Growing up, Star Trek was my favorite TV show. The characters still come alive every time I think about the voyages of the Star Ship Enterprise and bring a twinkle in my eye! I remember a friend of mine actually managed to lead several of us kids into believing that we could build a similar computer that could “do things as per our wish”. Many months of collecting old boxes, buckets, waste material, iron roads, used circuit boards led to no outcome and we abandoned the effort on a summer afternoon after one of the parents found the kids surrounded by garbage and junk strewn all over the corner of our neighborhood.
Today a lot of what we saw in Star Trek is a reality, though I am still waiting for the day when I could just ask Scotty to beam me up – I know it’s only some years away when we could do interplanetary travel in no time.
Artificial Intelligence(AI) and Machine learning(ML) have led to a new phase where we have the ability to automate and let machines do tasks that earlier were done by humans. Cars can drive themselves based on the interactions they can have with objects around them, how to navigate a bump, what to do when another vehicle swirls into the lane etc. all the things that we as drivers have heck of a time managing and multi- tasking with, machines are today able to do it accurately where they can not only bring efficiency but also let humans focus on other important things – some of us still are debating what those other important things are going to be when this becomes mainstream!
AI and ML at its intersection, Infosys is doing this with earnest with our own platforms like IIP, Mana, IDP, IKP and others – things that required human intervention, human effort to do tasks are being taken over if not completely to a great extent by the platforms. Our partners have made significant investments in similar platforms – IBM’s Watson, Microsoft’s work on Azure Machine Learning etc. are in this space which is beginning to get quite crowded. These platforms are changing something fundamental – the way we deal with data, both by drawing insights as well as using those insights for decision making and taking actions based on the decisions, the whole nine yards, if you will. Dealing with data can pose several challenges given the complexity, variety and size of the data (Big Data would ring a bell!). The key is accessing all that data in the right manner, right format, at the right time, analyzing it to draw the right insights and doing this in a very short span of time. For example IBMs Watson can consume the equivalent of a million books per second, talk about the size of the data. The technical problems of ingesting data, working with it etc. are getting solved with innovative ways every single day and I am happy to share some of our teams have been part of some of the solutions here.
Machine learning has the ability to let machines learn without being explicitly programed! While the obvious use cases of Machine Learning are known and plenty, the fact that this can help us create smarter applications is what excites me the most. Google leverages Machine Learning to improve its search results and is used extensively as part of its search algorithms. A lot of these advances are driven by the work in the Open Source world, communities are creating new and innovative frameworks and products to solve some of the complex problems in this space. It is also coming at a fraction of a cost today, for example a slice of some of the best computing capabilities could now be easily affordable to even an amateur developer and therefore further democratizes the whole process of development where anyone with an idea can add on to what is already being done by others.
What is interesting is to see how all this continues to change the way we work in our day to day lives. What would a typical team member’s or manager’s work day look like when some of these advances are available to every one of us as basic productivity tools in our work space. Do we even need to come to office to access these. Do we need to be driven to the office by humans. Do we need to write programs any more. Do we just explain the problem in simple English to our digital assistant (Siri, Cortana …though my own experience with Cortana tells me that it thinks of me as someone deeply interested in negative, crime related news stories and in the early days used to spam me with those and only those…) and by the time we are back from the lunch break, it has already solved the problem for us or done the task that we gave. These and many more possibilities lay in front of us today, the more we dive into the future, the better it looks to me!