Everyone who has used the popular digital assistants like Apple’s Siri and Google’s Alexa has had experience with these systems breaking down if asked something strange or unexpected. Even though this is slowly changing, with these companies using machine learning to make their systems more robust, they are still good examples of an Expert System designed around responses based on pre-programmed combinations of data.
To create an Expert system, first, you need an expert. For example, you could have a Doctor note down every possible combination of symptoms in a large database, and then use a series of IF-THEN statements to diagnose a patient. This system works very well and is very accurate but comes with some core limitations.
1. You need an Expert, and your system is limited by the expert’s knowledge and experience.
2. The expert needs to note down every possible combination and if something gets missed, or is presented with something new, the system cannot adapt.
3. As the complexity of the problem and number of possible combinations increases, it becomes physically impossible to create a database manually, this is often referred to as a Combinatorial explosion.
Machine learning is a programming technique that is quickly becoming popular because it frees us from the above limitations. The idea is to write a relatively simple program that can build its own database of combinations based on certain defined outcomes. The program can then be trained, by evaluating the results, and over time learns and gets better at the task it was designed for. This technique closely matches how the human brain processes information and get better over time through practice. The only difference is that a program can run 24/7 and never gets tired, so the learning process is very fast.
The result is an algorithm that gets better over time, does not need an expert, and can cope with new situations that it has not faced before.
This opens up a whole new set of possibilities for applications that involve processing very large amounts of data that has many complex combinations. If your business does any of the following, machine learning algorithms will be very useful:
1. Data Analysis
2. Scientific Research
3. DNA Classification
4. Computer Vision and Object recognition
5. Medical Diagnosis
6. Financial Market Analysis.
7. Language Processing
9. Targeted Advertising
10. Recommendation systems
11. Sentiment Analysis
12. Search Engines
13. Robot Locomotion
15. Computer Networking
Silstone Group is dedicated to bringing the best of Machine Learning based Applications to our Clients as well as discovering new ways this technology can used.
Triman S. Bhullar