Big Data and Deep Learning are two major trends that will impact and influence the future direction and potential of innovation in the United States.
From the deluge of information on both trends over the past year, it would appear that they may be key drivers for the future growth of the American economy. We’re not that sanguine.
In our opinion, both of these trends have substantial promise. But, they also have limitations that must be overcome to deliver on that promise. Let’s examine why.
Big Data is everywhere and the folks who are making a living warehousing and mining it abound. Big Data can be used to analyze web browsing patterns, tweets and transit movements, to predict behavior and to customize messages and product offerings.
Kenneth Cukier, Data Editor of The Economist, and Viktor Mayer-Schoenberger, Professor of Internet Governance and Regulations at the Oxford Internet Institute, exalt the emerging use and impact of Big Data in an essay for the May/June issue of Foreign Affairs. The essay is adapted from their new book, Big Data: A Revolution That Will Transform How We Live, Work and Think.
“Datification” is the label that Cukier and Mayer-Schoenberger give to the process of rendering “into data many aspects of the world that have never been quantified before.” They point out that currently almost everything is being datified from locations, to words to “likes”.
They assert that because of the ability to collect and use great volumes of information there will need to be three “profound changes” in how data is approached. (1) We will no longer have to rely solely on small amounts or samples and statistical methods for analysis. (2) We will have to tolerate some “messiness” and depend on the quantity of data as opposed to its quality. (3) In many instances, “we will need to give up our quest to discover the cause of things in return for accepting correlations.”
It seems to us that it is precisely because of these three considerations that there will need to be more rigor and objectivity in the data gathering and analysis process. Scientific methods will become more important rather than less. An informed intellect and an inquiring mind will become more essential in order to perceive “truth” and bring some order out of chaos.
Interestingly, the authors appear to agree with our perspective. At the end of their article, they emphasize, “Big data is a resource and a tool. It is meant to inform, rather than explain; it points toward more understanding, but it can still lead to misunderstanding, depending on how well it is wielded.”
We agree that Big Data is a “resource.” Big Data by itself, however, is not a tool. The tool is the research design that is employed to organize, aggregate, and analyze data in order to see patterns, extract meaning and make judgments. The person who creates and uses that data is the toolmaker. Today, we have an oversupply of Big Data and an under supply of Big Data toolmakers.
That’s a key point that Steve Lohr made in a 2012 year-end column for the New York Times about Big Data in which he wrote, “I think it is a powerful tool and an unstoppable trend.” Lohr cautioned appropriately, however, that putting Big Data to work may take some time because of a “workforce bottleneck.”
Lohr reported that the McKinsey Global Institute projected that the United States needed 140,000 to 190,000 more workers with deep analytical skills and 1.5 million more managers with the right training to use this data fully. He also stressed the need for experience and intuition to exploit the potential of this resource.
The message to us from this is straightforward. Even with mounds and mounds of Big Data, human insights and innovation must come into play to matter and make a difference. Big Data. Small Minds. No Progress! Big Data. Big Brains. Breakthrough!
Deep Learning stands in contrast to Big Data. Deep Learning is the application of artificial intelligence and software programming through “neural networks” to develop machines that can do a wide variety of things including driving cars, working in factories, conversing with humans, translating speeches, recognizing and analyzing images and data patterns, and diagnosing complex operational or procedural problems.
As the New York Times reported in November 2012, there have been rapid advances in this field lately “made possible because of greater computer power and especially the rise of graphics processors.” Deep learning programs have recently beat humans in some head to head competition.
Smart machines are here and they will continue to get smarter. As robots have taken over some of the jobs on the factory floor, they will take over functions in other areas of human endeavor. That’s the case that Erik Bryjolfsson and Andrew McAfee make in their book, Race Against the Machine.
The real innovation challenge to us then it seems will not be to apply deep learning to replace humans but to use it to create new ideas, products and industries that will generate new jobs and opportunities for skilled workers. We’re not certain exactly what those will be but we do know that it will demand the best in American entrepreneurship and innovation.
Getting the most out Deep Learning will require deep thinking. That’s where authentic human intelligence still trumps artificial machine intelligence. And, it’s what makes us optimistic about the upward movement of the American innovation curve and the potential it will bring with it.
In conclusion, Big Data and Deep Learning can be big deals and the bases for an American innovation and economic revolution. To start and complete that revolution successfully though we need to have the right complement of revolutionaries who are well equipped and motivated to accomplish the task.