The 7th International Conference of the Immersive Learning Research Network (iLRN 2021) will be an innovative and interactive virtual gathering for a strengthening global network of researchers and practitioners collaborating to develop the scientific, technical, and applied potential of immersive learning. It is the premier scholarly event focusing on advances in the use of virtual reality (VR), augmented reality (AR), mixed reality (MR), and other extended reality (XR) technologies to support learners across the full span of learning–from K-12 through higher education to work-based, informal, and lifelong learning contexts.
Following the success of iLRN 2020, our first fully online and in-VR conference, this year’s conference will once again be based on the iLRN Virtual Campus, powered by VirBELA, but with a range of activities taking place on various other XR simulation, gaming, and other platforms. Scholars and professionals working from informal and formal education settings as well as those representing diverse industry sectors are invited to participate in the conference, where they may share their research findings, experiences, and insights; network and establish partnerships to envision and shape the future of XR and immersive technologies for learning; and contribute to the emerging scholarly knowledge base on how these technologies can be used to create experiences that educate, engage, and excite learners.
Note: Last year’s iLRN conference drew over 3,600 attendees from across the globe, making the scheduling of sessions a challenge. This year’s conference activities will be spread over a four-week period so as to give attendees more opportunities to participate at times that are conducive to their local time zones.
##### TOPIC AREAS #####
XR and immersive learning in/for:
Serious Games • 3D Collaboration • eSports • AI & Machine Learning • Robotics • Digital Twins • Embodied Pedagogical Agents • Medical & Healthcare Education • Workforce & Industry • Cultural Heritage • Language Learning • K-12 STEM • Higher Ed & Workforce STEM • Museums & Libraries • Informal Learning • Community & Civic Engagement • Special Education • Geosciences • Data Visualization and Analytics • Assessment & Evaluation
##### SUBMISSION STREAMS & CATEGORIES #####
ACADEMIC STREAM (Refereed paper published in proceedings):
– Full (6-8 pages) paper for oral presentation
– Short paper (4-5 pages) for oral presentation
– Work-in-progress paper (2-3 pages) for poster presentation
– Doctoral colloquium paper (2-3 pages)
PRACTITIONER STREAM (Refereed paper published in proceedings):
– Oral presentation
– Poster presentation
– Guided virtual adventures
– Immersive learning project showcase
NONTRADITIONAL SESSION STREAM (1-2 page extended abstract describing session published in proceedings):
– Workshop
– Special session
– Panel session
##### SESSION TYPES & SESSION FORMATS #####
– Oral Presentation: Pre-recorded video + 60-minute live in-world discussion with
others presenting on similar/related topics (groupings of presenters into sessions determined by Program Committee)
– Poster Presentation: Live poster session in 3D virtual exhibition hall; pre-recorded video optional
– Doctoral Colloquium: 60-minute live in-world discussion with other doctoral researchers; pre-recorded video optional
– Guided Virtual Adventures: 60-minute small-group guided tours of to various social and collaborative XR/immersive environments and platforms
– Immersive Learning Project Showcase: WebXR space to assemble a collection of virtual artifacts, accessible to attendees throughout the conference
– Workshop: 1- or 2-hour live hands-on session
– Special Session: 30- or 60-minute live interactive session held in world; may optionally be linked to one or more papers
– Panel Session: 60-minute live in-world discussion with a self-formed group of 3-5 panelists (including a lead panelist who serves as a moderator)
Please see the conference website for templates and guidelines.
##### PROGRAM TRACKS #####
Papers and proposals may be submitted to one of 10 program tracks, the first nine of which correspond to the iLRN Houses of application, and the tenth of which is intended for papers making knowledge contributions to the learning sciences, computer science, and/or game studies that are not linked to any particular application area:
Track 1. Assessment and Evaluation (A&E)
Track 2. Early Childhood Development & Learning (ECDL)
Track 4. Inclusion, Diversity, Equity, Access, & Social Justice (IDEAS)
Track 5. K-12 STEM Education
Track 6. Language, Culture, & Heritage (LCH)
Track 7. Medical & Healthcare Education (MHE)
Track 8. Nature & Environmental Sciences (NES)
Track 9. Workforce Development & Industry Training (WDIT)
Track 10. Basic Research and Theory in Immersive Learning (not linked to any particular application area)
##### PAPER/PROPOSAL SUBMISSION & REVIEW #####
Papers for the Academic Stream and extended-abstract proposals for the Nontraditional Session Stream must be prepared in standard IEEE double-column US Letter format using Microsoft Word or LaTeX, and will be accepted only via the online submission system, accessible via the conference website (from which guidelines and templates are also available).
