Shaping the next decade of human and machine interactions…

There are a new generation of users growing up with a persona for human computer interactions and they are engaging with voice and messaging interfaces to consume content, make smart decisions and to stay on top of things they need to know and do. Developers on the other-hand view these interfaces as new opportunities for distribution and monetization.

While the potential of what AI can do is immense, at this point, it’s at a nascent stage and solves only more superficial or peripheral problems. AI, needs to evolve to offer more robust solutions to users — in a way that makes their lives simpler, helps them make decisions faster and drives productivity.

Bridging the user/developer expectation gap:

Technology advancement:

• For a start AI and NLP should be able to do some off my repetitive tasks easily. I have a 40min ride to office and during this time I catch up on the top news across tech, politics, science and sports, review my calendar, catch up on my to do’s and quickly scan my social media channels to see what’s going on. Deep expertise in NLP technology should enable this by providing me a daily digest to consume these use-cases.
• Voice and Messaging interfaces have made strides in understanding NLP by being able to understand the user’s intent but have a long way to go when it comes to ensuring that results are constantly rooted in context. Explicit preferences and personalization are areas where we need to continue to focus and invest in to be able to solve user problems. For example — It would be great if NLP can solve my family’s holiday planning to discover flights, hotels and tours for a group of 4 rather than having to ask me every time how many people would I need a flight for, rooms for etc.

Become Platform agnostic: Today there is a huge rush to build AI for messaging platforms but I believe that in the long-term AI will have to cut across the following platforms and solve for similar problems and will have to offer a seamless experience across platforms

• Website/Apps: Trends show that PCs and mobile devices will continue to co-exist, and thereby so will websites and apps and hence AI needs to be able to provide developers the ability to engage with consumers through NLP within their existing channels. This would mean that we make life easy for developers by enabling a conversational interface backed by NLP within their website/app.
• Messaging platforms: Over 2.5 billion people have at-least one messaging app installed and within a couple of years that number is expected to reach 3.6 billion which will be about half of humanity. Bot engagement will meet the same fate as app downloads if they do not focus on solving user problems rather just try to mirror their app experience within a bot.
• Voice enabled platforms: Devices like Amazon Alexa, Google Home and CarPlay will continue to become more mainstream to how users stay on top of all that they have to know and do.

Great consumer products help users behave online like they do offline and hence AI/NLP has the opportunity to help developers, businesses and users engage with each other like they would do in a real world. While there will always be the one or two players who stand out across platforms, I think the real winner will be companies who not only build AI applications, but actually invest in technologies that advance natural language processing to redefine the fundamental ways in which humans and machines interact.