Researchers at the University of Washington have developed a new system capable of monitoring factory and warehouse workers and warn them of risky behaviors in real time. The new system relies on machine learning to get this done.
There were about 350,000 incidents of workers taking time off due to muscle, nerve, ligament, or tendon injuries, according to the U.S. Bureau of Labor Statistics. The workers with the highest number of incidents were those working in factories and warehouses.
These incidents are normally musculoskeletal disorders that happen when people do certain tasks that cause strain on the body. These researchers looked for a way to detect these behaviors so that workers can be more aware.
The algorithm of the new system divides certain tasks, such as lifting boxes off high shelves and carrying objects, into individual actions. A risk score is then calculated for each one.
Ashis Banerjee, an assistant professor in both the industrial & systems engineering and mechanical engineering departments at the UW, is one of the senior authors.
“Right now workers can do a self-assessment where they fill out their daily tasks on a table to estimate how risky their activities are,” she said. “But that’s time consuming, and it’s hard for people to see how it’s directly benefiting them. Now we have made this whole process fully automated. Our plan is to put it in a smartphone app so that workers can even monitor themselves and get immediate feedback.”
Those current self-assessments rely on snapshots of tasks being performed. The position of each joint gets scored, and they are all added up to determine a risk score. This new algorithm will make it much more simple as it is able to score an entire action instead.
The team tested the algorithm by using a dataset with 20 three-minute videos of people doing 17 activities. These activities are commonplace among warehouses and factories.
“One of the tasks we had people do was pick up a box from a rack and place it on a table,” said first author Behnoosh Parsa, a UW mechanical engineering doctoral student. “We wanted to capture different scenarios, so sometimes they would have to stretch their arms, twist their bodies or bend to pick something up.”
The researchers then used a Microsoft Kinect camera to capture the dataset, and 3D videos were recorded. They then determined what was happening to the person’s joints during the tasks.
The algorithm was first able to determine risk scores for each video frame. Eventually, it was able to tell when a task started and finished so that it could give a risk score for the entire action.
The team’s next step is developing an app that these factory workers and supervisors can use. They want it to be able to detect and warn of moderately risky actions and high-risk actions.
In the long term, they hope that robots will be able to be used in these factories and utilize the algorithm to help keep workers safe.
“Factories and warehouses have used automation for several decades. Now that people are starting to work in settings where robots are used, we have a unique opportunity to split up the work so that the robots are doing the risky jobs,” Banerjee said. “Robots and humans could have an active collaboration, where a robot can say, ‘I see that you are picking up these heavy objects from the top shelf and I think you may be doing that a lot of times. Let me help you.'”
The research was published in IEEE Robotics and Automation Letters on June 26, and it will be presented at the IEEE International Conference on Automation Science and Engineering in Vancouver, British Columbia on August 23.
Researchers Carry Out Census Of Wikipedia Bots
Researchers from the Stevens Institute of Technology (SIT) have recently finished an analysis of all the Wikipedia bots which work to maintain and improve the massive online encyclopedia. According to TechXplore, the results of the research could potentially inform the way that bots are used to develop commercial applications in fields like microchip design and customer service.
When Wikipedia first launched back in 2001, it had around 13,000 articles. Fast forward 18 years later and Wikipedia is home to a vast amount of information, over 40 million articles total, contributed to by over 500 million monthly users. In order to maintain all these of these articles, Wikipedia leverages 137,000 volunteer editors in a large body of bots, driven by simple AI programs. These bots are in charge of fixing tags, broken links, fixing typos, eliminating junk entries, and more.
The research team utilized computer algorithms to classify the bots by the functions that they carried out. The researchers were able to conduct an analysis of how AI programs and humans interact when engaging in large scale projects like maintaining a large repository of data such as Wikipedia. Understanding the way people and bots interact is a major focus for the field of Human-Computer Interaction, and as such the study was recently published in the Proceeding of the ACM of Human-Computer Interaction.
