OYSTER BAY, NY--Advances in machine learning technology will help propel sales of smart cameras for machine vision applications to 197 million units and a total value of $35 billion by 2027, according to global technology intelligence firm ABI Research. "The shift from machines that can automate simple tasks to autonomous machines that can'see' to optimize elements for extended periods will drive new levels of industrial innovation. This is the innovation that machine learning offers to machine vision. Machine learning can augment classic machine vision algorithms by employing the range and reach of neural network models, thus expanding machine vision far beyond visual inspection and quality control," explains David Lobina, artificial intelligence and machine learning analyst at ABI Research. Smart cameras, embedded sensors and powerful computers can bring machine learning analyses to every process step.

Business technology investment no longer follows a predictable path to growth. The global venture capital (VC) investment in artificial intelligence (AI) was close to its peak in 2021 reaching $22.3 billion, according to the latest worldwide market study by ABI Research. This is just $400 million shy of the historical high of $22.7 billion recorded in 2019. Compared to the $15 billion recorded in 2020, the market made a remarkable recovery, with a 48.5 percent year-on-year growth. Will the future AI marketplace return to stable growth, or will it remain volatile?

The internet of things (IoT) is a huge market, with a forecast of 23.6 billion connections by 2026, according to ABI Research. This growth entails a wide range of devices and applications, opening up opportunities for different wireless connectivity technologies, improved security, and enhanced features with artificial intelligence. This month's issue covers all of it: wireless technologies, security, and AI in IoT. The Eclipse Foundation's 2021 IoT & Edge Developer survey revealed that the top three concerns of IoT developers are security, connectivity, and deployment. The significant increase in both security and connectivity concerns highlights the challenges that developers face in determining the right technologies for their applications, according to the report.

Video management platforms, equipped with technologies such as artificial intelligence (AI) and machine learning, are vastly expanding capabilities in the area of urban surveillance and public safety, according to research. The Covid-19 pandemic has spurred the use of technologies, such as crowd monitoring, which ABI Research believes are here to stay. It adds that other developments in urban surveillance from live video feeds to bodycams, will be assisted by the introduction of 5G. In its report, Urban Surveillance Technologies and Public Safety Strategies, global technology intelligence firm, ABI Research, forecasts a compound annual growth rate of 11.6 per cent with 1.4 billion closed-circuit television (CCTV) surveillance cameras in urban areas worldwide in 2030. "Currently, the main use of CCTV in public safety is to aid authorities to solve crimes retroactively," said Lindsey Vest, smart cities and smart spaces research analyst at ABI Research.

Edge computing is booming, with estimates ranging up to $61 billion in value in 2028. While definitions vary, edge computing is about taking compute power out of the data center and bringing it as close as possible to the device where analytics can run. The devices can be standalone IoT sensors, drones, or autonomous vehicles. Increasingly, data generated at the edge are used to feed applications powered by machine learning models," stated George Anadiotis, analyst, engineer and founder of Linked Data Orchestration of Berlin, Germany, working on the intersection of technology, media and data, writing in a recent account in ZDnet. However, "There's just one problem: machine learning models were never designed to be deployed at the edge.

Simultaneously, the value in the technology stack is shifting beyond the hardware and middleware to analytics and value-added services, such as machine learning and other kinds of AI. ABI Research estimates that machine learning and AI services in the IoT domain will grow at a compound annual growth rate (CAGR) of nearly 40%, to $3.6 billion (€3.04 billion) in 2026. While COVID-19 impacted many industries, the IoT data analytics market has been less affected. In fact, many newly emerging cloud-native, data-enabled analytics vendors have benefited from COVID-19. "Since industries are transitioning to'remote everything', out-of-the-box solutions for remote monitoring, asset management, asset visibility, and predictive maintenance are in high demand and exemplify market acceleration. "Vendors, such as DataRobot, are now easing access to ML and AI tool sets through different deployment options at the edge, on-premises, and the cloud, and through consumption using Platform as a Service (PaaS), and Software as a Service (SaaS)," explains Kateryna Dubrova, Research Analyst at ABI Research. "All and all, the COVID-19 pandemic highlighted the importance of rapid deployment solutions, such as hardware agnostic SaaS." Companies like AWS, C3, and Google also have been successful in promoting their products and analytics capabilities (tool sets and environment) by creating centralized repositories for COVID-19 data. Currently, these data lakes are public and are not monetised, but ABI Research expects those companies will attempt to use the data lakes to create products for sale to the healthcare market in the future. From a technology perspective, the data lakes could be the first step for creating and testing data visibility, and streaming analytics services. COVID-19 has showcased the public cloud's healthcare industry ambitions expanding into pharmaceutical, biomedicine, and telemedicine. Big data and data analytics might not have a remedy for the virus, but IoT-data enabled technologies proved essential to lessen public anxiety, to monitor patients, and prepare the infrastructure for new outbreaks. "AI and ML usage has accelerated during the pandemic – however, greenfield AI projects have seen a significant slowdown.

