Abu Dhabi-based agritech company Silal is teaming up with a Dutch horticultural-software provider Hoogendoorn to deploy IoT sensors across 100 farms this year in an effort to boost agricultural production in the United Arab Emirates. The project, to be known as the Digital Agronomy Service, aims to help local farmers and advisers make smart decisions when it comes to crop management, fertilization and irrigation and thereby maximize the amount of locally grown produce available for consumption. The service will deploy a wide range of sensors to measure environmental conditions that can affect the health of the crop, including temperature, humidity, vapor difference, radiation, pH, soil moisture, electric conductivity and more. The sensors will also track crop growth and needs, such as water, energy, CO2, fertilizers and agrichemical applications. Silal's agricultural engineers will then use AI-powered computers to process the data and help the company devise prudent crop-growth plans and projects.

TU-Automotive's editor, Paul Myles, reports that "British lawyers are calling for automakers to shoulder unlimited legal responsibility for a driverless vehicle's actions on the roads." Subsequently, drivers will become users-in-charge, absolving human drivers of any blame for the "vehicle's driving tasks in the event of an accident or breach of any highway regulations. As reported by the BBC, the UK's Law Commission was asked in 2018 to come up with a series of reports on the regulatory framework for automated vehicles and their use on public roads." While there is an ongoing discussion about connected and autonomous vehicle (CAV) liability in terms of who or what is responsible whenever an accident occurs, in a telephone conversation with TU-Automotive, the Society of Motor Manufacturers and Traders (SMMT) was keen to point out that there are currently no plans to make automakers 100% responsible for accidents caused by one of their vehicles in semi or fully autonomous mode. However, there is still a discussion to be had about it and in, certain circumstances such as an accident caused by a technical fault when a human driver is not in charge of a vehicle, there is the persistent view that they should be liable.

In the drive towards full autonomy, vehicle makers and solutions providers must tackle a nearly impossible variety of factors. Some of these, often necessarily, are more attention-grabbing than others but there are numerous ones just as critical that don't typically appear in auto technology headlines. One is the weather, which is somewhat odd because information about weather and its effect on road conditions has been crucial ever since humankind has climbed into a car. In our'assisted driving' age, that need remains acute and experts say it will be more so as we move up the levels of autonomy. Fortunately, with increasingly robust vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) technology, weather data and forecasts can be delivered ever more effectively.

The Internet of Things' killer app might be artificial intelligence. While it may be a stretch to classify artificial intelligence (AI) and its multifaceted offshoot machine learning (ML) as true applications, these techs can profoundly change IoT operations. AI makes IoT networks smarter and able to scale as needed without the risk of uncontrollable growth. IoT operations is an ongoing struggle to try to ensure that the thousands or more devices run properly and safely on an enterprise network and that the data that's being collected is both accurate and timely. While the sophisticated back-end analytics engines do the heavy lifting of processing the steady stream of data, ensuring the quality of the data itself is often left to somewhat archaic methodologies.

Despite silicon shortages, several new capabilities for embedded machine learning on Internet of Things devices will emerge in 2021, industry watchers predict. New capabilities mean severing the cord between so many Internet of Things ( IoT) devices and the cloud and instead running processes at the edge. The boost in chip processing capabilities--which will continue to increase next year, as Moore's Law dictates--means sidestepping cloud-based latency issues, among other benefits. Experts argue that moving processing to the edge – or "going to local execution," as Hiroshu Doyu,, an embedded AI researcher at Ericsson, puts it –will deliver five distinct advantages in 2021: Privacy will be less "porous," Doyu said, offering fewer opportunities for data to be stolen while in transit to the cloud or on the return trip. "Once the AI is more powerful, that kind of device can be installed without a power line," he said. "More powerful IoT AI chips will be shipped and more domain-specific IoT AI chips will be shipped.

Securing vast and growing IoT environments may not seem to be a humanly possible task--and when the network hosts tens or hundreds of thousands of devices the task, indeed, may be unachievable. To solve this problem, vendors of security products have turned to a decidedly nonhuman alternative: artificial intelligence. "Cyberanalysts are finding it increasingly difficult to effectively monitor current levels of data volume, velocity and variety across firewalls," CapGemini noted in a survey research report, "Reinventing Cybersecurity With Artificial Intelligence." The report also noted that traditional methods may no longer be effective: "Signature-based cybersecurity solutions are unlikely to deliver the requisite performance to detect new attack vectors." In addition to conventional security software's limitations in IoT environments, CapGemini's report revealed a weakness in the human element of cybersecurity.

A race is on to accelerate artificial intelligence (AI) at the edge of the network and reduce the need to transmit huge amounts of data to the cloud. The edge, or edge computing, brings data processing resources closer to the data and devices that need them, reducing data latency, which is important for many time-sensitive processes, such as video streaming or self-driving cars. Development of specialized silicon and enhanced machine learning (ML) models is expected to drive greater automation and autonomy at the edge for new offerings, from industrial robots to self-driving vehicles. Vast computer resources in centralized clouds and enterprise data centers are adept at processing large volumes of data to spot patterns and create machine learning training models that "teach" devices to infer what actions to take when they detect similar patterns. But when those models detect something out of the ordinary, they are forced to seek intervention from human operators or get revised models from data-crunching systems.

As connected devices proliferate, new ways of processing have come to the fore to accommodate device and data explosion. For years, organizations have moved toward centralized, off-site processing architecture in the cloud and away from on-premises data centers. Cloud computing enabled startups to innovate and expand their businesses without requiring huge capital outlays on data center infrastructure or ongoing costs for IT management. It enabled large organizations to scale quickly and stay agile by using on-demand resources. But as enterprises move toward more remote models, video-intensive communications and other processes, they need an edge computing architecture to accommodate data-hogging tasks.

Artificial intelligence is a vital component of digital transformation initiatives as the volume of data that organizations gather continues to swell. A total of 69% of survey respondents to the 2020 IoT Adoption Survey reported using artificial intelligence (AI)/machine learning (ML) as part of IoT initiatives. Top AI priorities include performance monitoring and trend forecasting in addition to sensor and business data integration. Other popular applications of artificial intelligence in IoT projects include ensuring security, computer vision and product design and testing. Those findings are consistent with other data.

Artificial intelligence is a bit of a buzz term these days – but what do people really mean when they say AI? And why should local governments care? First of all, AI is extremely misunderstood. We aren't talking about HAL from "2001: A Space Odyssey," necessarily; we're talking about what Alan Turing speculated about "thinking machines" back in the 1950s. According to the Brookings Institute, AI is generally thought to refer to "machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment and intention."