AI relies on data and on training algorithms. In this context, an algorithm is a set of rules for a computer to follow. In AI, data and training algorithms are what makes the computer seem intelligent, because they allow it to make decisions based on the rules that it has learned to follow.
For a computer to learn it needs to find links between things, develop understanding and make decisions based on the training data that they are given. Sometimes, people can think of AI as a ‘black box’ – where you put everything into a machine and a magic formula will give you the exact results that you want. However, it is very important to remember that you only get out of AI what you put into it. So, if you enter poor quality data into a training algorithm, you will get poor results. Later in this week and throughout the course, we will go into more detail about training data and why it is so important in Earth observation.
Machine Learning is a subset of artificial intelligence. It is the art of teaching a computer to learn and improve from experience rather than being explicitly programmed. Machine learning is an ‘empirical approach’, which means it learns from what is seen or experienced rather than theoretical models. We encounter machine learning in our day-to-day lives: for example, using face-detection to unlock our phones, or getting real-time traffic updates on Google Maps.
The next subset of machine learning is deep learning. This is a kind of machine learning that aims to solve problems by mimicking the biological structure of the brain. To do this, they use a type of model called a neural network, which follows the way that the brain makes connections between different pieces of information. Later in this course, you will learn more about different kinds of neural networks.
Today, deep learning is reaching such a high level of accuracy that it has the ability to replace many difficult, expensive and time-consuming tasks, which opens up huge opportunities for businesses and science, including in Earth observation.
Furthermore, another subset of machine learning is reinforcement learning. Reinforcement learning is the training of machine learning models to make a sequence of decisions, taking into account interaction with the environment. In reinforcement learning, the AI faces a game-like situation: the computer employs a trial and error system to come up with a solution to the problem that it has been given. To get the machine to do what the programmer wants, the artificial intelligence will either be rewarded or penalised based on the actions it performs. Its goal is to maximise the total reward and solve the problem.
An example of the use of reinforcement learning is the AlphaGo programme which was able to beat a professional human Go player for the first time.
In this week, we will go into more detail about the kinds of problems that can be worked on using AI, and the different kinds of algorithms that can be used to solve these problems. Later in the course, you will see how these concepts are applied in the field of Earth system monitoring to solve problems that we face in the real world.
