insideBIGDATA Magazine – “How AI Really Makes History: Beyond Hype”

Here is an excerpt from the insideBIGDATA article by Greg Council, published April 7, 2016. For a full view of the entire article click here

Significant advances in computing technology can yield less significant, yet still more successful and practical applications for businesses. And yet, it’s easy to become enamored with technology for technology’s sake. Successfully applied artificial intelligence requires taking small steps to learn and adapt.

AI is not as smart as you think

Breathless headlines trumpeting cognitive computing, machine learning, and artificial intelligence are everywhere, but few articles actually discuss what it all really means. For all the hype about similarities to human thinking, artificial intelligence is not all that intelligent. Yet. Keeping this in mind is the key to successfully applying AI to any business. The underlying concepts of AI can be placed under the umbrella of “machine learning,” which combines a number of technologies aimed at automating tasks that typically require humans at the center or, at a minimum, human interaction.

Just like us, machine learning needs inputs. It then needs guidance on providing the right outputs. Machine learning algorithms don’t know how to experiment like we do. Left without any guidance on inputs, humans gradually figure things out through trial and error. AI needs tons of data and guidance on what that data means in order to produce useful output at anywhere near the performance level of humans. Google’s self-driving cars are a case in point on needing a lot of data to solve a complex problem. Google has spent years on the technology driving cars several million miles in the process to obtain that data and feedback. Most businesses do not have the resources to supply that level of data and feedback.

The Difference is in the Scope: Applied AI

Where applied AI differs from general AI is that specific problems are solved with limited scope. In this way, businesses can apply AI capabilities in smaller projects and still realize substantial benefits in terms of lowered costs, increased efficiencies, and improved knowledge.

Gathering sufficient samples and providing the input guidance is still required, but good results can be obtained due to both the limited scope of needs as well as the reduced amount of input and user feedback to “train” the AI system. Let’s look at some practical areas that businesses can use AI when applied to multiple document types, common to many organizations.

How Applied AI Works

Let’s talk about how applied AI works using an expense management scenario. A company typically establishes spending policies and requirements for employee reimbursement. Keeping the receipts involved is mandatory to show proof of the expenditure as well as to approve each expense. Most organizations cannot automatically apply policies. The process requires a lot of effort on the part of employees submitting expenses and the staff who review them….

…For the entire article click here.