There is serious progress being made in this field. Many applications will be of such a fundamental nature that they won't even be visible. They'll just figure things out and deliver great decisions on all kinds of transactions. So, if you're a Chief Data Officer in a reasonably large enterprise, your first AI challenge will likely come from your COO and the IoT solutions that need to be implemented.
During this last year I've seen three presentations from unrelated institutions where deep learning techniques have been applied with great success to the reading of medical imaging. These apps are many times faster than doctors, and their error rates are every bit as good. Other neural nets are being applied in the context of forensic accounting in order to find patterns in the movements of hazardous materials. If there's an application where you want fast and error free, it's in tracking the way terrorists buy and collect their munitions.
The change in basic conputing environments in the last few years has changed what can be accomplished with the various forms of artificial intelligence. I've had friends playing around with neural nets for decades, but it's only been recently that the needed computing power has become available. Now an investigator can make real progress much more quickly. It becomes a matter of getting the questions composed in a way that AI applications can answer.
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The various descendants of artificial intelligence are becoming increasingly impressive. A variety of Meetups under the Data Community DC umbrella have sponsored very exciting presentations on the development and use of deep learning. Several groups have produced results for the interpretation of medical imaging that rival human accuracy and speed up readings by several orders of magnitude.
Another area of development pertains to data engineering. AI is being applied to search in efforts to improve search relevance. For example, based on simple geography it might look like the nearest coffee shop is two miles away. AI can dig a little deeper and tell you that just because there's coffee directly across the river doesn't mean you won't have a 20 mile drive to get it.
So there are all kinds of possible decision support applications here, and more of them are delivering good answers every day. The data science team that can build real-time machine learning tools ought to be able to clean up big in the near future.
Even in pure research contexts
it's all about problem solving.
Problem solving always begins with
careful problem characterization.
Innovation is the art of turning
a great solution into a great application.