Jeffrey Nickerson, one of the study’s authors and a professor at the School of Business at (SIT), explained that AI is making massive changes in the way knowledge is produced and maintained and that Wikipedia’s size and ubiquity make it an excellent place to study these changes. Nickerson explained that to TechXplore that in the future it is likely we will all be working alongside AI in some capacity and therefore it’s important to understand how bots impact people’s decisions and how bots can be made into more effective tools.
Wikipedia made a great case study for the researchers because of its detailed record-keeping and transparency. The research team employed automatic classification algorithms to apply labels to bots and develop a map detailing how bots interact with each other on Wikipedia. Certain clusters of functions could be analyzed and the bots that carried out those functions labeled with descriptions like “Advisor” or “Fixer”. Fixers take care of vandalism and repair broken links while Advisors give tips to editors and suggest new tasks. There were also “Connectors” which are responsible for establishing links between different resources or pages.
The researchers found that Wikipedia bots played nine main roles on the site, and that these bots accounted for around 10% of all Wikipedia activity. Furthermore, on certain subsections of the site, such as the Wikidata platform, bots account for about 88% of the site’s activity. Most activity is carried out by the approximately 1200 fixer-bots that repair the site and are responsible for over 80 million edits. In contrast, while there are fewer advisor bots, they help shape people’s interactions with the site, guiding what kinds of edits are made and what kinds of features are created.
One way Wikipedia leverages the power of bots is by greeting new members of the community. When people join online communities they are more likely to stay as active members if they are greeted by other members of the community. This apparently proves true even if the community member welcoming them is a bot. Bots encourage community members to stay around and contribute to the community by pointing out errors, as long as they were cordial about these corrections. As Nickerson explained to TechXplore:
“People don’t mind being criticized by bots, as long as they’re polite about it. Wikipedia’s transparency and feedback mechanisms help people to accept bots as legitimate members of the community.”
As bots become more and more important to the maintenance of growing online communities, studying how Wikipedia has leveraged bots can help other companies and entities create bots that help human users and encourage prosocial activity. Both Wikipedia’s successes and failures with bots should be critically examined.
“By studying Wikipedia, we can prepare for the future, and learn to build AI tools that improve both our productivity and the quality of our work,” said Nickerson.
Study Shows That Workers Now Trust A Robot More Than Their Managers
Technology giant Oracle Corporation recently published a study that examined the relation of workers that indicates a distinct change in which artificial intelligence is changing the relationship between people and technology at work. According to this research that was done on a global scale, based on the analysis of the data, Oracle came to the conclusion that now 64% of people are inclined to trust a robot more than their manager.
The study was done involving 8,370 employees, managers and HR leaders across 10 countries. As is stated in the company’s press release, it found that 2AI has changed the relationship between people and technology at work and is reshaping the role HR teams and managers need to play in attracting, retaining and developing talent.
These results counter some common fears present that AI will have a negative impact on jobs, employees, managers and as the release states, “HR leaders across the globe are reporting increased adoption of AI at work and many are welcoming AI with love and optimism.”
In presenting the results of the study, ItProPortal notes that the report’s results show that “the majority of people would trust a robot more than their manager. They’d rather turn to a robot for advice, than their manager.”
Summarising the main points of the report, the press release point to the following:
[ ] AI is becoming more prominent with 50 percent of workers currently using some form of AI at work compared to only 32 percent last year. Workers in China (77 percent) and India (78 percent) have adopted AI over 2X more than those in France (32 percent) and Japan (29 percent).
[ ] The majority (65 percent) of workers are optimistic, excited and grateful about having robot co-workers and nearly a quarter report having a loving and gratifying relationship with AI at work.
[ ] Workers in India (60 percent) and China (56 percent) are the most excited about AI, followed by the UAE (44 percent), Singapore (41 percent), Brazil (32 percent), Australia/New Zealand (26 percent), Japan (25 percent), U.S. (22 percent), UK (20 percent) and France (8 percent).
