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Researchers Have Developed A System to Monitor Risky Behavior in Factories

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Researchers Have Developed A System to Monitor Risky Behavior in Factories

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. 

 

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Alex McFarland is a historian and journalist covering the newest developments in artificial intelligence.

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Study Shows That Workers Now Trust A Robot More Than Their Managers

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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.”

 

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The Use of Artificial Intelligence In Higher Education

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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.”

 

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AI Being Used To Personalize Job Training and Education

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AI Being Used To Personalize Job Training and Education

The landscape of jobs will likely be dramatically transformed by AI in the coming years, and while some jobs will go by the wayside, other jobs will be created. It isn’t clear yet how the nature of job automation will impact the economy, whether or not more jobs will be created than displaced, but it is obvious that those who work in the positions created by AI will need training to be effective at them.

Displaced workers are going to need the training to work in the new AI-related job fields, but how can these workers be trained quickly enough to remain competitive in the workplace? The answer could be more AI, which could help personalize education and training.

Bryan Talebi is the founder and CEO of the startup Ahura AI, which aims to use AI to make online education programs more efficient, targeting them at the specific individuals using them. Talebi explained to SingularityHub that Ahura is in the process of creating a product that will take biometric data from people taking online education programs and use this data to adapt the course material to the individual’s needs.

While there are security and privacy concerns associated with the recording and analysis of an individual’s behavioral data, the trade-off would be that, in theory, people would acquire valuable skills much more quickly. By giving personalized material and instruction to learners, a learner’s individual needs and means can be accounted for. Talebi explained that Ahura AI’s prototype personalized education system is already showing some impressive results. According to Talebi, Ahura AI’s system helps people learn between three to five times faster than current education models allow.

The AI-enhanced learning system developed by Ahura works through a series of cameras and microphones. Most modern mobile devices, tablets, and laptops have cameras and microphones, so there is little additional cost of investment for users of the platform. The camera is used to track facial movements of the user, and it captures things like eye movements, fidgeting, and micro-expressions. Meanwhile, the microphone tracks voice sentiment, analyzing the learner’s word usage and tone. The idea is that these metrics can be used to detect when a learner is getting bored/disinterested or frustrated, and adjust the content to keep the learner engaged.

Talebi explained that Ahura uses the collected information to determine an optimal way to deliver the material to each student of the course. While some people might learn most easily through videos, other people will learn more easily through text, while others will learn best through experience.  The primary goal of Ahura is to shift the format of the content in real-time in order to improve the information retention of the learner, which it does by delivering content that improves attention.

Because Ahura can interpret user facial expressions and body language, it can predict when a user is getting bored and about to switch away to social media. According to Talebi, Ahura is capable of predicting when someone will switch to Instagram or Facebook with a 60% confidence interval, ten-seconds out from when they switch over. Talebi acknowledges there is still a lot of work to be done, as Ahura has a goal of getting the metric up to 95% accuracy, However, he believes that the performance of Ahura shows promise.

Talebi also acknowledges a desire to utilize the same algorithms and design principles used by Twitter, Facebook, and other social media platforms, which may concern some people as these platforms are designed to be addictive. While creating a more compelling education platform is arguably a more noble goal, there’s also the issue that the platform itself could be addictive. Moreover, there’s a concern about the potential to misuse such sensitive information in general. Talebi said that Ahura is sensitive to these concerns at that they find it incredibly important that the data they collect is never misused, noting that some investors immediately began inquiring about the marketing potential of the platform.

“It’s important that we don’t use this technology in those ways. We’re aware that things can go sideways, so we’re hoping to put up guardrails to ensure our system is helping and not harming society,” Talebi said.

Talebi explained that the company wants to create an ethics board that can review the ways the data the company collects is used. Talebi said the board should be diverse in thought, gender, and background, and that it should “have teeth”, to help ensure that their software is being designed ethically.

Ahura is currently in the process of developing its alpha prototypes, and the company hopes that during beta testing it will available to over 200,000 users in a large scale trial against a control group. The company also hopes to increase the kinds of biometric data they use for their system, planning to log data from things like sleep patterns, heart rate, facial flushing, and pupil dilation.

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