Problem-solving steps in AI
Introduction
Since the beginning of human existence, Humans have faced different problems in different aspects of life and trying to solve their problems in the best way. To find the best solution to solve their problems and lead a happy and comfortable life without facing problems they use different techniques to solve their problems.
The world has become too advanced and technology has become too grown. In this blog, we will study an advanced technology called artificial intelligence (AI). How artificial intelligence solves our every problem. What are the Problem-solving steps in AI? Why should we use artificial intelligence to find solutions to our problems? We will discuss all topics in detail in this blog
What are advanced Problem-Solving Steps in AI?
To solve any problem we use artificial intelligence. To find the best solutions for a problem artificial intelligence work step by step. So we will study each step in detail which AI is used to find solutions to problems.
1) Understanding the Problem
The first Problem-solving step in AI is to understand the problem. For solving any problems in any field AI first has to understand the problem. For understanding any problem artificial intelligence uses different methods to fully understand that problem. Clearly and concisely to take full command of that problem to find the best solutions for that problem.
2) Data Collection and Preprocessing
It is the second step of problem-solving in which AI first collects data about a particular problem from different sources. There are three sources of data collection.
- The primary source is firsthand information from sources like interviews.
- The secondary source is collecting data from already collected information for different purposes.
- A tertiary source is the collection of data from summaries, reports, etc.
After collecting data from these entire sources AI checks the quality and accuracy of data. AI ensures that the data is free from errors and outliers. Artificial intelligence removes the unwanted and false information from the data. They remove the false data so that not affect our result.
3) Data Preprocessing (Problem-solving steps in AI)
After collecting the data from different sources the AI starts processing the data in the following steps
- AI then arranges the data in a specific pattern.
- Remove errors from the data.
- Give specific structures to data and add headings and subheadings.
- Reduces the irrelevant data.
- Categories the data based on information.
- Add the relevant and missing data.
The main purpose behind this process is to get the required information easily.
4) Feature Selection and Engineering
AI understands each feature by itself and then compares it with the targeted data. then it selects those features that are more helpful in solving the problem. AI uses smart techniques to figure out the most relevant feature and remove unwanted data. What we should do? AI should adjust the data so that the computer can easily understand it. It should categorize the data for easy comprehension by computer. AI should create new things based on the already known knowledge about the topic. Mix the new and already known knowledge related to the topic to make a new idea.
5) Model Selection (Problem-solving steps in AI)
The fifth step in problem-solving in AI is the selection of a model to solve the problem. To select a model for the problem, first, we have to determine which category the problem belongs to. There are some basic categories such as classification, regression, etc. We then choose a category for the problem and assess whether this category is suitable or not. Based on the category, we select a model for our problem.
There are two types of models:
Simple models, used for solving straightforward problems, and complex models, used for tackling more intricate issues· Choosing the right model ensures that it can effectively handle the problem, resulting in accurate and concise results·
6) Training the Model (Problem-solving steps in AI)
After selecting the model to solve our problem artificial intelligence provides data to that model to solve our problem. And provide full information and train the model to solve the problem. And instruct that model that how to solve the problem. What to do or what to not do. And which steps the model has to follow to solve that problem. The purpose of training the model is to solve the problem in the way that we want. And get an accurate and exact outcome. The outcome depends on how artificial intelligence has trained the mode. Artificial intelligence trains the model on the base of data that we have provided. Also for data collecting, we can use artificial intelligence to collect accurate data to train our model in the best way.
7) Evaluation (Problem-solving steps in AI)
The next step in AI Problem solving is evaluation. In this AI study about the model how good the model is. This measure is done on the base of the working of the mode. How accurate and better results it provides. AI chick correctness of the model and which feature should we add to this model to get a more accurate and valuable answer. In this step, AI compares the mode with other related more to check its accuracy and make it better.
8) Iterative Improvement (Problem-solving steps in AI)
In this step, AI removes mistakes or errors from the model. Artificial intelligence looks at all categories of the model so that they are presented in a well-meaning form and arranged sequences. We study the whole model and point out the areas where data is not present in clear and concise form. And remove mistakes from that area. AI chick the model with different tools and software to arrange and remove mistakes from the model. So people can easily use it and AI adds different types of data to the model so it can solve complex and large problems. If the model has a great amount of information then it can make better decisions about the problem. Artificial intelligence keeps the model continually updated so it can solve newly generated problems.
Diagram
Table from
| No | Steps | Description |
| 1 | Understanding the Problem | AI comprehends the problem by employing various methods, gaining a clear and concise understanding to find optimal solutions. |
| 2 | Data Collection | | Collects data from primary (interviews), secondary (existing information), and tertiary (summaries, reports) sources for the problem |
| 3 | Data Preprocessing | | Arranges data, removes errors, structures it with headings, reduces irrelevant data, and categorizes information for easy comprehension |
| 4 | Feature Selection and Engineering | AI identifies and selects relevant features, eliminating unwanted data, and creating new insights based on existing knowledge. |
| 5 | Model Selection | Determines problem category (classification, regression), and selects suitable model type (simple or complex) based on the problem. |
| 6 | Training the Model | AI provides data to the selected model, instructs it on problem-solving steps, and trains it for accurate and desired outcomes. |
| 7 | Evaluation | Assesses the model’s performance, checks accuracy, compares with related models, and identifies areas for improvement. |
| 8 | Iterative Improvement | AI iteratively updates the model, corrects mistakes, organizes data, adds relevant information, and ensures adaptability to new problems |
Summary (Problem-solving steps in AI)
In this blog, the focus is on the problem-solving steps in artificial intelligence (AI). The process begins with AI understanding the problem through various methods, aiming for a clear comprehension. The subsequent steps involve data collection from primary, secondary, and tertiary sources, followed by thorough preprocessing to ensure accuracy and eliminate errors. Feature selection and engineering involve identifying and incorporating relevant features while removing unnecessary data. The model selection phase categorizes the problem and selects an appropriate model, whether simple or complex. Training the model involves providing data and instructing it on problem-solving steps. The evaluation assesses the model’s accuracy, comparing it with related models. The iterative improvement stage focuses on updating the model, correcting errors, organizing data, and ensuring adaptability to new problems. . The overall process is depicted in a table, highlighting the key steps in AI problem-solving·
FAQs
A1· Understanding the Problem: AI comprehends the problem by employing various methods to gain a clear and concise understanding, paving the way for optimal solutions·
A2· Data Collection: AI collects data from primary (interviews), secondary (existing information), and tertiary (summaries, reports) sources for the identified problem·
A3· Data Preprocessing: AI arranges, structures, and refines collected data, removing errors, reducing irrelevant information, and categorizing data for easy comprehension·
Feature Selection and Engineering: AI identifies and selects relevant features, eliminating unwanted data, and creating new insights based on existing knowledge to enhance problem-solving.
Evaluation: AI assesses the model’s accuracy, compares it with related models, and identifies areas for improvement based on its working and correctness.