“Data is the new oil.” This statement by tech commentator Conner Forrest, sums up the terrific frontier energy generated by the partnership between IoT and machine learning.
The IoT market is predicted to reach $457 billion by 2020. That’s not even two years down the road. While manufacturing, logistics and transportation (IIoT) are expected to lead the way in using these technologies, consumers will experience a transformed retail marketplace as well.
Google is getting barraged with “what is” questions about each member of this emerging tech trio. It’s nearly impossible to clearly distinguish IoT, AI, and machine learning as individual technologies without highlighting the relationship between them. But the logical place to start is with the overarching concept, artificial intelligence (AI).
Machines that mimic the action of the human brain. This concept has justifiably fascinated human beings since antiquity. In the early days of computers, people started considering the possibility of teaching computers how to learn (rather than simply carry out calculations.) However, since early robots and computing machines were isolated devices, there were limits to how much intelligence could be applied.
True AI remained largely a topic for science fiction until the emergence of the Internet. Once individual machines linked into a larger network, AI began to transition from concept to the real world implementation. The limits on its capacity continue to diminish.
Putting AI in action is where machine learning comes into play. Machines are becoming increasingly intelligent because they are now able to collect, store, share, and analyze vast quantities of data. Machine learning frameworks like Google’s open source TensorFlow streamline these processes. With this enormous quantity of data, digital devices are now able to make statements, develop models, and offer predictions. The degree of certainty with which they deliver results increases with the amount of data available to them. Feedback loops or “training” allow them to evaluate the outcome of their decisions and statements, constantly improving their validity.
IoT refers to physical devices (also commonly called connected or smart) that are collecting data, sharing it online, and, at times, performing tangible tasks in response to online feedback they process.
A common metaphor used to characterize the relationship between IoT and machine learning is that of the body (IoT) and the brain (machine learning). IoT devices always include some form of sensor. These sensors may perceive light waves, sound waves, electromagnetic radiation, chemical molecules, vibrations, and many other external elements. Their next step is sending the information back to the brain.
If you burn your hand on a hot pot, for example, your brain recognizes the stimulus as something to avoid, leading to a quick reflex to pull your hand away. When the sensed data is relayed back to the connected artificial intelligence, it recognizes the data and reacts–making decisions or predictions and mandating certain physical actions. The end results inform a user and/or other device, which drives an action.
The depth and utility of machine learning corresponds to how much data is available to enter into training algorithms. The more data available, the smarter systems can become. In turn, a more universal adoption of IoT technology depends on how useful implementation can be in delivering on business objectives and end user expectations.
IIoT, for example, is quickly gaining momentum as industrial brands recognize the benefits of connected, automated workflows as opposed to the legacy manual processes that have made them a little late to the party. But the bottom-line impacts, process efficiencies, and areas of user satisfaction are just starting to be realized in this arena.
Smart machines participate in what people say and see. Language (spoken and written) is full of inconsistencies and nuances that are especially challenging for software–and, to be fair, pretty much anyone trying to learn a foreign language. However, the accumulation of linguistic data now allows much more natural language processing (NLP). These language capabilities power chatbots and other speech recognition devices, and continuously advance the overall accessibility of our machines.
Likewise, smart machines increasingly attribute meaning to visual data collected by their sensors, so they can be said to “see” in way that’s analogous to human perception. These algorithms that mimic the structure of the human brain and nervous system are called “artificial neural networks.” This technology works to adapt to human habits and expressions, allowing us to communicate more readily with devices.
Whether it’s something you wear, ride, or use on a regular basis to be more efficient, businesses and consumers are already working and living in new, enhanced ways as a result of the data intelligence revolution.
Here are some examples of how IoT and machine learning are uniting digital and physical worlds through connected solutions:
As machine learning and IoT interact to propagate intelligent networks, businesses and consumers will adopt and inevitably have increased desires and expectations around efficiencies and information. We will rely on smart sensors to stay safe, informed, and forward-thinking. We will perceive machines as contributors and more personally informed than ever before. The early stages of a technological revolution don’t typically last long. Industry and consumers alike are getting ready to embrace and then quickly build upon a new foundation of data-driven promise that has generational implications.