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Tuesday, August 15, 2017

Educational Approach | University of Denver

Learn by doing at University of Denver

At DU, we believe the genius is in the doing. Students take part in small, focused classes, where they improve understanding through conversations that draw from the vast range of experiences and perspectives of our diverse community.

Photo: University of Denver

Learning here goes far beyond the lecture hall. We create opportunities for research, scholarship, performance and engagement. Our students collaborate with peers and faculty across disciplines and explore the subjects that move them. Our students develop important skills, find meaning and fuel their passions through exploration and collaboration with peers and faculty across disciplines.

Our students work with professors and patients to examine how chemicals in foods like tea and chocolate could help with the effects of ALS. They collaborate with Nike to develop footwear that enhances performance and reduces injuries. Immersion programs prepare students to provide aid in humanitarian crises and understand the health needs and difficulties of the homeless.

Students gain knowledge and direction from lively, discussion-based classes, internships and community engagement. They work here in Denver to ensure children in the foster system have the support they need and collaborate with locals in Panama to protect biodiversity. Wherever they are, we make sure our students can use their passion to create a better world.

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How A.I. Is Creating Building Blocks to Reshape Music and Art | New York Times - Technology

Follow on Twitter as @CadeMetz
"Project Magenta, a team at Google, is crossbreeding sounds from different instruments based on neural networks and building networks that can draw" notes Cade Metz

An artwork created using DeepDream, which researchers at Google developed in 2015. The newer work at Google, with Project Magenta, involves music, and has led to creation of a tool called NSynth.
Photo: New York Times

In the mid-1990s, Douglas Eck worked as a database programmer in Albuquerque while moonlighting as a musician. After a day spent writing computer code inside a lab run by the Department of Energy, he would take the stage at a local juke joint, playing what he calls “punk-influenced bluegrass” — “Johnny Rotten crossed with Johnny Cash.” But what he really wanted to do was combine his days and nights, and build machines that could make their own songs. “My only goal in life was to mix A.I. and music,” Mr. Eck said.

It was a naïve ambition. Enrolling as a graduate student at Indiana University, in Bloomington, not far from where he grew up, he pitched the idea to Douglas Hofstadter, the cognitive scientist who wrote the Pulitzer Prize-winning book on minds and machines, “Gödel, Escher, Bach: An Eternal Golden Braid.” Mr. Hofstadter turned him down, adamant that even the latest artificial intelligence techniques were much too primitive. But over the next two decades, working on the fringe of academia, Mr. Eck kept chasing the idea, and eventually, the A.I. caught up with his ambition.

Last spring, a few years after taking a research job at Google, Mr. Eck pitched the same idea he pitched Mr. Hofstadter all those years ago. The result is Project Magenta, a team of Google researchers who are teaching machines to create not only their own music but also to make so many other forms of art, including sketches, videos and jokes. With its empire of smartphones, apps and internet services, Google is in the business of communication, and Mr. Eck sees Magenta as a natural extension of this work.

“It’s about creating new ways for people to communicate,” he said during a recent interview inside the small two-story building here that serves as headquarters for Google A.I. research.

The project is part of a growing effort to generate art through a set of A.I. techniques that have only recently come of age. Called deep neural networks, these complex mathematical systems allow machines to learn specific behavior by analyzing vast amounts of data. By looking for common patterns in millions of bicycle photos, for instance, a neural network can learn to recognize a bike. This is how Facebook identifies faces in online photos, how Android phones recognize commands spoken into phones, and how Microsoft Skype translates one language into another. But these complex systems can also create art. By analyzing a set of songs, for instance, they can learn to build similar sounds.

As Mr. Eck says, these systems are at least approaching the point — still many, many years away — when a machine can instantly build a new Beatles song or perhaps trillions of new Beatles songs, each sounding a lot like the music the Beatles themselves recorded, but also a little different. But that end game — as much a way of undermining art as creating it — is not what he is after. There are so many other paths to explore beyond mere mimicry. The ultimate idea is not to replace artists but to give them tools that allow them to create in entirely new ways.

