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Experiences in Taylor Institute's 'forum' engage students in the art of dialogue and deliberation | UCalgary News

"Instructors invited to submit applications to teach in dynamic learning spaces; deadline for spring/summer applications is Jan. 30, 2018" inform Mike Thorn, Taylor Institute for Teaching and Learning.

“Public dialogues have deep historical roots across the world."
Photo: David Troyer, for the University of Calgary
Science in Society. Professional Communication and Interviewing. These course topics might not encourage immediate comparison, but three instructors who teach the courses in the Taylor Institute’s dynamic, adaptable forum — one of the building's three flexible learning spaces — find common ground in the value and importance of dialogue. In fact, these instructors argue that the forum comes to represent the content of the courses, manifesting the very act of learning through engagement.

Gwendolyn Blue, associate professor in the Department of Geography, emphasizes the crucial nature of respectful and critical conversation in learning about science in society. Critical exchanges help students work through challenging concepts and contentious topics that are part of everyday public dialogues.

“The course is grounded in dialogue and deliberation. We start with some ground rules, and those ground rules are that everybody speaks while appreciating that there are others in the room who may not hold similar assumptions and values,” she says. “We also are very conscious of some basics from rhetoric, such as no ad hominem attacks — criticize the argument, not the person. And so we keep our focus always on the argument. We’re also bound, because it’s about dialogue and deliberation, to consider all views on a topic, no matter how uncomfortable they might make us.”

Co-teaching a course called Professional Communication and Interviewing in the forum, social work instructors Sally St. George and Les Jerome believe that students benefit from watching instructors work together respectfully and thoughtfully. Watching collaborative teaching in action leads to effective collaborative learning.

Jerome reflects, “I think that students can clearly see that Sally and I both hugely respect each other, and I think that’s important for them to see.”

“We can’t predict everything that’s going to happen in the classroom,” St. George adds. “We can be quite well-planned, but we can’t predict, and so we also have to demonstrate that spontaneity. That’s so important; the students have to see us doing that.”

Learning by exchanging ideas
Both courses’ instructors appreciate the forum’s technological capacities, but more strongly emphasize the possibilities for engagement offered by the room’s most basic attributes: movable chairs and round tables...

Learning through dialogue
Both classes use the Taylor Institute forum’s movable round tables and chairs to incorporate regular group discussion and active learning. This method gives students the opportunity to engage in the kinds of collaborative processes that cut across disciplines. It’s all about having the space required for meaningful, learner-directed conversation...

The Taylor Institute invites instructors teaching university-level courses to submit applications to teach in TI learning spaces. 
Visit our Learning Spaces webpage to find out more information and to submit your application. 

Source: UCalgary News

NC teacher pursues ASU master’s degree through distance learning | Valley Courier - Community

Alamosa News writes, "Being a single mother of three and teaching full-time doesn’t stop Covey Denton from setting a high bar." 

Covey Denton, of North Carolina, appreciates the Adams State Teacher Education online master’s program, which helps her inspire students in science.
Photo: Courtesy
“My goal is to be the most amazing science teacher my students will ever have,” she says from her home in North Carolina. “I want to develop a profound love of science in my students through the activities and material I cover in my classroom. I want to spread my love of science to every single student that enters my room.”

Denton is pursuing her master’s degree through Adams State University’s graduate distance degree program. She enrolled in the fall of 2016 to the Adams State Teacher Education Department Master of Arts in Education Curriculum and Instruction with Endeavor STEM Leadership Certificate. She will graduate in December 2018.

The Adams State program has given Denton access to unique opportunities and resources she didn’t realize existed. “The forums to communicate with like-minded individuals have given me feedback and helped me grow as a teacher.”

The flexibility Adams State online master’s program works well with Denton’s schedule. “I am a single mom of three kids who has eight grades of lessons to prep.” She teaches preschool through 6th or 7th grade, depending on the year. “The online classes allow me to work ahead when I have spare time in my schedule and allow me to pace myself and plan.” She appreciates the well-organized classes and user-friendly format. “The NASA classes with the call-in classroom meetings are easy to schedule after the kids’ bedtime and allow me to really focus on the content being offered. I have enjoyed the prompt communication from my instructors and felt like I benefited a great deal from each course I have taken.”

The courses through Adams State’s online program have also increased Denton’s awareness of diversity in the classroom. “The courses through Adams State have helped me understand the needs of my students and best practices in the classroom, and allowed me to develop my own teaching philosophy and style.”Read more... 
Source: Valley Courier

Op-ed: Let science educators build new science standards | Deseret News

Photo: John R. TaylorJohn R. Taylor, serves as president of the Utah Science Teachers Association. He is also an Associate Professor of Biology and Assistant Dean for Integrative Learning at Southern Utah University notes, "As Utah begins the process of revising the state science standards for elementary and high school, it’s a good idea to take a moment to ask why we teach science to K–12 students at all?"

University of Utah graduate Margarita Ruiz teaches during a class at Bryant Middle School in Salt Lake City on Monday, May 22, 2017. 
Photo: Alex Goodlett, Deseret News
Science, engineering and the resulting technologies are interwoven into our lives and will be integral in meeting humanity’s most pressing future challenges. National data illustrate the need for highly skilled workers with strong backgrounds in these fields and the need is steadily increasing. 

Finally, the Utah Science Teachers Association believes that all citizens should have a scientifically based understanding of the natural world in order to engage meaningfully in public discussions, be informed voters and discerning consumers. 

Problems arise when nonscience ideals impede the teaching and learning of science, either through the use of pseudoscience or the avoidance of topics because they are politically charged. This unfortunately occurred, to no avail, during the process of developing the sixth-eighth grade SEEd standards with regard to evolution and climate change, in particular. 

