AI is not an overloaded term. It is here to stay and has just started to show its applicability. A lot of what we want to do in the world of AI has been happening for around a decade, it is just that we didn’t have the right techniques and technologies before to make it happen. But now is the right time. 

Sandeep Alur, Director – Partner Technology Engagements at Microsoft

What AI helps tremendously is in making predictions and making predictions cheaper. With AI the velocity of any business can start improving by orders of magnitude. That’s the real business value of AI. If you go do the math you will see that the business case for AI is phenomenal, ranging from 100x to sometimes 1000x. 

Madhusudan Shekar, Principal Technical Evangelist at Amazon

Everybody knows about the key skills in data science domain today. In my opinion, tools, technologies, and methods are commodities and are ever-evolving. I coded for over 4000 hours in SAS, but it is now almost redundant or irrelevant in many cases. Today the language of choice for coding is ‘Python’, and I don’t know what it will be in the next 5 years. Similarly, Hadoop was very sexy 2 years ago but does not have the same popularity today.  

Ravinder K Sharma, Global Sr. Director – Analytics, Insights & New Capabilities at AB InBev

The world of AI goes on a long journey starting with data. From collecting data to cleaning it, labelling it, training it and deploying it, it is a long journey. As an ecosystem, you need help on the entire spectrum. I have lived in Silicon Valley before and I can tell you that the Bangalore ecosystem is incredible. We got a lot of help from companies like Microsoft without which our journey would have been a little slow. From cloud to hardware to deployment, there is a great ecosystem that the city provides. 

Navaneethan Sundaramoorthy, Co-founder & CMO at Uncanny Vision

Being new to the media industry, it has been overwhelming with the kind of data that we collect and the kind of possibilities in terms of how AI can change the user experience. It is very different from a lot of other industries because this is directly a B2C business wherein every change you make to a product is reacted immediately by the user. 

Anubhav Shrivastava, Head – Product Data Sciences at Voot

Till two years ago not many people spoke about inference and the talk was mostly around how we developed models. But now we have crossed the early adoption stage and are talking about democratising AI. And that’s where inference is taking the centre stage. In many cases, target deployment is not as much as we see in the training environment. 

Sunil Kumar Vuppala, Principal Scientist at Philips Research

NLP tends to mean a lot of things these days and is an umbrella term, but in general, it means how the computer understands human language. There is a constant amount of research that is going on in this field and a lot of research is around making computer close to how humans interact. Named entity recogniser is one of the common techniques of NLP and is often not duly addressed but it continues to be a huge problem to be solved. 

Mathangi Sri, Head of Data Science at PhonePe

India today is number one in terms of data consumption. All the devices such as planes and car are going to generate terabytes and petabytes of data there are going to be 50 billion devices that are going to be connected on the internet to generate all sorts of data. There are going to be 200 billion sensors. Tomorrow time may come when a car may message to change tyres or refrigerator may say to replace my compressor. That is where the whole data revolution is happening 

Lakshmi Narasimhan, Manager – Consulting (IoT, HPC and AI) at Intel

A lot of time we have this feeling that AI is going to help us in terms of solving bigger problems such as genome mapping or detecting floods or detecting weather conditions. While this is true and AI definitely helps here but now AI has also become pervasive in our daily lives and issues that we face on a daily basis. There are many small instances where AI is being used, for instance, predicting if anyone would fall asleep behind the wheels when they are driving at night. Sometimes as AI engineers when we start, we think we don’t have big enough problems to solve but AI is quite useful in solving problems in daily lives too.

Praveen Srivatsa, Director at Asthrasoft Consulting

We work with a lot of companies and till five years ago, they were thinking about big data, three years ago companies were able to finalise architecture and now they say that their platform is all set, can you do something with it. Data has evolved from being unstructured to structured and now it is about adding vision and audio. Also, in terms of what AI can be applied for earlier, it was about bigger adoption, but now people are talking about smaller things and implementations to improve customer experience using AI.

Srinivasan Govindaraj, Director and Head of Data Sciences at Happiest Minds Technologies