From your nerd friend to weird neighbor, all talk about AI. But, that’s not the problem; overwhelming by AI-jargons is. Yes, you are not supposed to act like a child who’s in for ‘Disney Land’. Get on and keep up. That’s not so hard as you thought.
People are more likely to use the fancy terms ‘Neural Networks’ and ‘Deep Learning’, when they speak about AI. Of course, it does sounds like a super-spell that powers AI. However, the truth is ‘they are not actual creators’; just supporters.
It is simply, a sub-field of computer science with the goal of developing computer systems that can do human kinda work with real intelligence.
Garden-Variety: Computers aren’t creative. They can mimic the works of great artists; but possibly couldn’t beat a street-artist in your locale.
So, are we training them so that they could beat a street artist?!?
No! But, AI will conquer them spontaneously; we are focusing on some better purposes and collaborations. Here’s a needle in a haystack – Zebra.
Also learn: We still have time to hangout with NS5s.
What are ‘Neural Networks’ and ‘Deep Learning’?
Artificial Neural Networks are the biologically inspired simulations performed on the computer to perform certain specific tasks like clustering, classification, pattern recognition, etc.
Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.
The whole idea is to frame a brain i.e., Artificial Neural Network on computers and formulate its functions using a set of rules/sequential operations i.e., Deep Learning.
What Powers AI?
As you can see, we can create a full functional (literally) brain using Artificial Neural Network and Deep Learning Algorithms. But what powers a brain.
To humans, mitochondria; To AI, data.
So, ‘Data’ is what powers and makes your AI useful!
Your negotiating chat bot can’t be of much use in cooking. Because, their core algorithms and most importantly, ‘Training Data’ are completely different.
Next time, when you’re with AI try not to focus only on machine learning and neural networks. Get in the race with your ‘Data’. Bonus, IBM predicts demand for Data Scientists will soar 28% by 2020.
We’ll dive deep with data in AI next week.