[ ] Men have a more positive view of AI at work than women with 32 percent of men optimistic vs. 23 percent of women.
The results also indicate that most of the interviewed people believe that, as ItProPortal says, “robots would do a better job than their managers at providing unbiased information, maintaining work schedules, solving problems and maintaining a budget. At the same time, humans are considered better at understanding employee feelings, coaching and building a work culture.”
Also, it turns out that people weren’t afraid of losing their jobs to AI, with most of them being “optimistic, excited and grateful” to be able to work with the latest advancements in technology. The report quotes Jeanne Meister, Founding Partner of Future Workplace, which said that the company’s 2019 results “reveal that forward-looking companies are already capitalizing on the power of AI. As workers and managers leverage the power of artificial intelligence in the workplace, they are moving from fear to enthusiasm as they see the possibility of being free of many of their routine tasks and having more time to solve critical business problems for the enterprise.”
The Use of Artificial Intelligence In Higher Education
Lasse Rouhainen, the author of Artificial Intelligence: 101 Things You Must Know Today About Our Future and an international expert on artificial intelligence, disruptive technologies, and digital marketing wrote recently in Harvard Business Review about his views on how AI could be used in higher education.
In his opinion both the opportunities and challenges that the introduction of artificial intelligence could bring to higher education “are significant.” As he notes, the higher education today in general faces a wide range of challenges – from “disengaged students, high dropout rates, and the ineffectiveness of a traditional “one-size-fits-all” approach to education.”
Rouhainen sees the possibility to overcome these challenges with the correct use of big data analytics and AI, which would create “personalized learning experiences,” that would then overcome some of these challenges. This personalized learning experience would create “a completely unique educational approach,” which in turn offers the possibility of increasing student’s motivation and possibly lower the drop out rate. On the other hand, professors would have the ability to better understand the learning process of each of their students, making the teaching process much more effective.
As the author explains, “AI learning systems would be helping students to reach their full potential, quite possibly preventing them from dropping out by identifying problems early enough to allow the appropriate corrective measures to be taken.” To achieve that, though, and to make an AI-based learning system to work properly, “big data would be needed in order to train it.” Rouhainen adds the caveat that “data would need to be used ethically, and students would need to be informed about how their personal data might be shared and used by AI algorithms.”
Such ‘ethical use’ could be in the form of “ MyData.org, an international non-profit whose mission is to promote human-centered control and privacy of personal data.” The basis on which MyData.org operates is “to give users more control over which personal data they choose to share with AI systems.”
A successful AI testing was noted at The University of Murcia in Spain when an AI-enabled chatbot to answer students’ questions about the campus and areas of study. The results were quite surprising to school administrators – the chatbot was able able to answer more than 38,708 questions, answering correctly more than 91% of the time. As was also noted, this chatbot was “able to provide immediate answers to students outside of regular office hours, but university officials also found that the chatbot increased student motivation.” At the same time, there were no changes in the structure of the staff.
Similar advances were registered at several other universities that tested chatbots in handling repetitive tasks that otherwise need to be handled by either a professor or a member of faculty staff. One such task is answered student FAQ’s frequently asked questions. Staffordshire University in the UK and Georgia Tech in the U.S. have rolled out chatbots that offer 24/7 answers to students’ most frequently asked questions.
Rouhainen also mentions the use of AI in reducing stress among students and improving their motivation. This could be achieved with the use of chatbots and virtual assistants that would take a student’s mental well-being. As an example, he mentions Woebot, an AI-enabled chatbot designed to help users learn about their emotions with “intelligent mood tracking.”
One of the problems the author sees is the fact that students need to understand that“over time, more repetitive and routine tasks will be automated and performed by artificial intelligence, automation, and robots.” He stresses that right now, “many universities around the world are failing to teach students about the kinds of skills that will and will not be needed in their future careers.”