Machine Learning: What is that?

Where did this start?

Since ages unknown, man has been trying to offload tasks to others, in order to simplify his daily life. For example, before the advent of the stone age, people used their own hands to break fruits and nuts. Sure it hurt them; but they had no option. Some genius then came up with the idea of using stones instead. Everyone around must have opposed him, claiming that he is inviting dependence on those stones. But over time, everyone came to accept the benefits and started using stones. What they essentially did was to offload a painful task to an external tool, called the stone. Since then people have come up with various ways of offloading their own difficult tasks to external tools. That has been the driving factor behind most inventions - from stones to computers and mobiles.
We began with physical tasks and around a century ago, we had reached a point of time where almost all physical tasks could be performed by external machines. Though we had machines to take care of most physical actions, we had to help these machines with the decision of what needs to be done. We were burdened with the job of deciding which action is appropriate for now, and then getting the machine to do it. That is when mankind moved into the world of computing and later into Artificial Intelligence - to externalize the intellectual load. To help us with the task of taking routine decisions.
Any such decision can come from various aspects of our thought process.


We calculate based on specific parameters, and decide on an action that would lead to the desired result. For example, knowing well the equations of profit and loss, and with the information about availability of products, a trader can decides on the strategy that can maximize the profit. This calculation does not need anything more than the equations and the information about the current status of things. These calculations can increase in complexity, but they are feasible with these limited inputs. This is calculation based decision.


Over the years, we have learnt a lot from surroundings. These lessons may or may not be conscious. They may be based on experience or explicit teachings. But, over the years we have all accumulated "lessons" that impact the way we think. These may or may not add positive value to our decisions, but we use what we have learnt to analyse the current situation. Based on this analysis, we try to predict which action could lead to the desired result. Thus, our decisions are affected by what we have learnt. Such learning is hazy. We may not be sure about details about precise source of each lesson. But we have gathered them, and we use them as an input to our calculations. This is learning based decision.


Emotions are the major factors in our decisions. Emotions make us feel good or bad about events and make us want to choose based on this 'feeling'. It often happens that a particular action may not be the best as per what we know and what we calculate. Yet we do it because "I like it". We do it because it makes us feel happy. This is often the more appropriate approach than the above two. Such emotions play a major role in our decision. In fact, the influence of emotions is often much stronger than the previous two. These are emotion based decisions.


And there are times when we overlook all the above, and do something entirely different - just out of a gut feeling, or intuition. Such actions may not be justified under any of the above. Yet we intuitively feel that this is right, and often it turns out to be so. Such intuitions have been an important source of most innovations. They have been instrumental in the progress of mankind. This is the fourth source of our decisions.
These are just a few of the several aspects of our mental effort, the effort in deciding which action is appropriate for this moment. Anyone familiar with Roger Sperry's theory of Right Brain and Left Brain analysis can easily make out that each is based on the left brain as well as the the right brain - with increasing tilt to the right brain. The whole of AI is an attempt to extract and isolate the explicit, analytical activity out of each of these, and then get a machine to do. Thus, allowing you to focus further on the creative aspects of your work.
Machine Learning Specialization from University of Washington

Machine Learning

After offloading physical efforts to machines, we have also started offloading some of these mental efforts. We have successfully offloaded all possible calculations and information. Today, very few of us waste our effort of calculating and remembering stuff. We have computers and mobile phones that easily take care of this job. The next we are attempting is the ability to learn from surroundings, and use the learnt information to analyse events and take decisions based on what we have learnt. Machine learning deals with this aspect of artificial intelligence. This is not a new branch of computing. It has been several decades since we have worked on this science. But now, it has gained a lot of momentum because we have seen a lot of commercial value of its application.
Essentially, machine learning is the field of study that gives computers the ability to learn without being explicitly programmed. In more formal terms, a computer program is said to learn from experience with respect to some task and performance measure, if its performance at the tasks improves with the experience.
It's not as complex as it sounds! In order to understand machine learning, we should try to understand how humans learn. The most commonly quoted example - probably the most important learning we went through is standing up. A child is not able to stand up on its feet. There are hundreds of muscles in the body. He has no idea about which muscle leads to a given action. All that he knows is that everyone around him is able to stand up and walk. So he tries to follow, and falls several times. Subconsciously, his mind observes each such event. It notes the events and actions through all his attempts to stand up. The mind subconsciously makes a note of how a particular muscle in the legs seems to impact his position while he stands. Each attempt to stand up gives a fresh set of observations to the mind - on events that kept him standing and events that led to a fall; and the effect of various muscular movements on either. Based on this, the mind builds up a map of muscular movements against its effect on the position of the body.
The mind just "learns" from a generalization based on several consecutive observations. The process matures as this set of observations build up and one day, he is able to stand up, walk and run... None of these observations used any kind of calculations or measurements. The child does not need any knowledge of mechanical engineering to define the stability of the body. No knowledge of anatomy is required to identify which muscle in the body impacts a particular joint. Everything is just "learnt" from data gathered from observations over several events. Each such observation adds to what was learnt.
Now consider the state when the same person gets into a roller coaster. His observations are entirely different. The same fall is simulated again and again. The jerky motion tosses his body around. At this time, the machine moves in in various directions, in high speed - forcing him to use his muscles to hold on. The mind again tries to learn from these events. But, this gives an entirely different set of observations. These are quite different from the ones that the mind had gathered while it was learning to stand up. This confuses the mind. Because it is forced to rework what it had learnt. And, when the person gets off the roller coaster, he is not able to stand still - what the mind had learnt years ago is now disturbed by the recent observations. This confusion remains for a few minutes and as the mind continues to observe, it notices that what it had learnt before continues to hold. Then it just pushes aside the recent set of observations as an exception - possibly specific to the roller coaster - and carries on. If he is a regular at the roller coaster, he is pretty stable on the ride as well as on coming down. The mind soon identifies two different sets of observations - specific to the ground and the roller coaster.
Machine learning is attempting to replicate, in a very limited scope, this behavior of the mind - the mind's ability to classify and learn from events. The mind's ability to decide and conclude based on mere observation - without any need to know the low level details.