Why this book?
After years of being talked about and featured in science fiction, artificial intelligence is finally showing up in the world around us. But before we delve into artificial intelligence, do we understand what normal, natural human intelligence is?
For sure, there is no simple, single definition of intelligence, but it can be broken into a number of tasks, or categories of tasks. At one end of the spectrum we have raw, computational power that allows us to solve complex but logical problems, while at the other end we have abstract, philosophical concepts like being conscious and self-awareness and have motivation and morality. In between are four kinds of tasks that are seen as intelligent - the ability to predict future outcomes, to react successfully to unexpected circumstances, to resolve between ambiguous situations and to create something new and original.
Complex logical problems can be resolved with traditional computer programming while the philosophical issues are best kept aside for academicians and spiritualists. The other four namely predictions, uncertainty, ambiguity and creativity - is what practical artificial intelligence is all about. From this perspective certain tools and techniques have been found to be very useful. Deep learning -- the combination of a tool called artificial neural networks and a technique called machine learning -- is one approach that drives many of the most exciting innovations in the business world.
Managers in mainstream companies are cut-off from this surge of technology that is flowing past them but it will have a significant impact on their careers. Many tasks that were being carried out by humans are now done by machines. This was already evident in factories run by robots and now back-offices are adopting robotic process automation tools to eliminate white-collars. There is no escape from the relentless march of this technology.
Sun Tzu’s Art of War tells us to know the enemy. Even though AI is not an enemy in that sense, it is important that managers today understand the nuts and bolts of this critical technology. This book will help business managers enter this field, find their way around and prepare them to leverage the potential opportunities that lie within this magical world.
It is naive to believe that AI can be understood through text and slides and managers can leave the coding part to professional programmers. Zukerberg and Musk built global businesses but they did not outsource the coding to India. Computer coding is as much a part of the corporate landscape today as Mathematics and English is and every manager should have the ability to at least read, follow and understand computer code.
This book uses Python to demonstrate how intelligence arises, or manifests itself, from pure data through the medium of neural networks that are loosely modelled on human, biological, brains. If the reader invests a little effort in simply following along and understanding what the Python codes are doing they will be rewarded with five interesting applications of artificial intelligence :
- Predict the incidence of diabetes and cancer on the basis of pathological data
- Classify images based on what they contain.
- Predict what a person is likely to type next on a keyboard
- Get a Taxi to learn how to navigate around a city and Moon Lander to learn how to land safely on the moon
- Create original art-work
There are many books that talk about and describe deep learning but to really appreciate the magic that they bring to the table, it is important to actually build simple systems that demonstrate intelligent behaviour. No coding is required but readers with even a faint memory of computer coding should have little difficulty in navigating through this book.
This book was written for second year MBA students in Praxis Business School where Python is taught as a compulsory subject in the first year. The syllabus and pedagogy at Praxis has a strong bias towards high technology and the author is grateful to Dr Subhasis Dasgupta, a member of the faculty who teaches these subjects in the Data Science program, for his help with some of the examples used in this book.
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