Artificial Intelligence (AI) helps businesses to enhance decision-making, increase efficiency, and gain a competitive edge. Its uses extend to various departments within the company, including sales, marketing, product development, and security, with the ultimate goal of improving efficiency and unlocking new possibilities of growth.
Applications of AI in Business
Some of the top applications of AI in Business are −
Hiring and Recruitment
The job market is highly competitive, with hundreds of applications for a single role. This would be quite challenging for the HR team of the company to go through each resume in order to shortlist. Hence, companies use AI and natural language processing to filter through the resumes and shortlist the candidates based on a few attributes like skills, locations, education, etc.
Cybersecurity
While the internet has made storage and management very convenient, there is a possibility of data breaches and leakage. Every company should integrate security online to protect all the important databases, such as financial data, strategies, private information, and user information. With the help of AI, experts can understand and remove unwanted noise and data that helps track abnormal activities and prepare for any cyber attack.
Market Prediction
AI algorithms like Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are used to learn and predict patterns from the data. This analysis helps in predicting financial markets and providing efficient outcomes for companies.
Customer Analysis
Every business runs with customers, so it is extremely important for companies to analyze their customer base and strategize to improve customer engagement. AI helps companies to analyze customer feedback, preferences and past behavior which was manually not possible. These insights help to develop strategies to enhance sales and customer experience.
Billing and Invoice
Every business has its own financial responsibilities. For example, companies might often have bills, payment checks, and invoices exchanged with others. These accounting and financial processes can be stressful if done manually. Additionally, there is a chance of errors and calculation mistakes. Automating the process through AI has simplified and improved financial management.
Evaluating Proposal
Proposals are often exchanged in businesses, and if not checked and analyzed properly, they might lead the company towards losses and wrong clients. AI can easily analyze any given proposal using machine learning models. The company can automatically gain insights by tracking the past source of the proposal to predict the future outcome.
Virtual Assistants
Each company offers a unique range of services that must be described to the public in order to grow their customer engagement and boost sales. By using AI, companies integrate virtual assistants and chatbots into their websites to offer round-the-clock services to address users’ inquiries.
Targeted Marketing
Targeted marketing is a strategy in online advertising done with the help of NLP and AI that shows particular ads to specific set of audiences based on their past behavior, preferences and interests.
Social Media Insights
Social Media is one of the strongest platforms for brands to promote their business. If a company uses its social media platform effectively, it can attract a large number of customers. Due to the large number of users, businesses cannot collect and analyze feedback manually. Using AI allows brands to understand their market position and gain insights from their customer base.
Challenges of Adopting AI in Businesses
Some of the challenges that businesses face with the incorporation of AI are −
Data Privacy − The company has to adhere to strict protocols to safeguard sensitive data and prevent breaches and data misuse.
Integrating with Legacy Systems − Integrating advanced AI technologies with the existing legacy systems could be quite difficult.
High Costs − Adoption and incorporation of AI could be expensive, as it includes hardware, software, and the skills needed to design.
Artificial Intelligence (AI) plays a crucial role in business decision-making especially when data is processed by an AI platform. This aids in quantifying data, enabling precise decisions and accurate forecasts without human intervention. AI is capable of managing anomaly detection, processing data, conducting intricate analysis, making optimized decisions, and identifying trends.
Benefits of AI in Decision Making
Advantages of incorporating AI in decision making are −
Enhanced decision-making process in businesses by analyzing large datasets.
Boosts sales and marketing campaigns by using AI algorithms like natural language processing allowing businesses to understand how customers interact with different brands, tones, and words that are the most preferred.
AI tools and algorithms with machine learning and chatbots offer a better understanding of customer satisfaction and expectations.
Improves accuracy and efficiency of decision-making in companies dealing with complex datasets.
Applications of AI in Decision Making
Following is the list of practical examples of AI in decision making −
1. Data-driven Insights
AI analyzes extensive data to reveal concealed patterns, facilitating decision-making based on data. In the field of finance, it examines market trends, forecasts results, and reduces risks, providing timely investment advice. Constant learning helps in adapting to the dynamic changes in the market and improving decision precision.
