Introduction
Digitalisation is overgrowing day by day. Development and inventions in technologies are on the rise in any industry. It made people’s life much easier and better. But at the same time, it’s been a little tricky for us to learn how new technologies, how it works, which is better for us etc.
This article discusses such a topic about common confusion about Rule-Based AI and Machine Learning. It has been a topic for some time which among these is best for development. Let’s look into it.
People have to figure out existing technologies or development in technologies all from the internet. Likewise, every machine, including computers, should learn how to process its functions or tasks. So how do they learn?
What is Rule-based AI and Machine learning
Rule-based Artificial Intelligence or AI is implemented in a device or system. AI helps computers to learn about how to perform their assigned tasks. Programmers or developers comprise a set of directions and facts in rule-based AI. It works with pre-determined outcomes. It is considered the most accessible form of AI.
Example for rule-based AI
Gaming Applications: Rule-based AI is an essential feature in gaming applications. Game developers decide and install some rules that determine how the game should be played. These rules decide whether the player wins or loses, how much score he gets etc.
Advantages of Rule-Based AI
- Cost-efficient
- High Speed
- Availability
- Accuracy in result
- Steady Response
Disadvantages of Rule-Base AI
- Time-consuming
- Cannot solve complex problems
- Require pre-determined rules
- Lots of manual labour required
- Less learning capability
In Machine Learning, a machine can learn its work on its own. It does not demand any instructions to be given explicitly. A machine can also analyse and improve its performance itself. So how does a machine analyse itself? When we perform some task in a machine, it will measure its performance and increase accordingly. It sets its own rules based on the required data. Models implemented with machine learning require more data.
Example for machine learning
Speech recognition: It is an application of machine learning to convert speech or voice into text. We are very familiar with this feature. In this, software records the audio identifies the language and converts it into text format. It measures the wavelength and frequency of sound for converting it into text—for example, Amazon Alexa.
Advantages of Machine Learning
- Continuous improvement
- Automation for everything
- Pattern and trend identification
- Wide range of applications
- Less manual labour
Disadvantages of Machine Learning
- Data Acquisition
- Inaccurate results
- Algorithm selection can be a tedious task
- Time-consuming
- Lack of knowledge about how the system made its conclusion.
Difference between Machine Learning and Rule-based AI
Both Machine Learning and Rule-based AI are considered important branches of Artificial Intelligence. The primary focus of both methods is decision making and execution of given tasks. But some differences can be noted in the way they work.
- We can use machine learning only if we have a large set of valid data to make accurate conclusions, whereas we use rule-based AI for applications having fewer data and need direct or clear-cut rules.
- Machine Learning Systems work based on probability, whereas Rule-Based AI works as per pre-determined outputs.
- Machine Learning models are easily measurable, while Rule-Based AI is not measurable.
- Machine Learning systems are alterable objects, and Rule-Based AI models are permanent objects while.
- Machine Learning focus on accuracy when Rule-Base AI focus on success rate.
- The goal of Machine Learning is to learn from data and improve performance, and the goal of Rule-Base AI is to replicate human intelligence to resolve complex problems.
When to choose Rule-Based AI
- Pre-determined Outcomes: When we know the possible task results, Rule-Based AI can be used. Consider a submit button in a form. There can be two situations: the button can be pressed, the form can be submitted successfully, or the button is not pressed, and the form fails to submit.
- Accurate data: Situations where you cannot risk the accuracy of data.
- If not, Machine Learning: If you or your sustaining system lack knowledge of machine learning, Rule-Based AI can be utilised.
When to choose Machine Learning
- pre-determine rules do not apply: If the task cannot perform based on some fixed rules, Machine Learning is advised.
- Improving situations quickly: If the condition changes system requires a fast update and does not have time to compose new rules.
- Natural processing: Each person conveys things differently, and it is impossible to set rules for it. In such cases, adaptive intelligence of machine learning can be used.
Conclusion
As we conclude, which Rule-Based AI or Machine Learning is best is still a debatable topic. Choosing the correct model for your need is always important. Both technologies have their positives and drawbacks in various situations. Each technology is efficient in apt situations. Evaluating those against your requirements is a crucial criterion.
Author Info
Aarif Habeeb is the Technical Content writer at DigitalMarketingCrab – A digital marketing company in Jaipur. He is a technical writer and IoT writer of year 2020 by HackerNoon. His expertise includes Technical writing, Technical SEO, e-commerce, lead generation, and localized site-to-store strategies. He is the founder of Aarif Habeeb & Co. Feel Free to Follow him on Twitter and Linkedin.
