Over the past few years, and I really mean the past FEW years, we have been inundated with thousands of articles proclaiming the new age of data and how it interacts with and drives Artificial Intelligence. As a result, the three terms above have transitioned almost overnight from buzz words to standard vocabulary, and have become synonymous with the direction that our society is moving in. But how many of us can really enunciate the differences between those sacred terms? Let’s take a moment to look at each one briefly.
In the olden days, it was called Statistics. But now it has morphed and grown, like Thanos’s chin, until it became ‘data science’. Today, top flight universities offer degrees in it and everyone is calling it a career path that will never fail.
The first recorded reference to ‘data science’ was apparently by Peter Nauer, a Danish computer pioneer, at the White Hart Tavern in 1960 when he used it to replace the term ‘computer science’. Actually, the White Hart Tavern part is not true, he was probably in his office talking to a grad student, but for those of us who are Arthur C. Clarke fans it would have been really cool if it were true.
One of the earliest more modern references to it was by C. F. Jeff Wu in his 1997 lecture at the University of Michigan “Statistics — Data Science?”. In Dr. Wu’s universe, data science moved somewhat past traditional statistics, using the trio of data collection, modeling and analysis, and significantly, decision making. He would have broken modeling and analysis apart, making it a quartet but in his youth had become so incensed at the fact the Beatles pushed the Kingston Trio out of the top 40 that he vowed a life long hatred for all things four. And that’s not true either.
In 2001, William S. Cleveland, sort of named Data Science as a separate entity.
adding “advances in computing with data” to statistics as the differential. Oh, and that is all true.
Turing Award winner, Jim Gray, looked at it in 2007 as a ‘fourth paradigm of science’, augmenting the normal scientific methods by including the dimension of ‘data driven analysis’.
But it wasn’t until 2012 when the Harvard Business Review published their article “Data Scientist: The Sexiest Job of the 21stCentury” that things really began to take off.
In the end, it’s hard to succinctly and completely describe what a Data Scientist does because it does cross over into the artificial intelligence area but it certainly starts with some hard core statistical concepts and a really solid knowledge of Python which has many functions that make statistical analysis easier.
At the same time, it goes beyond statistics. Instead of just collecting and analyzing data using tried and true statistical methods, the data scientists asks the all important question — What If?
What if we looked at the data from a different perspective? What if we extended the modeling that our test data has given us in a number of independent ways? What if we let a machine analyze the data with no rules to guide it? What will it show in terms of relationships?
In the end, it is the input generated by the data scientists that will feed the two tools they will use to use the data to make decisions; Machine Learning and Deep Learning. So let’s move on to both of them.
Data Science — Wikipedia, https://en.wikipedia.org/wiki/Data_science
Harvard Business Review — https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century
Probably Wikipedia says it best. “Machine Learning is a field of computer science that uses statistical techniques to give computer systems the ability to learn . . . without being explicitly programmed.”
It’s that last clause that is key; without being explicitly programmed.
Much of the data intelligence work prior to this required extensive programming to help the computer account for every eventuality. And, of course, those efforts were doomed from the beginning because not every eventuality can ever be considered.
Machine Learning is different. In this case, a framework is set up that feeds statistically relevant information into the computer and lets it make decisions, lets it learn, from that data, rather than from programmer instructions. In other words, Machine Learning lets the computer discover what is probably true and what is probably false on it’s own based on data we provide and sometimes clues we also provide.
The more data you can feed in and the higher the quality of that data, the more the machine will learn. And when it is done learning, you can feed data in and get decisions from the machine on what to do with that data even if it involves cases you did not specifically train the machine on.
A relatively simple example of a machine learning system is the Spam filter on your computer. By looking at the various words that make up the email, and evaluating the probability of a given word or group of words being a danger, it is able to make a decision as to what should be filtered out and what should be left in.
Machine Learning is still in elementary school. That is, as with the spam filter, we have all seen cases where legitimate emails are spammed and things that should be marked are not. But most of the time, it is close enough on target for me. And as more and more spam filters start using true machine learning, that should improve.
Wikipedia — https://en.wikipedia.org/wiki/Machine_learning
SAS — What it is, Why it Matters — https://www.sas.com/en_us/insights/analytics/machine-learning.html
Of all the words we have encountered in this article, none is more forbidding, more laden with the unknown, more likely to send a terrifying chill down the length of your back, than Deep Learning. Is this indeed what unleashed SkyNet on an unsuspecting world. I know people who would swear it is. But I am guessing it is not. Or at least not yet. Not for a few years.
Again, Wikipedia nails it by calling Deep Learning “part of a broader family of machine learning based on learning data representations as opposed to task specific algorithms”. Yeah! You tell ’em, boy.
OK, let’s try this again. Deep Learning is part of machine learning, but it specifically, may I say, ignores anything that is specific, that is task oriented. It is not used if we want to define a system that will tell you what city in each state is the capital. Or the largest. Or the most fun. Well, that last one might be deep learning. I’m not sure.
Other names for Deep Learning are Deep Neural Networks, Deep Belief Networks, Recurrent Neural Networks, and Sheldon. And it has been applied to things as freaky weird as computer vision, speech recognition, natural language processing, audio recognition, social network filtering (something that is way overdue), etc. Broad things for a broad approach.
As noted above, Deep Learning is a subset of Machine Learning, a subset that is focused on two things.
The first is patterns rather than rules or facts. The machines are taught to look for patterns or even just portions of a pattern.
The second is mimicking the behavior of neurons, particularly those in the neocortex of the brain.
What difference does this make? One of the hallmarks of neurons in the human brain is that none of them work alone. There is not, for example, a neuron that is responsible for recognizing a dog verses a cat. Instead many neurons will work together, each one perhaps only responding to one very small part of the patterns the brain has for ‘dog’ and ‘cat’. But working together they are able to reach a consensus on whether it is a dog or cat and how strongly we feel about that decision.
That is what Deep learning is working on. It requires tremendous computing power, plus and in depth understanding of how the brain works, something that is still being studied and where our knowledge is constantly growing and changing.
Let Me End by Saying . . .
Data Science is based in statistics but Data Scientists go beyond just linear regressions. Remember? They’re sexy. The new data science goes beyond analysis to prediction, and to looking at data in ways that the traditional techniques do not. And the main reason for this is not a breakthrough in the mathematics but the adoption of powerful computers to quickly run analysis that would have been impractical in the past.
Machine Learning is about using data to let the computer learn on it’s own. Sometimes this learning is directed (we include rules or other parameters that guide how the machine makes it’s decisions) or undirected (we let the machine dig into the data and see what it can find). It’s all about separating intelligence from programming. In the Machine Learning world, the data is the teacher, not the coder.
Deep Learning is a subset of Machine Learning. It is based on data, but it uses a particular algorithum type, one that acts similar to the neurons in a human brain. Will it result in a Positronic Brain and the Three Laws of Robotics? Hard to say. But it is the future.
Ready, Set, Go.