2. Automated Decision Support Systems
AI-based decision support systems such as IBM Watson examine patient data to help doctors diagnose the disease. AI uses medical knowledge to compare symptoms, offering potential diagnoses and treatment options to speed up diagnosis and treatment planning, enhance healthcare outcomes, and decrease diagnostic mistakes.
3. Risk Assessment
AI takes into consideration customer information, such as demographics and past data, to construct detailed risk profiles. It will identify patterns or trends that might go undiscovered by a human, providing a more accurate estimation of the risks. This way of using data helps companies improve financial stability and manage risk through informed decision-making.
4. Complex Problem-Solving
AI has significant potential in decision-making, especially for intricate issues. The level of effectiveness relies on the quality of data and a methodical approach. For instance, introducing a new product in a market that is not well-known necessitates careful prioritization, optimization, forecasting, and experimentation, all of which AI can offer valuable insights on and enhance decision outcomes.
Risks of AI in Decision Making
Some of the risks related to using AI in business decisions are −
Bias − AI algorithms when trained on partial data leads to unfair outcomes. To overcome the risk of biased decisions, businesses should ensure that the data is diverse and relevant.
Understanding algorithms − Analyzing and understanding how AI algorithms make decisions is quite challenging. Hence, it is difficult to identify potential issues.
Over reliance − There is a risk of over trusting the algorithm for decision making with reduced involvement of human judgment.
Cost − Installing and maintaining AI systems can be expensive and not affordable to all businesses.
Artificial Intelligence (AI) in healthcare refers to the application of machine learning algorithms, NLP, and deep learning technologies to enhance health services, making it easier for both patients and doctors. The data processing capabilities and predictive capabilities of AI enable health professionals to manage their resources and encourage them to take a better approach to various aspects of healthcare.
Examples of AI in Healthcare
AI is used in many areas of healthcare; including finding new links between genetic codes, powering surgery-assisting robots, automating administrative tasks, personalizing treatment options, and many more like −
AI in Medical Diagnosis
AI helps in improving the diagnostic process since incomplete medical histories and large cases lead to human errors. Considering these reasons, AI predicts and diagnoses disease at a faster rate than medical professionals.
AI in Drug Discovery
AI helps cut down on development expenses and human hours in the drug development industry. Putting each drug under clinicians is expensive and requires human resources and time. Due to breakthroughs in technology, AI has sped up the process of drug design, predicting side effects and identifying ideal candidates for clinical trials.
AI in Patient Treatment
AI can be used to support digital communications, offering scheduled remainders, and curated health tips and suggestions. This ability of AI in healthcare improves the speed and accuracy of patient recovery, especially due to the faster response and personalized care. Integrating AI in personalized treatment will also help enhance patients’ experiences.
AI in Healthcare Data Management
Highly valuable information can sometimes be lost, especially in the growing amount of data each day. Additionally, not being able to gain enough insights from the data would slow down drug development, preventative medication, and proper diagnosis. In such cases, AI breaks down the data and connects the information within minutes that usually takes years to process. This reduces the time and costs of healthcare administrative processes, contributing to more efficient and easier operations.
AI in Robotic Surgery
Hospitals use AI and robots to help perform everything from minimal procedures to open heart surgery. Surgeons can control a robot’s mechanical arms while seated in front of a computer console. With robot-assisted surgeries, fewer errors and complications arise, and the patient recovers much faster.
Benefits of AI in Healthcare
The introduction of AI in healthcare enables several benefits and transforms the medical service delivery process. Some of the key benefits include −
Enhanced diagnostics
Personalized treatment plans
Improved patient care
Operational efficiency
Drug discovery and development
Predictive analytics
Robotic surgeries
Challenges of AI in Healthcare
The introduction of AI in healthcare also comes with challenges that have to be addressed. Some of the key challenges include −
Patient Safety − AI systems are often prone to making errors, leading to incorrect diagnoses or treatment recommendations.
Quality and Quantity of Data − High quality, relatable, diverse, and large amounts of data are crucial for AI systems to function effectively.
Implementation costs − The initial costs of implementing AI technologies can be high, which include hardware, software, and training health professionals.
Safety and Ethical Concerns − It is quite challenging to ensure that AI systems follow healthcare regulations and ethical standards. Issues such as maintaining the privacy of the patient, data security, and informed consent need careful consideration.
