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Sunday, May 19, 2019

AI vs. Machine Learning vs. Deep Learning | Artificial Intelligence - Datamation

Deep learning is a subset of machine learning, which is a subset of artificial intelligence. Each of these emerging technologies is reshaping IT across virtually all sectors, says Cynthia Harvey, Writer and Editor.

AI, machine learning and deep learning are each interrelated, with deep learning nested within ML, which in turn is part of the larger discipline of AI.
Since before the dawn of the computer age, scientists have been captivated by the idea of creating machines that could behave like humans. But only in the last decade has technology enabled some forms of artificial intelligence (AI) to become a reality.

Interest in putting AI to work has skyrocketed, with burgeoning array of AI use cases. Many surveys have found upwards of 90 percent of enterprises are either already using AI in their operations today or plan to in the near future.

Eager to capitalize on this trend, software vendors – both established AI companies and AI startups – have rushed to bring AI capabilities to market. Among vendors selling big data analytics and data science tools, two types of artificial intelligence have become particularly popular: machine learning and deep learning...

Artificial Intelligence 'Contains' Machine Learning and Deep Learning 
Computers excel at mathematics and logical reasoning, but they struggle to master other tasks that humans can perform quite naturally.

For example, human babies learn to recognize and name objects when they are only a few months old, but until recently, machines have found it very difficult to identify items in pictures. While any toddler can easily tell a cat from a dog from a goat, computers find that task much more difficult. In fact, captcha services sometimes use exactly that type of question to make sure that a particular user is a human and not a bot.

In the 1950s, scientists began discussing ways to give machines the ability to "think" like humans. The phrase "artificial intelligence" entered the lexicon in 1956, when John McCarthy organized a conference on the topic.
Read more...

Source: Datamation

Machine learning explained | Machine Learning - InfoWorld

Martin Heller, contributing editor and reviewer for InfoWorld explains, Machine learning systems create models from data. Because they learn from experience, you can improve their performance with training. 

Photo:Thinkstock
What is machine learning? 
Machine learning is a branch of artificial intelligence that includes methods, or algorithms, for automatically creating models from data. Unlike a system that performs a task by following explicit rules, a machine learning system learns from experience. Whereas a rule-based system will perform a task the same way every time (for better or worse), the performance of a machine learning system can be improved through training, by exposing the algorithm to more data.

Machine learning algorithms are often divided into supervised (the training data are tagged with the answers) and unsupervised (any labels that may exist are not shown to the training algorithm). Supervised machine learning problems are further divided into classification (predicting non-numeric answers, such as the probability of a missed mortgage payment) and regression (predicting numeric answers, such as the number of widgets that will sell next month in your Manhattan store).

Unsupervised learning is further divided into clustering (finding groups of similar objects, such as running shoes, walking shoes, and dress shoes), association (finding common sequences of objects, such as coffee and cream), and dimensionality reduction (projection, feature selection, and feature extraction)...

Neural networks and deep learning 
Neural networks were inspired by the architecture of the biological visual cortex. Deep learning is a set of techniques for learning in neural networks that involves a large number of “hidden” layers to identify features. Hidden layers come between the input and output layers. Each layer is made up of artificial neurons, often with sigmoid or ReLU (Rectified Linear Unit) activation functions.

In a feed-forward network, the neurons are organized into distinct layers: one input layer, any number of hidden processing layers, and one output layer, and the outputs from each layer go only to the next layer.
Read more... 

Source: InfoWorld

Microsoft to train 15,000 people on artificial intelligence by 2022 | Ai Technology - CNBCTV18

  • The training will revolve around technologies including data science, Machine Learning (ML), Cloud and data engineering among others.  
  • In order to create consistent standards for AI skills, Microsoft will be the founding member of GA's AI Standards Board which will later be joined by other industry-leading companies at the forefront of AI disruption.