Proposals for the Practitioner Stream are to be submitted via an online form, also accessible from the conference website.
A blind peer-review process will be used to evaluate all submissions.
##### IMPORTANT DATES #####
– Main round submission deadline – all submission types welcome: 2021-01-15
– Notification of review outcomes from main submission round: 2021-04-01
– Late round submission deadline – Work-in-progress papers, practitioner presentations, and nontraditional sessions only: 2021-04-08
– Camera-ready papers for proceedings due – Full and short papers: 2021-04-15
– Presenter registration deadline – Full and short papers (also deadline for early-bird registration rates): 2021-04-15
– Notification of review outcomes from late submission round: 2021-04-19
– Camera-ready work-in-progress papers and nontraditional session extended abstracts for proceedings due; final practitioner abstracts for conference program due: 2021-05-03
– Deadline for uploading presentation materials (videos, slides for oral presentations, posters for poster presentations): 2021-05-10
– Conference opening: 2021-05-17
– Conference closing: 2021-06-10
*Full and short papers can only be submitted in the main round.
##### PUBLICATION & INDEXING #####
All accepted and registered papers in the Academic Stream that are presented at iLRN 2021 and all extended abstracts describing the Nontraditional Sessions presented at the conference will be published in the conference proceedings and submitted to the IEEE Xplore(r) digital library.
Content loaded into Xplore is made available by IEEE to its abstracting and indexing partners, including Elsevier (Scopus, EiCompendex), Clarivate Analytics (CPCI–part of Web of Science) and others, for potential inclusion in their respective databases. In addition, the authors of selected papers may be invited to submit revised and expanded versions of their papers for possible publication in the IEEE Transactions on Learning Technologies (2019 JCR Impact Factor: 2.714), the Journal of Universal Computer Science (2019 JCR Impact Factor: 0.91), or another Scopus and/or Web of Science-indexed journal, subject to the relevant journal’s regular editorial and peer-review policies and procedures.
##### CONTACT #####
Inquiries regarding the iLRN 2020 conference should be directed to the Conference Secretariat at conference@immersivelrn.org.
Al is not lost though, as academics from UCL and several other institutions have developed a tool that may help us separate the fact from fiction. They have designed a machine learning tool which can cite domains that were created to spread what has now commonly become known as ‘fake news’.
Holland, B. (2020). Emerging Technology and Today’s Libraries. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 1-33). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch001
The purpose of this chapter is to examine emerging technology and today’s libraries. New technology stands out first and foremost given that they will end up revolutionizing every industry in an age where digital transformation plays a major role. Major trends will define technological disruption. The next-gen of communication, core computing, and integration technologies will adopt new architectures. Major technological, economic, and environmental changes have generated interest in smart cities. Sensing technologies have made IoT possible, but also provide the data required for AI algorithms and models, often in real-time, to make intelligent business and operational decisions. Smart cities consume different types of electronic internet of things (IoT) sensors to collect data and then use these data to manage assets and resources efficiently. This includes data collected from citizens, devices, and assets that are processed and analyzed to monitor and manage, schools, libraries, hospitals, and other community services.
Makori, E. O. (2020). Blockchain Applications and Trends That Promote Information Management. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 34-51). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch002
Blockchain revolutionary paradigm is the new and emerging digital innovation that organizations have no choice but to embrace and implement in order to sustain and manage service delivery to the customers. From disruptive to sustaining perspective, blockchain practices have transformed the information management environment with innovative products and services. Blockchain-based applications and innovations provide information management professionals and practitioners with robust and secure opportunities to transform corporate affairs and social responsibilities of organizations through accountability, integrity, and transparency; information governance; data and information security; as well as digital internet of things.
Hahn, J. (2020). Student Engagement and Smart Spaces: Library Browsing and Internet of Things Technology. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 52-70). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch003
The purpose of this chapter is to provide evidence-based findings on student engagement within smart library spaces. The focus of smart libraries includes spaces that are enhanced with the internet of things (IoT) infrastructure and library collection maps accessed through a library-designed mobile application. The analysis herein explored IoT-based browsing within an undergraduate library collection. The open stacks and mobile infrastructure provided several years (2016-2019) of user-generated smart building data on browsing and selecting items in open stacks. The methods of analysis used in this chapter include transactional analysis and data visualization of IoT infrastructure logs. By analyzing server logs from the computing infrastructure that powers the IoT services, it is possible to infer in greater detail than heretofore possible the specifics of the way library collections are a target of undergraduate student engagement.
