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How AI Really Makes History: Beyond Hype

Greg-CouncilIn this special guest feature, Greg Council, VP Marketing and Product Management at Parascript, discusses how applied AI can help businesses uncover and use valuable information that they are already storing. In his role at Parascript, Greg is responsible for market vision and product strategy. He oversees all aspects of Parascript software life cycles, leading the successful development and introduction of advanced technology to the marketplace. Greg has over 15 years of experience in marketing, product definition and development, competitive/market analysis, and channel engagement for both on-premise solutions and SaaS. Formerly, he led product management at Evolving Systems and Captaris, now OpenText. Mr. Council holds an BBA in Marketing with a minor in information systems and an MBA from the Isenberg School of Management at UMASS Amherst.

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.

Now let’s introduce applied AI. Almost every company has many samples of receipts along with expenses that were approved or rejected and their expense categories. The receipt samples and results for each expense can be input into machine learning algorithms to answer two questions:

  1. What expense category does each receipt belong to?
  2. What receipts are approved?

Because this information is already answered for each receipt (based on expenses already processed), the software locates data and identifies patterns in it to make inferences and answer each question. For instance, expenses for the category “dinner” above $50 are repeatedly not approved. The software identifies this pattern and automatically applies a rule for assigning an expense to the category of “dinner” and then determines if the expense can be approved. Going further, if the expenses for the category of “dinner” always have exceptions for alcohol, the software detects this pattern, identifies terms associated with those items, the amounts, and then automatically applies this outcome as well.

Here applied AI focuses on a specific problem and performs two functions: Assign expense categories and determine whether expenses are approved. This kind of problem is perfect for the level of AI that is commercially available and, while not creating earth-shattering changes, can have a dramatic effect on a time-consuming business processes.

Approaching the use of artificial intelligence requires identifying the problem, keeping the scope narrow, and then focusing on improvements that are achievable.

 

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Comments

  1. Helen Prista says:

    In the “AI is not as smart as you think” part, l don’t think l’m quite well convinced. It’s true that machine learning needs input, but this does not mean that it denies the possibility of AI having intelligence at the same level of humans. Even only big companies like Google has the ability to develop,once super-intelligence is achived,the aftermath shall be inconceivable. I mean, l sincerely hope l can persuade myself into believing in the advantages of applied AI,but the thought of super-intelligence always keeps me from relaxing. l’m so glad that this article gives me some hope,but l’d be most thankful if you could give me more reassurement that AI is not,or cannot be,as smart as l think.

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