Technical Complexity and Skill Gap − Developing, deploying, and maintaining AI systems require technical skills. This bridge can gap with trained individuals with skills and technical knowledge.
Integration of AI systems − Integrating AI systems into the existing healthcare infrastructure is complex and requires time. Often this process involves changes in the workflow and operations.
The manufacturing industry is experiencing a prominent change due to the incorporation of AI technologies to boost productivity, enhance efficiency, and lower expenses.
How is AI Used in Manufacturing?
The main AI applications in manufacturing include −
Supply Chain Management
Supply chain management is the process of managing the flow of goods, data, and finances related to a product or company. It plays a crucial role in the manufacturing industry, which is enhanced by incorporating artificial intelligence. AI in supply chain enables using predictive analytics and demand forecasting to improve the efficiency and cost-effectiveness.
Cobots
Cobots are collaborative robots that collaborate with human operators, especially for tasks like picking and packing. They are developed using machine learning to accelerate order fulfillment, streamline logistics, and improve operations.
Predictive Maintenance
Manufacturing industries use advanced predictive analytics and machine learning algorithms to enable companies to proactively monitor and predict equipment failures, minimize down time and optimize maintenance schedules.
New Product Development
The integration of AI in manufacturing industry has transformed and brought about innovative approaches and streamlined processes that enable companies to create and introduce new products to the market. Manufacturers gain insights from market trends, customer preferences, and competitor analysis by using machine learning algorithms which allows then to make data driven decisions and design products that align with market preferences.
Performance Optimization and Quality Assurance
AI algorithms enable manufacturers to analyze historical data, real-time sensor data, and other relevant variables to identify patterns, detect anomalies, and make data-driven predictions. This optimization helps minimize downtime and maximize equipment effectiveness in operations. Furthermore, computer vision algorithms can be used by manufacturers to analyze images or videos of machinery in order to detect defects, anomalies, and deviations from quality standards.
Benefits of AI in Manufacturing
Some of the benefits of adopting AI in manufacturing industries are −
Decision Making − AI analyses large amounts of data to identify trends and patterns, which are used for optimizing production tasks, improving product tasks, and making data-driven decisions.
Improved Efficiency and Product Quality − AI-based vision systems can examine products much more precisely and rapidly than a human, making it easier to identify problems with greater efficiency and quality of the final product. AI takes over repetitive tasks to accelerate production with minimum errors and waste.
Challenges of AI in Manufacturing Industries
Some of the challenges and concerns with AI in manufacturing industry are −
Data Quality −AI systems ensure data accessibility and reliability to function effectively. Many manufactures face challenges with integration and analysis of data that is segregated within various departments.
Cost − The installation of AI systems in manufacturing industries involve software, hardware, and expertise who can handle it which can be quite expensive.
Integration with Existing Systems − Integrating the AI systems will be at some point difficult as it depends on current infrastructure. This difficulty is multiplied with the need of compatibility between various machines and systems.
What is AI-driven Personalized Customer Experience?
AI-driven customer experience is the use of artificial intelligence technologies to elevate a brand and enhance customer interaction. These tools replace manual, time-consuming processes and simultaneously offer deep analytics capabilities.
AI-driven customer experience has the ability to analyze unstructured data, such as interpreting customer reviews, social media chatter, and even voice recordings from customer service interactions. On being able to evaluate this data, businesses and brands can identify customer needs, choices, and drawbacks.
How AI can Improve Personalized Customer Experience?
The strength of AI based customer engagement comes from its ability to manage data in several ways like −
Data Collection − AI based tools collect data on customer behavior and choices, segregate and categorize it and store it in a way that makes it useful.
Data Analysis − AI automates many tasks related to data analysis functions which include data processing, anomaly detection, and reporting.
Personalization − AI creates personalized experiences by identifying patterns and relations from data collected based on user behavior. These insights can further be used to recommend products, content, and messaging to the appropriate segments across a variety of different digital experiences.
Ways to use AI in Personalization
AI-based personalization can enhance user experience across domains like retail and e-commerce. Some of the effective ways to use AI for personalization are −
1. Personalized Campaigns
AI can analyze customer data to create personalized content, tailoring messages and recommendations based on individual preferences and behaviors. This helps businesses to increase user engagement.
