Let’s just dig into the simple economics of Predicting Machines by starting with the complementary assets of A.I. and we can call as the A.I. Canvas without which A.I. is just about making prediction and helping humans in an efficient way but we all know that A.I. is more than that.
The A.I. Canvas consists of following assets :
Input – Without the oil/fuel, even the cars don’t operate on the roads then how would A.I.? For A.I. the well suited input is the Data through which A.I. can predict patterns and make predictions. Also the cost of prediction will fall if the value of Data will increase. ( simple economics) #Win-win
Prediction – We all know how terrible humans are in making Predictions but they still make because it is inevitable. Fun fact is if the capability of Machine Prediction increases, the value of Human Prediction will fall as machines can make great predictions after observing the patterns hence, humans are benefited by the faster and cheaper prediction.
Judgement – Humans give guidance to the A.I. since A.I. do not make judgement, they make predictions which help humans to make the better Judgement.
Action – A.I. do tasks, they don’t do workflow ( turning input into an output is workflow ) they are programmed to make better predictions to ease the job of humans and humans take actions with the help of Deep Learning since without Actions, predictions are even worthless.
Outcome – Only after taking an action, we come to know whether it is a wrong outcome or the right one and based on the human judgement about the outcome, A.I. observe and predict the data to make sure that the further outcomes must be the good ones.
Learning – Practice makes the man perfect and learning makes the A.I. perfect. They learn and learn to make good predictions which helps the humans to do the job in an effective and efficient manner. They are not here to replace them but to help them for a better future.
Feedback – Until or unless we do not receive the feedback on any task performed, we would not be able to rectify our actions for future and keep making the same mistakes. Similar analogy goes with the A.I. They get feedback data and be ready to get programmed for making lesser mistakes in further tasks.
When chances of making prediction falls, the value of all these complementary assets go up ! These assets complement A.I. instead of being the substitutes.
If I were to state a fact to a question that – If A.I. is just about making predictions then why there is so much fuss about it ?
I would normally say – Prediction is the key input to Decision Making and guys, Decision Making is everywhere ! A.I. is new and Decision theory is old but we are in the age of predicting technology converting into the process of Decision Making. 99% of the companies today are investing in A.I.
They are dealing in mapping the tasks, ranking the data and simultaneously working 24*7 to make use of this cheaper and faster technology.
Google has almost 2000 A.I. tools under development, why ? The company must be witnessing the applications of A.I. in our daily routine and documenting the fact that A.I. can transform the economics by disrupting it and making Humans More Productive !
All we need to do is to keep learning and be open to possibilities coming our way be it a new technology/career/prospect or a machine playing with humans and with his intelligence. 
I hope by now, MyShiksha has been able to inundate the dynamics of the term A.I. into our reader’s world and if not then we will try to learn more and share as much as knowledge and wisdom amongst our students and professionals.
Happy Learning !
Cheers
MyShiksha