Let me be clear: Every major scientific organization in the country — indeed, around the world — is on record as firmly asserting the scientific credibility of evolution and anthropogenic influence on climate change. 

Science teachers have a professional responsibility to teach science topics as understood by the scientific community, as both the National Science Teachers Association and its state affiliate, the Utah Science Teachers Association, recognize. Furthermore, the UtSTA adamantly feels nonscience topics have no place in science classrooms.

State science standards play an important guiding role. 
Read more... 

Source: Deseret News

What AI can really do for your business (and what it can’t) | InfoWorld

Photo: Isaac Sacolick"Artificial intelligence, machine learning, and deep learning are no silver bullets. A CIO explains what every business should know before investing in AI" according to Isaac Sacolick, author of Driving Digital: The Leader’s Guide to Business Transformation through Technology.

Photo: InfoWorld
How can you tell whether an emerging technology such as artificial intelligence is worth investing time into when there is so much hype being published daily? We’re all enamored by some of the amazing results such as AlphaGo beating the champion Go player, advances in autonomous vehicles, the voice recognition being performed by Alexa and Cortana, and the image recognition being performed by Google Photos, Amazon Rekognition, and other photo-sharing applications.

When big, technically strong companies like Google, Amazon, Microsoft, IBM, and Apple show success with a new technology and the media glorifies it, businesses often believe these technologies are available for their own use. But is it true? And if so, where is it true?

This is the type of question CIOs think about every time a new technology starts becoming mainstream:
  • To a CIO, is it a technology that we need to invest in, research, pay attention to, or ignore? How do we explain to our business leaders where the technology has applicability to the business and whether it represents a competitive opportunity or a potential threat?
  • To the more inquisitive employees, how do we simplify what the technology does in understandable terms and separate out the hype, today’s reality, and its future potential?
  • When select employees on the staff show interest in exploring these technologies, should we be supportive, what problem should we steer them toward, and what aspects of the technology should they invest time in learning?
  • When vendors show up marketing the facts that their capabilities are driven by the emerging technology and that they have expert PhDs on their staff supporting the product’s development, how do we evaluate what has real business potential versus services that are too early to leverage versus others that are really hype, not substance?
What artificial intelligence really is, and how it got there 
AI technology has been around for some time, but to me it got its big start in 1968-69 when the SHRDLU natural language processing (NLP) system came out, research papers on perceptrons and backpropagation were published, and the world became aware of AI through HAL in 2001: A Space Odyssey. The next major breakthroughs can be pinned to the late 1980s with the use of back propagation in learning algorithms and then their application to problems like handwriting recognition. AI took on large scale challenges in the late 1990s with the first chatbot (ALICE) and Deep Blue beating Garry Kasparov, the world chess champion.

I got my first hands-on experience with AI in the 1990s. In graduate school at the University of Arizona, several of us were programming neural networks in C to solve image-recognition problems in medical, astronomy, and other research areas. We experimented with various learning algorithms, techniques to solve optimization problems, and methods to make decisions around imprecise data.

If we were doing neural networks, we programmed the perceptron’s math by hand, then looped through the layers of the network to produce output, then looped backward to apply the backpropagation algorithms to adjust the network. We then waited long periods of time for the system to stabilize its output.

When early results failed, we were never sure if we were applying the wrong learning algorithms, hadn’t tuned our network optimally for the problem we were trying to solve, or simply had programming errors in the perceptron or backpropagation algorithms.

Flash-forward to today and it’s easy to see why there’s an exponential leap in AI results over the last several years thanks to several advances.

First, there’s cloud computing, which enables running large neural networks on a cluster of machines. Instead of looping through perceptrons one at a time and working with only one or two network layers, computation is distributed across a large array of computing nodes. This is enabling deep learning algorithms, which are essentially neural networks with a large number of nodes and layers that enable processing of large-scale problems in reasonable amounts of time.

Second, there’s the emergence of commercial and open source libraries and services like TensorFlow, Caffe, Apache MXNet, and other services providing data scientists and software developers the tools to apply machine learning and deep learning algorithms to their data sets without having to program the underlying mathematics or enable parallel computing. Future AI applications will be driven by AI on a chip or board driven by the innovation and competition among Nvidia, Intel, AMD, and others.
Read more... 

Source: InfoWorld   

Machine vision firm runs AI deep learning on Nvidia platform | Electronics Weekly

"MVTec Software, a Munich-based machine vision specialist, says it it now possible to run deep learning functions on embedded boards with Nvidia Pascal architecture" continues Electronics Weekly.

HALCON's deep learning now on NVIDIA Jetson boards
The deep learning inference in the latest version of the firm’s Halcon machine vision software was successfully tested on Nvidia Jetson TX2 boards based on 64-bit Arm processors.

The deep learning inference, i.e., applying the trained CNN (convolutional neural network), almost reached the speed of a conventional laptop GPU (approx. 5 milliseconds), says MVTec...

Photo: Dr. Olaf MunkeltDr. Olaf Munkelt, managing director, MVTec Software, believes the rapidly growing market for embedded systems requires corresponding high-performing technologies.

“AI-based methods such as deep learning and CNNs, are becoming more and more important in highly automated industrial processes. We are specifically addressing these two market requirements by combining HALCON 17.12 with the NVIDIA Pascal architecture,” said Munkelt.

