AI software is a must to handle the complexity of the Internet of Things. AIOps enables IoT data management at speed and scale.
What Is the Relationship between AI and the Internet of Things?
There are two fundamental aspects that define the relationship and overlap between AI and the Internet of Things, and they’re quite distinct. The first is reasonably obvious: the Internet of Things enables the smart automation of many objects. The Internet of Things gives devices, vehicles, even buildings the ability to host algorithms and perform functions that can only be driven by software. When you examine the tasks that these objects need to perform, they frequently involve processes that are similar to human cognitive processes.
It’s a four-step process very similar to how the brain works. First, you need to observe to understand what is changing in a complex environment. Second, there is an analysis of what has been observed. Third comes the construction of an action plan to respond to what has been analyzed and observed. Finally the loop is closed by the execution of the action plan. In this scenario, the type of software deployed will be AI. That’s the first overlap: the Internet of Things turns a lot of the physical world into robots… and you need AI to drive robots.
Next, let’s look at the Internet of Things through the lens of AIOps. Examining the segments and domains of the Internet of Things, what is very characteristic it that it’s a highly complex interaction of individual objects whose individual behaviours are extremely difficult to predict. Due to this complexity, these environments are virtually unmanageable. If something goes wrong, it’s hard for the human eye to identity what has gone wrong and consequently, hard to fix it.
In essence, the human manager needs a cognitive boost so the team can use AI to cope with the complexity of the environment. In an Internet of Things scenario timeframes shrink, leaving the individual with microseconds to respond. The acceleration of state changes means that our minds struggle to comprehend what’s going on. Therefore, AI software is a must to handle the complexity of the Internet of Things. The software enables us to manage data at speed and scale. Without AI software, humans fail. These are the two major intersections of AI and the Internet of Things.
AI software is a must to handle the complexity of the Internet of Things. The software enables us to manage data at speed and scale, without the software humans fail. These are the two major intersections of AI and the Internet of Things.
Social AI and the Internet of Things
The Internet of Things will have a massive influence on the evolution of AI, and the changes will be quite significant. When you examine the AI that has been commercialized to date, the algorithms deployed are largely single agent, almost first-person algorithms. The algorithms are designed to see, analyze, and act. There is interaction, but all of the intelligence takes place independently. Now let’s look at other algorithms, for example those designed for a marketplace or trading floor. Here, prices get set as a result of many micro interactions between individual traders or reactions to issues in the supply chain or currency fluctuations. These interacting set of individual occurrences result in a price calculation for a wide variety of goods. This type of algorithm is what I like to refer to as social AI.
How is the world going to really benefit from the Internet of Things? It’s my view that it won’t be as a result of all these little robots interacting with one another individually, it will be the result of the planned, structured algorithmic outcome of that interaction. For example, look at self-driving cars, the algorithm is not just focussed on the individual car, it is also looking to optimize the distribution of traffic in a congested situation. So here we are interested in the social AI algorithm that is not just the sum of all these individual actors.
But how do you impose top down constraints i.e. mechanism design, to ensure you to get to the outcome you’re looking for? In the case of a market, you want the market to ensure that in a situation of scarcity goods are allocated to those who will provide the most value. The Internet of Things is a market of interacting individuals and forces, so what I think you will see is the forced commercialization of algorithmic game theory, mechanism design and social AI. Therefore, the next big thing in AI will be driven by the requirements of the Internet of Things which will culminate in a shift from individualistic AI to social AI.
The Merger of AI and the Internet of Things
The Internet of Things and AI are seen undoubtedly as separate from one another. However, these disciplines will be seen increasingly as two sides of the same coin. This isn’t a new concept. Rather than thinking of IoT as a collection of IP-enabled devices, we will start thinking of it as a system of distributed smart agents. What will we call this is anyone’s guess. Yet we are moving to a point where the default object won’t be a passive, dumb, software-driven device. It will be a combination of hardware and software that is making decisions within the context of other decision-making agents. That will be the real shift – moving from a bunch of devices connected to the Internet to a collection of interacting smart agents connected to each other.
Once we start looking at IoT as this assembly of smart agents, human beings will still interact in this community. Some of us will be flesh and blood, but other players will be purely digital. Very often individual participants won’t have a clue (or won’t care) if the interaction is with a robot or a human. Within five years, we won’t be talking about the Internet of Things. We will be talking about whatever new buzzword is describing smart agents interacting in this digital system.
Enterprise IoT Will Transact using AI
To date the obvious investment in the Internet of Things has focused on satisfying consumer demands with smartphones and smart homes. But IT, with its embrace of AI, has something to say about business demand for smart buildings and smart vehicles. Over time IoT investment will become more enterprise and B2B focussed.
In the future, many digital interactions between businesses will resemble a trading floor. When two algorithms are buying and selling, they are acting like business people. This will spread to the automation of the supply chain, not only day to day transactions. In many respects the way in which financial markets work is the model for how AI and IoT will converge and evolve. The structure of the economy is ready-made to be mechanized. All we’re doing is automating a process whose structure is already an algorithm; it’s just not coded. Let’s call this the “algorithmization” of the economy.
The IoT Evolution of AIOps
To manage the complexity of the Internet of Things, you need AIOps. It’s not an option. The reality is that IoT cannot be corrected without AIOps. IT teams orchestrating IoT projects will be aware that AI is required if that project is to be successful. However they will not be aware of the need for management disciplines to ensure the Internet of Things is delivering what it should.
AI is applied to complex environments, but we haven’t thought consciously about what it means to apply management to a community of smart agents. How do we manage AIOps, or IT Ops Management for that matter, when Skynet becomes self-aware? When we reach the eventual singularity of man and machine, which is not that far off, then we should expect a necessary reformation of management disciplines. One can imagine management disciplines of the future will come to resemble macroeconomic policy management techniques, which are currently used by organizations like the Bank of England or the Federal Reserve. It’s only one possible future for AIOps.
About the author
Will studied math and philosophy at university, has been involved in the IT industry for over 30 years, and for most of his professional life has focused on both AI and IT operations management technology and practises. As an analyst at Gartner he is widely credited for having been the first to define the AIOps market and has recently joined Moogsoft as CTO, EMEA and VP of Product Strategy. In his spare time, he dabbles in ancient languages.