What is the difference between Artificial Intelligence, Machine Learning and Deep Learning approaches?

Artificial intelligence is part of our daily life in many ways, but this technology remains a futuristic concept in several others.

In recent years, the potential for the practical application of artificial intelligence has exploded thanks to the rapid improvement in the costs of computing power and cloud storage as well as the increase in the production of data from images, text, software transactions and more.

The terms "artificial intelligence", "machine learning" and "deep learning" are often used interchangeably with little or no explanation as to their meaning. Identifying the different types of artificial intelligence, including machine learning and deep learning, will provide a better understanding of how this technology can be implemented.

For starters, artificial intelligence is a subset of computing where computer systems perform anthropomorphic tasks (for example, grading images, speech recognition, or even translating) as well or better than humans. The concepts of logic first appeared with the beginnings of computing in the 1950s.

Artificial intelligence consists of two segments: narrow artificial intelligence and general artificial intelligence. General artificial intelligence is a futuristic concept that considers robots to act and think like humans. Narrow artificial intelligence, refers to any computer system designed to perform specific anthropomorphic tasks.

Artificial intelligence is truly a discipline of creating intelligent algorithms. In its early days, artificial intelligence was just a set of programmed computer instructions. Today, it refers to anything between a complex set of logics and a self-taught algorithm that generates results based on benchmark examples and that requires minimal human programming.

Machine learning was developed as a subset of artificial intelligence and is considered a technique for achieving artificial intelligence. Machine learning refers to the use of algorithms to enable computer systems to acquire the ability to learn from data and make decisions.

Over the years, decision trees, cluster analysis, reinforcement learning and Bayesian networks, among many others, have enriched decision-making algorithms.

Deep learning algorithms are a subset of artificial intelligence that has recently gained prominence thanks to continuous advancements in technology. Deep learning benefits from advances in machine learning, but still has a few key differences.

Instead of relying on humans to program tasks using computer algorithms, deep learning achieves results through an example-based approach that mimics human learning. By leveraging neural networks, deep learning-based inspection applications make connections and recognize shapes in gigantic datasets.

Users can further improve the solution by providing it with more data from which the tool can learn. The more data the deep learning application has, the more efficient it becomes in finding anomalies.

The future of AI, ML and DL

As artificial intelligence continues to be used as a buzzword for marketing by many companies, it is important to understand what it is and what it can and cannot do, especially in a busy environment.

In the future, whether the approach used to meet the inspection needs is based on examples or rules, or even a combination of the two, will not matter. For now, however, each approach has its own strengths and weaknesses, and should be used accordingly.