Interpretative Intelligence

…or how to avoid drowning in the coming data flood.

According to a recent Cisco report, last year’s mobile data traffic was nearly 12x the size of the ENTIRE global Internet in 2000.  This “big data” is going to proliferate as most of the world still does not have smartphones (or affordable access to 4G networks). We are only at the very beginning of massive data collection from all of these devices.  Mobile devices are now acting as sensors – which means by default most humans (who are carrying these devices almost 24/7) are living, breathing sensors. Add wearable technology and even in body embedded sensors and the implications are significant.

Human to human interaction has been around since the beginning of human life, and the connectivity created by tech platforms has multiplied these interactions. Machine to human interactions are also increasing as evident by the fact that there are almost now more mobile phones in the world than people. Add machine to machine (M2M) communication, currently in use in security, smart grid and many other applications, and you can imagine the billions of bits of data that are exchanged every second around the world.

All this leads to a complex web of interactions that are ripe for intelligent analysis. In the late 1990s I was investing in enterprise integration, partner networks and business intelligence – the consumer web was still very much in its infancy. Over the years I have looked at recommendation engines, wearable technology (in this case, fabric that detected heart activity), biosensors, home monitoring, video analysis and natural language processing algorithms. Now we have the connectivity to integrate these different technologies (along with a host of others) in truly meaningful ways.

In my view, there is no greater opportunity in the next 5-10 years than the analysis and applications arising from these interactions, what I am calling “Interpretative Intelligence” (the thesis of my new VC fund). Data on its own is meaningless, and can create more challenges than benefits, unless it can be quickly be parsed, analyzed and distributed to relevant entities. The challenge of the future is doing this in a real-time, networked way instead of a linear method; and also knowing technology’s limitations so that decisions can be flagged for human intervention. I still believe that the greatest machine ever invented is the human body and particularly the human brain – and the more we can crack these processes the smarter machines can become. The smarter machines become, the more our brains are able to evolve out of mundane processes and hopefully create more value and even better decisions. Not only is there a lot of money to be made in the next decade in Interpretative Intelligence, there is massive amount of potential social impact (in healthcare, financial services, education and other verticals) – because it is about bringing the power of technology and data to the masses in a scaleable way, and not just the privileged few.


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