AI, VR and Quantum in Education
AI, VR and Quantum in Education
Ray Schroeder
Senior Fellow, UPCEA
Ray Schroeder
Senior Fellow, UPCEA
First: A word about presentation format. For the past dozen years, we have been "power-point-less" at the UIS Center for Online Learning, Research and Service. Rather than using a static, aging format, we prefer to create Web pages for our presentations to assure that they are easily accessible, updatable, and always available. The intent is that this will serve as a reference meta-site for you on the topic.
UPDATE notes promised in the session :
Adaptive Learning
Candace Thille was the foundress of modern adaptive learning! https://www.insidehighered.com/digital-learning/article/2018/01/29/amazons-high-profile-hire-higher-education-candace-thille
-ray
Make no mistake, we have crossed the threshold into the fourth industrial revolution that will most markedly advance this decade through maturing artificial intelligence, ultimately driven by quantum computing. The changes will come at an ever-increasing rate as the technologies and societal demands accelerate. Digital computers advanced over the past half century at approximately the rate described by Moore’s Law, with processing power doubling every two years. Now we are entering the era of Neven’s Law, which predicts the speed of progress of quantum computing at a doubly exponential rate. This means change at a dizzyingly rapid rate that will leave many of us unable to comprehend the why and barely able to digest the daily advances that will describe reality. New platforms, products and processes will proliferate in this new decade.
Now, some definitions:
Artificial Intelligence - the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. https://www.lexico.com/en/definition/artificial%20intelligence
Machine Learning - Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it... learn for themselves. https://www.predictiveanalyticsworld.com/machinelearningtimes/watch-3-videos-from-courseras-new-machine-learning-for-everyone/11555/
Deep Learning - In practical terms, deep learning is just a subset of machine learning. In fact, deep learning technically is machine learning and functions in a similar way (hence why the terms are sometimes loosely interchanged). However, its capabilities are different. While basic machine learning models do become progressively better at whatever their function is, but they still need some guidance. If an AI algorithm returns an inaccurate prediction, then an engineer has to step in and make adjustments. With a deep learning model, an algorithm can determine on its own if a prediction is accurate or not through its own neural network. https://www.zendesk.com/blog/machine-learning-and-deep-learning/
Algorithm - In computing, an algorithm is a precise list of operations that could be done by a Turing machine. For the purpose of computing, algorithms are written in pseudocode, flow charts, or programming languages. . https://simple.m.wikipedia.org/wiki/Algorithm [Ray - example Python]
Supervised and Unsupervised Learning - In a supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data. An unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns on its own. https://blogs.nvidia.com/blog/2018/08/02/supervised-unsupervised-learning/
Reinforcement Learning - In this kind of machine learning, AI agents are attempting to find the optimal way to accomplish a particular goal, or improve performance on a specific task. As the agent takes action that goes toward the goal, it receives a reward. The overall aim: predict the best next step to take to earn the biggest final reward. https://blogs.nvidia.com/blog/2018/08/02/supervised-unsupervised-learning/
Self-Coding - Now, using neural networks driven by AI, a host of companies including a group led by Intel, MIT and Georgia Tech are developing self-coding AI programs. These will be designed to enable authors to merely describe the porgrams they desire and the software will generate the code. https://www.technologyreview.com/2020/07/29/1005768/neural-network-similarities-between-programs-help-computers-code-themselves-ai-intel/
Artificial Intelligence in Higher Education: Applications, Promise and Perils, and Ethical Questions - EDUCAUSE Review
What are the benefits and challenges of using artificial intelligence to promote student success, improve retention, streamline enrollment, and better manage resources in higher education? AI is affecting all aspects of higher education: administration, teaching, assessment, student performance and more.