Source: New York Times

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New lunchtime learning classes to offer languages, art, music and more | Imperial College London

Photo: Andrew Czyzewski
"Imperial's Centre for Languages, Culture and Communication is due to launch a new programme of lunchtime classes for staff, students and visitors" summarizes Andrew Czyzewski, Central Faculty, Communications and Public Affairs.

Maria Fernandez, Spanish Teacher
Photo: Imperial College London

The classes at South Kensington include Spanish, French and Italian language at beginner or conversational level; Music for Listening; The Joy of Art; Creative Writing; Photography; and London (Art, Literature and People).

The Lunchtime Learning series builds on the success of Imperial’s ever-popular Evening Classes which have inspired many people, helped them navigate different countries and even launched new careers.

Photo: Anna Nyburg, 
Imperial College London
Dr Anna Nyburg is Coordinator of Evening Classes and has been involved in teaching humanities subjects at Imperial since 1989.

“This all came about because I represent the Centre [for Languages, Culture and Communication] at Imperial Insights - a meet and greet session for new staff. I was increasingly hearing people express an interest in the Evening Classes, but they simply couldn’t consider it because they had a young family or other important commitments. So we’re trialling it this year, and if it’s successful we’ll have a larger offering next year.”

Anna also notes that the lunchtime classes offer the chance to break up the day, and do ‘something for yourself’ and also helps with the overall work-life balance...

Registration for the Lunchtime Classes opens on 1 September, with classes commencing in October. For more information and to enrol, please visit:
Read more... 

Source: Imperial College London 

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How Machine Learning Is Helping Morgan Stanley Better Understand Client Needs | Harvard Business Review - Technology

Photo: Thomas H. Davenport
Photo: Randy Bean
"Investment advisers are using technology to build deeper relationships" says Thomas H. Davenport, President’s Distinguished Professor in Management and Information Technology at Babson College and Randy Bean, CEO and managing partner of consultancy NewVantage Partners. 

Photo:  Harvard Business Review

Systems that provide automated investment advice from financial firms have been referred to as robo-advisers. While no one in the industry is particularly fond of the term, it has caught on nonetheless. However, the enhanced human advising process — augmented by machine learning — that was recently announced by Morgan Stanley goes well beyond the robo label, and may help to finally kill off the term.
New York–based Morgan Stanley, in business since 1935, has been known as one of the more human-centric firms in the retail investing industry. It has 16,000 financial advisors (FAs), who historically have maintained strong relationships with their investor clients through such traditional channels as face-to-face meetings and phone calls. However, the firm knows that these labor-intensive channels limit the number of possible relationships and appeal primarily to older investors (according to a Deloitte study, the average wealth management client in the U.S. across the industry is over 60).

So Morgan Stanley’s wealth management business unit has been working for several years on a “next best action” system that FAs could use to make their advice both more efficient and more effective. The first version of the system, which used rule-based approaches to suggesting investment options, is being replaced by a system that employs machine learning to match investment possibilities to client preferences. There are far too many investing options today for FAs to keep track of them all and present them to clients. And if something momentous happens in the marketplace — for example, the Brexit vote and the resulting decline in UK-based stocks — it’s impossible for FAs to reach out personally to all their clients in a short timeframe.

The next best action system at Morgan Stanley, then, is focused on three separate objectives — only one of which is common in the robo-adviser market. There is, of course, a set of investment insights and choices for clients. In most existing machine advice, the recommended investments are strictly passive, that is, mutual funds or exchange-traded funds. The Morgan Stanley system can offer those if the client prefers them, but can also present individual stocks or bonds based on the firm’s research. The FA is given several ideas to offer the client and can use their own judgment as to whether to pass along any or all of them.
The second aspect of the system is to provide operational alerts. These might include margin calls, low-cash-balance alerts, or notifications of significant increases or decreases in the client’s portfolio. They might also include noteworthy events in financial markets, such as the aforementioned Brexit vote. FAs can combine personalized text with the alert and send it out over a variety of communications channels.