Source: Electronics Weekly

Why AI Could Be Entering a Golden Age | Knowledge@Wharton - Technology

The quest to give machines human-level intelligence has been around for decades, and it has captured imaginations for far longer — think of Mary Shelley’s Frankenstein in the 19th century. Artificial intelligence, or AI, was born in the 1950s, with boom cycles leading to busts as scientists failed time and again to make machines act and think like the human brain. But this time could be different because of a major breakthrough — deep learning, where data structures are set up like the brain’s neural network to let computers learn on their own. Together with advances in computing power and scale, AI is making big strides today like never before.
Photo: Frank ChenAfter years of dashed hopes, we could be on the brink of large breakthroughs in artificial intelligence for businesses thanks to deep learning, says Frank Chen of Andreessen Horowitz. 

Photo: Knowledge@Wharton
Frank Chen, a partner specializing in AI at top venture capital firm Andreessen Horowitz, makes a case that AI could be entering a golden age. Knowledge@Wharton caught up with him at the recent AI Frontiers conference in Silicon Valley to talk about the state of AI, what’s realistic and what’s hype about the technology, and whether we will ever get to what some consider the Holy Grail of AI — when machines will achieve human-level intelligence.

An edited transcript of the conversation follows.

Knowledge@Wharton: What is the state of AI investment today? Where do we stand?
Frank Chen: I’d argue that this is a golden age of AI investing. To put it in historical context, AI was invented in the mid-1950s at Dartmouth, and ever since then we’ve basically had boom and bust cycles. The busts have been so dramatic in the AI space that they have a special name — AI winter.
We’ve probably had five AI winters since the 1950s, and this feels like a spring. A lot of things are working and so there are plenty of opportunities for start-ups to pick an AI technique, apply it to a business problem, and solve big problems. We and many other investors are super-active in trying to find those companies who are solving business problems using AI.

Knowledge@Wharton: What brought us out of this AI winter?

Chen: There’s a set of techniques called deep learning that when married with big amounts of data really gets very accurate predictions. For example, being able to recognize what is in a photo, being able to listen to your voice and figure out what you’re saying, being able to figure out which customers are going to churn. The accuracy of these predictions, because of these techniques, has gotten better than it has ever gotten. And that’s really what’s creating the opportunity.

Knowledge@Wharton: What are some of the big problems that AI is solving for business?

Chen: AI is working everywhere. To take one framework, think about the product lifecycle: You have to figure out what products or services to create, figure out how to price it, decide how to market and sell and distribute it so it can get to customers. After they’ve bought it, you have to figure out how to support them and sell them related products and services. If you think about this entire product lifecycle, AI is helping with every single one of those [stages].

For example, when it comes to creating products or services, we have this fantasy of people in a garage in Silicon Valley, inventing something from nothing. Of course, that will always happen. But we’ve also got companies that are mining Amazon and eBay data streams to figure out, what are people are buying? What’s an emerging category? If you think about Amazon’s private label businesses like Amazon Basics, product decisions are all data-driven. They can look to see what’s hot on the platform and make decisions like “oh, we have to make an HDMI cable, or we have to make a backpack.” That’s all data-driven in a way that it wasn’t 10 years ago.

Source: Knowledge@Wharton

Overcoming The Challenges Of Machine Learning Model Deployment | BCW - Business

Yvonne Cook, General Manager at DataRobot UK summarizes, "Our societies and economies are in transition to a future shaped by artificial intelligence (AI)." 

Photo: BCW
To thrive in this upcoming era, companies are transforming themselves by using machine learning, a type of AI that that allows software applications to make accurate predictions and recommend actions without being explicitly programmed.  
There are three ways that companies successfully transform themselves into AI-driven enterprises, differentiating them from the companies that mismanage their use of AI:
  • They treat machine learning as a business initiative, not a technical speciality.
  • They have higher numbers of machine learning models in production.
  • They have mastered simple, robust, fast, and repeatable ways to move models from their development environment into systems that form the operations of their business.
Commercial payback from AI comes when companies deploy highly-accurate machine learning models that operate robustly within the systems that support business operations. 

Why Companies Struggle With Model Deployment
While hard data is scarce, anecdotal evidence suggests that it is not uncommon for companies to train more machine learning models than they actually put into production. Challenges to organisation and technology are in play here, and success requires that both are addressed. From an organisational perspective, many companies see AI enablement as a technical speciality. This is a mistake.
AI is a business initiative. Becoming AI-driven requires that the people currently successful in operating and understanding the business can also create tomorrow’s revenue and be responsible for both building and maintaining the machine learning models that grow revenues. To succeed, these business drivers will need collaboration and support from specialists, including data scientists and the IT team.
Machine learning models must be trained on historic data, which demands the creation of a prediction data pipeline. This is an activity that requires multiple tasks including data processing, feature engineering, and tuning. Each task, down to versions of libraries and handling missing values, must be exactly duplicated from the development to the production environments, a task with which the IT team is intimately familiar...

AI and machine learning offer companies an opportunity to transform their operations. IT professionals play a critical role in ensuring that the models developed by their business peers and data scientists are suitably deployed to succeed in serving predictions that optimise business processes. Automated machine learning platforms allow business people to develop the models they need to transform operations while collaborating with specialists, including data scientists and IT professionals.
Choosing an enterprise-grade automated machine learning platform will certainly make IT’s life easier. By providing guidance on organising for successful model deployment and the choice of appropriate technology, IT executives ensure their teams are recognised for their effective contribution to the company’s success as it transforms into an AI-driven enterprise.

Source: BCW

How Machine Learning Can Help Identify Cyber Vulnerabilities | Harvard Business Review - Analytics

Ravi Srinivasan, vice president of strategy and offering management at IBM Security notes, "Putting the burden on employees isn’t the answer."

 Photo: Pedro Pestana/EyeEm/Getty Images
People are undoubtedly your company’s most valuable asset. But if you ask cybersecurity experts if they share that sentiment, most would tell you that people are your biggest liability.