Treskon, M. (2020). Providing an Environment for Authentic Learning Experiences. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 71-86). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch004
The Loyola Notre Dame Library provides authentic learning environments for undergraduate students by serving as “client” for senior capstone projects. Through the creative application of IoT technologies such as Arduinos and Raspberry Pis in a library setting, the students gain valuable experience working through software design methodology and create software in response to a real-world challenge. Although these proof-of-concept projects could be implemented, the library is primarily interested in furthering the research, teaching, and learning missions of the two universities it supports. Whether the library gets a product that is worth implementing is not a requirement; it is a “bonus.”
Rashid, M., Nazeer, I., Gupta, S. K., & Khanam, Z. (2020). Internet of Things: Architecture, Challenges, and Future Directions. In Holland, B. (Ed.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 87-104). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch005
The internet of things (IoT) is a computing paradigm that has changed our daily livelihood and functioning. IoT focuses on the interconnection of all the sensor-based devices like smart meters, coffee machines, cell phones, etc., enabling these devices to exchange data with each other during human interactions. With easy connectivity among humans and devices, speed of data generation is getting multi-fold, increasing exponentially in volume, and is getting more complex in nature. In this chapter, the authors will outline the architecture of IoT for handling various issues and challenges in real-world problems and will cover various areas where usage of IoT is done in real applications. The authors believe that this chapter will act as a guide for researchers in IoT to create a technical revolution for future generations.
Martin, L. (2020). Cloud Computing, Smart Technology, and Library Automation. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 105-123). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch006
As technology continues to change, the landscape of the work of librarians and libraries continue to adapt and adopt innovations that support their services. Technology also continues to be an essential tool for dissemination, retrieving, storing, and accessing the resources and information. Cloud computing is an essential component employed to carry out these tasks. The concept of cloud computing has long been a tool utilized in libraries. Many libraries use OCLC to catalog and manage resources and share resources, WorldCat, and other library applications that are cloud-based services. Cloud computing services are used in the library automation process. Using cloud-based services can streamline library services, minimize cost, and the need to have designated space for servers, software, or other hardware to perform library operations. Cloud computing systems with the library consolidate, unify, and optimize library operations such as acquisitions, cataloging, circulation, discovery, and retrieval of information.
Owusu-Ansah, S. (2020). Developing a Digital Engagement Strategy for Ghanaian University Libraries: An Exploratory Study. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 124-139). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch007
This study represents a framework that digital libraries can leverage to increase usage and visibility. The adopted qualitative research aims to examine a digital engagement strategy for the libraries in the University of Ghana (UG). Data is collected from participants (digital librarians) who are key stakeholders of digital library service provision in the University of Ghana Library System (UGLS). The chapter reveals that digital library services included rare collections, e-journal, e-databases, e-books, microfilms, e-theses, e-newspapers, and e-past questions. Additionally, the research revealed that the digital library service patronage could be enhanced through outreach programmes, open access, exhibitions, social media, and conferences. Digital librarians recommend that to optimize digital library services, literacy programmes/instructions, social media platforms, IT equipment, software, and website must be deployed. In conclusion, a DES helps UGLS foster new relationships, connect with new audiences, and establish new or improved brand identity.
Nambobi, M., Ssemwogerere, R., & Ramadhan, B. K. (2020). Implementation of Autonomous Library Assistants Using RFID Technology. In Holland, B. (Ed.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 140-150). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch008
This is an interesting time to innovate around disruptive technologies like the internet of things (IoT), machine learning, blockchain. Autonomous assistants (IoT) are the electro-mechanical system that performs any prescribed task automatically with no human intervention through self-learning and adaptation to changing environments. This means that by acknowledging autonomy, the system has to perceive environments, actuate a movement, and perform tasks with a high degree of autonomy. This means the ability to make their own decisions in a given set of the environment. It is important to note that autonomous IoT using radio frequency identification (RFID) technology is used in educational sectors to boost the research the arena, improve customer service, ease book identification and traceability of items in the library. This chapter discusses the role, importance, the critical tools, applicability, and challenges of autonomous IoT in the library using RFID technology.