As Colleges Adopt AI, They’ll Need Ethical Frameworks - Alexander Huls, Ed Tech Magazine
The rapid advancement of artificial intelligence promises to be an innovative boon to many industries, including higher education. As nascent as AI may be as an emerging technology, colleges are already finding numerous ways to leverage this powerful tool for efficiencies in student services, classroom support and back-office operations. AI programs are reviewing student assignments for plagiarism and creating live transcriptions of faculty lectures. Chatbots are answering questions about financial aid, registration and campus life. Virtual teaching assistants are reminding students of office hours, exam logistics and essay file formats.
https://edtechmagazine.com/higher/article/2021/01/colleges-adopt-ai-theyll-need-ethical-frameworks
Future Of Emotion AI In The Education Sector - Ranjan Kumar, Business World Education
The on-going Covid-19 pandemic has disrupted sectors across the globe, and one of the worst-hit is education. Overnight, all educational institutes were compelled to shift to a completely virtual mode of learning, which was only a minor part of classroom learning until March. While an increasing number of EdTech firms are offering online learning solutions to students, the challenge remains in acknowledging and responding to the emotional nuances of students in a similar manner to that of face-to-face interaction.
Affective Artificial Intelligence: Better Understanding and Responding to Students - Ray Schroeder - Inside Higher Ed
Artificial intelligence is recognizing and responding to human emotions, oftentimes better than many humans. AI in many fields now applies affective communication algorithms that help to respond to humans. Customer service chat bots can sense when a client is angry or upset, advertising research can use AI to measure emotional responses of viewers and a mental health app can measure nuances of voice to identify anxiety and mood changes over the phone. “Machines are very good at analyzing large amounts of data,” explains MIT Sloan professor Erik Brynjolfsson. “They can listen to voice inflections and start to recognize when those inflections correlate with stress or anger. Machines can analyze images and pick up subtleties in microexpressions on humans’ faces that might happen even too fast for a person to recognize.”
Google Announces Neural-Network Chatbox Meena
Google has released a neural-network powered chatbot called Meena that it claims is better than any other chatbot out there. Data slurp: Meena was trained on a whopping 341GB of public social-media chatter—8.5 times as much data as OpenAI’s GPT-2. Google says Meena can talk about pretty much anything, and can even make up (bad) jokes. Google says it won’t be releasing a public demo until it has vetted the model for safety and bias, which is probably a good thing. When Microsoft released its chatbot Tay on Twitter in 2016 it started spewing racist, misogynistic invective within hours.
You are reading AI-written reports every day (many are written from AI-gathered research as well) - in Bloomberg (1/3 of all reports), Washington Post, Forbes, Guardian, Associated Press and many more.
https://www.nytimes.com/2019/02/05/business/media/artificial-intelligence-journalism-robots.html
https://techhq.com/2019/09/writing-and-research-tools-the-future-of-the-newsroom/
How VR In Education Will Change How We Learn And Teach - Nick Babich, Adobe
Virtual reality can be used to enhance student learning and engagement. VR education can transform the way educational content is delivered; it works on the premise of creating a virtual world — real or imagined — and allows users not only see it but also interact with it. Being immersed in what you’re learning motivates you to fully understand it. It’ll require less cognitive load to process the information.
https://xd.adobe.com/ideas/principles/emerging-technology/virtual-reality-will-change-learn-teach/
Hands-on Classes at a Distance and the Emerging Virtual Future - Ray Schroeder, Inside Higher Ed
The rush to remote learning this year has prompted concern and a flurry of work-arounds to meet the needs of hands-on classes and curricula. Many of us may have fallen behind the emerging practice in our fields as we focus on meeting the essential learning needs of our students. What previously had been a hands-on, manual process has often become, in this 4IR world, a technology-assisted, robotic or virtual practice. For example, telemedicine, virtual reality (VR), augmented reality (AR) and extended reality (XR) technologies are now essential tools in health care. They supplant some of the physical and manual diagnostic practices of the past.
