What are the differences between Artificial Learning, Machine Learning and deep learning?

Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now, and often seem to be used interchangeably.

They are not quite the same thing, but the perception that they are can sometimes lead to some confusion. So I thought it would be worth writing a piece to explain the difference.

Both terms crop up very frequently when the topic is Big Data, analytics, and the broader waves of technological change which are sweeping through our world.

In short, the best answer is:

Artificial Intelligence is a system, but it is not a system .AI is implemented in the system. There can be so many definitions of AI, one definition can be “It is the study of how to train the computers so that computers can do things which at present human can do better.”Therefore It is an intelligence where we want to add all the capabilities to a machine that human contains.

Machine Learning is the learning in which machine can learn by its own without being explicitly programmed. It is an application of AI that provides the system with the ability to automatically learn and improve from experience. Here we can generate a program by integrating input and output of that program.

For example, artificial intelligence and machine learning algorithms are widely used in the insurance domain. Insurance companies leverage these technologies as machines have great potential in providing better customer service. People seek individual approach and quicker service delivery with tailored solutions based on their specific needs. Today, chatbots and automatic models are being trained and continuously upgraded to improve customer experience in such spheres of AI/ML application as claims processing, insurance advises, risk management, fraud prevention, and others.

As an example, I can mention the recent solution designed by Altoros — Automatic Car Damage Recognition Model.

Car Parts Identification

• Parts localization and segmentation. The model identifies the main external vehicle parts, such as a hood, bumper, or lamps, and provides a comprehensive auto parts analysis.
• Photo quality control. The algorithms estimate and check the quality of photos in advanсe ensuring an auto-focus on the main car parts eliminating the risk of blurred, over- and underexposed images.
• Photostream processing. The model can process numerous vehicle’s photos at once with subsequent integration of the results.

Damage Understanding

• AI-generated car part damage level and cost estimation. As soon as the required part is defined, our algorithms estimate the extent and cost of the damage.
• Repair/Replace decision support. Based on the experience gathered from service stations working with multiple insurance cases and the overall damage level, the algorithms state whether there is a need for car parts repair.
• Overall car damage estimation. Estimating the damage level of the auto parts detected, our algorithms form a hypothesis about the general state of a vehicle.

you can start learning the basics of A.I and M.L with online courses like

Machine Learning and Artificial Intelligence eBook for Newbies

If you have any questions regarding artificial intelligence development services, please visit the New Machine Learning Project Course for Beginners