I remember feeling a little cynical when I first heard the phrase “Internet of Things” or IoT because at first, it sounded to me like just another marketing term for the tech giants to use to peddle their wares to the IT marketplace. But what else would you call a trillion internet-connected devices and objects, each addressable by the protocol that powers the Internet? As I reflect more about IoT, I can’t help but think about how we will process all the data that is being produced by the connected world.
On March 15th, at our inaugural Nimbix Developer Summit, I will talk more about the future in which machines will adapt to processing information gathered from trillions of objects attached to the Internet. I will discuss how the Internet of Things necessarily gives rise to a second Internet of Machines thus ushering in a more complete augmented world of knowledge.
Referring to an Internet of Machines at a basic level is admittedly silly, since after all, the Internet itself is by definition the inter-networking of computers enabling the rapid transmission of data and information between them. However, as we participate the next evolution of computing, the intersection of cloud computing and machine learning, it is easier to see how machines can take on millions of tasks to acquire, analyze and simulate data produced by other machines and people to derive new and incredibly valuable knowledge.
We are now routinely using basic neural net technology to “train” machines by feeding them hundreds of thousands, if not millions of pieces of data to do things like translate speech and recognize pictures. What happens when we start applying that technology in thousands of disciplines and domains to solve meaningful problems? Consider the advances achievable in fighting human disease when we can teach machines how to chunk through thousands of genomes and run thousands of simulations on all possible outcomes of therapies from a synthesized compound? What if the machines performed these kinds of functions 24 hours a day 7 days a week while being fed a constant stream of new data from connected sequencing machines? What if this was simultaneously being done across dozens of industries for testing new materials, reaching new consumers, predicting behaviors, simulating new products, running forensics, or fighting fraud?
What if we assume the above is happening on a global scale and add silicon that can be reconfigured on the fly by the machines themselves to adapt to a specific workflow or processing task? What does the future look like with machines that can “learn” and then adapt themselves for various programmatic tasks?
March 15th, I’ll dive in deeper on the evolutionary path of IoT and how machines will continue to help us build a bright future, tackling the toughest challenges our world has known, at the inaugural Nimbix Developer Summit.