Pratt Miller demonstrated its LAAD disinfecting robot at Gerald R Ford International Airport in Grand Rapids, MI, in July 2020. The impacts of the COVID-19 pandemic are likely to be felt for years to come, regardless of the presence and availability of a vaccine. Physical measures adopted by humans, such as social distancing or wearing masks, are likely to be utilized for years to come, along with technological developments deployed in both public and private spaces that are focused on enforcing social distancing, enabling more efficient cleaning and disinfecting of spaces, and driving more automation and intelligence to reduce humans' direct physical interaction with each other. Some companies and individuals feel the best way to avoid COVID-19 or other viruses is to simply avoid all unnecessary human contact. As such, many companies have introduced or fast-tracked the use of automation to lessen their reliance on human workers, as well as to enhance their responsiveness to customer queries.

In today's world, it is nearly impossible to avoid voice-controlled digital assistants. From the interactive intelligent agents used by corporations, government agencies, and even personal devices, automated speech recognition (ASR) systems, combined with machine learning (ML) technology, increasingly are being used as an input modality that allows humans to interact with machines, ostensibly via the most common and simplest way possible: by speaking in a natural, conversational voice. Yet as a study published in May 2020 by researchers from Stanford University indicated, the accuracy level of ASR systems from Google, Facebook, Microsoft, and others vary widely depending on the speaker's race. While this study only focused on the differing accuracy levels for a small sample of African American and white speakers, it points to a larger concern about ASR accuracy and phonological awareness, including the ability to discern and understand accents, tonalities, rhythmic variations, and speech patterns that may differ from the voices used to initially train voice-activated chatbots, virtual assistants, and other voice-enabled systems. The Stanford study, which was published in the journal Proceedings of the National Academy of Sciences, measured the error rates of ASR technology from Amazon, Apple, Google, IBM, and Microsoft, by comparing the system's performance in understanding identical phrases (taken from pre-recorded interviews across two datasets) spoken by 73 black and 42 white speakers, then comparing the average word error rate (WER) for black and white speakers.

Waverly Labs' Ambassador, an over-the-ear translation device, can support up to 20 languages and 42 dialects. The greatest obstacle to international understanding is the barrier of language," wrote British scholar and author Christopher Dawson in November 1957, believing that relying on live, human translators to accurately capture and reflect a speaker's meaning, inflection, and emotion was too great of a challenge to overcome. More than 60 years later, Dawson's theory may finally be proven outdated, thanks to the development of powerful, portable real-time translation devices. The convergence of natural language processing technology, machine learning algorithms, and powerful portable chipsets has led to the development of new devices and applications that allow real-time, two-way translation of speech and text. Language translation devices are capable of listening to an audio source in one language, translating what is being said into another language, and then translating a ...

Since their introduction more than a decade ago, smartphones have been equipped with cameras, allowing users to capture images and video without carrying a separate device. Thanks to the use of computational photographic technologies, which utilize algorithms to adjust photographic parameters in order to optimize them for specific situations, users with little or no photographic training can often achieve excellent results. The boundaries of what constitutes computational photography are not clearly defined, though there is some agreement that the term refers to the use of hardware such as lenses and image sensors to capture image data, and then applying software algorithms to automatically adjust the image parameters to yield an image. Examples of computational photography technology can be found in most recent smartphones and some standalone cameras, including high dynamic range imaging (HDR), auto-focus (AF), image stabilization, shot bracketing, and the ability to deploy various filters, among many other features. These features allow amateur photographers to produce pictures that can, at times, rival photographs taken by professionals using significantly more expensive equipment.

Agricultural businesses usually have a massive number of trackable assets (plants, livestock, and machinery), often operate in wide geographic areas in which these assets are located, and are subject to operational factors often beyond their control, such as the amount of sunlight or rainfall they receive, or temperature fluctuations. As such, agriculture is ripe for the adoption of new technologies to help monitor and manage assets on a granular level, and everything from Internet of Things (IoT) sensors, robots, and drones are being used by farms around the globe. The U.S. Department of Agriculture's National Institute of Food and Agriculture notes that the farms of today are avid users of agriculture technologies such as robots, temperature and moisture sensors, aerial imaging, and GPS technology, which are more precise and efficient than humans alone, and allow for safer, more efficient, and more profitable operations. One example of how technology enables new farming techniques is the use of robotic harvesting on indoor farms, which today account for a tiny fraction of the 900 million acres of traditional farmland in the U.S. However, these indoor farms are well suited to the growth of vegetables such as tomatoes, lettuce, and other leafy greens, are highly sustainable, generally feature an average yield per acre more than 10 times higher than that of outdoor farms, and represent a continuation of the agricultural sector's trend toward incorporating precision agriculture techniques to improve yields and become more sustainable.

