data scientists gravitate toward things like neural networks and support vector machines and tree-based methods. Not to say that they don’t cross paths with each other every once in a while, but there certainly tends to be more of an emphasis that aligns with things that they care more about.
“As far as machine learning and artificial intelligence, honestly, I’ll admit I haven’t heard it explained in quite the way that David explained that; that might be because he’s coming at it with a better understanding of some of the subfields of artificial intelligence.
“But the way I typically think of machine learning and artificial intelligence is that AI is the end goal. We’d like to have a machine that can perform a task competently, even when it is faced with an example we haven’t explicitly encountered before. And to get there, we use machine learning tools – a set of techniques and algorithms that we use to school the machine to help it to grow its intelligence. And hopefully, at some point, it’s ready to have a job.
“So that’s the way that I typically think about those terms. But I’ll say that they are nebulous terms, and you’ll very often hear people use AI and machine learning interchangeably. I don’t really see where that is causing major problems or confusion, so we may not converge on a common understanding of those terms in the near future. But maybe that’s OK.”