Educational Setting


A university leader's glossary for AI and machine learning Inside Higher Ed

#artificialintelligence

Artificial intelligence, it seems, is infiltrating every corner of higher education. From improving the efficiency of sprinkler systems to supporting students with virtual teaching assistants, AI has quickly become a near-ubiquitous presence on some campuses. Colleges and universities are being asked to do more with less as they grapple with shifting demographics and the need to not just respond to, but also anticipate, the needs of today's students. And early returns suggest that AI can play a role in helping institutions tackle pernicious challenges -- from "summer melt" to student engagement -- and enable students to navigate the complexity of financial aid, admissions, campus life and course scheduling. In response, a growing number of products are touting AI and machine learning as part of their sales pitch.


TTEC Recognized for Use of AI, Machine Learning and Digital Innovation in Learning and Development, Earns LearningElite Silver Award

#artificialintelligence

TTEC Holdings, Inc. (NASDAQ: TTEC), a leading digital global customer experience (CX) technology and services company focused on the design, implementation and delivery of transformative customer experience, engagement and growth solutions, has recently been recognized by Chief Learning Officer magazine as a 2019 LearningElite Silver Award winner. This robust, peer-reviewed ranking and benchmarking program recognizes those organizations that employ exemplary workforce development strategies that deliver significant business results. Special emphasis was placed this year on how these learning teams are helping their organizations adapt to and prepare for change. Winners were recently announced during the ninth annual LearningElite Awards program at the CLO Symposium conference. "TTEC is honored to be recognized as an elite learning organization and appreciates this award from Chief Learning Officer," said Steve Pollema, Executive Vice President, TTEC Digital.


Teen Inventor Designs Noninvasive Allergy Screen Using Genetics and Machine Learning

#artificialintelligence

One of Ayush Alag's earliest memories is of biting into a chocolate bar with cashew nuts and suddenly feeling his throat get itchy. For most of his childhood, the Santa Clara, California resident avoided eating anything with cashews and other nuts that caused irritation as best as he could. By his middle school years, he and his parents wanted to know for sure: did he have a serious food allergy, like 32 million other Americans, or was it just a food sensitivity? They sought the help of an allergist, Joseph Hernandez of Stanford University. Hernandez told them that the difference between an allergy and a food sensitivity is huge.


Taking the lead on digital and AI - Education Technology

#artificialintelligence

From TED Talk speakers, to a futurologist's keynote at an event, those who make predictions about the future usually live safe in the knowledge they won't retrospectively be pulled up on forecasts that don't come to pass. The picture is very different for those in government, who must ensure citizens and businesses are adequately prepared for challenges. Government predictions must convert to real-world planning that puts building blocks for future success and prosperity in place – it can't be a'finger in the air'. Education is at the foundations of preparation. Governments and educators must identify trends early enough to update curriculums, develop the right courses, and equip people with skills that put us in a strong position to compete on the world stage.


What's wrong with the approach to Data Science?

#artificialintelligence

Data science is the application of statistics, programming and domain knowledge to generate insights into a problem that needs to be solved. The Harvard Business Review said Data Scientist is the sexiest job of the 21st century. How often has that article been referenced to convince people? The job'Data Scientist' has been around for decades, it was just not called "Data Scientist". Statisticians have used their knowledge and skills using machine learning techniques such as Logistic Regression and Random Forest for prediction and insights for decades.


Detect discrimination with help of artificial intelligence

#artificialintelligence

Washington D.C. [USA], July 14 (ANI): Researchers developed a new artificial intelligence (AI) tool for detecting unfair discrimination such as race or gender. Preventing unfair treatment of individuals on the basis of race, gender or ethnicity, for example, been a long-standing concern of civilized societies. However, detecting such discrimination resulting from decisions, whether by human decision-makers or automated AI systems, can be extremely challenging. This challenge is further exacerbated by the wide adoption of AI systems to automate decisions in many domains including policing, consumer finance, higher education, and business. "Artificial intelligence systems such as those involved in selecting candidates for a job or for admission to a university are trained on large amounts of data. But if these data are biased, they can affect the recommendations of AI systems," said Vasant Honavar, one of the researchers of the study presented at the meeting of The Web Conference.


Identifying perceived emotions from people's walking style

#artificialintelligence

A team of researchers at the University of North Carolina at Chapel Hill and the University of Maryland at College Park has recently developed a new deep learning model that can identify people's emotions based on their walking styles. Their approach, outlined in a paper pre-published on arXiv, works by extracting an individual's gait from an RGB video of him/her walking, then analyzing it and classifying it as one of four emotions: happy, sad, angry or neutral. "Emotions play a significant role in our lives, defining our experiences, and shaping how we view the world and interact with other humans," Tanmay Randhavane, one of the primary researchers and a graduate student at UNC, told TechXplore. "Perceiving the emotions of other people helps us understand their behavior and decide our actions toward them. For example, people communicate very differently with someone they perceive to be angry and hostile than they do with someone they perceive to be calm and contented."


Learnin' Good All This AI Stuff for Product Management

#artificialintelligence

I've invested a considerable amount of time taking numerous courses, so I dug into my emails to collect some of the suggestions I've doled out. First, it's worth addressing the extent to which a product manager even needs to understand how AI works in order to be effective. There is an endless stream of business articles about what AI is, what it does and how it is going to disrupt this and that, all of which is great, but I am talking about understanding how it works (e.g. As Marty Cagan pointed out in Inspired (a must-read), product managers can come from a variety of different vertical disciplines, including those that are not necessarily technical, such as marketing or sales. Can these individuals, or even product managers who come from engineering but don't necessarily have a background in AI, be successful managing AI products?


Using artificial intelligence to detect discrimination

#artificialintelligence

Preventing unfair treatment of individuals on the basis of race, gender or ethnicity, for example, been a long-standing concern of civilized societies. However, detecting such discrimination resulting from decisions, whether by human decision makers or automated AI systems, can be extremely challenging. This challenge is further exacerbated by the wide adoption of AI systems to automate decisions in many domains -- including policing, consumer finance, higher education and business. "Artificial intelligence systems -- such as those involved in selecting candidates for a job or for admission to a university -- are trained on large amounts of data," said Vasant Honavar, Professor and Edward Frymoyer Chair of Information Sciences and Technology, Penn State. "But if these data are biased, they can affect the recommendations of AI systems."


Machine learning: 3 ways to tell hype from reliable research

#artificialintelligence

Augmented intelligence (AI) and related branches--such as machine learning and natural language processing--offer lots of promise for health care, but how can physicians and other health professionals distinguish between clinically safe and useful innovations and hot air? That question is at the heart of a recent JAMA Pediatrics editorial on machine learning, a branch of AI, that outlines some rules of thumb to help doctors tell the difference between hype and reliable research on machine learning in medicine. New health care AI policy adopted at the 2019 AMA Annual Meeting provides that AI should advance the quadruple aim--meaning that it "should enhance the patient experience of care and outcomes, improve population health, reduce overall costs for the health care system while increasing value, and support the professional satisfaction of physicians and the health care team." The AMA House of Delegates also adopted policy on the use of AI in medical education and physician training. This built on the foundation of the AMA's initial AI policies adopted last year that emphasized that the perspective of physicians needed to be heard as the technology continues to develop.