Software major Microsoft has decided to train and certify 15,000 workers on Artificial Intelligence (AI) skills by 2022, in partnership with education provider General Assembly (GA) by IANS.


The training will revolve around technologies including data science, Machine Learning (ML), Cloud and data engineering among others.

"According to the World Economic Forum, up to 133 million new roles could be created by 2022 and to address this challenge, Microsoft and GA will power 2,000 job transitions for workers into AI and ML roles in year one and will train an additional 13,000 workers with AI-related skills across sectors in the next three years," the company wrote in a blog post on Friday.

In order to create consistent standards for AI skills, Microsoft will be the founding member of GA's AI Standards Board which will later be joined by other industry-leading companies at the forefront of AI disruption...

As part of the partnerships, together, the organisations will establish an AI Talent Network to source candidates for hire and project-based work.

GA will leverage its existing network of 22 campuses and the broader Adecco ecosystem to create a repeatable talent pipeline for the AI Talent Network, the post added.
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Source: CNBCTV18 

Saturday, May 18, 2019

Google Doodle Celebrates Persian Mathematician, Poet Omar Khayyum | World - NDTV

With a doodle, Google celebrated the birth anniversary of Omar Khayyam, the first mathematician to give a general method for solving cubic equations, as NDTV reports.

Google Doodle paid tribute to the mathematician Omar Khayyam
Photo: google.com

Today's Google Doodle celebrates the birth anniversary of Persian mathematician, astronomer and poet Omar Khayyam. The Iran-born mathematician was known for his work on the classification and solution of cubic equations...

He is known for his work on the theory of parallels and geometric algebra. He was the first mathematician to give a general method for solving cubic equations. His methods were sufficient to geometrically find all real roots of cubic equations.
Read more...

Source: NDTV

Free 'Statistics Facts and Snacks' Summer Workshop for High Schoolers | UK Happenings - UKNow

High school students interested in learning about statistics and related careers are invited to participate in the 2019 "Statistics Facts and Snacks workshop," offered by the University of Kentucky Center for Clinical and Translational Science, June 5 to 7 from 9 a.m. to noon. 

Photo: Getty Images
Students in any grade level may participate, and no statistics or computer science experience is required. Snacks will be provided. 

This hands-on workshop will explore the fun of data analysis through basic statistical techniques taught using R, a free statistical programming language. Students will also learn about what a statistician does and what the requirements are to pursue higher education in statistics. The workshop will include visits from expert statisticians in a variety of different statistical careers, and students will have the opportunity to chat with them about their background and work life.
Read more...

Source: UKNow

UNSW Business School Ranked 1st in the World for Risk and Actuarial Studies | UNSW Home Newsroom

UNSW Business School has topped the new Global Research Rankings of Actuarial Science and Risk Management & Insurance, inform Julia Jones, Communications Specialist at Sydney, New South Wales (NSW)


UNSW Business School has topped the new Global Research Rankings of Actuarial Science and Risk Management & Insurance.

Set up by the University of Nebraska at Lincoln (UNL), the global rankings list the top 50 business schools worldwide based on the number of papers published in leading insurance and actuarial journals between 2014 to 2018.

UNSW Business School led the list with 60 published articles. Five Australian universities were named in the top 50.

“This is an excellent result for the Business School,” said Professor Michael Sherris, founding Professor of the Actuarial Studies program at UNSW.

“One of our aims when we set up the program was to have an impact internationally in terms of our research. Over the past 5 or 10 years we’ve seen that pay off as more UNSW graduates move into top finance and investment positions. This number one ranking is the result of 20 years of hard work by academics in the school, the success of research grants and success in recruiting and hiring good people who do ground breaking research.”...

The leading journals tracked for the Actuarial Science and Risk Management & Insurance rankings include: Journal of Risk and Insurance (JRI); Insurance: Mathematics and Economics (IME); North American Actuarial Journal (NAAJ); ASTIN Bulletin: The Journal of the International Actuarial Association (ASTIN); and Scandinavian Actuarial Journal (SAJ).

