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Synchronous work between humans and technology will change how we work.

Artificial intelligence (AI) is changing the world around us. From how we travel to how we communicate, it has become part of our daily lives. It’s used in farming to kill weeds resulting in increased crop yield without the need for additional resources. It helps keep your finances safe by helping banks monitor and alert you of fraudulent transaction patterns. Chatbots provide quick answers to quick questions, allowing support agents to spend more time with clients on more pressing needs. Sports trainers use AI to closely monitor the impact games have on athletes’ bodies to help prevent injury, all while using the same technology to improve their skills beyond what was ever possible before. And recently, even the field of environmental air quality is advancing as well, thanks to AI.

From the stone age to the information age and beyond, technology has continuously changed how humans work. AI has been described as the “fourth Industrial Revolution,” and with this emerging technology, companies are learning to interface AI and the human worker together to attain better outcomes. This type of synchronous work is how problems that have plagued humans for years are being solved.

Worker safety is always a preoccupation of facility owners. OSHA reported over 5,000 worker deaths in 2019. To make facilities safer for workers, some organizations are turning to AI-based solutions. Some safety professionals use AI to sort through data sets and incident reports, observations and inspections to identify near misses or incident patterns. By training AI on these elaborate data sets, new patterns can emerge to help operators know if particular instances are happening at the same time of day or in specific regions of a facility. Machine learning excels in situations where traditional statistical analysis falls short. Machine learning can process datasets with potentially hundreds of inputs and outputs to inform decisions and predictions, and unlike statistical analysis, machine learning can do this without knowing the underlying probability distribution for the variables. Utilizing the power of data this way allows facilities to make changes that improve worker safety that we could have never known using traditional methods.

Risk assessments of the future have the potential to be much more impactful by using the power of AI. Another great example of machine learning in work is collision avoidance. In 2020, nearly 1,000 workplace fatalities were caused by workers colliding with objects or equipment. Workplaces such as warehouses and construction sites have begun implementing AI-based collision avoidance systems on machinery and workers to help protect them from being struck by machinery, as well as help gather large amounts of data that can be used to improve these systems. Research in this field is directly related to similar work being done in the self-driving car space. An even more practical use for computer vision AI in the workplace is monitoring systems that can see if workers are wearing the correct safety equipment. AI software has been developed to tell if a worker has a hard hat on, is wearing hi-vis clothing or if they are tethered correctly when working at height.

The applications for AI in the workplace go beyond just making the workplace safer. There are a number of researchers using AI to plan smart workspaces where humans and robotics co-work alongside each other in the same space. It could be that the future distribution center has humans paired with biomechanical “exoskeletons” working alongside autonomous forklifts. In order for this to work, AI is needed to plan smart paths, create collision avoidance and perform advanced biometric modeling to name a few. AI is also being used to run elaborate simulations that can prepare response plans for catastrophic events. Imagine that extreme weather has damaged a chemical plant, or an explosion has occurred in a complex mining operation. Machine learning models can help with adaptive changes to ventilation systems, evacuation routing and more.

Another advancement—and perhaps how technology will change the workplace the most in the next decade—is the tools that are being built that change how workers perform the everyday work that they do. For example, all over the world, clinical labs are experiencing a shortage of qualified lab analysts. A lab analyst’s job is difficult, tedious and both physically and mentally exhausting. Additionally, special training is required to perform the necessary tasks at a high level. These jobs also tend to typically be done by workers who are aging out of the workforce, and employers are having a hard time replacing them when retirement comes. Pair this with a growing aging population, and you have a perfect storm of a sharp increase in the need for more lab tests with a decrease of qualified workers to perform the analysis. Labs are turning to AI-driven diagnostics with human support to find sustainable solutions. This process involves digitizing work and using various forms of AI to assist humans.

First, samples are prepared and then scanned by a digital microscope. Once the slide is digitized, it can be analyzed by AI. In this stage, the AI can be trained to look for specific objects of interest, for example, white blood cells, parasites, cancerous cells, bacteria and many others. Next, the AI counts and classifies those objects to either label the slide as positive or negative. A human can then confirm the predictions made by the AI. In some cases, this technology can find hard-to-locate pathological organisms more often than humans, resulting in more accurate diagnoses. In others, it can reduce the time it takes for technicians to analyze a slide from three to five minutes to under 30 seconds3 per slide while improving accuracy and consistency.

In a recent study to compare the results of manual microscopy and AI-driven diagnostics, digital diagnostics were five times more sensitive. A similar study showed that digital diagnostics found two additional positive cases that had been missed by manual microscopy out of 135 positive results by technicians. As one can imagine, the accuracy and sensitivity demonstrated by this type of technology can impact lab productivity along with patient care and health across the globe.

This AI-driven diagnostics technology isn’t just being used for human healthcare. There are new AI-based analyses that industrial hygienists and environmental professionals are utilizing. AI is being used to help perform analyses on spore traps for mold and pollen, dust characterization and characterizing nanomaterial emissions. For spore trap analysis, software companies are taking existing laboratory methodologies that have been around for decades and actually improving them. The inconsistency of spore trap analysis is no secret in the indoor air quality space. In order to make these analyses economical for commercial laboratories, most laboratory SOPs only analyze about 20 percent to 30 percent of the sample and then extrapolate the data to get an estimated total for the entire sample. Since the particulate loading in a spore trap is not uniform, this can lead to huge variability in sample results. AI can analyze an entire spore trap sample in a fraction of the time it takes a human. Since the whole sample trace is analyzed, this results in more reliable and more repeatable data in an industry that has been stagnant with progress in microscopic analyses.