Artificial Intelligence is a trillion-dollar industry with almost all of the companies across the globe investing in it to drive their businesses better or to increase efficiency to a great degree. When the stakes are this high, it is always advantageous to know about the technology, the trends, and how best we can make use of it and leverage it to get the tasks at hand get done easily.
This is the exact topic of discussion on this blog where we will be checking out the following aspects of Expert Systems in Artificial Intelligence:
Let us begin this Expert System in AI blog by taking a quick look at the introduction to AI.
Introduction to Artificial Intelligence
Let’s take a quick second to understand what Artificial Intelligence is. It is the culmination of making a machine think like a human. The process of training a computer, passing on some good amount of data to it, and letting it understand the data is considered to be the foundational step in today’s world of Artificial Intelligence.
When you think about it, Artificial Intelligence is everywhere around us. From a very simple ‘Hey Google’ voice to smartphones to cars that can drive themselves, it has come a long way.
The implementation of AI has been subtle since Day 1, and this is the reason you feel that it is being implemented slowly. On the contrary, AI is rapidly expanding to every field you can think of.
Some of the most prominent fields are as shown below:
An Artificial Intelligence entity will have access to a lot of information, and it will be programmed in a way, wherein it uses this information efficiently and gives out a response as fast as it can.
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Let us begin to understand what the Expert Systems in AI are and where are they used.
What is an Expert System?
An expert system, as the name suggests, is a complex computer application that can solve a majority of problems in any set domain. It was the brainchild of the researchers at Stanford University.
Adding on to this, it gets its name because it learns from humans who are experts in their domains. This gives the machines the highest level of cognition at this point. An expert system is a system that can solve any complex problem by taking inputs such as facts and heuristics and producing a meaningful solution to the problem.
Next up on this ‘What is Expert System in AI’ blog, you need to understand where this expert system in AI is used.
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Expert System Domains
Here’s an Expert System example – when one thinks about making use of an expert system in AI to leverage profits for a business, ROSS is the expert system that often comes up. It is an Artificial Intelligence-based attorney that uses a variety of techniques to mimic human intelligence. Everything from data mining, pattern recognition all the way to implementing complex Deep Learning models, ROSS is a complete package, bringing together AI and businesses.
With multiple domains comes multiple areas where an application becomes the niche rather than being generalized. When that is the case, it is always good to check out various areas in which an expert system in Artificial Intelligence can be actively placed.
Teaching: Helping students in learning the concepts quicker
Planning: Smart set of actions that can help achieve a goal faster
Design: Customised solution to a situational model
Interpretation: Provision of systemic conclusions to problems based on facts
Diagnosis: Active detection of diseases and contagions across the world
Control: Behavioral analytics and change of management in a system
It is extremely vital that you understand why the Expert System in AI is being put to use today. Read on!
Why do you need Expert Systems?
Today’s world requires more and more experts in the ever-growing technological feats that humans are achieving. The important thing here is to see if you can put the power of computing to good use. Expert systems in AI are the way computers replicate the knowledge and the skills of a person who’s an expert in a field.
Some of the biggest advantages that Expert Systems provide us are these four aspects:
- Maximum efficiency
- High-level understandability
- Unbeatable performance
This process of taking an expert human’s knowledge and adding high amounts of computation power to it has proved nothing but immensely beneficial in today’s world.
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Hence, it would be fitting at this point to take a look at the advantages of an expert system.
Human Experts vs Expert Systems
|Human Experts||Artificial Intelligence ES|
|Can get tired||Works without breaks|
|Perishable knowledge||Permanent knowledge|
Advantages of Expert Systems
Storage: They have the ability to store large amounts of data easily and add levels of access.
Training: Expert systems can be mapped to help train new employees or incoming employees, thereby, saving a lot of money involved in training and preparing employees for their roles.
Decision-making: The ability to implement computing power to derive expert skills and use it to drive decisions is very much possible. This is leaps and bounds ahead of the conventional ‘educated-guess’ approach.
