AI could be the key to understanding the data collected by the IoT.
Big data is useless and all the sensors in the world are not enough. Contentious? Maybe. I’ve talked in the past about the prospects for digitizing the world and it’s true that we have many of the ingredients to make this happen: tiny, low-power sensors including optical and MEMS inertial sensors that provide contextual awareness; connectivity technologies for almost every practical and budgetary constraint; low-cost processing power and mass storage.
We’re well on the way to seeing almost 30 billion devices connected to the IoT in the next couple of years, and there is no practical limit to this. We have enough IPv6 addresses to cover the earth’s surface many times over with smart “things.” We can easily collect the data we need to digitize the world.
The bigger challenge is to understand what that data are telling us and, from there, determine suitable responses. The sheer volume, velocity and variety of data we can now capture through IoT devices easily exceed the capacity of humans to analyze and extract meaningful insights manually. AI is the perfect companion to the IoT, capable of providing the assistance we need. Bringing them together as the AIoT is the key to tackling complex challenges such as sustainability. Studying the climate and humans’ impact, the effects of using natural resources such as energy, and the prospects for controlling and managing these are subject to huge numbers of variables that are impossible for us to analyze effectively.
AI empowers us to deal with the masses of data that we can collect quickly and easily with our IoT applications. Moreover, given its ability to adapt and improve, AI can both automate responses and generate insights and recommendations to optimize our systems for energy savings and carbon reduction. It’s exciting to consider how AI can show us how we can achieve our goals by changing our behavior.
One example is reducing the pollution from urban traffic by improving traffic flow: something human planners have failed to accomplish satisfactorily for generations. There are simply too many variables to contemplate and the number of vehicles on the roads is increasing continually. Taipei has shown us a great example of how AI can help address this. Historically, the city’s police officers have analyzed images from the network of traffic cameras to detect problems and restore flow. Their response rate was about 16%. Now, the Taipei Traffic Density Network (TTDN) is using AI to improve traffic-flow management by dynamically managing controls at intersections and leveraging current and predicted traffic density information. The AI also lets Taipei’s systems adapt to changing patterns such as different daytimes, weekdays, and seasons including vacation periods. Keeping things moving ensures cleaner air and a lower carbon footprint; a goal shared with ultra-low-emission initiatives being promoted in other cities across the world. Equally important, it also helps shorten journey times and reduce accidents.
The infusion of AI is also changing the ways car vendors are managing customer relationships. With large datasets gathered from the field and augmented with data about product recalls and repairs – and even information from social media – carmakers can implement better predictive maintenance schedules. These can ensure better reliability, improve their brand image, and improve vehicle lifecycle management to minimize the burden placed on the environment.
The effects of AI on smart grids will be transformative as these acquire more data about generating capacity and consumer patterns. It will help improve planning and scaling, including sizing and positioning generating capacity and storage. Similarly, smart agriculture is leveraging AI applications fed by data from IoT sensors and drone cameras to direct farming activities and optimize crop yields.
Some changes may appear to be tiny, or even imperceptible. It’s said that many people have been interacting with AI on a daily basis in their lives for some years already and are unaware of it. Some Taipei motorists may never know about TTDN and may perceive only a small reduction in journey time. On the other hand, the cumulative effect on lowering the city’s carbon emission can be huge.
But small changes can be more acceptable, generally, to human beings who are typically comfortable with the present and fearful of the unknown. As technologists and engineers, our own passions are to create new solutions and we are excited to exercise our skills to deliver them. It is natural for us to have doubts about where all this may lead, however. Even some of today’s most vocal futurists have expressed concerns about the power of AI, particularly if permitted to develop unchecked.
We can be confident that AI is not going to subjugate and supersede in the foreseeable future. This is not to deny that its effects are already transformative. Today’s AI-powered generative tools like ChatGPT can significantly accelerate creative activities and can already offload routine processes such as writing letters and emails. Although it’s clear that businesses will need to adopt them if they are to remain competitive, the standard of work produced is not high enough to take over more sophisticated tasks. Yet. There may be a warning in Amara’s law, named for Roy Amara, scientist and former head of the Institute for the Future. “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.”
But perhaps we can draw comfort from the fact that AI is the perfect learner. It improves every time without repeating mistakes. We can hope to see our cities and infrastructures get closer to our concepts of sustainability and become more supportive of cleaner living.