Database Reference
In-Depth Information
In clearly defining the separation of the human and computing aspects of Data Science,
LeCun captures the essence and the poetry of this interaction, and how one aspect enhances
the value of the other. “It's very much an interplay between intuitive insights, theoretical
modeling, practical implementations, empirical studies, and scientific analyses,” says
LeCun. “The insight is creative thinking, the modeling is mathematics, the implementation
is engineering and sheer hacking, the empirical study and the analysis are actual science.
What I am most fond of are beautiful and simple theoretical ideas that can be translated
into something that works.”
Still, the value of Deep Learning as applied to Data Science, even though over-hyped,
should nonetheless not be underestimated. Gary Marcus, cognitive professor at NYU ex-
plains the technology in the most succinct and understandable manner. He writes that: “A
computer is confronted with a large set of data, and on its own asked to sort the elements of
that data into categories, a bit like a child who is asked to sort a set of toys, with no specific
instructions. The child might sort them by color, by shape, or by function, or by something
else. Machine learners try to do this on a grander scale, seeing, for example, millions of
handwritten digits, and making guesses about which digits looks more like one another,
'clustering' them together based on similarity. Deep Learning's important innovation is to
have models learn categories incrementally, attempting to nail down lower-level categories
(like letters) before attempting to acquire higher-level categories (like words).”
This subtle interaction of Deep Learning technologies and human intuition is what
Eric Berridge, CEO of Bluewolf, alludes to when he says: “Perfect Data Science takes
every customer interaction and identifies patterns that can be repeated and proactively ac-
ted upon. In this ideal scenario, humans and artificial intelligence systems collaborate in an
amalgamation of contextual, timely insight to better understand their customers and, ulti-
mately, to predict their future behaviors.”
