The Meaning of (Artificial) Life
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The Hitchhiker's Guide says the meaning of life is 42. Considering that the field of Data Science is going through a period of exponential growth it too could soon find that the meaning of an artificial life is also 42. But if you are not involved on a day-to-day basis, the expansion can seem bewildering. The story of how disparate disciplines have combined to produce Data Science is fascinating.
In this talk, we will walk through a journey of scientific discovery. Following how, from humble beginnings, a multitude of sciences (and a surprising number of hacks) converged into the incredible advancements that you see in the media today. With these building blocks, we will be able to succinctly describe what these disciplines are and how they relate. The result will be the decomposition of a "rockstar" data science application; you will see that it is not so complicated after all. But the interesting result is that this generates a philosophical and political minefield; we can decompose the application and clearly see how it is built, but it also mimics or surpasses human capabilities. Are these human qualities? Is a more efficient or productive algorithm better than a human? Can we call them "intelligent"?
Attendees will gain a fundamental understanding of the field of data science. You will leave understanding exactly the difference between machine learning and deep learning and how they are different. You will be able to describe how data mining can help your business run analytics tasks to improve efficiencies. You will be able to explain to your children why big data techniques were invented to solve a specific problem. This will suit anyone interested in the history of data science and also serve as a broad introduction to the rest of the day's in-depth talks.
So, is the meaning of life 42? Possibly. But maybe all we need is a science algorithm to ask a better question.
Prerequisite attendee experience level of the topic: Beginner
Phil masters the hard art of backing theory by real-life experience. There is always more to learn about data and reinforcement learning... #data