Finding Information
9 AI Speak: Machine Learning
An algorithm is a fixed sequence of instructions for carrying out a task. It breaks down the task into easy, confusion-free steps, like a well written recipe.
Programming languages are languages that a computer can follow and execute. They act as a bridge between what we and a machine can understand. For a computer , images, videos, instructions are all 1s (switch is on) and 0s (switch is off).
When written in a programming language, an algorithm becomes a program. Applications are programs written for an end user.
Conventional programs take in data and follow the instructions to give an output. Many early AI programs were conventional. Since the instructions cannot adapt to the data, these programs were not very good at things like predicting based on incomplete information and natural language processing (NLP).
A search engine is powered by both conventional and Machine learning algorithms. As opposed to conventional programs, ML algorithms analyse data for patterns and use these patterns or rules to make future decisions or predictions. So, based on data, good and bad examples, they find their own recipe.
These algorithms are well suited for situations with a lot of complexity and missing data. They can also monitor their own performance and use this feedback to become better.
This is not too different from humans, especially when we see babies learning skills outside the conventional educational system. Babies observe, repeat, learn, test their learning and improve. Where necessary, they improvise.
But the similarity between machines and humans is shallow. “Learning” from a human perspective is different, and way more nuanced and complex than “learning” for the machine.
A classification problem
One common task a ML application is used to perform is classification – is this a photo of a dog or a cat? Is this student struggling or will they pass the exam? There are two or more groups, and the application has to classify new data into one of them.
Let us take the example of a pack of playing cards – group A and group B – divided into two piles and following some pattern. We need to classify a new card, the ace of diamonds, as belonging to either group A or group B.
First, we need to understand how the groups are split – we need examples. Let us draw four cards from group A and four from group B. These eight example cases form our training set – data which helps us see the pattern – “training” us to see the result.
As soon as we are shown the arrangement to the right, most of us would guess that the ace of diamonds belongs to Group B. We do not need instructions, because the human brain is a pattern-finding marvel. How would a machine do this?
ML algorithms are built on powerful statistical theories. Different algorithms are based on different mathematical equations that have to be chosen carefully to fit the task at hand. It is the job of the programmer to choose the data, analyse what features o