Machine Learning
Self-aware
I'm not worried about robots taking over there Earth just yet. Mostly because if you've ever built a machine learning model, you know how much it relies upon you as a human to tell it exactly what to do. And even when you give it clear instructions, it usually gets it wrong. You have to be so explicit with these systems that the chance of it suddenly becoming sentient is remote.
ML Predict
If you're just tagging your organization employee faces in pictures, you're not using a machine learning model. If you are capturing through cctv camera a new photo and suddenly it tells you who each person is, then you're cooking with machine learning gas. The whole point of machine learning is to predict things based on patterns and other factors it has been trained with. It can be anything;
Example: Housing prices based on zip code and a number of bedrooms, likelihood of a flight delay based on time of year and weather, tagging of objects or people in pictures etc.
ML Requires Training
You have to tell a machine learning model what it's trying to predict. Think about how a human child learns. The first time they see a apple, they have no idea what it is. You then tell them it is a apple. The next time they see one, they'll identify it like a apple (like several objects). Machine learning works in a similar way. You show it as many pictures of a apple as you possibly can, tell it its a apple, and then test it with a picture of a apple it wasn't trained on. This is an oversimplification a bit because I'm leaving out the part where you also have to tell it what isn't a apple and show it different kinds of apple, different colors, pictures from different perspectives and angles etc.
80% accuracy for success
We are not at the point in technology where a machine learning platform will achieve 100% accuracy in identifying apple in pictures. But that is ok. It turns out that humans aren't 100% accurate either. The unspoken rule in the industry is that a model with 80% accuracy is a success. If you think about how useful it is to identify 800,000 images correctly in your collection, whilst MAYBE not getting 200,000 correct, you're still saving yourself 80% of your time. That is huge from a value perspective.
UPDATE for 2018: The 80% rule is now more like the 90% rule.
Know About ML, AI, DL / NN
AI - Artificial Intelligence just means a computer that is as good as (or better than) humans at doing specific tasks. It can also mean a robot that can make decisions based on lots of input, not unlike the Terminator or C3PO. It's a very broad term that isn't very useful.
ML - Machine learning is a method for achieving AI. It means making a prediction about something based on training from sets of parsed data. There are lots of different ways an ML platform can implement training sets to predict things.
DL / NL - Deep Learning / Neural networks are one of these ways a machine learning model can predict things. Neural networks work a bit like your brain, by tuning itself through lots and lots of training to understand what a banana is supposed to look like. You create layers of nodes that get very deep.