Finally, the Morgan Stanley system includes content on life events. If, for example, a client had a child with a certain illness, the system could recommend the best local hospitals, schools, and financial strategies for dealing with the illness. That life-event content isn’t found in other machine advisor systems, and has the potential to help create a trusting and value-adding relationship between clients and FAs.
Read more... 

Recommended Reading

Photo:  Harvard Business Review
How the Imagined “Rationality” of Engineering Is Hurting Diversity — and Engineering by Joan C. Williams, Distinguished Professor of Law and Founding Director of the Center of WorkLife Law at the University of California, Hastings College of the Law and Marina Multhaup, Research & Policy Fellow for the Center for WorkLife Law at the University of California, Hastings College of the Law.
"Just how common are the views on gender espoused in the memo that former Google engineer James Damore was recently fired for distributing on an internal company message board"

Source: Harvard Business Review

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The 35 New Skills You Can Now Learn at LinkedIn Learning | The Learning Blog - New Courses

"Each week presents a new opportunity for you and your team to learn the skills necessary to take on the next big challenge" inform The Learning Blog.

And, at LinkedIn Learning Solutions, we want to do everything we can to help make that happen.

So, each week, we add to our 10,000-plus course library. And this week was no different, as we added 32 new courses covering everything from managing high performers to creating a go-to-market plan to data visualization.

The new courses now available at LinkedIn Learning are:

Source: The Learning Blog  

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Was the First Eclipse Prediction an Act of Genius, a Brilliant Mistake, or Dumb Luck? | Atlas Obscura - Eclipse Madness

Photo: Natasha Frost
It is hard to predict an eclipse when you think the world is flat, notes Natasha Frost, British-Kiwi hybrid and Atlas Obscura Editorial Fellow.

Thales, in an 18th-century engraving by F. Ramberg.  
Photo: Wellcome Images/Public Domain

The year was 585 B.C., and the Lydians and the Medes had been warring for half a decade in what we now know as Turkey. No clear victory was in sight. Sometimes the Lydians were on top, on other occasions, the Medes seemed to have matters in hand. Once they even fought a battle in the dead of night. But, in the sixth year of their war, as they brandished their arms on the battlefield, something amazing happened. The skies began to darken. The moon passed in front of the sun. The armies, astonished, lay down their weapons—and called a truce.

This story comes to us via Herodotus, the Greek historian, who lived about a century after the fight. What’s perhaps more remarkable about this story is the line that follows it: “Thales of Miletus had foretold this loss of daylight to the Ionians, fixing it within the year in which the change did indeed happen.”

The ancient philosopher Thales of Miletus had no access to the scientific knowledge or equipment to successfully predict a solar eclipse. As a result, this story has puzzled and divided classicists and scientists for centuries. Was it preternaturally sophisticated astronomy, a myth, or just a happy accident?

Researchers believe that the eclipse Herodotus describes over the battlefield is the one that took place on May 28, 585 B.C. Its path ran from Nicaragua, over the Atlantic, then across France and Italy—and, finally, Turkey. Thales’s home, the ancient city of Miletus, on the Mediterranean coast, is just outside the path of totality. He would have seen an impressive partial eclipse from there. There are other eclipses around that time that are possible candidates, but none that would have plunged the Lydians and Medes into abrupt darkness in the way that Herodotus describes.

It is particularly strange, if the historian is to be taken at his word, that Thales predicted the year of the eclipse, rather than the exact date. In fact, wrote mathematician Dmitri Pachenko in the Journal for the History of Astronomy, “if one can predict an eclipse at all, one can predict it to the day.” Astronomy is an extremely precise science. If you know a major celestial event is coming, and where it will be visible, you’ll most likely have some precision about when it will take place. Thales, however, was at a marked disadvantage for making astronomical predictions. He didn’t know that the Earth is spherical—and seems to have thought of it as a flat disc, resting on water. 