Historically, no matter how much money an organization spends on cybersecurity, there is typically one problem technology can’t solve: humans being human.  Gartner expects worldwide spending on information security to reach $86.4 billion in 2017, growing to $93 billion in 2018, all in an effort to improve overall security and education programs to prevent humans from undermining the best-laid security plans. But it’s still not enough: human error continues to reign as a top threat.

According to IBM’s Cyber Security Intelligence Index, a staggering 95% of all security incidents involve human error. It is a shocking statistic, and for the most part it’s due to employees clicking on malicious links, losing or getting their mobile devices or computers stolen, or network administrators making simple misconfigurations. We’ve seen a rash of the latter problem recently with more than a billion records exposed so far this year due to misconfigured servers. Organizations can count on the fact that mistakes will be made, and that cybercriminals will be standing by, ready to take advantage of those mistakes.

So how do organizations not only monitor for suspicious activity coming from the outside world, but also look at the behaviors of their employees to determine security risks? As the adage goes, “to err is human” — people are going to make mistakes. So we need to find ways to better understand humans, and anticipate errors or behaviors that are out of character — not only to better protect against security risks, but also to better serve internal stakeholders.

There’s an emerging discipline in security focused around user behavior analytics that is showing promise in helping to address the threat from outside, while also providing insights needed to solve the people problem. It puts to use new technologies that leverage a combination of big data and machine learning, allowing security teams to get to know their employees better and to quickly identify when things may be happening that are out of the norm.

To start, behavioral and contextual data points such as the typical location of an employee’s IP address, the time of day they usually log into the networks, the use of multiple machines/IP addresses, the files and information they typically access, and more can be compiled and monitored to establish a profile of common behaviors. For example, if an employee in the HR team is suddenly trying to access engineering databases hundreds of times per minute, it can be quickly flagged to the security team to prevent an incident.

Source: Harvard Business Review 

AI and machine learning: Looking beyond the hype | - Comment

Photo: Erin Hawley"In every federal agency, critical insights are hidden within the massive data sets collected over the years" reports Erin Hawley, DataRobot's vice president of public sector. 

But because of a shortage of data scientists in the federal government, extracting value from this data is time consuming, if it happens at all. Yet with advances in data science, artificial intelligence (AI) and machine learning, agencies now have access to advanced tools that will transform information analysis and agency operations.

From predicting terror threats to detecting tax fraud, a new class of enterprise-grade tools, called automated machine learning, have the power to transform the speed and accuracy of federal decision-making through predictive modeling. Technologies like these that enable AI are changing the way the federal government understands and makes decisions.

To use tools like automated machine learning to their full potential to accelerate and optimize data science in the federal government, it’s important to start by understanding the terms used and what they mean.

Data science — the art of analyzing data 
Data science is a broad term, referring to the science and art of using data to solve problems. Rooted in statistics, this practice blends math, coding and domain knowledge to answer specific questions from a certain data set. Advances in computing power have transformed this from calculator-based statistical modeling into predictive algorithms that transform historical analysis into forecasts about future behaviors.
Read more... 


There is a new chapter in Harry Potter's story — and it was written by artificial intelligence | Business Insider

  • An artificial intelligence tool read all of the "Harry Potter" books and automatically generated a new, self-written chapter out of what it learned.
  • The output text was mostly raw and incomprehensible, so a few writers intervened to make it understandable.
  • The writing is mostly weird and borderline comical, but the machine managed to partly reproduce original writer J.K. Rowling's writing style.
There is a new chapter in Harry Potter's story, but it wasn't written by the original author, J.K. Rowling. Instead, an artificial intelligence (AI) algorithm did most of the hard work, The Verge first reported

A young Daniel Radcliffe in one of the Harry Potter movies.
Photo: Warner Brothers
The people over at Botnik Studio fed a computer's algorithmic tool with all of the original novels from Harry Potter's saga, and in return, it generated a three-page chapter titled "Harry Potter and the Portrait of What Looked Like a Large Pile of Ash."

The AI churned out the bulk of the text, but in order to transform it from your typical predictive-text word salad to something actually intelligible, a number of writers were involved. 

Chief among them is Jamie Brew, a former writer for The Onion and Clickhole, who had already worked on similar automated text prediction writings on Tumblr, where his objectdreams page includes procedurally generated, fictional work on X-Files, grammar rules, and even Craiglist ads...

The writing is as weird as it is fun, and it might be worth a few minutes of your time; if you agree, you can read the whole chapter here.  

Source: Business Insider

Artificial intelligence helps accelerate progress toward efficient fusion reactions | Princeton University

Before scientists can effectively capture and deploy fusion energy, they must learn to predict major disruptions that can halt fusion reactions and damage the walls of doughnut-shaped fusion devices called tokamaks. Timely prediction of disruptions, the sudden loss of control of the hot, charged plasma that fuels the reactions, will be vital to triggering steps to avoid or mitigate such large-scale events.
"Today, researchers at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University are employing artificial intelligence to improve predictive capability" argue John Greenwald, Science Editor.

Image of plasma disruption in experiment on JET, left, and disruption-free experiment on JET, right. Training the FRNN neural network to predict disruptions calls for assigning weights to the data flow along the connections between nodes. Data from new experiments is then put through the network, which predicts “disruption” or “non-disruption.” The ultimate goal is at least 95 percent correct predictions of disruption events.
Photo: courtesy of Eliot FeibushPhoto: William Tang
Researchers led by William Tang, a PPPL physicist and a lecturer with the rank of professor in astrophysical sciences at Princeton, are developing the code for predictions for ITER, the international experiment under construction in France to demonstrate the practicality of fusion energy. 