2. Optimizing Customer Segmentation
AI algorithms can segregate customers more accurately based on their past behavior, interests and location. This allows for more targeted marketing strategies and customer satisfaction.
3. Data-Driven Content Recommendations
AI technology can recommend content, news, videos, or interesting products based on their previous engagement with the application to improve the interaction among users.
4. Segmenting and Targeting with AI Predictive Analytics
Machine learning, which is a subset of AI, is used critically for segmenting and targeting. ML algorithms use data analysis to identify micro-segments (derived from subtle patterns in user behavior) of users. This is what becomes the foundation for hyper targeted messages.
Examples of AI-based Personalization
The following are some examples Artificial Intelligence in Personalization −
AI in E-commerce and Retail
AI in E-commerce uses complex algorithms for recognizing the behavior, past purchase history, and user preference for offering customized product recommendations and pricing strategies. It also facilitates better interaction with the use of chatbots or virtual assistance, personalized marketing campaigns, and so on, and involves the use of advanced algorithms to analyze behavior, past purchases, and preferences of users for customizing product recommendations and devising pricing strategies. AI algorithms are developed for customer segmentation and review analysis to have appropriate marketing and continuous brand enhancement
AI-Driven Content Recommendations
Content recommendation based on AI algorithms can identify user’s interest by evaluating the browsing history, preferences, and behavior. By identifying these points, the application makes the necessary content for developing user engagement in diverse forms, be it the news website or a music-streaming platform, social media platforms, or even dating services. AI allows brands to stay intent with the changing tastes of an individual, focusing on improving customer satisfaction.
AI in Personalized Healthcare Solutions
AI in personalized healthcare solutions looks into the patient’s information to cover his or her generic details, medical history, and lifestyle factors so that treatment and medicines can be tailored for everybody. Personalized medicines improve individual treatment plans that enhance the rate of curing.
AI in Personalized Learning
Personalized learning with AI transforms education by ensuring the content and approach depend on the needs of the learner. It analyses patterns of learning, strength areas, and weaknesses of the learners and provides a learner-specific learning experience. The support for students includes customized tutoring systems, adaptive learning and AI based evaluations to help them learn at their own pace.
Challenges of AI in Personalization
Some of the key challenges that have to be addressed while integrating AI with Personalization are −
Balancing Personalization and Privacy − It is crucial to rightly balance between providing personalized experiences and respecting the privacy of the user.
Quality of Data − The quality of data is important for personalization to be effective. Inaccurate and insufficient data can result in poor personalization and customer dissatisfaction.
Implementation Costs − The cost associated with developing and maintaining AI personalization systems is quite expensive, requiring investment in technology and expertise.
Predictive Analytics is the process of using data analysis to make predictions about future trends and behavior based on past events. In predictive analysis, AI technology is employed in the form of machine learning algorithms and structures that enable the system to gain knowledge from the provided information. Most models are created using previously obtained data in order to look at relationships and other patterns that can help to forecast specific results.
How do you perform Predictive Analytics using AI?
The combination of AI and Predictive Analytics creates a system that predicts future outcomes that are effective and accurate. Following is the process of how this integration works −
Collecting and Preparing Data − Historical datasets should be gathered from various sources that are relevant to your problem statement and combined into a large dataset. The next step would be preparing data, which includes identifying missing values, errors, handling inconsistencies, formatting and structuring.
Model Development and Training − After the preprocessing data, the next step would be building and designing the model. The collected historical data is fed to the developed model to identify patterns and relations. The type of model you choose will have a huge impact on its ability to learn and the final outcome.
Validating and Testing − Once trained, the model has to be tested to check its efficiency and performance. This process involves providing the model with new, unseen data that hasn’t been used in the training process.
Deploying − After validating the model, the next is using the model in a productive environment to make real-time predictions.
Continuous Learning − AI-based Predictive analytic models are dynamic, i.e., they are designed to learn and adapt to new data. Further, the model can be fine-tuned and trained on the new dataset to improve efficiency and accuracy.
Advantages of AI-based Predictive Analytics
AI-based predictive analytics offers multiple benefits which include −
Enhanced Decision-Making − Using AI algorithms, you can identify relations and patterns. This will reduce manual work and enhance strategy planning.