Despite the challenges facing the wider manufacturing industry during the coronavirus pandemic, collaborative robots continue to attract attention and investment, due to their ease of use, redeployability, and convenience to end-users who struggle to afford more traditional forms of automation. According to a new report from global tech market advisory firm ABI Research, the cobot market is set to grow substantially over the coming decade. The market had a global valuation of $475 million in 2020, which will expand to $600 million in 2021 and $8 billion in 2030, with a projected annual growth of 32.5 percent. Rian Whitton, principal analyst at ABI Research, says: "The most direct benefit of cobots is not in their ability to collaborate with humans. "Rather, it is in their relative ease of use, improved interface, and the ability of end-users to redeploy them for different tasks." This has made cobots popular with small and medium-sized enterprises (SME's) which value flexibility and incremental automation where the maintenance cost is not prohibitive. Universal Robots is currently the dominant player in the market, with 50 percent of the total shipments and posting $219 revenue for 2020, but challengers like Fanuc, ABB, and others are beginning to catch up after initially lagging in the space. They have done this by improving user interface and the usability of their systems. Whitton says: "The barriers between cobots and standard industrial robots are beginning to breakdown, as many vendors are experimenting with dual-mode robots that can have a cobot and industrial mode.

The value of data is increasing, and that value is stimulating the Internet of Things (IoT) Advanced Analytics Market, with the emergence of accessible out-of-the-box and off-the-shelf machine learning (ML) and artificial intelligence (AI) solutions. Vendors are now easing access to ML and AI toolsets by expanding availability through deployment options that include the edge, on-premises, cloud, Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS). Global tech market advisory firm, ABI Research, finds that the IoT ML and AI market will reach US$1.09 billion in 2020 and grow to US$10.6 billion in 2026. Edge ML/AI is more prevalent in manufacturing and industrial segments, where there is an immediate need to assess, transform and augment data as it is being generated through functions of quick pattern recognition, labeling, and protocol optimization. "The IoT Edge Advanced Analytics Market is essentially operationalized ML and AI products and services targeted at Operational Technology (OT) teams to understand and extract insights," explains Kateryna Dubrova, Research Analyst at ABI Research.

Despite the current pandemic-related downturn, the demand for edge AI chips will grow to outstrip demand for cloud AI chips for the first time in 2025, according to a new report from ABI Research. By 2025, the edge AI chip market will reach $12.2bn in revenue, whereas cloud AI chip revenues will reach $11.9bn in the same time frame. While most AI training and inference workloads are handled in the cloud today, ABI Research predicts that growth in the edge AI chipset market will be driven by increasing demands for low latency and data privacy plus the availability of low-cost ultra-low-power chips designed specifically for this application. AI training and inference will be processed in gateways and all types of edge devices, right down to sensor nodes in the next five years. Save Your Seat for: the Upcoming Webinar on "Mind of Engineer" Survey results "By integrating an AI chipset designed to perform high-speed inference and quantized federated learning or collaborative learning models, edge AI brings task automation and augmentation to device and sensor levels across various sectors," said Lian Jye Su, principal analyst at ABI Research.

A budget proposal from the White House would boost funding for AI by around 30 percent as the US aims to retain its technological supremacy. Countries around the world are vastly increasing their budgets for AI, and with good reason. Just look at Gartner's Hype Cycle released yesterday to see how important the technology is expected to be over the next decade. Russian president Vladimir Putin famously said back in 2017 that the nation which leads in AI "will become the ruler of the world". Putin said that AI offers unprecedented power, including military power, to any government that leads in the field.