Form of ‘deep learning’ 
The new predictive software, called the Fusion Recurrent Neural Network (FRNN) code, is a form of “deep learning” — a newer and more powerful version of modern machine learning software, an application of artificial intelligence. “Deep learning represents an exciting new avenue toward the prediction of disruptions,” Tang said. “This capability can now handle multi-dimensional data.”

FRNN is a deep-learning architecture that has proven to be the best way to analyze sequential data with long-range patterns. Members of the PPPL and Princeton machine-learning team are the first to systematically apply a deep learning approach to the problem of disruption forecasting in tokamak fusion plasmas.

Chief architect of FRNN is Julian Kates-Harbeck, a graduate student at Harvard University and a DOE-Office of Science Computational Science Graduate Fellow. Drawing upon expertise gained while earning a master’s degree in computer science at Stanford University, he has led the building of the FRNN software...

Princeton’s Tiger cluster 
Princeton University’s Tiger cluster of modern GPUs was the first to conduct deep learning tests, using FRNN to demonstrate the improved ability to predict fusion disruptions. The code has since run on Titan and other leading supercomputing GPU clusters in the United States, Europe and Asia, and has continued to show excellent scaling with the number of GPUs engaged.

The researchers seek to demonstrate that this powerful predictive software can run on tokamaks around the world and eventually on ITER.

Also planned is enhancement of the speed of disruption analysis for the increasing problem sizes associated with the larger data sets prior to the onset of a disruptive event.
Support for this project has primarily come to date from the Laboratory Directed Research and Development funds that PPPL provides.

PPPL, on Princeton University’s Forrestal Campus in Plainsboro, New Jersey, is devoted to creating new knowledge about the physics of plasmas — ultra-hot, charged gases — and to developing practical solutions for the creation of fusion energy. PPPL is managed by Princeton for the U.S. Department of Energy’s Office of Science, which is the largest single supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time.
Read more... 

Source: Princeton University

Artificial intelligence just discovered two new exoplanets | Popular Science - Technology

Mary Beth Griggs, assistant editor at Popular Science contributed research to this report.
Follow on Twitter as @robverger"This is what happens when you turn machine learning loose on the cosmos" says Rob Verger, Assist. tech editor at at Popular Science.

The Kepler-90 system; AI helped discover the planet called Kepler-90i.
Photo: NASA/Wendy Stenze
A machine learning technique called a neural network has identified two new exoplanets in our galaxy, NASA scientists and a Google software engineer announced today, meaning that researchers now know about two new worlds thanks to the power of artificial intelligence.

Discovering new exoplanets—as planets outside our solar system are called—is a relatively common occurrence, and a key instrument that scientists use to identify them is the Kepler Space Telescope, which has already spotted a confirmed 2,525 exoplanets. But what’s novel about this announcement is that researchers used a AI system to spot these two new worlds, now dubbed Kepler-90i and Kepler-80g. The planet known as 90i is especially interesting to astronomers, as it brings the total number of known planets orbiting that star to eight, a tie with our own system. The average temperature on 90i is thought to be quite balmy: more than 800 degrees Fahrenheit.

Just as exoplanet discoveries are common, so too are neural networks, which is software that learns from data (as opposed to a program that have had rules programmed into it). Neural networks power language translation on Facebook, the FaceID system on the new iPhone X, and image recognition on Google Photos. A classic example of how a neural network learns is to consider pictures of cats and dogs—if you feed labeled images of cats into a neural network, later it should be able to identify new images that it thinks has cats in them because it has been trained to do so.

“Neural networks have been around for decades, but in recent years they have become tremendously successful in a wide variety of problems,” Christopher Shallue, a senior software engineer at Google AI, said during a NASA teleconference Thursday. “And now we’ve shown that neural networks can also identify planets in data collected by the Kepler Space Telescope.”

Astronomers need tools like telescopes to search for exoplanets, and artificial intelligence researchers need vast amounts of labeled data. In this case, Shallue trained the neural network using 15,000 labeled signals they already had from Kepler. Those signals, called light curves, are measures of how a star’s light dips when a planet orbiting it passes between the star and Kepler’s eye, a technique called the transit method. Of the 15,000 signals, about 3,500 were light curves from a passing planet, and the rest were false positives—light curves made by something like a star spot, but not an orbiting planet. That was so the neural network could learn the difference between light curves made by passing planets and signals from other phenomena.

Source: Popular Science

Making a career change? Get a comprehensive tour of computer science with these online courses | Mashable

"If you thought you missed your chance to major in computer science when you opted for art history in college, there's good news after all" says Mashable.

There are such things as second chances, and thanks to the influx of online learning, gaining a new skill set won't require you to dip into your savings. This Computer Science training is just $39 — that's equivalent to just 4.8 months of Netflix.

134 hours of all things robots and computer tech.
Photo: Pexels The Computer Science Advancement Bundle features eight classes that will help you make a career in tech, no matter what you do now. Here's a breakdown of each course:

First, learn how to code 
Break Away: Programming And Coding Interviews
Photo: Pexels A great introduction to tech jobs, this course will walk you through the job interview process for programming and coding careers. The team behind this course has conducted hundreds of interviews at Google and Flipkart, so they know what they're talking about and will give you the heads up on the kind of programming problems that might come up in an interview.

The Fintech Omnibus: Theory and Practice in Python, R, and Excel 
The Fintech Omnibus will walk you through risk modeling, factor analysis, numerical optimization, and linear and logistic regression using real models and examples. You'll learn a ton about value-at-risk, Eigenvalue decomposition, modeling risk with covariance matrices, and the method of least squares...

Get in on Big Data and Machine Learning 
The Big Data Omnibus: Hadoop, Spark, Storm, and QlikView
Photo: PexelsAfter these 120 lectures on big data, you'll be able to install Hadoop in different modes, manipulate data in Spark, run a Storm topology in multiple modes, and use the QlikView In-memory data model. Using these tools, you'll glean insights from enormous amounts of data in the way both major and minor corporations do.

