If you are familiar with the expression AI, then surely you have also come into contact with the concepts Machine Learning and Deep Learning? These three expressions are often used synonymously, but actually, they do not mean quite the same thing. I would like to take the opportunity to explain which similarities and differences there are, a kind of Artificial Intelligence, Machine Learning and Deep Learning for dummies.
Consider AI, Machine Learning and Deep Learning to be three generations of the same family, AI being the first generation, Machine Learning the second and Deep Learning the third. In other words, they are very much related but have come to be and evolved in 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 how to speak, form thoughts, concepts and solve problems which had previously required a human to be involved. In other words, fairly broad. As research progressed and our technical possibilities expanded, the concept AI was broken down and specified further. As a result, new research areas within AI have emerged.
AI and Machine Learning
Machine Learning is an area of research within AI which aims to develop the ability of machines to independently understand and handle large amounts of data. The key word here is
To achieve independence, algorithms are used which enable computers to interpret and learn from datasets in order to then create an opinion or prediction about something. With Machine Learning, the computers expand their learning datasets as algorithms are exposed to, process and analyze new information, thus becoming smarter with time.
Machine Learning enables businesses to easily draw conclusions from massive amounts of customer data and quickly customise offers based on the newfound information. Machine Learning is often mentioned in healthcare, where algorithms can process significantly more information and see more patterns than humans can. One study made use of Computer Assisted Diagnosis (CAD) to go through early mammography pictures of women who later on developed breast cancer, and the computer discovered more than 52% of the cancer cases more than a year before an official diagnosis was determined.
Machine Learning and Deep Learning
In the same way that Machine Learning is a research area within AI, the concept Deep Learning has emerged from Machine Learning. Deep Learning focuses on particular tools and methods to enable the implementation of Machine Learning and subsequently solve more or less any problem which requires human or artificial ways of thinking.
In line with the principle of Machine Learning, Deep Learning is basically about feeding enormous datasets to computers, which then make up the knowledge base that the computer uses to interpret new data. The concept then builds on the idea of creating and using artificial and neural networks as a method of processing and making decisions based on given datasets. These artificial systems are logical constructs which ask a series of binary true/false questions or provide a numerical value for each dataset they come into contact with in order to then categorize them according to the answers.
The research on Deep Learning focuses on continually developing these networks to handle datasets as large as Google's image bank or every tweet ever posted on Twitter. Deep Learning can be used on all types of data - machine signals, audio, video, speech and text - in order to conclude in the same way that humans do today, but considerably faster.
Today, traces of Deep Learning can be found behind the doors of many well-known companies. Google makes use of Deep Learning in its voice and image recognition algorithms. Netflix and Amazon use Deep Learning to collect customer data and provide recommendations for new books, movies or series. In the same way, Deep Learning is responsible for Spotify's customised playlists which are sent out to users every week.
Time to get started
Essentially, AI, ML, and DL provide businesses with the opportunity to better predict and understand the needs of their customers and then customise and create new offers and procedures. The potential benefits are enormous and entirely industry-independent. Most businesses should, therefore, ask themselves the question: how and where in the company can AI be implemented?