Updated: May 21, 2019
The tech space, like the idea space, often brims with non-tangibles. It’s heavy in buzzwords - concepts many of us talk about but that typically stay on a conceptual level. Their full implementation remains distant for most of us, though we know we should be paying attention to them. Those concepts mean different things to different people and often serve as more an umbrella for a collection of ideas than one clearly defined technological element. That’s OK. After all, working in innovation sometimes means aiming for an idea we don’t entirely understand - or with potential that we don’t entirely understand but we know exists and is waiting for us to explore and benefit from.
But three of those buzzwords represent incredible potential for how we will live and do business in the future - and how your company’s products or services will be received in the market - especially when fused together. We believe the intersection of IoT, machine learning, and blockchain will transform our products, services and lifestyles. Ignoring the power of all three, combined, or not planning for how they will affect what your business offers is a misstep and a lost opportunity.
I’ll start by explaining how we define these three buzzwords, so we can get a little more concrete - and then lay out the business applications and what you can do now to be ahead of your competitors in the coming transformations.
Internet of Things is simply decentralized computing - processing and data generation that happen through a series of devices and the interactions of those with people. Because it requires physical objects (the things), IoT is hardware-driven, and therefore capital-intensive. Often, these physical things integrate with larger data processing through sensors, with algorithms either running locally on devices or pumping data up to the cloud where a more advanced algorithm can make it into something useful. Or, it could be a hybrid of local- and cloud-based computing. Some of the value in IoT that businesses should understand: It can be used to generate data points that did not previously exist as well as some exhaust data - data that’s not critically important now but might become valuable if you collect enough or seek new methods to use it for either cost-savings or increased value to consumers or clients.
This is close to what it sounds like, though the implications and operations are a little more nuanced.
The “machine” could just be a software; unlike IoT, it does not require a physical object. Any system or computer or program that operates on an algorithm that teaches it to not make the same mistakes and to get better and smarter with use - to evolve on its own - is using machine learning.
A word of encouragement: People worry that machine learning will take jobs, but its best implementation is to aid and complement people in their jobs so that they can produce more without increasing their stress. They can get more done in a shorter period of time and ease their burden. But you still need the human element for oversight. In fact, the process of creating and training machine learning requires human, not computer, intelligence.
A caution: Because we are training an algorithm to learn on its own and correct itself, it’s possible to lose oversight of low level details of how it’s functioning (I.E., how it eventually comes to make decisions). This is the “black box” issue of machine learning. Humans provide objectives, train it to steer away from the wrong outcomes - but, with sophisticated machine learning, we can lose our ability to track what’s happening “inside the box.” One solution is our third component: blockchain.
Blockchain is intelligent, fully automated software that provides auditing, tracking and oversight that’s missing in machine learning and other technologies. Here’s an example: One of the important factors of self-driving cars is not just preventing fatal or injury-inducing accidents from occurring, but determining who’s accountable for them. As they continue to develop, we need some means to recognize whether it was a human error - something built into software - or whether it was an erroneous decision made by artificial intelligence. The same is true for any machine learning algorithm. It gives accountability that wouldn’t exist without it or something like it.
Because it stores and delivers unique, trackable data - unlike, say, a CD, which you could copy repeatedly and it would appear to be the same as the original - it also creates digital scarcity, which is where cryptocurrencies generate their value. It now identifies data that exists in the cloud that is unique, which helps identify counterfeits and provides a permanent, unalterable record of past transactions.
Machine learning and blockchain both are software driven; IoT is what makes those things physical. It’s the actual bracelet face on your wrist that calculates and stores your steps. It’s the touchscreen on your laptop. It’s the sensors equipped on your door that monitor entry.
As humans, we engage with physical things. As much as we believe we interface with software, we really interact with things that house software. We use a computer. That’s physical. We keep our fitness data in the cloud but we use a bracelet to collect and process it. The software side, compared to hardware, is relatively inexpensive. Silicon Valley, Boston, New York City have exploded with software companies, which are low-capital endeavors. You can copy and paste your software and, if it’s useful, reap exponential revenue.
But there’s inherent value in the physical side of software-generated data. Consider Amazon. The giant is purely logistics, but it specializes in the movement of physical goods. Amazon is thriving because it focused on providing people with physical things, fast, and at the best price possible. In IoT, the focus has been on how we make more physical things and connect those to non-tangibles that (should) make our lives better, but the struggle has been creating value that justifies the cost because physical things are expensive. Software is what generates the value of that physical good - but you can’t undervalue the physical hardware and the thoughtful design of the optimal version of that hardware. (Shameless plug: This is where places like Pittsburgh, and the Rust Belt as a whole, have greater value. We have an established advantage in manufacturing.)
The intersection of those three advances is what most science fiction is based on. Terminator. Skynet. Most of our dystopian doomsday stories. It is “the singularity” where we achieve a universal intelligence that is perfect, automatic and all knowing. We remain a ways off from the perfect application, but businesses can and should be aware of how that intersection can affect what they do, or risk being left behind. Here are three thoughts on how to approach these technologies:
Prioritizing value for your end users
Building-in ways to improve your product or service
Making the evolution of those three buzzwords an ongoing process
From a business perspective, it’s easy to romanticize the creation of devices and, in our enthusiasm, to develop devices that don’t have value. That’s a costly mistake. Because hardware is so much more expensive than software, designing the ideal device/object for your objectives means means understanding the human interaction with that device and what value it provides to the person who will (hopefully) be using it in data, convenience or lifestyle improvement. Creating a device that measures something doesn’t necessarily have value. It may have no value. You have to gauge the value to the end user through every step of development.
If you are a CEO or leader of a company that’s pursuing ways to augment what you offer with the convergence of IoT, machine learning and blockchain, keep that perfect implementation in mind as your team is building your product. Don’t rule out areas where it could apply or assume it doesn’t apply and leave a window for competitors to do better than you. Uber is an example of a step in that direction. You’re talking about logistics of moving people from A to B in the most efficient and safest way possible. That requires a level of automated consciousness with some oversight - blockchain - to safely move people. The value to consumers is that they efficiently use their device to set up a ride and get where they need to go without worrying about safety, parking, etc. Look for opportunities to build in steps to make sure the evolution happens in a way that’s positive for your business, your product, and your customers.
And don’t just do it once.
As a savvy and forward-looking CEO or other executive, you have to make this process - asking those questions, seeking opportunities to integrate IoT, machine learning and blockchain into your value proposition - an ongoing process. It can be part of a constant assessment of whether you are delivering the right value to the customers, the value that saves them money or time, brings them joy or makes their lives more convenient.
That is the value of humans still being in charge. We can use our technology and our data points to provide the analysis we need - for example, which features consumers are using most (or not using) in your product - to make intelligent, human decisions by constantly questioning whether we are creating the most value possible. Humans are the ones who direct the technology and make it most useful to other humans.
As with all product development and improvement, it all boils down to same fundamental question: What is the value to your end users and how does it help them? That drives the decisions, and the combination of these three powerful and evolving technologies can help you achieve the answers.
Nicholas Anthony, an optimistic futurist with an eye on the hardware side of distributed and decentralized intelligence, is CEO of PiMios, a western Pennsylvania-based engineering firm that designs and ramps up manufacturing for businesses wanting to compete in the "IoT" market at an elite level. Read more about their offerings at https://pimios.com.