- For business leaders, investing just a little bit of time to pick up the basics of machine learning can help them formulate solvable problems and navigate the sea of potential technological options that can be used to realize business value
- Those with even a basic degree of comfort with numbers (high school math, really) can quickly come up to speed on machine learning and go on to become leaders who contribute to gains on hundreds of challenging real-world data analysis problems from a wide span of industries and sciences.
- They are hiring. Do you have the skills? More and more companies are looking at data to increase productivity and competitiveness. What are you waiting for?
Thursday, September 5, 2013
Riding on the wave of a new movement requires that you learn new skills. I am often asked “What do I need to learn to profit from the Big Data trend?” My answer is “Learn some Machine Learning.”
Why do I say that? Three reasons
Machine Learning has arrived
After all, no less an authority than Bill Gates recently said, “When it comes to technology, there are four areas where I think a lot of exciting things will happen in the coming decades: big data, machine learning, genomics, and ubiquitous computing …” What is more, Bill Gates is also reported to have declared that “a breakthrough in machine learning would be worth ten Microsofts.”
Even if you happen to think that the above stand is debatable, there is no question that machine learning has arrived and is moving center stage. Large computational capital is readily available, and simple algorithms are waiting for someone to leverage them for handsome returns.
Numerous aspects of our life, both significant and trivial, have become incredibly measurable. Several aspects are already finely measured and stored. Privacy concerns aside, this might seem like a good thing till we realize that most of this data lies in deep slumber. Unused, unsorted and unedited, this data, rather than boosting the bottom line, kills productivity – cluttering up storage space on devices and slowing down connections. Just as most of us store more digital pictures than we can handle, companies frequently find that – although a treasure troves of data is on hand – the very volume of data makes it difficult to find what they need or glean actionable insight.
But we mustn’t blame the data. We must celebrate it. Astute business leaders have realized that tremendous insights exist in Big Data and want to use it in order to leap ahead of their competitors. Businesses are looking for expertise in machine learning to parse, reduce, simplify and categorize data.
Practitioner of this craft, sometimes called data scientists, are not quite software engineers. Although they might be quite competent at coding, their expertise lies in developing or adapting algorithms that operate on data to reveal order that can lead to insight. Data scientists can understand a data-centric problem, come up with a solvable formulation and recommend a practical solution. They may provide working prototypes to demonstrate the solution, and even sometimes provide segments of final code. They work closely with others more grounded in software engineering whose job is to translate their work, optimize it exquisitely and plug it into the larger framework of efficient runtime code implemented in the final product.
You are already a Data Scientist
Lest we forget, the ability to tame Big Data is the ultimate hallmark of intelligent beings. Your brain runs the best machine learning algorithms known. It absorbs massive data sets every moment of every day. The processing and directing of thought and action that you so effortlessly accomplish is amazing. Even more so once you consider that no neuron in your brain fires faster than 1 kHz, the speed of a circa 1980 PC. Of course, your brain’s biological computing system does not work the way a computer does. Rather than laboriously finding and opening large files loaded with complex information, your brain calls up millions of individually stored simple data elements, processes them in parallel in multiple locations, sometimes breaks rules leading to profoundly creative solutions and generally causes an emergence of sensible outcomes. This happened even before you spoke your first word, and now happens even when you sleep.
So, it looks like we already know machine learning without knowing that we know it. Many practitioners that I know report that the process of learning machine learning is accompanied by fleeting feelings of understanding how we think. This seems natural considering that machine learning is all about the problem of finding essential and distinguishing properties of a category, a quest that we are constantly engaged in.
Just good for fun and games?
Let me end this blog by taking another question that I am sometimes asked, “Is Big Data only useful for superficial stuff like recommending good movies to watch?”
My answer is that machine learning is used in several efforts to improve the quality of security, public health and safety. Methods for the automatic detection of events and other patterns in huge data sets find natural applications in areas such as flagging shipping containers likely to hold banned goods, timely alarms of disease outbreaks, warning of possible but yet-to-be-committed crimes, predicting dangerous sinkholes using satellite data, improved methods of assessing a patient’s risk of developing diabetes, proactively abating traffic congestion, early detection and containment of forest fires before they become too big, the list goes on ...
To be sure, Big Data and machine learning will be used to earn big bucks for corporations. But it is being, and can be, put to more noble uses in the wider world.
Friday, May 24, 2013
“Statistics is boring! I can prove it to you!” I sometimes declare in statistics classes that I teach. I do that in order to make the class lively. As proof, I ask the class to name a famous physicist (Einstein's name comes up every time), a famous biologist (Darwin's name comes up every time), and so on. I finally, ask them to name a famous statistician. This is usually greeted with silence, laughter, and the inevitable smart aleck response, “Shashi.” Statistics has no celebrities to boast of? It must be boring! Is it really? I proceed to challenge the class with “I bet you definitely know the name of a famous and pioneering statistician.” This is indeed true, but the class has to wait till the end of the day to know who it is.
