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Monday, March 27, 2017

Location Can Determine How Successfully Teachers Work Together, Study Finds | Education Week Teacher

"A study by Northwestern University finds that teachers' physical proximity to one another plays an important role in the way they interact and, ultimately, in how successful they are at collaborating." says Teaching Now Blog contributor.

Teaching Now

A teacher's work is often done in the seclusion of the classroom. But new research finds that teachers' physical proximity to one another plays an important role in the way they interact and, ultimately, how successful they are at collaborating.

Source: Image by Flickr user dcJohn, licensed under Creative Commons

A study from Northwestern University's school of education and social policy, published this month in Sociology of Education, measured how distance in the school building—teachers' proximity to each other's classrooms as well as to other areas where teachers spend their time, such as restrooms and the lunchroom—affects the way teachers connect with one another to talk about academics, problems, and support. 

Researchers examined school staff interactions about instruction as well as floor plans in 14 elementary schools, and conducted surveys and interviews with more than 1,000 elementary school teachers and administrators over the course of four years.

They found that the closer teachers are physically, the less time and effort they need to put into working together. This is especially true for teachers in the same grade level. While planned staff meetings are helpful, there are more benefits to the day-to-day interactions that result from working close by; impromptu conversations increase and teachers can more immediately collaborate on ideas or share issues while they are still fresh.

Even small distances can make a difference. One 5th grade teacher reported collaborating most often with a colleague who was next door, rather than going to talk with other teachers across the hall, because it was easier. 

And a 6th grade teacher said that grade-level planning meetings with all teachers were helpful for thinking about lessons, but more informal exchanges with nearby teachers were better for discussing day-to-day teaching issues. If a lesson "didn't happen in math the way I wanted it to," she would go into another teacher's room and say, "Oh my God, you'll never guess what happened in math today.''

These interactions can have a positive effect on student performance and other school outcomes, by giving those at the front of the classroom greater access to resources, information, materials, and encouragement.

Source: Education Week

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Can Harvard’s most popular professor (and Confucius) radically change your life? | Harvard University - Education - The Guardian

Photo: Tim Dowling
Please take a closer peek at this article as below by Tim Dowling, journalist for the Guardian.

Professor Michael Puett: what we really are is ‘a messy and potentially ugly bunch of stuff’.
Photograph: Linda Nylind for the Guardian

The School of Life’s Sunday sermons could be described as lectures for people who don’t believe in God but still like church. They sing secular songs before and after the sermon (when I arrive, the large congregation at Mary Ward House in London is on the second verse of A Spoonful of Sugar), and everybody seems to share an abiding faith in the power of open-mindedness.

On this particular Sunday, the sermon is to be delivered by Michael Puett, professor of Chinese history at Harvard University, and is based on his book The Path, which applies the lessons of ancient Chinese philosophers to modern life. These philosophers may have done their best work 2,500 years ago, but they were trying to answer the same big questions we still ask. How do I live my life? How do I live my life well? 

“I forewarn you,” Puett tells the congregation: “At first it’s gonna sound really bleak.” 

The back cover of The Path describes Puett as “Harvard’s most popular professor”. It is unclear how this distinction is awarded, but the book grew out of a 2013 magazine article written by his co-author, Christine Gross-Loh, about the undergraduate course Puett teaches – classical Chinese ethical and political theory – said to be the third most popular class at Harvard.

“That’s still the case,” Puett says when I meet him. “No 1 and No 2 are the introduction to economics class and the introduction to computer science class.” Third biggest means his lectures are delivered to around 750 students. Puett exposes them to the writings of Confucius, Mencius, Zhuangzi and Xunzi, among others, but he also promises that the course will do more than just fulfil Harvard’s required ethical reasoning module.

“I do give them a guarantee,” he says. “The guarantee I make is if they take these ideas seriously, by the end of the course, these ideas will have changed their lives.”

