From Sci-Fi to Reality : Exploring the wonders of Artificial Intelligence (AI) and Machine Learning (ML)
Whether you’re a technology enthusiast or not, you’ve most definitely heard of the concept of artificial intelligence or more commonly referred to as AI, somewhere down the line. In the highly sophisticated digital era we live in, it’s almost impossible to carry on an intellectual conversation with somebody, without addressing one of today’s most trending subjects of debate — AI.
However, one may question, is the hype surrounding AI (Artificial Intelligence) and ML (Machine Learning) justified? Is AI as big of a deal as techies are making it out to be? Well, in case you haven’t already been convinced of AI’s wizardry and jumped on the bandwagon (mostly comprising of starry eyed techies), allow me to guide you on a virtual tour where we explore the fascinating, or should I say — jaw-dropping marvels of AI (Artificial Intelligence) and ML (Machine Learning), layman style.
Ranging from AI powered chatbots, autonomous vehicles to robotic surgeons, come rain or shine, AI and its inventions is always going to be a matter of discussion, especially after OpenAI most recently took the world by storm with the release of its language model based chatbot — ChatGPT.
Now that we hopefully understand why AI is quite important, let’s take it from the top by tackling the fundamental question :
What exactly is Artificial Intelligence? (And should we be fearing it?)
In layman terms, Artificial Intelligence quite literally translates to “human made thinking power” and is about creating computer programs that can learn from data and make smart decisions or help us solve problems. AI can do things like recognizing pictures, understanding languages, driving cars, and even playing games. It’s like teaching computers to be clever helpers that can make our lives easier and more convenient.
However, If you wished to sound more intelligent by using more technical jargon, you could define AI to be the simulation of human intelligence in computers or machines, allowing them to perform tasks that typically require human-like cognitive processes such as being able to comprehend natural/spoken and written language, analyze data,etc. AI systems leverage algorithms and data to learn from past experience, improve over time, and make informed decisions or predictions.
Although it seemed fair Elon Musk dubbed AI to be our “biggest existential threat”, when you chance upon memes like the one below, you can’t help but question AI every now and then. (Just saying)
Machine Learning (ML)
With AI being out of the way, let’s unpack the second topic for this blog — Machine Learning or more commonly referred to in its abbreviated form, as ML.
“Machine Learning is the next internet’’ — Tony Tether (Director — DARPA)
Let’s commence with the primary question — What is Machine Learning?
Machine learning is a subset or an umbrella category of artificial intelligence that focuses on automatically allowing a machine or system to learn and improve from past experience. Machine learning makes use of algorithms such as linear and logistic regression, to analyze copious amounts of data and then make informed autonomous decisions based on the data, all along without the need for explicit programming. Instead of being explicitly programmed to perform specific tasks, a machine learning system learns from past experience and given examples.
As quite simply shown in the meme above, machine learning involves the creation of mathematical models that are able to identify patterns within a database. These machine learning models are designed in such a way they are able to improve their performance over time as they’re more exposed to data. Machine Learning can be divided into 3 main types that are Supervised learning, Unsupervised learning and Reinforcement Learning.
Although machine learning is an equally (and sometimes more) trendy topic as AI in the current day and age, interestingly enough, the concept of machine learning actually dates back to the 1950s when Arthur Samuel, a computer scientist at IBM and a pioneer in AI and computer gaming, coined the term “Machine Learning” for the very first time when he designed a computer program for playing checkers. Arthur found out that the more the program played the game, the more it learned from its experience : which proved to be the first and a classic example of how machine learning works.
Quote — “Machine Intelligence is the last invention that humanity will ever need to make” — Nick Bostrom
So, you may raise the question — Are AI and ML cut from the same cloth? Or do they differ?
Now although Machine Learning is often used as a synonym for Artificial Intelligence, the two are most definitely not the same. AI allows a machine to mimic human behavior whereas ML is a subset of AI that enables a machine to automatically learn from past experience without the need to be explicitly programmed. The scope of the two fields is another distinguishing factor ; while AI is a broad field with a very wide scope covering aspects like computer vision, robotics and speech recognition, Machine Learning (ML) has a limited scope due to its specific approach with creating models based on past data in order to make predictions and decisions based on the data.
From a layman’s point of view, it’s easy to get overwhelmed and intimidated by the sheer advancements in AI and rapid growth in ML, this may even lead you to start questioning everything as you believed it to be. You may be plagued with doubts over whether a day will dawn when intelligent machines propelled by AI will take over the world, but hold your horses, for the truth is — you’ve just been watching too many sci-fi movies.
If anything, we shouldn’t fear AI in itself, instead we should be fearing what people are choosing to do with it.