I often try to make up simple experiments to help the class realize that statistics is merely common sense quantified. Sadly, this is not the message most people take away from statistics classes or training that they might have endured. So, I was quite struck by the uniqueness of the Prudential commercial demonstrating statistics in an interactive way. In this commercial, filmed at a park in Austin, Texas, a group of 400 people were asked the question “How old is the oldest person you've ever known?” Each person was given a blue dot and asked to stick it on a 1,100-square-foot wall, lined up with the age of that person. The results, and the way the results develop into a neat histogram, are striking.
|A participant in Prudential's commercial adds her sticker.|
The final array of stickers is on the right.
The visual effect is spectacular because the blue dots organically pile up into a mountain well to the right of a reference line showing the retirement age of 65. Prudential prudently realized that the public cannot be expected to show sustained interest in static graphs or percentages (even when rendered in arresting, moving colors and fonts) attempting to communicate details of life expectancy and the cost of retirement. In a refreshing move, they harnessed the wisdom of the crowds to create a compelling visual for them.
Compelling, it is. But the thought that is so compellingly communicated – “Oh my gosh! Just look at this huge number of very very old people! If they retired at 65, what in the world are they going to live on in their 90s and beyond? Now, I better do something about this for myself!” – is quite far removed from the statistical facts that happen to be actually relevant to retirement and ageing.
Besides, there is a bit of a problem with the question “How old is the oldest person you've ever known?” This was revealed when I attempted to duplicate the experiment using my friends as volunteers: Given more time to think and more opportunity for family members to jog our memory, we often find that we have known someone older than what we first thought. And, as it often happened in my experiment, we can recall knowing someone really old without knowing their exact age. A statisticians job is to prevent such problems by helping develop a solid protocol for the experiment.
But never mind. I don't want this blog to turn out to be a critique of Prudential’s approach to selling retirement planning products. On the contrary, I very much appreciate Prudential’s effort in bringing complex statistical concepts to the masses. In fact, a number of interesting statistical and computational ideas can be illustrated by taking Prudential’s experiment as a starting point. Let us explore.
|Fitting a bell curve to the histogram of blue stickers|
Next we ask questions. “Why does the mountain of blue stickers have the appearance that it does? Why does it look orderly even though the participants did not plan for creating the order? Why does it have a peak around 90 years?” We can answer these questions by doing our own blue sticker experiment, this time virtually (and at near-zero cost!)
Let us start with the 2011 US Census, which makes data on age distribution available on its website. From this data, we can apply curve fitting again to build an approximate mathematical model of age distribution, shown as the blue curve which approximately follows the red data points.
|Age distribution data and its mathematical model|
Having a mathematical model allows us to perform all manner of useful thought experiments, or simulations – or indulge our fancy. When done carefully, these experiments give rise to insight, help make predictions and often provide easy answers to real world questions. For example, from the mathematical model of age distribution, we can generate a random group of virtual people and be sure that the number of retirees (i.e. those past 65) is about what would be expected in an actual sample of the same size (roughly 12%). We can then create an easily understandable picture of the age distribution of that group.
Since we have a mathematical model, we can repeat this process of identifying the oldest individual as often as we like, each time using a different virtual group. Each repetition of the process corresponds to one instance of an individual in Prudential’s experiment mentally going over everyone ever known to him or her and identifying the oldest person known. The result of repeating the process a large number of times – each time placing a virtual sticker on a virtual wall – is shown in the figure below.
|A random group of virtual people. Those aged 65 and older are colored orange.|
If I were a participant in Prudential’s experiment and if the people depicted in this picture were the ones (and only ones) ever known to me, I would go up to the wall and place my sticker above the 92 mark. How old is the oldest person you've ever known? 92.
|Virtual blue stickers generated by running a simulation of Prudential's experiment.|
The histogram and the bell curve fit closely follow the result from the live experiment.
Even though the experiment involves generating random ages, we see that the pattern of stickers is not quite random. It follows what seems to be the familiar bell curve again (shown as a blue curve). What is more, it even peaks at around 90 years. The agreement between the actual (shown as a black dashed curve) and virtual versions is quite close. In aggregate, a predictable pattern emerges from randomness. The wisdom of the crowd ensures that the stickers fall into an orderly and familiar histogram, rather than be scattered all over the place.
So, that’s it for now. I hope we see more such creative attempts to infuse freshness into commercials. Everyone I know enjoyed this commercial. I am sure there were statisticians behind the scene who worked hard, (well before the experiment was conducted live) to make sure that it did not turn out to be a public flop. There is lots more material than can be covered in this blog. Does it really follow a bell curve? Does it matter? What is the distribution that is actually relevant to funding retirement plans?
As always, I will enjoy talking to you about this or any other numerical and insightful topics.