NOVEMBER 20188 MANUFACTURINGTECHNOLOGYINSIGHTSIN MY OPINIONBy Richard Bradley, Director, Digital Supply Networks, DeloitteArtificial Intelligence Explained.... with Cats and Dogs...Artificial Intelligence is, at its heart, the discipline of building machines that think like humans in particular the development and display of learning and problem solving capabilities.There are hundreds of articles out there that talk about the socio-economic impact of AI­how it will change the way in which we work and view "value" ­but relatively few articles that explain some of the basic concepts in simple terms­and the crucial principles of how machines "learn".Whilst the big pictures and headlines are important­the ability to understand some of the science behind the data can be at once reassuring, profoundly interesting and ­in some cases­terrifying.In my case, when researching this article­I learned two things:1. Any analogy like this can only ever skim the surface of the topic.2. Eliezer Yudkowsky was right you conclude you understand AI at your peril!Hot Topic: Machine LearningLet's get started. Machine Learning allows computers to do things without being explicitly programmed to do so. How? By learning from patterns and experience just as a child does.Imagine a child is shown a picture of a cat and a picture of a dog ("training data sets") by their parents ("supervised machine learning") and every time they mistake a dog for a cat (or vice-versa), their parents correct them. Future cats and dogs that the child sees may be of different colors, shapes, breeds­however the child still knows that they are seeing either a cat or a dog because from the previous data he/she has been shown, they can determine the patterns which give the highest likelihood of that animal being a cat or a dog.Now imagine the child's parents simply left them with a pile of pictures of cats and dogs without explicitly telling the child which was which ("unsupervised machine learning"). As the child goes through the pictures, they could probably make a fairly good grouping between two types of animals (even though they miss the label `cat' and the label `dog') based upon the characteristics or patterns between the two.Finally, imagine the child makes a series of deductive steps to determine whether the picture is a dog or a cat­and every time they reach an ultimate correct determination of whether it is a cat or a dog they receive a sweet ("reinforcement learning"). Ultimately the child would alter their way of selecting dogs from cats based upon how the outcomes of a series of decisions was rewarded or penalized.Richard Bradley
< Page 7 | Page 9 >