A nurse asks a patient to describe her symptoms. A fast-food worker greets a customer and asks for his order. A tourist asks a police officer for directions to a local point of interest. For those with all of their physical faculties intact, each of these scenarios can be viewed as a routine occurrence of everyday life, as they are able to easily and efficiently interact without any assistance. However, each of these interactions are significantly more difficult when a person is deaf, and must rely on the use of sign language to communicate.

Sensor technology is designed to allow machines to interact with real-world inputs, whether they are humans interacting with their smartphones, autonomous vehicles navigating on a busy street, or robots using sensors to aid in manufacturing. Not surprisingly, three-dimensional (3D) sensors, which allow a machine to understand the size, shape, and distance of an object or objects within its field of view, have attracted a lot of attention in recent months, thanks to their inclusion on Apple's most-advanced (to date) smartphone, the iPhone X, which uses a single camera to measure distance. Indeed, the TrueDepth system, which replaces the fingerprint-based TouchID system on the Apple handset, shines approximately 30,000 dots outward onto the user's face. Then, an infrared (IR) camera captures the image of the dots, which provides depth information based on the density of the dots (closer objects display a dot pattern that is spread out, whereas objects that are farther away create a denser pattern of dots. Altogether, the placement of these dots creates a depth map with 3D data that is used to supply the system with the information it needs to check for a facial identity match, which then unlocks the device.

The construction of New York's Empire State Building is often seen as the figurative and literal pinnacle of construction efficiency, rising 1,250 feet and 102 stories from the ground to its rooftop spire in just over 13 months' time, at a human cost of just five lives. Indeed, most of today's construction projects would be lucky to come close to that level of speed, regardless of the building's size. While the construction industry traditionally has been slow to change the way it operates, several new technologies are poised to usher in a new era of faster and more automated construction practices. Three-dimensional (3D) printing is among the key technologies that are expected to change the way structures are built in the future, as construction engineers and contractors seek methods for completing buildings more quickly, more efficiently, and, in many cases, with a greater attention paid to sustainability. Large printers that can print construction materials such as foam or concrete into specific shapes can drastically speed up the creation of walls, decorative or ornamental pieces, and even certain structural elements.

Customer contact centers are most efficient when they are able to automate routine tasks and quickly route callers to human agents who can solve issues in a timely and courteous fashion. In years past, rules-based decision matrices (such as "press 1 for sales, press 2 for technical support") were the de facto standard for "intelligent" customer service systems, and often left customers frustrated and angry by the time they reached a live human being. Advances in artificial intelligence are yielding significant benefits for organizations that deploy the technology in their call centers. Indeed, rather than simply being used to replace contact center workers, artificial intelligence (Al)-based technologies, including machine learning, natural language processing, and even sentiment analysis, are being strategically deployed to improve the overall customer experience by providing functionality that would be too time-consuming or expensive to do manually. "It's a lot more prevalent than people think," explains Justin Robbins, content director for the International Customer Management Institute and HDI.

Silke Pan of Team PolyWalk EPFL in the powered exoskeleton race. Most physical competitions are based around the idea of participants pushing themselves physically, demonstrating to the world that they are the fastest, strongest, or otherwise physically gifted. For those with significant physical disabilities or injuries, however, simply accomplishing basic everyday tasks can be an Olympic-level feat. That's where Cybathlon, a new competition designed to promote innovative assistive devices, may accomplish two goals: providing a competitive forum for disabled athletes, and highlighting the specific advances that are being made in robotic assistive aids designed to help those with significant physical disabilities. Conceived and developed by Switzerland's ETH Zurich (a science and research university) and National Centre of Competence in Research (NCCR) Robotics professor Robert Riener, the first iteration of Cybathlon took place last October in Zurich.