Efficiency: In the process of problem-solving, most of the time, the requirement is time-sensitive. Cutting the process short by making everything more efficient, thereby, solving the problem quickly is a possibility with the use of expert systems.
Error handling: Expert systems not only reduce human errors but also make sure that errors can be handled and solved in a structured way, which is an important advantage in most situations.
Wrangling: With the usage of expert systems, one can look at data or any process and understand it in a way that would’ve been incomprehensible to the naked eye.
So, what makes an expert system an expert? To understand this, let us take a look at the expert system architecture in Artificial Intelligence.
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Components of Expert Systems
There are many types of Expert Systems out there. But all of them have similar characteristics to Expert Systems in terms of components and their usage. Following are some of the most important components that combine to bring about this intelligent entity:
This is also called the knowledge base, which is basically a huge container of data. The processed part of the data and the output of the learning equate to the data creation called ‘knowledge.’ Be it man or machine, only using the knowledge you can validate the accuracy of the solution to a problem. Without which, it would just be facts, right?
It is the storage for the raw data, which is used as an input for models to train and function. The important aspect here is the variety of methodologies and techniques used to store the data for immediate access when required.
Next up on the components of Expert Systems in AI, we have the most important component on this list
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Inference engines are the heart of Expert Systems. These processes power through the knowledge hub and analyze the problem at hand to arrive at a solution.
If it is rule-based, then the engine ensures to iterate through facts, apply the knowledge obtained, and resolve conflicts where required (in case of multiple rules).
These systems are put into place to supply the information that helps in clarifying the problem domain and the structure. This has multiple use cases not only in the field of expert systems but otherwise as well.
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Building an Expert System
You might be wondering about the process that goes into the building of an expert system. The following is a brief explanation of the same:
- First, you need to begin by determining the characteristics of the Expert Systems and the requirements of the problem at hand. Doing this gives you a thorough understanding of the problem.
- The second step is to use the expertise of the domain experts and the knowledge engineers to create and define a structured problem statement.
- Next, the knowledge engineers help convert the knowledge packages into high-level, easy-to-understand verbiage. The inference engine and a structured approach to the solution are designed at this point.
- Lastly, the knowledge experts will ensure how these raw knowledge sources can be integrated with the reasoning system, and they assess the segregation of the explanations that would help solve the problem.
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Limitations of Expert Systems
After checking out the features of the Expert Systems, we need to take a look at its limitations as well. When looking at Artificial Intelligence, one must understand that it is a technology that is under continuous development and improvement every day. Most of the time, when one wishes to use AI to solve a problem, the stakes are high and so are the expectations. To know and understand where the limit is, let’s check out some of the limitations of Expert Systems.
Creativity: No matter how good an Expert System is designed to be, there are many situations where it lacks finesse and brings out a response that can be deemed as plain and commonplace.
Result validation: If there is a situation where the input data to the knowledge hub is not accurate, the validation engine might not be able to figure this out and, therefore, might furnish inaccurate results.
Running cost: As you might expect, designing, maintaining, and running these expert systems require a lot of finances. Once set up, it will serve for longer durations efficiently than human experts driving in more return on investment overall.
Conflict of solutions: When you consider the human approach to solving a problem, each person can come up with a unique solution. This is not the case when working with Expert Systems, which happen to be one of the biggest limitations today.
Key Applications of Expert Systems
Following are some of the most popular applications where Expert Systems are used:
- Information technology
- Hospitals and healthcare
- Loan provision analytics
- Planning and scheduling tasks
- Warehouse optimization
- Stock trading
- Process control and automation
- Airline scheduling
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With the ever-rising power of Artificial Intelligence, the creation of Expert Systems, which are interactive and reliable in providing decisions based on facts and heuristics, has been proven to work. One might argue that they are expensive to create and maintain, but they do provide enough benefits and ample return on investment if put to the right use in the right environment.
What do you guys think about Expert Systems in Artificial Intelligence? Where are they used best and are they reliable for all analytical decisions in the future? Head to the comments section and let us know!
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