For more information visit the University of Nebraska-Lincoln website.
Read more...

Source:  UNSW Home Newsroom  

Tips for Teaching Students ‘What to Learn’ and ‘How to Learn’ During Lectures | Effective Teaching Strategies - Faculty Focus

Tiffany Zielinski Culver, associate professor of psychology at Sul Ross State University, Uvalde, Texas says, It was soon after my son enrolled in a local junior college that I realized something was wrong. 

Photo: Faculty Focus
Success, which seemed to come so easy to him in high school, was suddenly out of reach. In fact, he was failing every course! I quickly learned that in high school he did not have to exert any effort and was taught to simply memorize material.

Sadly, this high school experience resulted in a new high school graduate who had no concept of time management, study skills, or critical thinking (McGuire, 2015). He had no idea how to take responsibility for his own learning, and despite my pleas that he needed to “study differently” in college, he had no idea what this meant or how to go about this task.

As a college instructor, this caused me to deeply reflect on my own teaching practices. I needed to help my own students develop learning skills they never had the opportunity to develop. After all, these students (like my son) were probably very intelligent, but never had the opportunity to develop these skills...

A very important component of this process is the explaining to my students why I use these teaching strategies. They need to understand the benefit of these strategies and how the strategies will help them learn in my class and in other courses.

I have asked students about my teaching techniques and I have found they are grateful for them. My students state that they have never had an instructor who teaches like me and despite my persistent questions, they are happy to have the opportunity to “learn how to learn.” 
Read more... 

Source: Faculty Focus

Big Data Science: Establishing Data-Driven Institutions through Advanced Analytics | Editors' Picks - EDUCAUSE Review

Data analytics can drive decision-making, but to optimize those decisions, stakeholders must couple effective methods with a shared understanding of both the domain and the institutional goals.

Photo: Cecilia Earls
From improving student success to forming optimal strategies that can maximize corporate and foundational relationships, data analytics is now higher education's divining rod. Faculty and administration alike make daily decisions that impact the future of our institutions and our students, recommends Cecilia Earls, Data Scientist in Information Technologies at Cornell University.


Departments establish curriculums; labs invest in new technologies; we admit students, hire faculty, monitor meal plans, and define security protocols. How can we optimize those decisions over the coming years? How do we know if we are meeting our goals? Can we use our data to make better decisions?

I think the answer is yes, but only if we couple the use of state-of-the-art analytical methods with a focused approach to how and when we engage our data to make decisions. Our data strategy must reflect not only our institutional goals, but also the novel ways in which we can now collect and analyze data to attain those goals. Part of my role as a data scientist at Cornell University  is to help guide this strategy by establishing a common understanding of, and vocabulary around, the data-driven decision-making process. 

A Team Effort
Simply hiring a data scientist does not create a data-driven organization. Identifying and realizing relevant and measurable goals through a well thought-out data strategy does, and this requires collaboration. It is essential that data scientists' partner with four types of stakeholders:
  • Visionaries. These are the leaders with a vision of our organization's future. They can identify the areas in which, when influenced by informed decision-making, would result in the greatest impact toward achieving our institutional goals. Bottom line: they know what our "big questions" really should be.
  • Subject experts. Members of our community who deeply understand the area chosen for analysis. We rely on them to identify which variables are important. Subject experts can help guide the analysis because they understand the types of change that are truly possible. If we offer a "solution" that cannot be implemented, it is the wrong solution.
  • Data experts and archivists. These individuals know where the relevant data are stored and how they can be accessed. This group also includes experts on data quality and how the data have been collected.
  • Technology experts. Setting up a secure data ecosystem requires substantial computer expertise and resources. Many data scientists do not have this expertise and need support from those who do.
While this list is not exhaustive, it makes a key point clear: data-driven decision-making is a team effort. To have the desired impact, data solutions in higher education depend on the collective knowledge of visionaries and experts in subject matter, technology, data, and data science who work collaboratively to ensure that our goals are well defined and that our approach is practical. To this end, all engaged team members must have a common understanding of our framing themes, terms, and processes...

