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  • Writer's pictureMark Heymann

AI: Is The Intelligence Artificial Or Amplified?

Updated: Oct 4, 2023



In today’s environment, there’s barely a day that goes by when there isn’t some discussion or article written about the latest in artificial intelligence. It’s a very exciting time as we look at what computers can accomplish with or without human intervention.


To take a half a step back and to even the playing field in order to ensure clarity in the discussion that’s going to ensue, I will highlight four key areas of what is called artificial intelligence.


  • Machine Learning: This is a simple process by which a system gains more information that enables it to parse data. Based on all of this historical information, it makes predictions about what is going to happen in the future.

  • Deep Learning: This refers to a machine learning approach that utilizes artificial neural networks, employing multiple layers of processing to progressively extract more advanced features from data.

  • Natural Language Processing: Natural language processing (NLP) employs machine learning techniques to unveil the underlying structure and significance within textual content. Through NLP applications, businesses can analyze text data and gain insights about individuals, locations and events, enabling a deeper comprehension of social media sentiment and customer interactions.

  • Cognitive Computing: Cognitive computing pertains to technology frameworks that, in a general sense, draw from the scientific domains of artificial intelligence and signal processing. These frameworks encompass a range of technologies, including machine learning, logical reasoning, natural language processing, speech recognition, visual object recognition, human-computer interaction, as well as dialog and narrative generation, among other capabilities. There is currently no agreed-on definition for cognitive computing in the industry or academia.


Computers And Decision Making


My intent here is not to rehash a group of definitions, but with this as a baseline, I want to specifically turn to decision making and how much involvement computers should have in this process.


I think one of the keys to where the final decision lies depends upon not just the impact of a decision on the business but also the risk profile of the decision’s outcome. Further, when that decision is assessed and reviewed, who will be held accountable for the result? This does not seem to be an area that discussions of artificial intelligence focus on very much.


Years ago—literally over 40 years ago—we developed some initial technology to help hotels predict revenue center activity. These centers not only accounted for daily room occupancy but also factored in the anticipated number of guests to other facilities, such as restaurants and bars. This process resembled the familiar task of forecasting widget production to align with demand while avoiding any significant inventory excesses.


The approach at that time was what we now commonly call machine learning. Over time, these technologies and algorithms have evolved to now fall more into the category of deep learning. But at the end of the day, regardless of any computer-generated predictions, it was still up to the manager of the specific revenue center or production environment to make the final decision on projected volume.


Once that decision was made, one of the key areas influenced by these projections was staffing levels. This pertained not only to daily staffing but, in the service industry, often extended to staffing levels in half-hour increments as needed.


As systems have advanced and the scope of data analysis has expanded, the accuracy of predictions has consistently improved. However, it remains a rarity for the manager overseeing this specific aspect of the operation to be fully removed from the final predictions, which encompass staffing and cost levels that will be incurred.


Where Human Intervention Is Needed


Turning now to the broader economy and taking a look at where AI is being tried, we see examples where the systems that are being used have no human intervention whatsoever. At times, it is clear that human intervention is absolutely needed.


Consider, for example, trading systems within the stock market. In such systems, human intervention has proven critical in preventing excessively wide market fluctuations. This is just one area, but I’m sure if you take a moment to sit back and think about other areas where computers are making decisions based on some level of AI, you’ll find many more examples of where human intervention is still crucial.


The Business Impact Of Decisions


As we look at the application of what is broadly called artificial intelligence, it becomes more and more important to understand the risk impact of specific decisions on business results. Simply put, the larger the impact of a decision on an operation, the more important it is to ensure that the decision is not left completely to the computer.


If the decision going to be made has a very low risk of business failure and/or the cost of failure is very low, then it’s easy to turn to the computer for determination.


We all remember when Deep Blue played chess and, at first, suffered defeat. However, as it continued to learn, it won chess matches, sparking our excitement about the computer's capabilities. Nevertheless, it's important to recognize that winning a chess game, which holds little real-world consequence, is quite different from the task of making decisions such as estimating the demand for breakfast service or predicting the number of travelers heading to Chicago.


The cost of getting that number wrong or the impact on other revenue centers can be significant, counting both direct and indirect impacts.


Therefore, I believe it benefits us to understand the consequences of the decisions being made, as well as the associated costs and risks of potential failures. This understanding can guide us in determining the appropriate level of management involvement in making the final decision. Final accountability for decision making in key areas needs to remain with management, especially when the cost of failure is high.


Over time, computer information and interpretation will become more important and enlightening. But as we look for accountability in management decisions, we may want to think more about AI being defined as "amplified" intelligence as compared to purely "artificial."


Originally Published in Forbes

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