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.”
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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