Improved efficiency − AI algorithms can analyze large datasets much faster than manual analysts, leading to increased efficiency and accuracy.
Risk Management − AI predictive analytics can identify risks and issues before hand, which allows you to take measures. This can be especially valuable in weather forecasting, finance, and healthcare.
Ability to Personalize − In sectors like e-commerce and retail, AI based predictive analytics can be used to customize product recommendations by predicting what they might purchase based on the past behavior with the website.
Challenges in AI-based Predictive Analytics
Along with benefits there are quite a few challenges when AI combines with predictive analytics. Some of them include −
Quality and Quantity of Data − The performance and accuracy of AI prediction models depend on the quality and quantity of the data gathered. Only good quality and large datasets can yield accurate outcomes.
Interpreting Model − Some AI models and algorithms can be complex, making it difficult to understand the logic behind their decision.
Ethical considerations and Privacy − There have to be some regulations and ethical standards especially while working with sensitive data to maintain trust.
Integrating Model − Integration of AI into existing systems is quite challenging, especially for organizations with developed IT infrastructure.
An Expert System is a computer program that mimics human decision-making in a specific area. Similar to a human, it resolves problems, provides suggestions, and makes decisions based on a knowledge base and rules of logic.
Expert systems analyze information and make decisions based on pre-established rules and logic they do not think like humans.
They are often utilized to assist professionals in resolving complex issues in manufacturing, finance, law, and medicine.
NASA uses expert systems on space missions to assist astronauts and control autonomous spacecraft. During the Deep Space 1 mission, they employed an expert system called Remote Agent. This system had an impact on diagnosing system problems making real-time decisions, and adjusting navigation without human involvement. This AI-based system reduced the need for constant human oversight by allowing the spacecraft to operate on its own in deep space.
Benefits of Expert Systems
Following are the advantages of Expert Systems −
Availability: They are easily available due to mass production of software.
Making Decisions Consistently: Expert systems remove human inconsistencies caused by emotions or fatigue by arriving at consistent, objective decisions.
Less Production Cost: Production cost is reasonable. This makes them affordable.
Knowledge Preservation− These methods make sure that crucial information is not lost as a result of employee retirements or turnover by keeping specialized knowledge.
Speed: They offer great speed. They reduce the amount of work an individual puts in.
Scalability: Once developed, expert systems can be easily expanded and deployed to support multiple users across various locations simultaneously.
Less Error Rate: Error rate is low as compared to human errors.
Education: They shorten the learning curve in specialized areas by offering high-quality coaching to newcomers.
Reducing Risk: They can work in the environment dangerous to humans.
Steady response: They work steadily without getting motional, tensed or fatigued.
Expert Systems Limitations
No technology can offer easy and complete solution. Large systems are costly, require significant development time, and computer resources. ESs have their limitations which include −
Lack of Common Sense: Expert systems lack human intuition and general knowledge they just follow pre-established rules and facts which are stored in knowledge base.
Incapacity to Learn on Their Own: In contrast to humans who learn from experience, they cannot develop or change unless specifically revised.
High Development Cost and Time: Developing an expert system requires a lot of programming, knowledge acquisition, and maintenance.
Limited to Specific Areas: They work well in certain areas and work badly when applied to more general or unrelated problems.
Relying on engineers: with expertise is essential for updating or enhancing an expert system, which can make implementing changes quite difficult.
Challenges with Missing or Uncertain Data: Expert systems often struggle to make conclusions when they encounter missing or ambiguous information.
Lack of emotional intelligence: limits their use in some situations because they are unable to understand empathy, human feelings, or human interaction such as in customer support. For example, expert systems may struggle to handle sensitive situations in customer service where a human touch, empathy, and comprehension are needed.
Expert systems are artificial intelligence (AI)-based computer programs created to simulate human decision-making in particular domains. They provide responses based on specific information, rational principles, and reasoning, which lowers human error and boosts output. Many industries, including finance, healthcare, and manufacturing, rely heavily on expert systems.
Following are few applications of Expert Systems in artificial intelligence −
Expert System in Medical Domain
Expert systems utilize test results, medical history, and symptoms to assist healthcare professionals in diagnosing illnesses. They are especially beneficial in complex cases and can provide treatment suggestions.
