Cognitive Technologies: The Challenges Ahead

By Avjit Singh

Machine Learning. Artificial Intelligence. These are words heard almost everywhere on a diverse and technology centric campus such as that of UC Berkeley. After a brief discussion with a friend involved in ML@B (Machine Learning at Berkeley), my curiosity got the better of me.

What exactly is machine learning?

Machine Learning is essentially a method of data analysis that automates analytical model building. Using specific algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.

To begin with, Machine Learning and it’s implemented applications are all around us. We observe Machine Learning in action, everyday, without realizing it. Those Netflix and Amazon suggestions? Fraud detection online? The heavily hyped Google self driving car? Yes, all forms of Machine Learning executed in the real world.

Cognitive technologies, which are technically products of the field of Artificial Intelligence, incorporate Machine Learning, Computer vision, Natural language processing, and speech recognition. These very technologies are now being implemented in almost all business sectors of the economy and in both the private and public sectors.

Across industries, cognitive technology is in high demand. Businesses, whether they are investment banks, startups, or tech companies, are trying to incorporate these new problem solving techniques into their products, processes, and future endeavors. The benefits of incorporating cognitive technologies are immense. Not only do they produce potentially efficient and cheap solutions to previously challenging and expensive problems, but they open up a whole new avenue for companies to reach large numbers of people. The challenge businesses now face is finding specific applications to solvable problems and facilitating them through human capital.

The first effect of cognitive technology is specialization of product development and industry. There are companies like Blippar and Catchoom whose sole focus is image recognition, and there are other companies like Inside Sales and Metamarkets who work specifically with predictive analytics and self -learning. Yet, the industry giants including Amazon, Google, Apple, and others have found ways to incorporate the necessary components of cognitive technology.

The second effect of this challenge is the shift in workforce. Critics of cognitive technology warn of the dangers it poses to employment options. However, the current capabilities of machine learning and artificial intelligence are not advanced enough to make humans obsolete. To fully capture the productive benefits of advances in technology, companies must provide some means of transition to these “machine complementing” jobs.  The human capital will now be even more skewed to experts in computer science, machine learning, mathematics, engineering, and more and more in physics. This is not a particularly new phenomenon, however it now is the responsibility of the companies to utilize this capital in effective ways working alongside cognitive technology. The risk of hiring skilled labor is low. Brilliant minds and creative ideas are invaluable to the development of companies and therefore are necessary to succeed.

Another way in which businesses can best utilize cognitive technology is through accessing new markets and disrupting current structures. A great example of this is online banking. It incorporates many artificial intelligence and machine learning solutions that have allowed people to more actively participate in banking and investments.  This not only simplifies the user process but in addition has attracted large numbers of millennials, previously not as involved in their own finances.

These cognitive technological solutions are reaching immense numbers of people and changing the way we not only interact but also the whole structure of commerce.