AI, ML, Neural Networks and Deep Learning are some of the buzzwords of today’s world. They are disrupting the way in which traditional business operate. Many service-based organizations are branding themselves as pioneers and leaders in these frontiers. However, before putting money into any of these AI branded assets, it becomes very important to understand the business use-case of these technologies (on which I shall write later). Currently, I shall focus upon facilitating the beginner’s understanding of these buzzwords.
Artificial Intelligence, in the easiest language, is used when machines can take decisions and perform actions (easy or complex) intelligently and smartly, implying, it can mimic human activities, like learning and solving problems.
AI may be classified into two categories: Applied AI & General AI. Applied AI is what is creating the buzzword in today’s world: autonomous cars (Volvo S60 Drive Me), virtual agents (Louise, the virtual agent of eBay), playing strategic games (Go & Chess) against humans etc. Hence, they are specific to a case in point. General AI is what we have seen in movies like Ultron (from Avengers series) & Ava (from Ex Machina) i.e. they have the capability to mimic the human and can perform actions like those that humans do.
An interesting observation is that actions taken by machines, which were once categorized as intelligent, are no longer considered intelligent.
For e.g. Optical Character Recognition. Hence, just like human’s approach (metrics to measure) to intelligence (psychology) varies, the metrics as to what actions define artificial intelligence and what not, may require continuous change.
Now, let us try to understand Machine Learning. ML, in the simplest form, is the ability of the machines to parse data, categorize it, learn that categorization and then perform some actions or give some predictions on cases for which it was not trained. So, rather than the traditional IF… ELSE statements, using algorithms like clustering, decision tree, inductive logic & Bayesian networks, the machine is trained using large volumes of data after which it can perform some task for which it was trained for.
I shall take a very novice example to explain it. Suppose the machine is trained on all the past matches of Roger Federer. His opponents, tournaments, practice sessions, performances at all levels, etc. Now based upon this training, if the machine is able to identify Federer’s odds of winning against any opponent.
Two more “learning” keywords used frequently are Supervised learning & Unsupervised learning.
Supervised learning happens when a bot is trained on corpus of data and the output is defined. If the outputs are defined as classes, then it is a classification problem. If the output is continuous, then it is a regression problem. There are many use-cases defined for classification. For e.g.:
1. To classify, if the financial transaction is fraudulent or not
2. To classify the different types of objects in an image (fruits, vegetables)
3. To classify the given texts into different categories (if the tweet is about football, cricket etc.) in Natural Language Processing (NLP).
Unsupervised learning takes place when the bot starts to learn and take decisions from itself (a concept called self-learn).
Let us understand these two concepts from a real world example.
Case 1: Supervised Learning
Vipul is a kid. He sees different kinds of fruits. His father tells him that this particular fruit is an apple, orange etc. Now a new fruit comes in front of Vipul, which he has not seen before. Vipul identifies it as an apple – and not as a mango, papaya etc.
Here, I had a teacher to guide me and help me learn new concepts, so that when a new object came my way to which I had not been trained, I was still able to categorize and identify it.
Case 2: Unsupervised Learning
Vipul is a kid. He went to North Korea, a country about which he had no prior knowledge – no information on their culture, food, tradition, language etc. However, Vipul tries to learn and make sense of his surrounding – what to eat, how to greet people, how to pray etc.
This is unsupervised learning because in this case, though, I had many data around me, I did not know how to derive meaning out of it or rather what to do with it. Here I had no teacher to guide me and I had to figure out a way on my own. Then, after some time, based upon certain learning, I started processing these data into information categories that made sense.
(The rest of the knowledge will be shared in the second part. )
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