Artificial intelligence is the deception of human intelligence processes by machines, especially computer systems. This umbrella term covers Robotics process automation, Machine Learning, Deep Learning, Machine Vision, Natural Language Processing, Pattern recognization, Robotics, etc. Introduced by John McCarthy, the American Computer Scientist at The Dartmouth Conference in 1956, it has gained prominence due to big data and its role in increasing the speed, size, and variety of data businesses. Artificial Intelligence can do the tasks such as recognizing data patterns more efficiently than humans, enabling businesses to achieve more details of their data. Artificial intelligence has gained significant importance in almost all domains like healthcare, business, education, finance, law, and manufacturing.
In this blog, we will discuss some of the critical areas of Artificial Intelligence by answering some important questions on it. The answers will give us a brief idea about artificial intelligence, difficulties with the real-time approach of artificial intelligence, advantages, and disadvantages of four Artificial Intelligence systems over traditional business processes. We will get an idea about how artificial intelligence can be applied to different business processes. This will also explain how a decision support system can be effective for small businesses—a brief highlight on Porter’s value chain in AI.
Related post – Why AI in edge computing is the next possibility
Difficulties with new Information Technology (IT) approaches in general
The typical difficulties with a new IT approach, in general, are that the new IT approach needs to be understood and accepted, as well as, people need to be trained to use the new system. Artificial Intelligence is the science of enabling machines to imitate human thinking and behavior.
Advantages and disadvantages of Artificial intelligence systems over traditional business processes.
Artificial Intelligence is the human-defined intelligence mechanism for machines, robots, and a special branch of computer science targeted to create it. The artificial intelligence systems used in businesses can be categorized into the following major categories: expert systems, neural networks, genetic algorithms, and agent-based technologies. Each of these systems has its advantages and disadvantages.
Expert systems
Advantages –
- It reduces problem-solving time.
- It is the implementation of many experts derived problem solution
- It improves the quality of customer/patient services and the solution quality of a problem.
- It has the ability to predict future problems as well as it can solve current problems
- As the solution turnaround time is less, it can save the money of an organization.
- It can reduce the manpower requirement in some areas, especially car fault diagnosis, which is again cost-effective for a company.
Disadvantages :
- Initial set up cost is high for an Expert system
- Expert systems users need proper training for using it, and it is time-specific as well as costly.
- It is a continuous updating process so that it can be out of use for some time.
- In an organization, one specialist will be needed for each unit.
Neural Network
Advantages :
- The development of neural networks needs heuristic analysis as well as robust methodologies for developing algorithms.
- It is significantly accurate for a whole range of problems, including recognizing image and sound, analyzing text and time series, etc.
Disadvantages :
- It is difficult to learn it well, which may cause hurdles in debugging.
- We cannot get any explanatory power in this system. The system can extract the signal in the best possible way and classify the data accordingly. But it is unable to explain how it has come to that solution.
- These are computationally intensive for training, and one needs a lot of chips as well distributed run-time to be trained based on massive datasets.
Genetic Algorithm
Advantages:
- Using chromosome encoding, a Genetic algorithm can solve any optimization problem.
- For a particular problem, it can derive multiple solutions.
- One can solve any types of problems like multi-dimensional or non-differential, from non-continuous to non-parametrical problems using the Genetic algorithm as it is not dependent on the error surface.
- We can get the possibility to solve the solution parameter problems and solution structure using the Structural genetic algorithm. We can do the same thing using the genetic algorithm.
- The genetic algorithm is easy to understand method. We do not need any mathematical knowledge to understand the genetic algorithm.
- Genetic algorithms can be easily transferred to existing simulations and models.
Disadvantages:
- Genetic algorithms cannot solve specific optimization problems, which are usually known as variant problems. This happens when poorly known fitness functions generate bad chromosomes. Whereas the fact is only good chromosome blocks can cross over the problem.
- There is no guaranteed assurance that a genetic algorithm will find an optimum solution as a whole. When the populations have a significant number of subjects, the algorithm fails to get the optimum solution.
- The genetic algorithm cannot provide assured constant optimization response time, which may be possible in other AI techniques. We can see a larger response time between the shortest and the longest optimization if we use the conventional gradient method. Because of this disadvantage, genetic algorithm use is limited in real-time applications.
- Genetic algorithm applications are limited in real-time control systems as it gives random solutions and convergence. However, it is not applicable for an individual within this population. So it is required to test the genetic algorithm first on a simulation model before using it for online controls.
Intelligent Agents
Advantages:
· We can get higher productivity using intelligent agents. We can get much amount of work using intelligent agents than with a closed system. For example, you may have an agent for website searching with required information about your interests.
· We can materialize the idea of distributed computing using intelligent agents. We can make applications using different agents. These applications can perform their specific task using networked computers.
· Using an intelligent agent is economical because agent-based computing is demands less computing power. We can use a network of computers in agent-based computing with equivalent computing power.
· Intelligent agents demand less network traffic. Because for a single intelligent agent, we need to send a single command over the network. Whereas in the traditional RPC model, we need to send more commands across the network from computer to computer. In this case, the number of commands is proportional to the work volume.
Disadvantages :
· Security is a demanding concern for Intelligent Agents. There is a potential possibility that intelligent agents can destroy the contents of a host computer. So it is necessary to ensure that agent programs must not harm the host computer.
· There is no standard defined for intelligent agent-based communication. If no standard is defined, then only agents of the same languages can talk to each other.
· Artificial Intelligence intelligent agents need more improvement. It is seen most of the intelligent agents do not work well or up to the mark.
A Simple Case Study
Say you were selling specialty teas and had a brick and click stores. What are the types of Artificial Intelligence systems applicable to each part of your business?
As I am selling the same types of items, I can use the same kind of Artificial Intelligence system. Although my business uses both online and offline modes of transaction, for customer information analysis purposes, the same AI systems can be used. Like I can use an expert system for both the types of stores which can help customers to find a tea as per their choice.
The way you would use them.
But we can use different IT tools for the different parts of the business. For example, we can use neural networks to analyze the web traffic for my Website.
A place for decision support and artificial intelligence techniques in small specialty businesses
A decision support system might not give many benefits for a small business. As the small business consists of fewer resources and finance, getting an artificial intelligence technique might be costly for them, which will ultimately not produce any benefit.
The way decision support add value in business
With decision support systems, we can collect data, analyze it and shape the data accordingly. Using decision support, we can make sound decisions, construct business strategies based on the analysis. For determining customer taste and preference, Decision support may take an added value.
Decision Support System (DSS) or an Artificial intelligence system would be valued reducing (in terms of Porter’s value chain theory)
A value chain has defined as a collection of activities that an organization follows to create value for its customers. Porter suggested a value chain for general-purpose that organizations can use to examine all of their functional activities. This is also used to relate how they’re connected. This value chain theory is useful to analyze the sources of costs, profits, etc.
Decision Support System can produce a significant cost advantage by increasing overall efficiency and eliminating value chain activities. For example, using a DSS, a bank, or a loan firm can reduce the overall costs.DSS can be used to consolidate the number of steps for processing a loan which will ultimately reduce the number of manpower required to approve loans.
Conclusion
Artificial Intelligence is the best field for people with dreams to play around. It is the thought that we can make human-machine using artificial intelligence. Many scholars conclude that making a human-machine is next to impossible, but research will still achieve the final objective. Using human-machine is one way advantageous as they are emotionless and serve better in terms of faster and longer performance. We can get the ultimate result with time, proving whether artificial intelligence can attain human-level or beyond that and what is the future for artificial intelligence.