Well it all kind of started with Frankenstein them came to terminator and Skynet and now Jarvis. Robots, Artificial Intelligence are just things that just wow us humans. There is just something about robots maybe it is the soul it is missing, but robots have always fascinated us humans. We had robots or robot like things in our movies even before we had computers. From then to now technology has made big strides in the field of robots. Japan is kind of like the hub of it. Now we have humanoids -Sophie and it even got citizenship of Dubai even humans can't get it. They are already taking over us. Making robots always came with big problems and a subconscious that always tells us "don't create them they are going to rule you".
At the initial stage when computer science started showing its hands in making robots, the numbers of problems faced was unparallel. The thing with robots is you programme a robot by defining all the moves it is supposed to do and then there is a flaw and you have to start all over again. How can you anticipate and program every action of a robot, and even if you could do that, what about any new functionality you want to put in the Robot. You have to reprogram the computer for that.
Another problem is computational complexity. For computers real time vision is a real hard problem they can't master it like humans do it. Binary language is there forte they recognize patterns out of these. Robots finds things that are easy to us problem of epic proportion to them. The easy one eye things for us are zero's and one's for robots, for them every slightly change is a new set of zero's and one's.
It can enormously. Machine Learning, the poster boy of data science is here to help. What machine learning does is to make a computer or robot learn by providing it labelled examples of any kind of behaviour like recognizing human hand writing recognition. What it does is that it takes thousands of ways in which alphabet "a" is written by humans, then label them as "a", and ultimately train the computers on that data. Once the computer is trained, it is tested on new unseen data to get an idea of how accurate it is, and accuracy numbers are going through the roof because of two reasons.
First is the universal availability of data in the data deluge era plus the mammoth computational power we have to compute the most computationally intensive problems like the computer vision problem I talked about. Google has just announced Tensor Processing Units which are much more powerful than GPUs for training Deep Learning Algorithms.
Last but not the least, a branch of Machine Learning called Reinforcement Learning is the closest it can get to the way humans learn. In Reinforcement Learning, the rewards and penalties are set and a system learns by making mistakes. If it makes a mistake, it is penalized so that it remembers, and is rewarded on required behavior. Isn't this how we learn? This is a facile explanation of Reinforcement Learning to explain the concept of it.
There are gazillions of real world applications of Reinforcement learning but my personal favorite if teaching your computer to play Super Mario Bros using Reinforcement Learning.
Machine Learning is helping humans unfold their greatest fascination and much more