So how did he do it? A common suggestion is that Thales had coopted the expertise of the ancient Babylonians. Their astronomers, based near modern Baghdad, kept careful records of the sky, including how Venus, Mercury, the Sun, and the Moon moved in the heavens. In 1063 B.C., their records document a total eclipse “that turned day into night.” These records led them to discover what we now call the Saros cycle, which governs the recurrence of eclipses. After three 223-month Saros series, eclipses do return to the same geographic region, but they are a complicated way to make an eclipse prediction. At any given moment, there are approximately 40 Saros cycles taking place at once, carrying on for over 1,000 years. As old sets of cycles end, new ones begin. Understanding them enough to be predictive, at the very least, requires the knowledge that the Earth is round and accurate, detailed observations—not to mention accounting for those missed eclipses that take place on cloudy days.

Thales did feats of mathematics that might have looked like magic to his contemporaries, including calculating the height of the pyramids from the length of their shadows. He was a legend. It’s possible, then, that his famous prediction was, too. People so readily accepted his claims—that magnets have souls because they make things move, that earthquakes happen because the Earth is floating on water, that all things are full of gods—that it wasn’t much of a stretch to believe he could have predicted mysterious happenings in the sky.

Natasha Frost ends his article with the following: Thales did feats of mathematics that might have looked like magic to his contemporaries, including calculating the height of the pyramids from the length of their shadows. He was a legend. It’s possible, then, that his famous prediction was, too. People so readily accepted his claims—that magnets have souls because they make things move, that earthquakes happen because the Earth is floating on water, that all things are full of gods—that it wasn’t much of a stretch to believe he could have predicted mysterious happenings in the sky.

Recommended Reading
The so-called Venus Tablet of Ammisaduqa, from ancient Mesopotamia, shows detailed astrological forecasts.
Photo: Fae/CC BY 3.0
 "Some methods are easier than others." 

Source: Atlas Obscura

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Philosopher of the month: Sir Karl Raimund Popper [timeline] | OUPblog

"This August, the OUP Philosophy team honours Sir Karl Raimund Popper (1902–1994) as their Philosopher of the Month" inform Catherine Pugh, Marketing Assistant at Oxford University Press in Oxford, England and John Priest, Marketing Assistant at Oxford University Press in New York.

Photo: Stanford Encyclopedia of Philosophy

A British (Austrian-born) philosopher, Popper’s considerable reputation comes from his work on the philosophy of science and his political philosophy. Popper is widely regarded as one of the greatest thinkers of the twentieth century. 

Born to a middle-class Jewish family in Vienna, Popper studied mathematics, physics, and psychology at the University of Vienna, graduating with a doctorate in psychology in 1928. His first book The Two Fundamental Problems of the Theory of Knowledge was shorted down to become arguably his most famous work and also the first to be published by the philosopher, Logik der Forschung (1934). The Vienna Circle became interested in Popper’s work after this despite it contesting some of their basic concepts. Popper shared their interest in distinguishing between science and other activities, but in contrast to them never supported the idea that non-scientific activities were meaningless. He instead disapproved of pseudo-science, believing that the fundamental feature of a scientific theory is that it should be falsifiable. An example of this pseudo-science which could not be falsified was Freud’s psychoanalytic theory which Popper contrasted with true science from the likes of Einstein. 

Source: OUPblog (blog)  

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15 Smartest Countries In Science, Not Math | Stock Review

Do you live in one of the smartest countries in science, not math? continues Stock Review.  


I tend to agree with this belief, as an open mind will always be able to learn and achieve more when compared with a mind which has always relied on memorization. After all, if you tell a child to do something without telling him why, he will go on simply accepting things as they are, without understanding them.

Belgium is one of the smartest countries in science. The beauty of living in a small continent, Europe, surrounded by different countries that spoke several languages, Belgium is regulated by the government and run by three communities. 

Another one is also a small European country, Slovenia. Besides that Slovenia is one of the smartest people, it has a fantastic education system. Science in a broad sense existed before the modern era and in many historical civilizations. Modern science is distinct in its access and successful in its results, so it now defines what science is in the strictest sense of the term. Science in its original sense was a word for a type of knowledge rather than a specific word for the pursuit of such knowledge.

