- 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.