The latency issue and VR:
The Speedy Future of Delivering Online Learning: 5G-10G Confusion and Potential - Ray Schroeder, Inside Higher Ed
We are poised at the beginning of an exciting new era in low-latency, high-bandwidth networking that will enable awesome interactions, 3-D displays and incredibly rich data collection. We have seen it in the advertising. Some of us are experiencing it, given the right wireless provider, smartphones and proximity to transmission point -- 5G is spreading rapidly across the country and in other countries. South Korea, China and the United States are the countries in the lead in deploying 5G, with the U.K. and Australia among others that are close behind. Five G has the potential to replace Wi-Fi in many applications, perhaps even on campuses. A 10-gigabits-per-second potential and a latency down to one or two milliseconds sounds great. Thales provides a good graphic site that makes the comparison of 5G to 4G to give you a good indication of the difference of change in generations from that which most mobile phones use to connect (4G) versus the innovative new generation (5G). However, field experiences of 5G have, on average, resulted in somewhat lesser performance, as seen in this Tech Radar update.
Quantum Computing will enable advance AI in ways we have yet to full understand, however, we know it will allow incredibly massive datasets to be analyzed in the blink of an eye. It will bring breakthroughs in fields such as meteorology, medicine, and education where we can assemble massive-scale data that can be combed for causality, prediction, and previously unrecognized connections.
Quantum Computing Is Poised to Change Everything
Ray Schroeder, Inside Higher Ed
It is truly rare that an advancement comes along that changes every aspect of society; quantum computing is poised to do just that in the 2020s. Do you recall Moore’s law? That’s the axiom developed by Gordon Moore some two dozen years ago that the processing power of computers would double every 18 months to two years. Now, quantum computing has ushered in Hartmut Neven’s law. His law predicting growth in quantum computing power is one that is doubly exponential. That is two to an exponent of two to a second increasing exponent. Charted on a graph, that growth rate appears to become nearly vertical.
Quantum Information and AI - Alex Moltzau, Towards Data Science
“In physics and computer science, quantum information is the information of the state of a quantum system. It is the basic entity of study in quantum information theory, and can be manipulated using quantum information processing techniques.” “Quantum neural networks (QNNs) are neural network models which are based on the principles of quantum mechanics. There are two different approaches to QNN research, one exploiting quantum information processing to improve existing neural network models (sometimes also vice versa), and the other one searching for potential quantum effects in the brain.”
https://towardsdatascience.com/quantum-computing-and-ai-789fc9c28c5b
Quantum Computing in Higher Ed
Quantum computing is not an incremental step in the advancement of computing. It is - pardon the phrase - a quantum leap!
According to the Financial Times report, the paper said that Google’s quantum processor was able to perform a calculation in three minutes and 20 seconds that would take today’s most advanced supercomputer, known as Summit, around 10,000 years.
Quantum Computing Roundup - HPC
Advances are taking place in the blink of an eye. Universities are assisting in the development and refinement of ever-larger quantum computers. Applications in education - as will almost every field - are astounding. Unimaginably large datasets and nearly unfathomably complex algorithms combined with superposition and entanglement features to make these computers the key component in changing civilization and life as we know it.
Einstein called it "Spooky": Quantum Entanglement
It affords connected actions at speeds exceeding the speed of light by means we do not yet fully understand. It opens the door to a whole new level of privacy and connectivity. Quantum entanglement is thought to be one of the trickiest concepts in science, but the core issues are simple. And once understood, entanglement opens up a richer understanding of concepts such as the “many worlds” of quantum theory.
https://www.quantamagazine.org/entanglement-made-simple-20160428/
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Online Learning Update http://people.uis.edu/rschr1/onlinelearning/blogger.html/
Recession Reality in Higher Education http://recessionreality.blogspot.com/
Educational Technology http://people.uis.edu/rschr1/et/blogger.html/
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Contact Ray
rschr1@uis.edu ~ rayschroeder@gmail.com - ray@upcea.edu
Associate Vice Chancellor for Online, Professor Emeritus
University of Illinois Springfield
Senior Fellow, University Professional and Continuing Education Assn.
217-206-7531