Computational Thinking
Computational thinking is a technique that solves complex problems very easily. Four essential techniques are employed in computational thinking:
- Decomposition
Breaking down a complex problem into smaller and manageable parts
- Abstraction
Ignoring any irrelevant detail and focusing only on the critical information
- Pattern Recognition
Looking for similarities within the smaller problems and essential information
- Algorithms
Developing rules or step-by-step solutions to solve the problems.
Practicing Computational Thinking
With the help of computational thinking, it becomes easy to take a complex problem and then decompose it into a series of small and manageable parts. Afterward, the step of pattern recognition comes. During this step, each of the smaller problems is looked upon individually, and it is determined how the previous issues of this sort were resolved. Then comes the abstraction stage, where all the irrelevant information is ignored, and the entire focus is shifted onto the crucial details. In the last step, i.e., algorithm, simple steps are devised to solve these smaller problems. In the final stage, these simple steps are used for programming a computer to solve a problem efficiently.
The Concept of Computational Thinking
In simple words, the concept of computational thinking is that the person tells the computer how to respond to a particular problem. A person can easily convert a complex problem into an easy-to-understand form via computational thinking.
Example of Computational Thinking
An example of computational thinking can be taken in a scenario where a person is playing a game. In order to win, he might need to consider the following:
- The necessary items he needs to collect, the procedure of collection, and the total estimated time of collection
- Determining the exit and finding the best route that can take him there in the shortest possible time
- Kinds of enemies he will encounter on his way and how will he deal with them
All of these details can help the player develop a strategy with which he can complete the level efficiently.
Employing Computational Thinking in the Above Example
The four essential techniques of computational thinking, i.e., decomposition, abstraction, pattern recognition, and algorithms, can be employed in the above example as follows:
- Decomposition: Breaking down a complex problem into smaller and manageable parts (e.g., where to go and how to complete the level)
- Abstraction: Ignoring any irrelevant detail and focusing only on the important information (e.g., determining the location exit)
- Pattern Recognition: Looking for similarities within the smaller problems and essential information (using the knowledge of similar previous problems)
- Algorithms: Developing rules or step-by-step solutions to solve the identified problems.
Artificial Intelligence and Collective Intelligence
Artificial Intelligence
Artificial Intelligence (AI) is the technology where machines, computers, or software simulate human intelligence. Some noteworthy examples of AI are
- Text-prediction in emails
- Traffic prediction in Google Maps, etc.
Collective Intelligence
Collective Intelligence (CI) refers to the shared intelligence of multiple people. CI is the knowledge that arises when people collaborate in the decision-making process. It is also known as shared or group knowledge. The aim of CI is to use collective human knowledge for better decision-making.
Reinforcing CI with AI
It is best to combine AI and CI together for added benefits. With AI, governments can harness the power of CI as it provides them with tools for managing, analyzing, and translating various contributions made by the citizens. For instance, the algorithms can understand the comments and group them. Afterward, these similar ideas can be placed on a map, and all the highlighted comments can be discussed.
If this automated assistance is absent, civil servants will have to work and manage the input manually. In that case, this whole process will become time-consuming and unreliable overall.
The Role of CI in AI
CI helps keep AI ethical and humane. There can be many faults in the algorithms; therefore, it is necessary to check them regularly for bias and prejudice. Also, CI will help define the relevant angles of research, and it will enable to use of the power of AI only for the public interest.
Alysidia can help!
When it comes to technology, Alysidia is one of the leading solution providers in the field. We provide the best services from professionals who are well-versed in AI, CI, and computational thinking. Our AI, CI, and computational thinking solutions have helped a lot of industries in terms of supply chain control, privacy, compliance, and information sharing. We are also open to providing our services to government agencies. If you belong to an industry or a governmental agency that is interested in our AI or CI solutions, contact us through our website Alysidia.com