Priya, A., & Sahana, S. K. (2020). Processor Scheduling in High-Performance Computing (HPC) Environment. In Holland, B. (Ed.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 151-179). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch009
Processor scheduling is one of the thrust areas in the field of computer science. The future technologies use a huge amount of processing for execution of their tasks like huge games, programming software, and in the field of quantum computing. In real-time, many complex problems are solved by GPU programming. The primary concern of scheduling is to reduce the time complexity and manpower. Several traditional techniques exit for processor scheduling. The performance of traditional techniques is reduced when it comes to the huge processing of tasks. Most scheduling problems are NP-hard in nature. Many of the complex problems are recently solved by GPU programming. GPU scheduling is another complex issue as it runs thousands of threads in parallel and needs to be scheduled efficiently. For such large-scale scheduling problems, the performance of state-of-the-art algorithms is very poor. It is observed that evolutionary and genetic-based algorithms exhibit better performance for large-scale combinatorial and internet of things (IoT) problems.
Kirsch, B. (2020). Virtual Reality in Libraries. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 180-193). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch010
Librarians are beginning to offer virtual reality (VR) services in libraries. This chapter reviews how libraries are currently using virtual reality for both consumption and creation purposes. Virtual reality tools will be compared and contrasted, and recommendations will be given for purchasing and circulating headsets and VR equipment. Google Tour Creator and a smartphone or 360-degree camera can be used to create a virtual tour of the library and other virtual reality content. These new library services will be discussed along with practical advice and best practices for incorporating virtual reality into the library for instructional and entertainment purposes.
Heffernan, K. L., & Chartier, S. (2020). Augmented Reality Gamifies the Library: A Ride Through the Technological Frontier. In Holland, B. (Ed.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 194-210). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch011
Two librarians at a University in New Hampshire attempted to integrate gamification and mobile technologies into the exploration of, and orientation to, the library’s services and resources. From augmented reality to virtual escape rooms and finally an in-house app created by undergraduate, campus-based, game design students, the library team learned much about the triumphs and challenges that come with attempting to utilize new technologies to reach users in the 21st century. This chapter is a narrative describing years of various attempts, innovation, and iteration, which have led to the library team being on the verge of introducing an app that could revolutionize campus discovery and engagement.
Miltenoff, P. (2020). Video 360 and Augmented Reality: Visualization to Help Educators Enter the Era of eXtended Reality. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 211-225). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch012
The advent of all types of eXtended Reality (XR)—VR, AR, MR—raises serious questions, both technological and pedagogical. The setup of campus services around XR is only the prelude to the more complex and expensive project of creating learning content using XR. In 2018, the authors started a limited proof-of-concept augmented reality (AR) project for a library tour. Building on their previous research and experience creating a virtual reality (VR) library tour, they sought a scalable introduction of XR services and content for the campus community. The AR library tour aimed to start us toward a matrix for similar services for the entire campus. They also explored the attitudes of students, faculty, and staff toward this new technology and its incorporation in education, as well as its potential and limitations toward the creation of a “smart” library.
large data is inherently noisy. \In general, the more “democratic” the production channel, the dirtier the data – which means that more effort has to be spent on its cleaning. For example, data from social media will require a longer cleaning pipeline. Among others, you will need to deal with extravagancies of self-expression like smileys and irregular punctuation, which are normally absent in more formal settings such as scientific papers or legal contracts.
The other major challenge is the labeled data bottleneck
crowd-sourcing and Training Data as a Service (TDaaS). On the other hand, a range of automatic workarounds for the creation of annotated datasets have also been suggested in the machine learning community.
Algorithms: a chain of disruptions in Deep Learning
Neural Networks are the workhorse of Deep Learning (cf. Goldberg and Hirst (2017) for an introduction of the basic architectures in the NLP context). Convolutional Neural Networks have seen an increase in the past years, whereas the popularity of the traditional Recurrent Neural Network (RNN) is dropping. This is due, on the one hand, to the availability of more efficient RNN-based architectures such as LSTM and GRU. On the other hand, a new and pretty disruptive mechanism for sequential processing – attention – has been introduced in the sequence-to-sequence (seq2seq) model by Sutskever et al. (2014).
Consolidating various NLP tasks
the three “global” NLP development curves – syntax, semantics and context awareness
the third curve – the awareness of a larger context – has already become one of the main drivers behind new Deep Learning algorithms.
A note on multilingual research
Think of different languages as different lenses through which we view the same world – they share many properties, a fact that is fully accommodated by modern learning algorithms with their increasing power for abstraction and generalization.