Machine Learning and TensorFlow on the Google Cloud 
TensorFlow is an open source software library for machine intelligence. Using TensorFlow and Google Cloud, you'll learn all about neural networks and machine learning principles...  

Get the Computer Science Advancement Bundle now for just $39 — a massive 97% discount off its $1,492 retail price.  

Source: Mashable

Just released: LinkedIn’s 2017 U.S. Emerging Jobs Report | LinkedIn Economic Graph Team

"The job market in the U.S. is brimming right now with fresh and exciting opportunities for professionals in a range of emerging roles" inform LinkedIn Economic Graph Team.

New types of jobs means new potential for workers at all levels, especially for those looking to change careers. Overall, job growth in the next decade is expected to outstrip growth during the previous decade, creating 11.5 million jobs by 2026, according to the U.S. Bureau of Labor Statistics.

Even further, it’s estimated that 65% of children entering primary school today will ultimately hold jobs that don’t yet exist.

To help find those up-and-coming roles and to better understand what skills are needed to succeed, we analyzed LinkedIn data from the last five years, as well as some survey data, to identify which jobs and skills are on the rise, what they’re replacing, and what these trends indicate about the jobs market in the years to come.

Here’s what we found:
  • Tech is king: Jobs with the top growth potential are tech-focused, with demand coming from tech and non-tech companies alike. Machine learning engineer, data scientist, and big data engineers rank among the top emerging jobs -- with companies in a wide range of industries seeking those skills.
  • Soft skills matter: Not all of the emerging tech jobs require technical skills. Sales development representative, customer success manager, and brand partner rank among the top emerging jobs at companies where a technical background is not a necessity. Traditional soft skills like communication and management underpin all of these emerging jobs.
  • Jobs with high mobility on the rise: Several top emerging jobs reflect broader societal trends, such as wellness, flexibility and location mobility. More people are getting healthy which could explain why barre instructor featured among our emerging jobs. Not quite as surprising, licensed realtors ranked highly as the post-Great Recession recovery of the real estate market rolls forward. Just in the past year, the number of licensed realtors has surged 40 percent. These type of roles tend to be more widely distributed across U.S. regions.
  • Low supply of talent for top jobs: Data scientist roles have grown over 650 percent since 2012, but currently 35,000 people in the US have data science skills, while hundreds of companies are hiring for those roles - even those you may not expect in sectors like retail and finance - supply of candidates for these roles cannot keep up with demand.
  • Future-proofing skills is critical: Some of these emerging skills didn’t even exist five years ago, and many professionals are not confident their current skill set will be relevant within the next 1-2 years.
Read on to see how skills and experience might play into the jobs of tomorrow, and the paths to get there.

The explosion of tech roles over the past five years comes as no surprise given the impact of technology in every sector. Our November Workforce Report noted that hiring is up nearly 10% in the hardware industry, and nearly 15% in the software industry from October 2016.
  • Specifically, the growth and widespread application of more sophisticated technology - like artificial intelligence - we are seeing more specialized machine learning and data-specific roles top the list of emerging jobs. These jobs are also widely available outside the technology industry.
  • The number of customer experience roles that made the list indicates that the “age of the customer” was more than jargon. These jobs are among some of the non-automatable jobs on the market today, and the skills associated with them aren’t necessarily taught in university, as they rely heavily on soft skills.
There can’t be emerging jobs without some jobs being replaced. When looking at the roles that are starting to lose steam in today’s professional landscape, two trend stands out:
  • Comprehensive sets of skills that cover multiple disciplines are seemingly in higher demand. Many of the roles on this list cover multiple disciplines and are applicable to multiple industries.
  • Certain specialist roles are on the decline. From specialized developer roles, to legal specialists, and even specialized logistics roles, we are seeing these roles be replaced in favor of more comprehensive skill sets and job titles. For example, Flash-related roles are on the decline as the technology loses steam in favor of more big data and machine learning roles.
We looked back at the career paths of professionals who hold these top 5 roles to get a sense for where they were in their careers 5 years ago, and what we found is encouraging for professionals no matter their career journey or the types of skills they have.
  • In analyzing the career path of professionals who hold one of the top 5 emerging jobs, there was a common thread throughout: software engineers are feeding into all of the technology-related professions.
  • Sales Development Representatives, while growing rapidly, is still an entry-level role and a great option for those looking to break into any industry. In fact, this is one of the most popular roles for recent graduates.
Machine Learning Engineer
1. Software Engineer          
2. Research Assistant
3. Teaching Assistant
4. Data Scientist
5. System Engineer
Data Scientist
1. Research Assistant        
2. Teaching Assistant
3. Software Engineer
4. Data Scientist
5. Business Analyst...
Methodology: The results of this analysis represent the world seen through the lens of LinkedIn data. As such, it is influenced by how members choose to use the site, which can vary based on professional, social, and regional culture, as well as overall site availability and accessibility. These variances were not accounted for in the analysis.

We looked at all members who list dated work experience on their profile and grouped the millions of unique, user-inputted job titles based on common job roles (which have many permutations). For example, the “machine learning engineer” job title includes user inputted titles such as “machine learning software engineer” and “machine learning engineer II.” We then counted the frequencies of job titles that were held in 2012 and compared the results to job titles that were held in 2017. “Emerging jobs” refers to the job titles that saw the largest growth in frequency over that 5 year period.

