Imagine that I own a company that can manufacture babies. The babies can be delivered in the color as preferred by the customers. It will have all the organs as human beings and will have artificial intelligence. It can learn from the environment using Neural networks and can make decisions using Fuzzy Logic and Decision Trees. Some Artificial babies can become sports persons and some scientists. It all depends on the environment and learnings given to the babies by the customers. Can this imagination become reality? If yes, then what will be the value of a baby manufactured from the company? Is it in a million or a billion or even more? The answer is priceless because it is only imagination at this point in time. The take-away from this imagination:
There is no doubt that the financial services industry is in a period of technological transformation. There are a lot of statistics that highlight the potential risks of artificial intelligence (AI) and automation related to the job market. There is a prediction that one-third of the jobs in the financial sector are under threat due to the advances in AI and automation. Instead of viewing these emerging technologies as a threat, it is far more productive to view them as an opportunity to add strategic value to the business. Even an e-auditor can reduce human intervention but cannot remove human intervention.
Human intelligence also needs changes in thinking to adapt to automation and strike the right balance with Artificial Intelligence. The major problem with Human intelligence is that sometimes it is not able to list down the solution as a step-by-step process. For example, my mother is good at cooking and can make delicious food ‘n’ number of times with the exact same taste, but she is not good at documenting her recipes. If recipes are not there, then cooking cannot be automated. The solution for this problem is Algorithmic Thinking.
Algorithmic Thinking provides a data driven approach to come up with innovations. Historically, a ‘computer’ was a device that performed mathematical calculations. The word was used this way until the early 20th century. The study of algorithms and computer science has deep roots within the study of mathematics. This made human intelligence to adopt Algorithmic Thinking for solving problems.
One common misconception about algorithmic thinking is that it is the study of computers and requires an understanding of coding, programming, or the user of a computer. Algorithmic thinking does not require these – it is essentially being able to arrive at a solution to a problem via a series of clearly defined steps. It’s not necessary to have an understanding of technology. Human Intelligence needs to develop an algorithmic thinking mindset to solve a problem and it helps defining the problem clearly. In my above example about recipe for cooking, if we have the correct recipes then it is easy to automate the cooking .
To strike a balance between Artificial and Human Intelligence, our thought process requires changes. We should not consider automation as a threat but an opportunity, and adopt changes in the thinking process. Adopting the change in thinking process for Artificial intelligence will help us accelerate the evolution of automation.