One of the first expert systems created to identify bacterial infections and recommend antibiotics was MYCIN. Nowadays, advanced systems such as IBM Watson Health analyze data from cancer patients to offer tailored treatment options.
Financial Analysis
Banks and other financial institutions apply expert systems to credit risk assessment, fraud detection, and investment advice. The systems analyze huge quantities of financial information in order to help make decisions.
For example, banks use FICO scores to determine the credit history and loan eligibility of a borrower. Systems for preventing fraud at Visa and PayPal look for trends in transactions in order to detect and prevent suspicious behavior.
Manufacturing and Process Control
By monitoring equipment, predicting failures, and enhancing production efficiency, expert systems significantly boost industrial performance. They support resource management and assist in making quality control decisions.
For instance, Digital Equipment Corporation benefited from XCON (Expert Configurer) in effectively configuring computer systems. To avoid failures, modern AI-driven expert systems identify mechanical faults in various industries.
Systems for Legal Advice
These systems offer contract analysis, legal guidance, and compliance verification. By examining extensive legal datasets, they support lawyers in conducting case research and ensuring regulatory compilance.
For instance, ROSS Intelligence utilized AI to perform case research and suggest arguments. Businesses can evaluate contracts for potential risks and compliance issues through automated contract analysis software.
Customer Support and Help
AI-driven expert systems at workstations address customer inquiries, resolve technical problems, and deliver automated assistance. They alleviate the workload on human agents and enhance response times.
For example, chatbots employed by companies like Google and Amazon provide rapid answers to customer questions. Telecom firms use virtual assistants to identify network problems and offer solutions.
Cybersecurity and Fraud Detection
Through analysis of trends and suspicious activity, anti-fraud and cybersecurity expert systems are critical in identifying security threats and preventing fraud. They significantly enhance cybersecurity efforts through real-time risk identification.
For instance,IBM QRadar closely monitors network traffic to identify potential threats. Similarly, fraud prevention systems at banks monitor transactions to prevent illegal behavior.
Agriculture domain
AI-powered expert systems assist farmers by giving them information on how to control pests, keep soil healthy, and optimize agricultural production. This approach maximizes output and minimizes losses.
A great example of this is the Decision Support System for Agrotechnology Transfer (DSSAT), which assists farmers in selecting the optimal crops based on weather and soil conditions. AI-based analysis is also employed by smart irrigation systems to optimize water usage.
Education and Training
Expert systems assist in tutoring, grade automatically, and provide customized learning suggestions. Depending on their performance, they give students individualized learning paths.
For example, Coursera and Khan Academy employ AI to suggest courses based on student performance. Automated tutoring software assists students by providing practice problems and explanations.
The following table shows where ES can be applied.
Application
Description
Design Domain
Camera lens design, automobile design.
Medical Domain
Diagnosis Systems to deduce cause of disease from observed data, conduction medical operations on humans.
Monitoring Systems
Comparing data continuously with observed system or with prescribed behavior such as leakage monitoring in long petroleum pipeline.
Process Control Systems
Controlling a physical process based on monitoring.
Knowledge Domain
Finding out faults in vehicles, computers.
Finance/Commerce
Detection of possible fraud, suspicious transactions, stock market trading, Airline scheduling, cargo scheduling.
Expert systems (ES) are one of the prominent research domains of AI. It is introduced by the researchers at Stanford University, Computer Science Department.
What are Expert Systems?
The expert systems are the computer applications developed to solve complex problems in a particular domain, at the level of extra-ordinary human intelligence and expertise.
Characteristics of Expert Systems
High performance
Understandable
Reliable
Highly responsive
Capabilities of Expert Systems
The expert systems are capable of −
Advising
Instructing and assisting human in decision making
Demonstrating
Deriving a solution
Diagnosing
Explaining
Interpreting input
Predicting results
Justifying the conclusion
Suggesting alternative options to a problem
They are incapable of −
Substituting human decision makers
Possessing human capabilities
Producing accurate output for inadequate knowledge base
Refining their own knowledge
Components of Expert Systems
The components of ES include −
Knowledge Base
Inference Engine
User Interface
Let us see them one by one briefly −
Knowledge Base
It contains domain-specific and high-quality knowledge.
Knowledge is required to exhibit intelligence. The success of any ES majorly depends upon the collection of highly accurate and precise knowledge.