Source: LinkedIn Economic Graph Team

5 Reasons that Electric Bikes Are Like Blended Learning | Inside Higher Ed - Technology and Learning

Follow on Twitter as @joshmkim"My new obsession is electric bikes. Not that I own one." says Dr. Joshua Kim, Director of Digital Learning Initiatives at the Dartmouth Center for the Advancement of Learning (DCAL).
Technology and Learning
Being an academic, I’ll need to do 10,000 hours of research before I am comfortable contemplating any action. At this rate, I expect to be in the market for an e-bike purchase in spring of 2018.

Electric bikeLike all my obsessions, I understand electric bikes through the lens of learning and technology.

Here are 5 ways that electric bikes are exactly like blended learning:

1 - The Passion of Early Adopters:
A growing number of my colleagues are commuting to campus on an electric bike. They are replacing a drive to campus in a car with a ride to campus on an e-bike. Reasons vary. Some are riding their electric bike because they live too far away to ride a traditional bicycle. Others ride their e-bike to campus because they can arrive without getting sweaty, avoiding the need to shower. What all of these electric bike owning colleagues have in common is their passion for e-bikes. They are electric bike evangelists. They talk about how their e-bike changed their life. Not only do they get more exercise, they look forward to their morning and early evening ride. The purchase price of the e-bikes were justified by saving on the parking passes and gas, but these practical commuting decisions gave rise to a larger belief that electric biking is the future of transportation.

We hear much the same things from those educators who have gotten into blended learning. Talk to faculty teaching online courses, and they marvel at how the medium enables them to deeply interact with their students. The asynchronous nature of much of online learning creates space for all the students in the class to contribute to discussions and debates - through the mechanisms of discussion boards and blogs and wikis - space that is normally constrained and finite in a traditional 50 or 90 minute residential class. Flipping a mostly residential course, by having course content and curriculum be delivered before the class through online lectures, creates new space in the face-to-face discussion for active learning.  Class is invigorating when the teaching model moves from delivering content to coaching and mentoring.

2 - A Dedicated Community of Practice:
The small and growing number of electric bike people on my campus have started to find one another. They are meeting to talk about how they chose their e-bike, where they get it serviced, and what rides in the area (with big hills) they are now willing to tackle. These campus electric bike pioneers are starting to convert others. There seems to be many more of us who are talking about getting an e-bike than who actually own one.  The enthusiasm of these early electric bike owners is contagious.

This small group of e-bike converts reminds me of those faculty who were amongst the first to teach online and to use technology to flip their residential classes. The first professors to make the transition to online and blended learning faced a good degree of skepticism from their colleagues. Most were skeptical themselves. They wondered if technology would get in the way of what they love best about teaching. They worried about what would be lost when eye contact was replaced by screen time. When the give and take of a good lecture was substituted for recorded video presentations and discussion boards.

What most faculty found, to their surprise, was that online and blended teaching is pretty great. Maybe not better than traditional face-to-face teaching, but usually better than a straight lecture based (large enrollment) course. Online and blended learning encouraged, rather than inhibited, interactions with students.  The medium of online and blended learning still required all the expertise of an experienced educator. The difference being that now online faculty could teach students who were also full-time workers, who were unable to move to campus, and who relied on online learning to participate in higher education. For those teaching blended courses, the technologies of classroom flipping opened up more time for active learning and intensive instruction.

Source: Inside Higher Ed (blog)

Composer incorporates math and art in McBain music | Cadillac News

Photo: Karen Hopper Usher"Composing is alive and instrumental music isn't just recycled from the past" summarizes Karen Hopper Usher, Cadillac News.

Composers like Andrew Perkins are constantly writing new tunes for young musicians.
The challenge is to write a piece of music that gets kids to really think.

Andrew Perkins, a music teacher and composer,
wrote \"Gradients,\" which McBain middle school
band students will perform in January at the
Michigan Music Conference.
Photo: Courtesy In January, middle school band students at McBain Rural Agricultural School will perform Perkins's piece, "Gradients," at the Michigan Music Conference.

When Perkins saw a Cadillac News article about McBain's upcoming performance, he contacted band teacher Heather Wiggins to congratulate her.
He also had a proposition — He had a recently completed composition. Would her students be interested in debuting the music at the conference?
Yes, they would.
"For composers, performances at big conferences are a big deal," Perkins said. "It's kind of a win-win."
Perkins, who teaches music in Fenton, near Flint, understands the complexities of finding music that's appropriate for middle school musicians, suits the instrumental make-up of the band and is entertaining.
A lot of middle school band literature isn't written "about anything," Perkins said. It's music for the sake of teaching a specific musical concept.

Perkins wanted to teach something more sophisticated, he said.
He settled on the idea of gradients, which has different but related applications in mathematics, physics, philosophy and art. The mathematic concept of "rise over run," is something kids tend to learn in middle school math, he said...

...perform Perkins's pieces at the Michigan Music Conference in January. Besides McBain, Warren Mott will perform "Alcatraz" and Okemos will perform "Asylum," which is about the old asylum in Traverse City.

Source: Cadillac News

Five management books that will help you kickstart 2018 on a high note | - Resources

Photo: Tamanna Mishra"For professionals – C-suite and mid-level management alike – the process of learning does not – and more importantly, should not – stop" inform Tamanna Mishra, seasoned communications professional. 

As you grow in your role, so do the challenges that arise directly or through your close circle of mentors and leaders. How does one learn to respond to such situations? The answers lie in books.

From human challenges such as persuading customers and motivating employees to operational challenges that involve creating order in a system that seems to be built on the premise of chaos, there is a lot that business books have spoken about in the past and continue to do so. It is this advice from management experts and business leaders that can steer you in the right direction.

The year-end holiday season is the best time not just to reflect on your personal achievements but also to catch up on the lessons learnt by businesses across the globe. So here’s a reading list featuring books on entrepreneurship, leadership, human relations, and every other topic a professional might be interested in.
Read more... 


