Artificial Intelligence (AI) can combine the benefits of IoT by adding human-like perception and decision-making capabilities to an existing environment, with the ultimate goal of increasing efficiency and improving steps.
IoT and AI are two of the hottest topics in the tech world lately, which is why we should learn more about them. These two technologies can now be said to be in a stage of symbiotic development, so it is important that we can have a clear understanding of how they can support each other, thereby being able to develop new technologies. applications or solutions that bring greater benefits to businesses.
What is IoT?
IoT is a network of devices connected through the Internet, IoT applications are often built from sensors that are able to collect and perceive real-world conditions and then trigger them. actions to respond in some way. Usually the responses include steps that affect the real world. A simple example is that through data from a sensor, the system will process the data and thereby make actionable decisions, such as turning on/off a light or a certain device, but many IoT applications require more complex rules for linking triggers and actions.
Messages that represent triggers and actions/commands in IoT go through what is commonly known as a control loop. The part of an IoT application that records triggers and initiates actions is the center point of that loop and is where the IoT rules reside.
The control loop is just one part of the total information flow in an IoT application – the part that actually receives information about real-world process conditions and generates real-world responses. Most IoT applications also generate some specific activities in running the business. For example, reading a shipping manifest at the entrance to the warehouse could trigger an action to open the door for the driver – a round of control decision – and also generate an action to receive the goods shown on the manifest. inventory – a business transaction. Decisions made in the control loop must meet the application’s latency requirements, often referred to as the length of the control loop.
Usually control loops only require simple handling to close the loop and create a real-world response to an event. Entering the code to open the gate is an example of this. In other cases, the processing required to make the decision is more complex. As processing has to apply more determinants, the time it takes to make these decisions can affect the length of the control loop and the ability of the IoT system to deliver the desired features. wait. For example, requiring workers to scan manifests half a minute late before loading trucks into the warehouse can reduce optimal warehouse utilization. The IoT system can read the QR code on the manifest and make the necessary decisions much faster, speeding up the movement of goods.
What is Artificial Intelligence (AI)?
AI is a class of applications that interpret conditions and make decisions, similar to how people respond to their senses, but without direct human intervention.
There are three common types of AI in use today, which are:
• Simple or rule-based AI is software that has rules or policies related to triggering events with actions. These rules are programmed, so some people may not realize this is a form of AI. However, many AI platforms are relying on this strategy.
• Machine Learning (ML) is a form of artificial intelligence in which the application learns behavior instead of being programmed. The learning process can take the form of monitoring a live system and relating human responses to events, then repeating them when similar conditions occur, by analyzing behaviors in past, or ask an expert to provide sample data.
• Inference or Neural Networks use AI to build an “engine” designed to mimic a simple biological brain and make inferences that generate responses to with triggers based on conditions that the engine “infers”. Today, this technology is most often applied to image analysis and complex analysis.
All three of these forms of AI are designed to replace human intelligence, but their ability to represent something even closer to actual human intelligence grows larger as you progress. set through the three forms in the order above.
How can IoT and AI support each other?
In IoT, real-world events are signaled and processed to produce an appropriate response. In a simple sense, any IoT application that uses software to generate a response to a trigger event is at least a basic form of AI, and then AI is essential for IoT. The question for IoT users and developers is not whether or not AI should be used, but how much AI can be leveraged. That depends on the complexity and variability of the real-world system that the IoT supports.
A simple rule-based AI would say “If the trigger switch is pressed, turn on light A” and a more sophisticated evolution might say “If the trigger switch is pressed and it’s dark, turn on light A.” This represents not only event recognition (trigger switch) but also state recognition (it’s dark). Programmers use the state/event table to describe how to interpret a sequence of events in multiple states, but this only works if there is a limited number of states that can be easily discerned.
Referring to the example of a truck arriving at a warehouse with goods to be stored, simple AI can provide a means for the driver to enter a code to get through the security gate. This will eliminate the cost of hiring gatekeepers. It is also possible to read barcodes or RFID tags on the vehicle itself and allow entry without entering the code. This will allow the truck to continue moving once its entry has been authenticated, speeding up the process even further.
If more conditions must be analyzed to determine the response to an IoT event, the process is beyond the capabilities of a simple AI application. If the dark state is replaced by the called state, I need more light and the IoT system does not respond to a specific trigger switch but to the task one is trying to perform, simple AI won’t be enough.
In that situation, the ML form of the AI could monitor the arrival of a cargo truck at the warehouse. Over time, it can tell when drivers and workers need more light and trigger a switch without the person having to act. Alternatively, a professional can perform the expected tasks and “teach” the software when more light would be appropriate. Then, AI/ML software will eliminate the need for programmers to build each IoT application.
In the inference form of AI, the IoT application tries to collect as much information as possible, mimicking what humans perceive. It then applies inference rules, such as people can’t work in a place with light levels below X, and from the perceived conditions and the application of those rules, The decision to turn on the light will be made by the system.
Inference-based AI requires more sophisticated software to collect conditions and determine inference rules, but it can fulfill more conditions without programming. The same level of inference processing can determine whether more unloading workers should be assigned, because the goods are in great need, the work is behind schedule, or simply because workers are available. All of this can improve the movement of goods and the overall efficiency of truckers and warehouse workers.
IoT is about using computer tools to automate real-world processes, and like all automation tasks, it is expected to reduce the need for direct human involvement. While IoT aims to reduce human work, it does not eliminate the need for human judgment and decision. That’s where AI can step in and dramatically improve the IoT system.