by Noah D'Mello

The understanding of artificial intelligence should be better

Interview
Jan 26, 2016
Big DataBudgetingBusiness

Dr. Kailash Nadh, who holds a PhD in artificial intelligence from London’s Middlesex University and is the CTO of financial technology firm Zerodha, talks about why AI hasn’t picked up yet and what lies in the future.

A technology which hasn’t been experimented much is always difficult to put into practice. Artificial intelligence falls under that bracket. The biggest challenge in building models is the process of building itself, says Dr. Kailash Nadh, CTO of fintech firm Zerodha. “Artificial intelligence is very broad. If you’re coming up with a model, you have to first experiment and try and apply existing models with new data and see what happens,” he added.

Lack of research material is one more issue that most of the companies are plagued with when implementing artificial intelligence solutions. “You can use artificial intelligence for a million things. In our sector—stock broking and risk analysis—there is nothing that has been done. So we have to start from scratch,” he said.

Why has artificial intelligence not become mainstream like other technologies?

“Technologies like big data and cloud are very easy to implement. But artificial intelligence has no end, it is limitless. The ultimate aim is to create intelligence… There are tiny multiple subsets of artificial intelligence and you cannot make generic artificial intelligence that does everything. You have to adapt your models to the process that you’re trying to do,” he said.

Commercialization of artificial intelligence is a thing which is recent. But Nadh says in India organizations and educational institutions do not understand artificial intelligence right from the start. “In India it hasn’t picked up because the research has been limited. Moreover, India has always had really poor finance funding and when you can’t get grants and funds for mainstream topics, how can you expect it for artificial intelligence?,” he questions.

Self-driving cars as well as Google’s release of artificial intelligence computation engine have highlighted the commercialization of cognitive computing.

Looking at this, Nadh says that 2016 can be good for artificial intelligence. “The whole idea of artificial intelligence is to be able to put through commercial productions and use in the real world—this has slowly sunk in,” he elaborates.

Nadh also says that the awareness about what artificial can really do is not reported aptly by the media, which is something that may not help its case in getting mainstream. “When the press covers it, they portray artificial intelligence as a doomsday machine, which will solve all your problems and come to eat your jobs. The understanding of artificial intelligence should be better,” he concludes.