What is Knowledge?
The data is collection of facts. The information is organized as data and facts about the task domain. Data, information, and past experience combined together are termed as knowledge.
Components of Knowledge Base
The knowledge base of an ES is a store of both, factual and heuristic knowledge.
Factual Knowledge: It is the information widely accepted by the Knowledge Engineers and scholars in the task domain.
Heuristic Knowledge: It is about practice, accurate judgment, ones ability of evaluation, and guessing.
Knowledge representation
It is the method used to organize and formalize the knowledge in the knowledge base. It is in the form of IF-THEN-ELSE rules.
Knowledge Acquisition
The success of any expert system majorly depends on the quality, completeness, and accuracy of the information stored in the knowledge base.
The knowledge base is formed by readings from various experts, scholars, and the Knowledge Engineers. The knowledge engineer is a person with the qualities of empathy, quick learning, and case analyzing skills.
He acquires information from subject expert by recording, interviewing, and observing him at work, etc. He then categorizes and organizes the information in a meaningful way, in the form of IF-THEN-ELSE rules, to be used by interference machine. The knowledge engineer also monitors the development of the ES.
Inference Engine
Use of efficient procedures and rules by the Inference Engine is essential in deducting a correct, flawless solution.
In case of knowledge-based ES, the Inference Engine acquires and manipulates the knowledge from the knowledge base to arrive at a particular solution.
In case of rule based ES, it −
Applies rules repeatedly to the facts, which are obtained from earlier rule application.
Adds new knowledge into the knowledge base if required.
Resolves rules conflict when multiple rules are applicable to a particular case.
To recommend a solution, the Inference Engine uses the following strategies −
Forward Chaining
Backward Chaining
Forward Chaining
It is a strategy of an expert system to answer the question, What can happen next?
Here, the Inference Engine follows the chain of conditions and derivations and finally deduces the outcome. It considers all the facts and rules, and sorts them before concluding to a solution.
This strategy is followed for working on conclusion, result, or effect. For example, prediction of share market status as an effect of changes in interest rates.
Backward Chaining
With this strategy, an expert system finds out the answer to the question, Why this happened?
On the basis of what has already happened, the Inference Engine tries to find out which conditions could have happened in the past for this result. This strategy is followed for finding out cause or reason. For example, diagnosis of blood cancer in humans.
User Interface
User interface provides interaction between user of the ES and the ES itself. It is generally Natural Language Processing so as to be used by the user who is well-versed in the task domain. The user of the ES need not be necessarily an expert in Artificial Intelligence.
It explains how the ES has arrived at a particular recommendation. The explanation may appear in the following forms −
Natural language displayed on screen.
Verbal narrations in natural language.
Listing of rule numbers displayed on the screen.
The user interface makes it easy to trace the credibility of the deductions.
Requirements of Efficient ES User Interface
It should help users to accomplish their goals in shortest possible way.
It should be designed to work for users existing or desired work practices.
Its technology should be adaptable to users requirements; not the other way round.
It should make efficient use of user input.
Expert Systems Limitations
No technology can offer easy and complete solution. Large systems are costly, require significant development time, and computer resources. ESs have their limitations which include −
Limitations of the technology
Difficult knowledge acquisition
ES are difficult to maintain
High development costs
Applications of Expert System
The following table shows where ES can be applied.
Application
Description
Design Domain
Camera lens design, automobile design.
Medical Domain
Diagnosis Systems to deduce cause of disease from observed data, conduction medical operations on humans.
Monitoring Systems
Comparing data continuously with observed system or with prescribed behavior such as leakage monitoring in long petroleum pipeline.
Process Control Systems
Controlling a physical process based on monitoring.
Knowledge Domain
Finding out faults in vehicles, computers.
Finance/Commerce
Detection of possible fraud, suspicious transactions, stock market trading, Airline scheduling, cargo scheduling.
Expert System Technology
There are several levels of ES technologies available. Expert systems technologies include −
Expert System Development Environment
The ES development environment includes hardware and tools. They are −
Workstations, minicomputers, mainframes.
High level Symbolic Programming Languages such as LISt Programming (LISP) and PROgrammation en LOGique (PROLOG).
Large databases.
Tools
They reduce the effort and cost involved in developing an expert system to large extent.