5 new books you won't want to miss this week | USA TODAY - Life - Books

Follow on Twitter as   
Jocelyn McClurg, USA TODAY's Books Editor, scopes out the hottest books on sale each week.Photo:
1. Mad Hatters and March Hares, edited by Ellen Datlow (Tor, fiction, on sale Dec. 12)

Mad Hatters and March Hares: 
 All-New Stories from the World
of Lewis Carroll's Alice in WonderlandWhat it’s about: A collection of new stories inspired by Lewis Carroll's psychedelic 19th century classic, Alice's Adventures in Wonderland...The buzz: Datlow, a Hugo Award winner for editing, has drawn in fantasy/sci-fi contributors including Seanan McGuire, Jane Yolen and Catherynne M. Valente.
4. Charles Darwin: Victorian Mythmaker by A.N. Wilson (Harper, non-fiction, on sale Dec. 12)
Charles Darwin:
Victorian Mythmaker
What it’s about: New biography offers a “reappraisal” of the scientist who developed the theory of evolution.The buzz: “Illuminating,” says Kirkus Reviews.Read more...

Daemon Voices review – wise words from a craftsman | The Guardian - Books + Reviews

"Philip Pullman’s collection of insightful essays on the power of storytelling." 

Daemon essayist: Philip Pullman.
Photo: Suki Dhanda for the Observer Published alongside La Belle Sauvage, the first in a three-part prequel to His Dark Materials, Daemon Voices is a compendious collection of Philip Pullman’s talks, essays and newspaper articles spanning several decades. 
Exploring themes as diverse as art, politics, science and faith, Pullman is eloquent on the craft and power of storytelling and the folk tales and fairytales that are his personal touchstones.

A lecture entitled “Let’s write it in red”, inspired by two little girls writing a story together on a train, reflects on the need to “keep the old stories burnished and bright and new by telling them over and over again”. Something of this telling and retelling is revealed in the themes, images and literary allusions that repeat like refrains throughout the collection, offering an insight into Pullman’s own creative preoccupations.
Daemon Voices 
by Philip Pullman • Daemon Voices by Philip Pullman is published by David Fickling Books (£20). To order a copy for £17 go to

Recommended Reading The Guardian

2017: A Year To Remember | Phoenix Conservatory of Music (PCM)

”I’ve been involved since I was seven or eight. Ever since then, I’ve been building my skills.” 

Phoenix Conservatory of Music named as an Arizona State Charitable Tax Credit Organization, Phoenix Conservatory of Music’s College Prep Program serving the Phoenix Metropolitan Area cited as one of the best creative youth development programs in the country and wins a National Arts and Humanities Youth Program Award, and Phoenix Conservatory of Music Wins Mayor’s Arts Award!
Will Kratzenberg, a sophomore in High School and PCM student says. “[I started with piano and then went to audio production.] I had no place to go for sound production or learn how to build or reconstruct sound. PCM showed me how to work inside sound production DAWs and how to mix and even create my own songs. At first, I thought it wouldn’t be all that great…all these people doing what I’m doing. If anything, that is the key to learning- learning what people are doing around you, learning from them and taking some of their knowledge into your own. I feel a sense of family here.  Coming here is an escape and you can really improve your skills being here and learning with your family.  [As a family] we are being able to build what we’ve been trying to build for a very long time.  [As an organization], we finally made it up there, and after all these years …it’s finally paying off.”

And this is the absolute truth.  This has been an incredible year for Phoenix Conservatory of Music, a Phoenix based nonprofit community school of music with a very big reach.  “It often feels as if we are a very best kept secret”, says Regina Nixon, Executive Director of Phoenix Conservatory of Music, ”but we have amazing stories with all of our students and some fantastic outcomes.”

This past November, at a special ceremony in Washington, D.C. this November, the nation’s top cultural agencies honored twelve Creative Youth Development programs from across the country for their work in providing excellent arts and humanities learning opportunities to young people.  Three of Phoenix’s own students had the honor of traveling to our Nation’s Capital to represent Phoenix, Arizona and a top after school arts program, Phoenix Conservatory of Music’s College Prep Program. Marcus Wolf (17), Michael Rodriguez (15) and Lourde Childs (13) were the student representative and performers for The Phoenix Conservatory of Music as it was recognized with a 2017 National Arts and Humanities Youth Program Award.  The award was presented by The National Endowment for the Arts and their partners.  Michael and Lourde, the only two performers for this prestigious Washington D.C. awards ceremony, performed Man In The Mirror recorded by Michael Jackson, written by Glen Ballard and Siedah Garrett; produced by Quincy Jones, and received a standing ovation.  (Video of the performance: )

The award honors the nation’s highest best programs for after school arts and humanities programs. Chosen from 350 nominations from across the country, PCM, was one of twelve organizations across the country to receive the honor, which recognizes effectiveness in promoting learning and life skills in young people by engaging them through creative youth development programs.
“Phoenix is home to an incredible arts community, and organizations like the Phoenix Conservatory of Music are key to the city’s cultural vibrancy,” Phoenix Mayor Greg Stanton said. “This award is testament to the great work PCM is doing to expose children in our community to the arts and music education.”

The award is the latest recognition to highlight the Conservatory’s work with students. Earlier this year, Phoenix Conservatory of Music was a recipient of the 2017 Mayor’s Arts Awards for Innovative Organization of the Year, and in 2015 received the Arizona Governor’s Arts Award for Arts Education Organization.  In addition to all of the accolades, there are the direct outcomes of the program- a 95% High School Graduation Rate, 71% attend college or university, and in the last 7 years, they have earned over $1M+ in addition to scholarship offers.

Source: Phoenix Conservatory of Music (PCM) - Blog


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