AI: Ethical Underpinnings of Artificial Intelligence
The following excerpt is based on the book Tomorrow’s Jobs Today, available at fine booksellers from John Hunt Publishers.
Futurist Roy Amara says that “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” In Tomorrow’s Jobs Today we interviewed over twenty of today’s most innovative business leaders, like Dr. Anand Rao, to offer solid perspective on where we are today with Artificial Intelligence, Big Data, Block Chain, Privacy, and the Internet of Things, as well as a near-magical crystal ball into what tomorrow holds.
“Deep learning is not equal to deep understanding. I think we must go beyond it and look at all other forms of learning and intelligence.”
Dr. Rao has over 30 years of experience in behavioral economics, risk management, and statistical and computational analytics. He has co-edited four books, published over 50 peer-reviewed papers, and previously served as the Program Director for the Center for Intelligent Decision Systems at the University of Melbourne. Dr. Rao received his Ph.D. in finance technology from Melbourne University, his master’s from the University of Sydney, and his MSc in computer science from the Birla Institute of Science and Technology.
From the interview
Anand, what sage advice might you have for those drawn to AI domains like machine learning, robotics, and neural networks? How do they break-in to an industry that seems so daunting and sophisticated?
I guess for the AI data scientists, the first advice I would give is deep learning is not the same as deep understanding. And I know there’s a lot of excitement around deep learning, but I think we must go beyond it and look at all other forms of learning and intelligence. The way deep learning traditionally works is based on what patterns you can draw from the data. That’s just one way that we learn, and we also learn in other ways. And not enough work is being done in other ways of learning. So, continuous learning. Learning at the symbolic level. Learning not just from patterns, but by inference. I’m sure that at the highest level, even some of the leading researchers of deep learning are very conscious of some of the drawbacks, and are trying to address that. Also, as an AI scientist, you need to be open-minded in embracing different things, to be able to move forward in the creation of AI. So, that would be one piece of advice.
For someone wanting to break into the industry, from a business point of view, there are some easy things that you can do with AI to give you or your business a big return on investment and lead to career growth. We call it using “cool” AI to solve boring problems. What I mean by boring problems is the back office. With most businesses, there’s a lot of invoices, and there’s a lot of text documents. People are just going through them, extracting information. That’s a very tedious task or a boring task if you like. AI can help a lot in those areas. It can remove all the drudgery. There is a lot of it left in the service economy as well that AI professionals can help remove. Of course, removing that drudgery means you then need to start adding value, rather than just replacing mechanical tasks. But, once you accept that challenge, I think there are indefinite opportunities to start doing more exciting things in the space.