Powerful editors and debugging tools with multi-windows.
They provide rapid prototyping
Have Inbuilt definitions of model, knowledge representation, and inference design.
Shells
A shell is nothing but an expert system without knowledge base. A shell provides the developers with knowledge acquisition, inference engine, user interface, and explanation facility. For example, few shells are given below −
Java Expert System Shell (JESS) that provides fully developed Java API for creating an expert system.
Vidwan, a shell developed at the National Centre for Software Technology, Mumbai in 1993. It enables knowledge encoding in the form of IF-THEN rules.
Development of Expert Systems: General Steps
The process of ES development is iterative. Steps in developing the ES include −
Identify Problem Domain
The problem must be suitable for an expert system to solve it.
Find the experts in task domain for the ES project.
Establish cost-effectiveness of the system.
Design the System
Identify the ES Technology
Know and establish the degree of integration with the other systems and databases.
Realize how the concepts can represent the domain knowledge best.
Develop the Prototype
From Knowledge Base: The knowledge engineer works to −
Acquire domain knowledge from the expert.
Represent it in the form of If-THEN-ELSE rules.
Test and Refine the Prototype
The knowledge engineer uses sample cases to test the prototype for any deficiencies in performance.
End users test the prototypes of the ES.
Develop and Complete the ES
Test and ensure the interaction of the ES with all elements of its environment, including end users, databases, and other information systems.
Document the ES project well.
Train the user to use ES.
Maintain the System
Keep the knowledge base up-to-date by regular review and update.
Cater for new interfaces with other information systems, as those systems evolve.
Benefits of Expert Systems
Availability: They are easily available due to mass production of software.
Less Production Cost: Production cost is reasonable. This makes them affordable.
Speed: They offer great speed. They reduce the amount of work an individual puts in.
Less Error Rate: Error rate is low as compared to human errors.
Reducing Risk: They can work in the environment dangerous to humans.
Steady response: They work steadily without getting motional, tensed or fatigued.
Forward chaining is a data-driven inference method that begins with established facts and applies rules to derive new information until a specific goal is reached. This approach is widely used in expert systems, recommendation engines, and automated decision-making processes.
Example
A fire alarm system is designed to detect both smoke and heat. The principle “If smoke and heat are detected, sound the alarm” is activated when both conditions are met, prompting the system to sound the alarm.
Backward Chaining
Backward chaining, on the other hand, is a goal-oriented reasoning technique that starts with a hypothesis and works backward to verify supporting evidence. It is commonly utilized in AI-driven diagnostic systems, legal reasoning, and troubleshooting applications.
Example
When a doctor is diagnosing a fever, they may begin with the statement, “The patient has the flu.” To verify this diagnosis, the doctor looks for additional symptoms like fever, body aches, and fatigue.
Differences between Forward Chaining and Backward Chaining
The following table highlights the major differences between Forward Chaining and Backward Chaining −
Forward Chaining
Backward Chaining
Forward Chaining starts from known facts and applies rules to derive new facts until the goal is reached.
Backward Chaining starts from the goal and works backward to find supporting facts.
Starts with known facts and observations.
Starts with a hypothesis or goal.
Searches through large amount of unnecessary data.
Searches only relevant parts of the knowledge base.
Moves from facts to conclusions (goal).
Moves from goals to facts
Data-Driven Reasoning − Processes all possible facts until a goal (or multiple goals) is reached.
Goal-Driven Reasoning − Checks only relevant rules needed to prove the goal.
Can be inefficient if the number of rules is large because it may derive unnecessary facts.
More efficient if the goal is known in advance, as it only searches relevant rules.
Used when exploring all possible outcomes from given facts.
Used when there is a specific goal to prove.
More complex due to the need to process all facts.
Less complex when there are fewer goals.
An approach known as “breadth-first search” is used in forward chaining reasoning.
A depth-first search methodology is used in backward chaining reasoning.
For example, an AI assistant processing a user’s query and applying all related knowledge to provide multiple suggestions.
For example, a doctor starts by suspecting a disease (goal) and asks about symptoms to confirm it.
Best suited for applications where new knowledge must be discovered (e.g., recommendation systems).
Best suited for applications where a decision needs justification (e.g., expert systems).
