The Basics of Artificial Intelligence, Machine Learning and Deep Learning
Artificial intelligence is now a reality. Companies, products and services are all incorporating AI into many of their processes. It has become a sales pitch, a sign of innovation, and simply a way to show that something is advanced, futuristic and efficient. However, on many occasions, the term is used incorrectly, or is mixed up with other concepts like machine learning or deep learning. These are parts of similar processes, but they aren’t exactly the same. In order to understand these concepts, it’s best to start with a basic artificial intelligence review.
What is artificial intelligence (AI)? Artificial intelligence is an umbrella term that covers different computational processes. AI can be defined as a series of computer algorithms that imitate the cognitive functioning of the human brain, allowing machines to perform intrinsically human actions like learning, understanding their environment and solving problems.
That being said, there are many definitions, typologies and schools of thought that explain what artificial intelligence, machine learning and deep learning really are. This article will go over the basics of each of these concepts, including what they mean, their applications in the real world and what the difference between them is so you can fully understand what the future of AI holds.
Artificial intelligence comes first
The history of artificial intelligence is complex. There are different branches, objectives, philosophies and ethical problems to consider when creating intelligence and dealing with the possible consequences. The original definition from the first books on the subject is that artificial intelligence is a field that studies “intelligent agents,” meaning any device that perceives its environment and takes measures to maximize the possibility of successfully achieving goals. Essentially, how AI works is by emulating human intellect and ability.
From this definition, we can start to analyze AI applications in specific technological areas. One of AI’s key tasks is automating processes in an autonomous and intelligent way that gets optimal results. That is why AI is a crucial part of different software and tech products like self-driving cars, virtual reality games, and retail marketing engines. The applications of artificial intelligence are endless. For example, our Minerva solution uses AI to personalize content on digital signage screens based on various parameters including the demographic profile of the person in front of the screen, sales data and inventory data.
Machine learning is when machines interpret data and learn
Let’s move on to machine learning. Machine learning is a subfield of computation and a branch of artificial intelligence. It is when a computer can execute the human function of learning, meaning it interprets the data it absorbs, detects patterns and creates new algorithms to incorporate into its programming without the need for human intervention. The basis of machine learning is data.
Even though we are currently in the early phases of big data and data processing for machine learning, there are fields that are already applying it with great results. For example, there are algorithms that detect browsing patterns on websites, and ones that make usability improvements or predictive functions for e-commerce (like our Audience Score solution). In the retail sector, for example, companies are already successfully collecting data on a daily basis and seeing a positive impact on sales.
Deep learning, the next step beyond ML, is when machines learn from complex data
The main obstacle facing machine learning is the complexity of problems. Machines can’t detect certain patterns through purely statistical analysis. However, in many cases, if the statistics extracted through data aren’t complete, trustworthy and applicable to real life, the learning will not be useful. That’s where deep learning comes in.
Deep learning is based on neural networks, which are algorithmic models capable of mathematically transforming data using intermediate layers. These “hidden layers” deconstruct data and detect patterns or underlying rules. A neural network is responsible for breaking down, interpreting and putting back together the data. These algorithmic engines vary in function of the type of data and how it’s used. They are the next step to achieving success with machine learning.
As you can see, the field of artificial intelligence is very complex, but fascinating. We hope this helped you get a better idea of what these concepts mean, and how they represent the future of technology.