AI - Agile Intelligence

AI – Agile Intelligence

7 Mar 2019

Artificial intelligence is one of those concepts that is thrown at, into or added whenever someone wants to demonstrate they are using this new concept. Traditional AI is not something that is achieved or ever finished; it is a process by which you incorporate various kinds of machine technology with human efforts. Today various kinds of machine intelligence in the form of cognitive behavioral analysis, natural language processing, analysis of vast arrays of data and larger computing efforts are used to solve all the problems we face each day. 

In the Tata Communications White Paper called “AI and the Future of Work,” Tata drew the analogy that the steam engine closed the gaps in physical distances. Today, we need to close gaps in more than physical ways but in mental intelligence, as the problems are not between two towns but global, environmental, economic, political and other problems that cross all spectrums of the world. Each alone might require all the efforts we have devoted to AI so far. Combining them together creates a limitless need for new agile ways to bring real solutions before global warming or pandemic obliterates us.

Seeing the solution – even knowing the solution – is what humans are really good at. Bringing the financial, talent and political resources to fix infrastructure, environmental, immigration and really tough problems is not something that AI can solve. What then can AI solve today or be of help to solve problems that arise in the future? 

I have spent a considerable amount of time involved with expert systems that portend to solve one or more problems to which the system was designed. In simple terms, an expert system (ES) is a computer program or system that organizes knowledge within rules or procedures to solve problems for a particular problem or task. If properly designed and maintained, an expert system can perform at or near the level of a human expert. The key issue is that an expert system is a machine. Current systems often have the constraints of the background and limitations of its creator-designer, and the skill and knowledge of the person who uses it. Presently most expert systems fail because (1) they require too much expertise from the user – it takes an expert to use an expert system or (2) they solve only certain classes of problems – helps you make chicken gumbo soup but not cream of chicken soup.

An expert system must have a diverse background reference to be effective, as opposed to an incredible ability to be efficient. Expert systems reflect the rule-based side of decision-making; mathematical models, formulas, algorithms, and heuristics can easily be applied, allowing expert systems to be developed and efficiently utilized. However, key point is a great algorithm applied against bad or biased data will only make the outcome worse. In other words, the algorithm must be as agile as the problem it is trying to solve. Where management or business procedures dictate a certain realm of finite possibilities to the decision makers, an expert system can be a vital management tool. Expert systems are more like productivity aids than truly intelligent software systems. They are tools that help managers improve the flow of information. These AI-assisted "power tools" provide an effective agile means for improving understanding, problem solving, or decision-making.

These capabilities suggest that a wide range of expert systems will be developed to guide clerks using payroll systems, help engineers with design, and aid doctors in diagnosis. A manager might also use such a tool to develop new models for organizational development, training, and policy analysis. These systems service the areas of business, computing, engineering, finance, geology, manufacturing, medicine, resource management, and science. Expert systems can provide expertise and insights into everything from providing estate planning and investment advice to selecting auditing procedures, configuring computers, diagnosing infectious diseases, and assessing problems with oil wells. In comparing expert systems with other knowledge technologies, the term "expert system" is used to describe rule-based technologies where the information is packaged or premixed. The term knowledge network is used to describe a network of people in a thought-processing system. To define a point of reference and to signify the role of people in the process and the resulting differences, knowledge networking refers to human-based activities. This is not to say that expert systems are nonhuman. In an expert system, experts program their knowledge into a computer. This is the real challenge of how to get the information on which to make decisions into useable and frankly, re-useable decision-making tools for others to use. It really requires an “agile” approach to intelligence. 

This concept is not without underlying support. Tata noted that “Most (75%) executives envision AI creating new roles in their businesses. AI has potential to free employees from tedious repetitive tasks, allowing them to focus much more on communication and innovation. Work will move from being task-based to strategic, enabling workers to enhance their curiosity and creative thinking.” This is where I find AI can evolve into agile intelligence bringing traditional thinking with new machine intelligence to accelerate better decision-making. 

Summary – AI is upon us and there seems to be no end to the problems that AI can solve. I can only hope that companies will find better uses for AI than to optimize bus routes, parking spaces, organizing resumes and other simple business processes. If an agile approach can be developed, better forms of AI can emerge and they can evolve to solve real problems just as we humans are faced with. I would like to thank Tata Communications for the opportunity to share some thoughts on artificial intelligence and the future of emerging technologies.


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