The Analysis: Machine Learning and Statistical Inference  
At their core, supervised and unsupervised machine learning and statistical analysis are simply sets of algorithms used to extract useful information from data. While you can expect your data scientist to choose which algorithm to use, everyone on the team should have a basic understanding of what these algorithms do.
Both supervised machine learning algorithms and traditional statistical inference depend on historical data for either:
  • prediction—accurately estimating future outcomes; or
  • estimation—determining which variables are related to the outcome, and how and to what degree they are related to the outcome.
For a given question, decision makers may be interested in prediction, estimation, or both; in any case, this interest must be established prior to the analysis...

Conclusion 
Big data science is taking purchase in higher education, and our diverse institutions provide an exceptionally fertile ground for impactful data-driven decision-making. We are not corporations; we are small, vibrant communities that make decisions every day regarding critical issues such as safety, facilities management, risk management, housing, recruitment, admissions, research support, academic freedom, instruction, campus life, alumni relations, athletics, career services, support services, and healthcare. Each of these components creates independent data stores that, when analyzed collectively, can offer valuable insights for the institution as a whole.

To realize this potential, however, requires that the entire community of decision makers, data and subject experts, technological experts, and analysts work collaboratively and communicate effectively.
Read more...

Source: EDUCAUSE Review 

Friday, May 17, 2019

Music Boosters Name Owatonna Music Hall of Fame Class | On-Air - krforadio.com

See who makes up the inaugural class of the Owatonna Music Hall of Fame, presented by Music Boosters of Owatonna, reports Roy Koenig, Sports Director and afternoon announcer at KRFO. 

Photo: Roy Koenig/Townsquare Media
The Music Boosters of Owatonna recently named the inaugural class of the Owatonna Music Hall of Fame. 

MBO will host a meet-and-greet event Tuesday, May 21 in addition to a presentation for the honorees. The OHS Pops Concert is also that night.

The first class of the hall of fame includes Harry Wenger, Arnold Krueger, Roger Tenney, James VanDemark, Arnold Krueger Jr and Wenger Corporation. The meet-and-greet begins at 5 pm in the OHS Commons and Small Group Forum. The presentation is scheduled for 6 pm.

The Pops Concert Tuesday, May 21 is an Owatonna tradition with the Huskies, Varsity and Concert Bands, Choirs and Orchestra plus the Symphony Orchestra all performing. The concert begins at 7 pm.

On their website, MBO states their goal this way, "The mission of the Music Boosters of Owatonna is to support and advocate for music and music education as essential to the lifelong learning of our students and community."
Read more... 

Source: krforadio.com

Slice of life: Learning about music and life | Music - Mail and Guardian

I was 16 and living on my own in Johannesburg, attending high school at the National School of the Arts, summaizes Franny Rabkin, Mail & Guardian.

Photo: Daniel Hutchinson (Paul Botes)
I was practising myself to a standstill, trying to get into the school’s annual concerto festival with a Mozart piano concerto. I wasn’t sleeping — well, sleeping very little — I was just working, spending all my time at the piano, every moment I had.

The auditions were the following day. I went to Ros [Liebman] for my final piano lesson in preparation. She took one look at me and cancelled the piano lesson. She gave me a large vegetarian meal, with freshly made beetroot juice, and drove me home to rest. She said : ‘Any practice now is pointless, you have to rest’.  I didn’t play well enough the next day to make it through for the festival, but her kindness stayed with me. Ros used to say she really didn’t feel like a piano teacher, she couldn’t be a piano teacher because, every week, there was a different challenge. And some weeks, the challenge wasn’t the piano. So she used to say some weeks she was a psychologist, some weeks a doctor, you know. 
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Source: Mail and Guardian