From big data to autonomous vehicles, artificial intelligence (AI) has undoubtedly transformed the way many industries operate today. However, despite playing a larger role in our daily lives, not many are still aware of what AI and machine learning (ML) do. This publication aims to break down some of the basic concepts related to AI into easy-to-understand parts. In simplest terms, machine learning (ML) is a subset of the AI.
Its core lies in the idea that computer systems can learn on their own from data obtained by performing previous tasks and past experiences. This means that there is no need to pre-program an artificial intelligence device every time you need it to do a job. This takes ML to a higher level. This subset of AI refers to the ability of a system to take unstructured data from multiple sources, analyze it, and apply it to solve new problems.
Deep learning is also known as “differential programming”. The artificial neural network refers to a system or algorithm used in deep learning that mimics the functioning of the neural circuits of the human brain, for example, when they make sense of things and events. The amount of resources on artificial intelligence (AI) can be overwhelming. If you want to start learning about it, you're likely to come across confusing acronyms and terms, such as ML, NLP, deep learning, or reinforcement learning, that will make you wonder why you started learning AI in the first place.
But don't worry, there are 8 basic concepts and applications you should know in the field of Artificial Intelligence and they are summarized in this post. However, broadly speaking, machine learning algorithms are divided into 3 types: supervised learning, unsupervised learning, and reinforcement learning. Reinforcement learning is a part of artificial intelligence in which the machine learns something in a way similar to the way humans learn. As an example, let's say the machine is a student.
Here, the hypothetical student learns from their own mistakes over time through trial and error. When you use Netflix, do you get recommendations for movies and series based on your previous choices or the genres you like? This is done by Recommender Systems, which provides you with guidance on what to choose next from the extensive options available online. Computer Vision uses artificial intelligence to extract information from images. This information can consist of the detection of objects in the image, the identification of the content of the image to group several images, etc.
An application of artificial vision is the navigation of autonomous vehicles by analyzing images of the environment, such as the AutoNav used in the Spirit and Opportunity explorers that landed on Mars. Artificial intelligence is concerned with creating systems that can learn to emulate human tasks using their previous experience and without any manual intervention. The Internet of Things, on the other hand, is a network of several devices that are connected via the Internet and can collect and exchange data with each other. Now, all of these IoT devices generate a large amount of data that must be collected and extracted for actionable results.
This is where artificial intelligence comes into play. The Internet of Things is used to collect and manage the enormous amount of data required by artificial intelligence algorithms. In turn, these algorithms convert data into useful and actionable results that LoT devices can implement.
