In the future imagined by science fiction, artificial intelligence will reign supreme and take over pretty much everything humans can do. Frankly this sci-fi vision isn’t helpful when it comes to applying technology because it distracts us from thinking about what people do well and what advanced techniques such as deep learning do well.
In the world of data science, great strides are being made in the area of deep learning. We’ve made so much progress that it is easy to think that instead of having to embrace data science as a discipline, we can somehow wait a little big longer and have a Watson-like box to perform all of these tasks for us. If you think this way, you are going to miss the boat. Here are three reasons why.
1. We Dole Out the Work
Deep learning, and much of data science, is limited to a narrow set of tasks. Deep learning is the term that has come to describe the most advanced forms of machine learning, systems that attempt to perceive in data complex patterns using a variety of statistical and algorithmic techniques based on fairly raw data. Deep learning has proven effective at perceptual tasks and recognizing certain attributes, often called features, of images or other data sets. Much of the time you can boil down a huge amount of processing to a much simpler model that then allows you to predict something or find some signal that was hidden. Deep learning victories include language translation (such as Google Translate and Baidu Translate) and speech recognition (which powers Apple Siri and Google Now) as well as image recognition and even playing video games and flying model helicopters.
There is no reason not to be happy about the victories of deep learning. But so far, deep learning systems can do some specialized tasks well, but only under certain circumstances. In a fascinating blog post, Zachary Chase Lipton surveyed a variety of papers that pointed out the flaws in deep learning systems. It turns out that they are often brittle and easily fooled. The key lies in understanding when a certain technique works and when it doesn’t.
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2. We Provide Context
Driverless cars don’t know where to go or why. Humans are needed to provide context, to frame the problem, to generate the hypothesis, and to decide what deep learning or data science to apply. Even today’s most advanced systems are “idiot savants” that perform a single task really well, but don’t have a broader context.
In any machine learning or analytics problem domain, one of the most important roles people play is to define what the goal is. It’s easy to build a system to optimize a value, only to discover you picked the wrong problem. Humans will for a long time be the ones who solely define problems, understand what is really important, and verify that a system is functioning as expected against an intuitive understanding of a problem domain.
Advanced systems don’t know when to turn themselves off. After the crash in 2008, humans turned the off switch on lots of program trading systems because the assumptions on which they were based no longer held. In any event, systems that make recommendations about life and death decisions or those with momentous financial consequences are always white box systems that are monitored, improved, and have decisions approved by people.
3. You Can’t Buy Deep Learning
Productization of deep learning and data science is not happening quickly. Toyota is going to invest a billion dollars to determine how deep learning applies to driverless cars. So far, such cars don’t really use much deep learning. Google and Facebook are eager to productize deep learning, but most of it is still R&D. It all looks promising, but let’s face it: most of us have seen Watson television commercials, not Watson-powered products.
Let’s say a whiz-bang deep learning product does become available. Because everyone can buy it, it won’t provide a competitive advantage.
The true winners in the realm of deep learning and data science will be the companies who understand the limits of these powerful tools and apply them in the right ways to find out what can be known with high confidence. Smart people who have data science skills are the key to getting this right now and for the foreseeable future.
The world of data science and machine learning is exciting and will only continue to grow. However, we must realize that machine learning still requires much human maintenance and oversight to succeed. We must continue to strengthen and integrate our data science teams into business organizations and invest in the architecture to allow our machines to communicate better with one another.
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