If you want to read more about 15 smartest countries in science, not math, check Insider’s Monkey list of 15 Smartest Countries In Science, Not Math and find out more about this interesting topic.

Source: Stock Review

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6 books on science Mark Zuckerberg thinks everyone should read | Business Insider - Science

"In January 2015, Facebook CEO Mark Zuckerberg made it his mission to read one book roughly every two weeks. He called it A Year of Books" reports Chris Weller, senior innovation reporter.

Mark Zuckerberg is a big fan of science — just look at his personal reading list.
Photo: REUTERS/Albert Gea

The goal was to learn more about the world and humanity's place in it, so he set about recommending books on culture, history, and science. Many of his recommendations spanned the entirety of human history, ranging from topics like the history of violence to evolution to artificial intelligence.

We've rounded up the books on science Zuckerberg thinks everyone should read.

'Sapiens' by Yuval Noah Harari 
We weren't always the only species of human on Earth. Roughly 100,000 years ago, there were actually six varieties of people, but homo sapiens were the only ones who made it to the future. How come?

"When I read 'Sapiens,' I found the chapter on the evolution of the role of religion in human life most interesting and something I wanted to go deeper on," Zuckerberg wrote on his Facebook page.

In "Sapiens," Harari looks toward a future in which genetic engineering and artificial intelligence make our definition of "human" even more fluid.

Additional resources

Photo: Rick Wilking/Reuters
8 books Richard Branson thinks everyone should read 

"We can't guarantee you'll follow in the entrepreneur's footsteps, but the books could certainly set you in the right direction." 

Photo: Collage
11 books the world's most influential people think you should read  

"After some careful tallying, the social-media site narrowed down the most recommended books to a list of just 11 titles."

Source: Business Insider

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Monday, August 14, 2017

Busting 4 blended learning myths | TrainingZone - Develop

Photo: Stephanie Morgan
Stephanie Morgan, Director of Learning Solutions at Bray Leino Learning breaks apart a few of the myths surrounding blending to get you to the next step.

Photo: 3dsculptor/iStock

Recent years have seen a rise in organisations reducing their use of face-to-face training in favour of a blended learning approach. However, Towards Maturity reports that only 22% of learning is delivered through fully blended solutions, and while this is has grown in recent years, there is still room for improvement.

At Bray Leino Learning, we believe blended learning is the answer to your prayers if you want fresh, focused, agile learning that focuses on performance. But what is stopping more organisations from taking the leap?

Myth #1: eLearning + face-to-face = blend 
In the beginning, all we had in terms of delivery methods was classroom learning.

If you wanted to learn you had to book onto a course, possibly in a hotel somewhere. People had to take time away from their work, and to travel. It was expensive and inconvenient. It’s no surprise L&D got a bad rep with some people.

Then eLearning was born, and it was the best thing since sliced bread. Anything that could be made into eLearning, was. The natural next step was to combine these methods to get the benefits of each – and so, ‘blended learning’ was born.

Initially, when attempting to blend learning, many people thought: I’ll keep the classroom element, and incorporate some eLearning—hey presto, blended learning! Or is it?

Actually, this is the definition of a classroom sandwich—which is not true blended learning.

A classroom sandwich centres around a classroom session, rather than understanding what the most engaging delivery method would be. The knowledge element is pulled out of the classroom-based experience and delivered as pre-reading—often digitally or as eLearning—usually to make the classroom part quicker or less costly.
I have rarely seen this work!
However, when we design learning based on the key drivers of ‘making it digital’ and ‘reducing face-to-face’, we are often not considering the best, most desired, way to deliver the learning—so how can it be the best possible method if we’re limiting ourselves like this?

Myth #2: Face-to-face training is dead  
People have been suggesting that time was up for face-to-face learning when eLearning came on the scene.
I disagree. I do, however, want to encourage L&D professionals to think about balance, and to employ methods that will best achieve the results they want—rather than starting with the method and hoping to 'solutioneer' the desired effect.

Source: TrainingZone

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