Buying clothes in person can be a frustrating experience. You go to the fitting room, try on the item, and find you've picked the wrong size. You then have to get dressed, go back onto the shop floor, get the right-sized item, and go through the whole process again in the fitting room. Finally, you find the right item in the right size -- but now you have to wait in a long line to make your purchase. What you thought was going to be a quick and easy procedure has turned into a bit of a slog.

Artificial Intelligence (AI) has been making headlines over the past few months with the widespread use and speculation about generative AI, such as Chat GPT. However, AI is a broad topic covering many algorithmic approaches to mimic some of the capabilities of human beings. There is a lot of work going on to use various types of AI to assist humans in their various activities. Note that all AI has limitations. It generally doesn't reason, like we do and it is generally best at recognizing patterns and using that information to create conclusions or recommendations.

Vending machines, which allow people to easily purchase items without interacting with a human worker, have been around since the 1st century, when a Greek engineer and mathematician named Hero Alexandria created a machine that accepted a coin before dispensing holy water at a temple, to prevent people from taking more than their share of holy water. Two millennia later, a far greater number and variety of products can be purchased from vending machines, thanks in part to the advent of new technologies including always-on, Internet of Things (IoT) connectivity, advanced physical and digital controls that allow these machines to be placed in a wide variety of settings, and the use of artificial intelligence (AI)-based algorithms that can capture and analyze customer insights, improve stocking efficiency, and deliver greater levels of personalization to customers. The global installed base of connected vending machines reached an estimated 2.4 million units in 2019, according to Berg Insight, a research firm that tracks the installed base of connected vending machines. Connected vending machines are equipped with an always-on Internet connection, which allows data to be sent between machines in the field and management software, enabling real-time payments, monitoring, and remote management of the machines. Advanced feature sets and functionality are projected to drive the market to nearly nine million units by 2024, according to Berg Insight, helped along by the desire of organizations to better serve customers without needing to attract and retain relatively costly human workers.

In order to place a CNN inside an embedded device, we need to let the model learn the task to solve to gain a well-performing network. The fundamental steps needed to achieve this are summarized in the Figure below for sake of clarity. The first key ingredient is represented by the dataset, that represents the data from which the model learns. There can be two different choices, based also on the goal of the project: use a benchmark or a custom dataset. The latter is the common case in the industrial field, where there is the need to have data that are not general, but specific to the problem under consideration.

An ongoing project intends to enable military and other scientists to monitor and even enhance the ways in which a soldier's brain sleeps and, importantly, attains rest and repair. The effort – a collaboration between the U.S. Army Medical Research and Development Command (USAMRDC) Military Operational Medicine Research Program (MOMRP) and scientists and engineers at Rice University (Houston, Texas) – is only one of a group of sensor-driven military projects seeking to create wearable technology to track and improve soldier performance and outcomes. Scientists at the Houston-based university are developing a noninvasive "sleeping cap" that analyzes the glymphatic system, the flow of fluid that is thought to cleanse and rid the brain of common metabolic waste during sleep. The cap will be used to further understand how the human brain deals with that waste, and if that function actually prepares and refreshes people for the next day. A team at Rice University's NeuroEngineering Initiative – together with teams from Rice's Institute of Biosciences and Bioengineering (IBB) and physicians from Houston Methodist Hospital and Baylor College of Medicine in Houston – are developing a lightweight skullcap that can analyze the wearer's glymphatic function and stimulate proper flow to treat sleep disorders and improve wakefulness and day-to-day function.

In 2020, the number of digital payment transactions grew to 779 billion, representing a vast acceleration in the adoption of digital payments around the world. The nature of these digital payments is also changing, as solutions come to market that can automate swift, convenient payments made from IoT devices. So what are IoT payments? In simple terms, IoT payments are payments triggered by IoT devices with a high degree of autonomy. IoT devices can include sensors, appliances, robots, drones, or other equipment.

The fourth industrial revolution is well underway with artificial intelligence (AI) at its heart powering new technologies and Internet of Things (IoT) devices from smartwatches to smart fridges, autonomous cars to home assistants, and security systems to a vast array of sensors. Using conventional computer architecture in the practical application of AI in IoTs leads to large power demands arising from the repetitive shifting of tremendous amounts of data between processors and memory units. These demands are only set to increase as AI improves and even larger amounts of data is generated. This increased power consumption comes with a potential impact on the environment via the emission of greenhouse gases through the generation of electricity through the burning of fossil fuels. The need to lower energy consumption in IoT technology has led to need for alternative, low-power alternatives that can implement AI.

Data infrastructure artificial intelligence company Torch.AI has won a five-year contract from the U.S. Navy to provide next-generation AI and data infrastructure software capabilities for the Navy's Digital Warfare Office (DWO), the company announced in a statement. The company will provide AI and machine-learning capabilities that are intended to allow the Navy to "better operate and maintain their operational fleet across a complex, siloed IT environment including cloud compute, storage, hardware, and cloud edge devices used for data lakes at unclassified, secret, and top-secret levels," the statement reads. The DWO was established in 2016 to better utilize the data that Navy platforms collect. Torch.AI will be responsible for combining proprietary sensor, vessel, and other maritime data sources from multiple Navy platforms into "commercially consistent data payloads," the statement reads.

Whether you're hip to it or not, conversational AI--which is really the sequencing of technologies like NLU/NLP, code-free programming, RPA, and machine learning inside of organizational ecosystems--has already begun reshaping the world at large. Lemonade, a tech- and user-centric insurance company is upending its industry by providing customers with a rewarding experience buying insurance that's facilitated by Maya, an intelligent digital worker described as "utterly charming" that can quickly connect dots and get customers insured. Maya is essentially an infinitely replicable agent that is always learning and doesn't make the same mistake twice. Compare that with whatever it costs Allstate to retain more than 12,000 agents in the US and Canada who are likely using outdated legacy systems and it's clear to see which way ROI is trending. Even bigger successes have been enjoyed by Ant Group (formerly Ant Financial) a nimble, Chinese financial giant that had surpassed the number of customers served by today's largest US banks by more than 10 times back at the start of 2020. Their IPO--which would have been the world's largest to date--collapsed after Chinese Communist Party leader Xi Jinping allegedly intervened. Subsequently, the company has broadened its scope past fintech to include sustainability and inclusive services (whatever those might be). Still, its core operations were built around a streamlined business structure that uses conversational AI to deliver meaningful experiences. While this kind of adoption of conversational AI in business settings is roundly expected to boom in the coming years, it will quickly seep into our daily lives as well, going beyond how we interact with the many companies in our lives and taking root in our interactions with all of the different technologies we regularly touch. I've always taken an interest in these topics, but like many cutting-edge things, they're hard to approach. Especially if you have no idea where to start. Especially if you don't have the expertise; the lexicon, the mindset, the lived experience. I'm a bit of an accidental Luddite, someone perpetually late to the party when it comes to the latest and greatest. Not to say that I'm completely unfamiliar with these things, just that integrating them into my work, and my life, is hard.

In this article, we are going to look at the biggest tech trends that you can expect to see in 2022. Let us go through the top tech trends and business trends we are likely to see over the next few years. We must define and invest our time in the top emerging technologies that will draw huge markets in 2022. Artificial intelligence, aka A.I., was one of the emerging tech trends of 2021. Emerging technologies are what you need to monitor in 2022.