The Forbes article Demystifying Artificial Intelligence points out that AI is on the minds of 96% of business executives of leading corporations. Another Forbes article, Deep Learning Must Move Beyond Cheap Parlor Tricks, warns that one should avoid creating a fragile house of cards. A third Forbes article, This Week in AI States: Up to 50% Failure Rate in 25% of Enterprises Deploying AI, points out that there is a 50% failure rate in the approximately 25% of organizations implementing AI globally do to lack of skilled staff and unrealistic expectations.
Yet, a PWC study in 2017 points out that:
“Global GDP will be 14% higher in 2030 as a result of AI – the equivalent of an additional $15.7 trillion. This makes it the biggest commercial opportunity in today’s fast changing economy”
AI is hard work as The AI Ladder points out. It requires proper tools, proper methods, and the right mindset.
Here is, in my view, an example of AI done right. A software engineer and I created an extensive working proof of concept of what amounts to an expert system for creating financial reports. If you really want to understand the application, watch the set of videos in this play list.
How did we do it? First, we did not make the "rush to detail" mistake that most people make. We created a solid foundation then we built on top of that foundation. I created a set of principles. With some help I created a theory. We created a model. We put together a framework. We created a method. We did the necessary testing.
All this resulted in our working proof of concept. This document, Guide to Building an Expert System for Creating Financial Reports, helps you to understand important details. While we essentially created a good old fashion expert system, we used the right tool for the job. All this very high-quality metadata will serve as the necessary training data to enable additional functionality using machine learning.
Don't misinterpret what you see. As the Innovator's Dilema points out, "A disruptive product appears as if it's doing everything wrong. Large companies with sophisticated and demanding clients cannot adopt such a technology."
This could be the future of financial reporting. Yes, it has to work well. It can.
A disruption is when new products and services create a new market and significantly weaken, transform or destroy existing product categories, markets or industries.
Maybe we will turn this into a product and get a piece of that $15.7 trillion.
If you are still confused about AI, read this.
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Artificial Intelligence for the Real World, Harvard Business Review