Artificial Intelligence – Objective & Approach
Intelligence
We scientifically call ourselves Homo Sapiens – man the wise – because our intelligence is so important to us; For thousands of years, we are trying to understand how we think; that is, how a mere handful of matter can perceive, understand, predict, and manipulate a world far larger and more complicated than itself.
Artificial Intelligence
AI is one of the newest field in Science and Engineering. Work started after World War 2, and the name itself was coined in 1956. Along with molecular biology, AI is regularly cited as the “field I would most like to be in” by scientists in other disciplines.
A student in Physics might reasonably feel that all the good ideas have already been taken by Galileo, Newton, Einstein and the rest. AI, on the other hand, still has openings for several full-time Einstein and Edison.
AI currently encompasses a huge variety of sub fields, ranging from the general (learning and perception) to the specific, such as playing chess, proving mathematical theorems, writing poetry, driving a car on a crowded street, and diagnosing diseases. AI is relevant to any intellectual task; it is truly a universal field.
Objective & Approach
As for the terms Data Science, Machine Learning and AI, nobody can blame you for being confused. It’s unfortunate these terms get tossed around so casually by those more interested in hype then solving problem. Forget the term AI for now.
The only practical application today for AI is using Machine Learning. Machine Learning is the technology you are expected to understand. Data Science is the likely how you wish to apply that machine learning. Data Science is the real-world application of machine learning, with the goal of creating products people use:
Artificial Intelligence:
- Sounds Sexy
- What we all hope is the future
Machine Learning:
- The only real AI
- Traditionally an academic discipline
- Not concerned with real-world software
Data Science
- Applies machine learning to create actual products
- Deals with real-world complexity
“The languages you learn, the technology you use, and the way you frame your thoughts will be a byproduct of your attempts to solve the problem.”
When you are trying to solve challenges you don’t use a language because it happens to be hyped. You don’t use technology stack because some expert of the day said this is how you do big data. You don’t use specific implementation of lean because you read an article by a millionaire who swears by it. When you are solving problems the only thing that matter is SOLVING THE PROBLEM. What is it the client actually needs?
Put all the toys that the cool kids are playing with to the side and have an honest conversation about the problem that needs a solution. This is the only criterion that should govern what the approach you take and … here’s the key... your skills in the decided-upon tools of choice will grow as a result.
Not only will this be a much better way towards the solution, but it will deeply ingrain in you the specifics of that particular language and technology or overall approach. You will learn where it works and where it doesn’t. You will witness the week points and strong points first-hand and will actually see how the solution maps to the pain points of the organisations you are trying to assist.