What is Machine Learning?

One of the most promising subfields of artificial intelligence is machine learning. It involves the process where systems can “learn” through statistics, trial and error as well as data to enable them optimize processes and also innovate much faster. Machine learning empowers computers to develop human-like capabilities that make it possible for them to resolve various challenges facing the world like climate change, cancer, HIV/AIDS and several others. So, in what ways is machine learning empowering computer systems with human-like capabilities?

The process of machine learning is automated and all through the learning process, it is usually fine-tuned based on the machines’ experiences. The machines are fed with high-quality data and machine learning models are developed with different algorithms which we shall look at shortly. The type of algorithm used is based on the available data as well as the kind of activity that is being automated. One question that comes to mind at this point is, how exactly does machine learning differ from traditional programming?

The answer is simple – we feed the input data (and a well-developed and tested program) into a machine to enable it generate an output. But this is not the case with machine learning as input and output data are both fed into the machine in the course of learning and the machine will work out a program for itself. Take a look at the illustration below.

Figure 9: Machine learning process

Generally, computer programs often depend on code to inform them on the things they should do or the information they should store. This is also regarded as “explicit knowledge” which encompasses things that can easily be recorded or written such as videos, manuals or textbooks. Presently, computers are acquiring tacit knowledge – knowledge acquired from context and personal experience – courtesy of machine learning. It is hard to transfer this kind of knowledge from one individual to another through verbal communication or text.

An excellent example of tacit knowledge is facial recognition. Have you observed that when we recognize people’s faces, it is not always easy to explain how or why we even recognize them accurately? What happens is that when we see a person, we depend on our personal knowledge database to tacitly make the conclusions and recognize an individual based on their face. Have you ever tried explaining how to ride a bike to a friend or family member before? You will agree with me that it is usually an easier task to just show them exactly how to ride a bike than trying to explain how it is done.

This is also what machine learning is all about. It is no longer compulsory for computers to depend on billions of lines of codes before they can execute calculations. With machine learning, they now have the power of tacit knowledge and this enables them easily make such connections, identify patterns and also leverage the things they already learned before in making predictions. The use of tacit knowledge by machine learning has undoubtedly made it extremely useful for virtually all industries – government, fintech, weather, healthcare, etc. We will be looking at how AI and machine learning are being used in different industries in section II.

Deep Learning

One subfield of machine learning that is also increasingly gaining traction is deep learning. Deep learning is getting more useful because of its unique ability to accurately extract data. To extract higher-level features from raw data, deep learning leverages Artificial Neural Networks (ANN). More on deep learning later in this chapter.

Common Types of Machine Learning

Figure 10: Types of machine learning

For machine learning to establish parameters, actions and end values, it also requires algorithms just like all systems with AI. The purpose of these algorithms is to serve as a guide for machine-learning-enabled programs while they go through several options and evaluate various factors. Computers actually use hundreds of algorithms based on different factors such as diversity and data size. I will not be going through all the available machine learning algorithms because that is beyond the scope of this book. But I will briefly discuss