Mobarak Inuwa — Published On July 6, 2022 and Last Modified On July 6th, 2022
Intermediate Machine Learning Use Cases
This article was published as a part of the Data Science Blogathon.

Introduction

When you think of “robotics,” the first thing that comes to mind is cutting-edge modern technology. It is also frequently imagined as a mechanical system that can perform amazing feats and takes the form of a person, thanks to science fiction films. All of this is true, yet a robot may just be a software system operating on a desktop computer rather than a real machine.

Robotics
Clockwork photo created by kjpargeter

As you may be aware, developing a robot may easily involve a wide range of scientific disciplines, making it an extremely complicated system. We will just focus on one of these numerous fields: Machine learning. Both machine learning and robotics first gained popularity in the 1950s. Since then, these technologies have evolved into larger and more sophisticated disciplines.

Automation and Robotics

One thing is synonymous with the two: automation. The term “automation” refers to the process of significantly reducing human interaction and involvement in tasks. Machine learning models are created to learn from datasets and then automate future operations without the need for human intervention. Robots, on the other hand, are taught human behaviors or built to mimic actions in order to do tasks without the need for human participation.

Robots, as previously stated, can be either physical machines or software systems. They must keep the characteristics of automation in both circumstances. Siri, for example, is a software system that operates on a device, as a software application. However, because it is classified as a robot, it is capable of doing tasks when instructed to do so without the need for human intervention.

Case Study: Real Estate House Price Prediction Project

If you create a model to predict the price of a property and then deploy it as a web application, users can enter the details of a house and then click on a predict button to see a predicted price. Will you refer to it as a robot? You could be experiencing doubts, but hang in there. What if a large real estate business decided to take the model established for forecasting houses and incorporate it into a humanoid robot, allowing it to simply speak the price aloud when people give it the qualities of a house? Machine learning is used in both applications to accomplish the same goal. One is working on automation in a web app, while the other is working on software within a human-like computer (humanoid robot).

Robotic concepts aren’t restricted to a single form. It is diversified, just like many other areas of computer science. It can be in the form of software programs/software robots, or it can take any physical form. Dogs, birds, humans, and other animals in boxes, balls or any shape form are just a few examples. It is acceptable to name a system robot if it can carry out tasks on its own without being directed on what to do in changing settings.

Robotics
Business plan photo created by rawpixel.com

A robot could simply be a deployed machine learning algorithm from the standpoint of machine learning. It is deployed to stand alone in a mechanically programmed physical machine, rather than being embedded in a PC operating system as software or an app, or hosted online as a web application with flask. Machine learning systems can be thought of as robots.

Except in some cases, they can be deployed into dedicated standalone systems to perform. This is based on the reality that a standalone robot may contain additional intellect. For example, the real estate company’s robot could be enhanced in terms of functionality and capability.

Using the example of predicting the price of a house, let’s say apart from predicting the price of a house, the robot can also be made to measure the land mass of a plot. And maybe even on its own, physically detect and read the features of a house and say the price. To add the ability to get the details of a house physically onsite and then use the value to predict the house price, will add more work to our Machine learning model predictive model.The robot may need some form or ability to see a physical environment in real-time where it can see the house and analyze what it sees. Thereby getting the details of the house directly onsite. This idea can be done with computer vision, which is also a broad area in computer science.There are algorithms in machine learning that also tackles this type of problem. A way to implement it may be to make the robot take pictures and record the pictures and use them to compare with the houses sold previously and train the model with it. This will make the robot use, maybe, a camera to take pictures and compare it with the previous houses or dataset before predicting the house price.
The robot may also need the ability to move, manoeuvre obstacles, etc. All this may also be implemented with machine learning. Or there may also be more optimized means to implement this idea in the robot outside machine learning. This is just an attempt to show the concepts of robots and machine learning working together. When we bring in the concepts of reinforcement machine learning it becomes even more realistic and articulated to implement this case.

Does it mean Robotics and Machine learning are the same?

The answer is a big No. In this light, let’s start with robots. They are some robots that are developed such that they can perform tasks with automation, on their own yet without any learning or intelligence! A car manufacturing company may develop a robot that just performs fixed repetitive tasks on its own without any learning! It does not stop it from being a robot but it does not use Machine learning. Another thing could be that the robot could have a means of taking all these variables during the repetitive tasks and making knowledge out of it in the future, thereby making it still become learned latter like a human. But if it doesn’t exhibit any form of learning, we can not call it intelligent or smart even though it is automated. This creates a good explanation for intelligence and automation. Hence, repeating tasks on its own makes it automated, while performing tasks according to the changing and unspecified scenarios make it intelligent. There is a feeling of freedom here. Automation is kind of hardwired while intelligence is free and dynamic.

This explanation shows that Machine learning has both automation and learning embedded in it while robots may lack the later. This may be why Machine learning has kept trending and gaining huge interest and investment than robotics. Hence we can call almost all Machine learning applications robots but not the other way round.

Conclusion

Finally, let’s define a robot as a machine that executes predetermined duties without the assistance of a person.
To balance everything, we can just use the term “machine learning robots” to smooth things out. On these grounds, watch out for a future post that delves more into how Artificial Intelligence fits into the picture of these technologies (automation and robotics), as well as Machine Learning and Robotics.

To summarize, we can say;

  • A robot is an extraordinarily complex system that can readily involve a wide range of scientific disciplines.
  • Automation is the process of considerably lowering human engagement and involvement in tasks.
  • Robots are trained to mimic human behavior or are designed to do so in order to complete tasks without the involvement of humans.
  • From the perspective of machine learning, a robot could be a deployed machine learning algorithm.
  • There are certain robots that have been created to the point where they can automate tasks and do them on their own, but they lack intelligence or learning.

References:

Clockwork photo created by kjpargeter
link: https://www.freepik.com/free-photo/3d-render-robots-with-transmission-wheels_1111364.htm#query=Robotics&position=41&from_view=search
Business plan photo created by rawpixel.com
link: https://www.freepik.com/free-photo/automation-production-system-operation-precess concept_18422664.htm#query=automation&position=10&from_view=keyword

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