Spurred by the global AI hype, the NLP field is exploding with new approaches and disruptive improvements. There is a shift towards modeling meaning and context dependence, probably the most universal and challenging fact of human language. The generalisation power of modern algorithms allows for efficient scaling across different tasks, languages and datasets, thus significantly speeding up the ROI cycle of NLP developments and allowing for a flexible and efficient integration of NLP into individual business scenarios.
third Library 2.019 mini-conference: “Emerging Technology,” which will be held online (and for free) on Wednesday, October 30th, from 12:00 – 3:00 pm US-Pacific Daylight Time (click for your own time zone).
Tomorrow’s technologies are shaping our world today, revolutionizing the way we live and learn. Virtual Reality, Augmented Reality, Artificial Intelligence, Machine Learning, Blockchain, Internet of Things, Drones, Personalization, the Quantified Self. Libraries can and should be the epicenter of exploring, building and promoting these emerging techs, assuring the better futures and opportunities they offer are accessible to everyone. Learn what libraries are doing right now with these cutting-edge technologies, what they’re planning next and how you can implement these ideas in your own organization.
This is a free event, being held live online and also recorded. REGISTER HERE
350 researchers, 200 graduate students, and 150 professionals in the AI sector. “We need AI, we need machine learning, we need the development of new technology to get people more efficient
Eureka: machine learning tool, brainstorming engine. give it an initial idea and it returns similar ideas. Like Google: refine the idea, so the machine can understand it better. create a collection of ideas to translate into course design or others.
Netlix:
influencers and microinfluencers, pre- and doing the execution
a machine can construct a book with the help of a person. bionic book. machine and person working hand in hand. provide keywords and phrases from lecture notes, presentation materials. from there recommendations and suggestions based on own experience; then identify included and excluded content. then instructor can construct.
Design may be the least interesting part of the book for the faculty.
multiple choice quiz may be the least interesting part, and faculty might want to do much deeper assessment.
use these machine learning techniques to build assessment. how to more effectively. inquizitive is the machine learning
students engagements and similar prompts
presence in the classroom: pre-service teachers class. how to immerse them and practice classroom management skills
First class: marriage btw VR and use of AI – an environment headset: an algorithm reacts how teachers are interacting with the virtual kids. series of variables, oppty to interact with present behavior. classroom management skills. simulations and environments otherwise impossible to create. apps for these type of interactions
facilitation, reflection and research
AI for more human experience, allow more time for the faculty to be more human, more free time to contemplate.
Jason: Won’t the use of AI still reduce the amount of faculty needed?
Christina Dumeng: @Jason–I think it will most likely increase the amount of students per instructor.
Andrew Cole (UW-Whitewater): I wonder if instead of reducing faculty, these types of platforms (e.g., analytic capabilities) might require instructors to also become experts in the various technology platforms.
Dirk Morrison: Also wonder what the implications of AI for informal, self-directed learning?
Kate Borowske: The context that you’re presenting this in, as “your own jazz band,” is brilliant. These tools presented as a “partner” in the “band” seems as though it might be less threatening to faculty. Sort of gamifies parts of course design…?
Dirk Morrison: Move from teacher-centric to student-centric? Recommender systems, AI-based tutoring?
Andrew Cole (UW-Whitewater): The course with the bot TA must have been 100-level right? It would be interesting to see if those results replicate in 300, 400 level courses
Way back in 1983, I identified A.I. as one of 20 exponential technologies that would increasingly drive economic growth for decades to come.
Artificial intelligence applies to computing systems designed to perform tasks usually reserved for human intelligence using logic, if-then rules, decision trees and machine learning to recognize patterns from vast amounts of data, provide insights, predict outcomes and make complex decisions. A.I. can be applied to pattern recognition, object classification, language translation, data translation, logistical modeling and predictive modeling, to name a few. It’s important to understand that all A.I. relies on vast amounts of quality data and advanced analytics technology. The quality of the data used will determine the reliability of the A.I. output.
Machine learning is a subset of A.I. that utilizes advanced statistical techniques to enable computing systems to improve at tasks with experience over time. Chatbots like Amazon’s Alexa, Apple’s Siri, or any of the others from companies like Google and Microsoft all get better every year thanks to all of the use we give them and the machine learning that takes place in the background.
Deep learning is a subset of machine learning that uses advanced algorithms to enable an A.I. system to train itself to perform tasks by exposing multi-layered neural networks to vast amounts of data, then using what has been learned to recognize new patterns contained in the data. Learning can be Human Supervised Learning, Unsupervised Learningand/or Reinforcement Learning like Google used with DeepMind to learn how to beat humans at the complex game Go. Reinforcement learning will drive some of the biggest breakthroughs.
