In this post, we work out similarities and differences between the concepts of AI – Artificial Intelligence, Machine Learning and Deep Learning.
If you have heard the term AI, have you probably also come in contact with the concepts Machine Learning and Deep Learning? Often these three terms are used synonymously but they do not actually mean the same thing. I thought about explaining the similarities and differences, some kind of Artificial Intelligence, Machine Learning and Deep Learning for dummies.
One could see AI, Machine Learning and Deep Learning as three generations within the same family where AI is the first generation, Machine Learning second and Deep Learning third. They are thus very much related, but have come to and developed at different times with different research conditions.
When Artificial Intelligence was first mentioned in a research proposal in 1955, the purpose was to find a way to teach machines to speak, form thoughts, concepts and solve problems that previously required the involvement of a human being. Quite broad in other words. As research has made progress and our technical capabilities expanded, the concept of AI has been broken down and specified. As a result, new research areas within AI have emerged.
AI and Machine Learning
Machine Learning is a research area within AI that focuses on developing machines’ ability to independently understand and manage large amounts of data. The keyword here is autonomous as, by using large datasets, machines are trained to perform tasks independently of rule-based programming.
To gain independence, algorithms are used that allow computers to interpret and learn lessons from the dataset and then create an idea or prediction of something. With Machine learning, computers expand their learning dataset as algorithms are exposed to, process and analyze new information and therefore become smarter with time.
Machine learning enables companies to easily draw conclusions from large amounts of customer data and quickly adapt offers to newly found information. There is a lot of talk about Machine Learning in healthcare and health where algorithms can prove to process significantly more information and see more patterns than people. A study used Computer Assisted Diagnosis (CAD) to review early mammography images of women who later developed breast cancer and the computer detected 52% of cancer cases over a year before an official diagnosis was established.
Machine Learning and Deep Learning
Just as Machine Learning is a research area within AI, the concept of Deep Learning has emerged from Machine Learning. Deep learning focuses on selected tools and methods to enable the implementation of Machine learning and then to solve virtually any problem that requires human or artificial thought paths.
In line with the principle of Machine Learning, deep learning is basically about feeding computers with huge data sets which then constitute the knowledge base that the computer uses to interpret new data. The concept then builds on the idea of creating and using artificial neural networks as a method for processing and deciding on given data sets. These artificial networks are logical constructions that ask a series of binary true/false questions, or generate a numeric value for each dataset they come into contact with and then categorize them according to the answers.
Deep learning research focuses on constantly developing these networks, to handle data sets as large as, say, Google’s image bank or any tweets ever written on Twitter. Deep learning can be used on all types of data – machine signals, audio, video, speech and text – to draw conclusions in the same way that people do today, but much faster.
Traces of Deep learning can be found today inside the doors of many well-known companies. Google uses Deep learning in their voice and image recognition algorithms. Netflix and Amazon use Deep learning to collect data on customers and suggest new books, movies or comics. Likewise, Deep Learning is behind Spotify’s custom playlists that reach out to users every week.
Time to get started
Basically, AI, ML and DL companies offer the opportunity to better anticipate and understand their customers’ needs and then adapt and create new offers and working methods. The benefits that are available are enormous and completely industry independent. Most companies should therefore ask themselves how and where in the company AI can be implemented