Autonomous computing uses advanced A.I. tools such as deep learning to enable systems to be self-governing and capable of acting according to situational data without human command. A.I. autonomy includes perception, high-speed analytics, machine-to-machine communications and movement. For example, autonomous vehicles use all of these in real time to successfully pilot a vehicle without a human driver.
Augmented thinking: Over the next five years and beyond, A.I. will become increasingly embedded at the chip level into objects, processes, products and services, and humans will augment their personal problem-solving and decision-making abilities with the insights A.I. provides to get to a better answer faster.
Technology is not good or evil, it is how we as humans apply it. Since we can’t stop the increasing power of A.I., I want us to direct its future, putting it to the best possible use for humans.
Artificial intelligence could erase many practical advantages of democracy, and erode the ideals of liberty and equality. It will further concentrate power among a small elite if we don’t take steps to stop it.
Ordinary people may not understand artificial intelligence and biotechnology in any detail, but they can sense that the future is passing them by. In 1938 the common man’s condition in the Soviet Union, Germany, or the United States may have been grim, but he was constantly told that he was the most important thing in the world, and that he was the future (provided, of course, that he was an “ordinary man,” rather than, say, a Jew or a woman).
n 2018 the common person feels increasingly irrelevant. Lots of mysterious terms are bandied about excitedly in ted Talks, at government think tanks, and at high-tech conferences—globalization, blockchain, genetic engineering, AI, machine learning—and common people, both men and women, may well suspect that none of these terms is about them.
Fears of machines pushing people out of the job market are, of course, nothing new, and in the past such fears proved to be unfounded. But artificial intelligence is different from the old machines. In the past, machines competed with humans mainly in manual skills. Now they are beginning to compete with us in cognitive skills.
Israel is a leader in the field of surveillance technology, and has created in the occupied West Bank a working prototype for a total-surveillance regime. Already today whenever Palestinians make a phone call, post something on Facebook, or travel from one city to another, they are likely to be monitored by Israeli microphones, cameras, drones, or spy software. Algorithms analyze the gathered data, helping the Israeli security forces pinpoint and neutralize what they consider to be potential threats.
The conflict between democracy and dictatorship is actually a conflict between two different data-processing systems. AI may swing the advantage toward the latter.
As we rely more on Google for answers, our ability to locate information independently diminishes. Already today, “truth” is defined by the top results of a Google search. This process has likewise affected our physical abilities, such as navigating space.
So what should we do?
For starters, we need to place a much higher priority on understanding how the human mind works—particularly how our own wisdom and compassion can be cultivated.
Artificial intelligence (AI) and machine learning are no longer fantastical prospects seen only in science fiction. Products like Amazon Echo and Siri have brought AI into many homes,
Kelly Calhoun Williams, an education analyst for the technology research firm Gartner Inc., cautions there is a clear gap between the promise of AI and the reality of AI.
Artificial intelligence is a broad term used to describe any technology that emulates human intelligence, such as by understanding complex information, drawing its own conclusions and engaging in natural dialog with people.
Machine learning is a subset of AI in which the software can learn or adapt like a human can. Essentially, it analyzes huge amounts of data and looks for patterns in order to classify information or make predictions. The addition of a feedback loop allows the software to “learn” as it goes by modifying its approach based on whether the conclusions it draws are right or wrong.
AI can process far more information than a human can, and it can perform tasks much faster and with more accuracy. Some curriculum software developers have begun harnessing these capabilities to create programs that can adapt to each student’s unique circumstances.
For instance, a Seattle-based nonprofit company calledEnlearn has developed an adaptive learning platform that uses machine learning technology to create highly individualized learning paths that can accelerate learning for every student. (My note: about learning and technology, Alfie Kohn in https://blog.stcloudstate.edu/ims/2018/09/11/educational-technology/)
GoGuardian, a Los Angeles company, uses machine learning technology to improve the accuracy of its cloud-based Internet filtering and monitoring software for Chromebooks. (My note: that smells Big Brother).Instead of blocking students’ access to questionable material based on a website’s address or domain name, GoGuardian’s software uses AI to analyze the actual content of a page in real time to determine whether it’s appropriate for students. (my note: privacy)
serious privacy concerns. It requires an increased focus not only on data quality and accuracy, but also on the responsible stewardship of this information. “School leaders need to get ready for AI from a policy standpoint,” Calhoun Williams said. For instance: What steps will administrators take to secure student data and ensure the privacy of this information?