When he speaks publicly, Puett’s voice ranges between a low rumble and an enthusiastic squeak. At first it sounds almost muppet-like, but after a while it becomes a little incantatory – you can see why he is a popular lecturer. He doesn’t refer to notes, and he has no visual aids. His sermon, like his course, begins by shattering some commonly held preconceptions about the self: there is no self, he says. The idea that we should look within, discover our true nature and act accordingly is, according to Confucius, nonsense. What we really are, Puett says, is “a messy and potentially ugly bunch of stuff”, a collection of emotions and conditioned responses, with no guiding inner core. We think we are self-determined, but in reality we are so set in our patterns that Google exploits our predictability to sell us stuff without us noticing. 

Puett’s School of Life audience is very open to this notion – I think most of us already figured as much – but apparently when he tells this to his students, it blows their minds. Is this, I wonder, a generational thing?

Additional resources
The Path:
A New Way to Think About Everything

Source: The Guardian

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Some of the most exciting (and scary) aspects of machine learning that you may not know about | MedCity News

Photo: Stephanie Baum
"The decibel of chatter around artificial intelligence is rising to the point where many are inclined to dismiss it as hype. It’s unfair because while certain aspects of the technology are a long way away from becoming mainstream tech, like self-driving cars, it’s a fascinating topic." notes Stephanie Baum, Digital Health Editor for

Photo: Andrzej Wojcicki, Getty Images

After listening to a talk recently by Eric Horvitz, Microsoft Research managing director, I can appreciate that the number of applications being conceived around the technology is only matched by the ethical dilemmas surrounding it. But in both cases, they are much more varied than what typically dominates the conversation about AI.

For fans of the ethical roads less traveled in AI, Horvitz offered a fair few items for his audience to consider at the SXSW conference last week that alternated between hope for the human condition and fear for it. Although I previously highlighted some of the healthcare applications he discussed, there are plenty of issues he raised that one day could be just as relevant to healthcare. I have included a few of them here...

Adversarial machine learning
One fascinating topic addressed in the talk was how machine learning could be used with negative intent —referred to as adversarial machine learning. It involves feeding a computer information that changes how it interprets images, words, and how it processes information. In one study, a computer that was fed images of a stop sign could be retained to interpret those images as a yield sign. That has important implications for self-driving cars and automated tasks in other sectors.

Another facet of adversarial machine learning is the use of information tracking individuals’ Web searches, likes and dislikes shared in social networks and the kinds of content they tend to click on and using that information to manipulate these people. That could cover a wide swathe of misdeeds from manipulation through fake Tweets designed by neural networks in the personality of the account holder to particularly nasty phishing attacks. Horvitz noted that these AI attacks on human minds will be an important issue in our lifetime.

“We’re talking about technologies that will touch us in much more intimate ways because they are the technologies of intellect,” Horvitz said.

Source: MedCity News

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Microsoft Is Betting Big on Artificial Intelligence | GuruFocus

Photo: Naman Shukla
"Company's Azure platform will continue to be a key revenue driver in the forthcoming year." reports Naman Shukla, GuruFocus Contributor. 

Microsoft (NASDAQ:MSFT) ended 2016 in the green, up more than 10%. Moreover, the stock is off to a good start this year, with a 5% rise year to date. As a matter of fact, the company’s upward movement started in early 2013. Since then, it has continued rewarding investors every year.

Microsoft reported second-quarter results in January. The company detailed earnings per share of 83 cents, exceeding analysts' estimate by 4 cents. The company's revenue came in at $26 billion, again exceeding analysts' estimate by $790 million. Moreover, that represents growth 4% compared to a 3% decilne in the same quarter of the previous fiscal year.

According to a report from, the artificial intelligence market is projected to reach $16.06 billion by 2022, a compound annual growth rate of approximately 63%. Keeping in mind the positive outlook, the company is making several smart moves to gain huge benefits from this trend.

Recently, the tech giant acquired Maluuba, a Canadian startup focused on natural language processing technology. As per the latest report from Synergy Research Group, Amazon (AMZN) holds the leading position in the public cloud market with approximately 40% worldwide revenue market share, significantly greater than Microsoft (NASDAQ:MSFT), IBM (IBM) and Google (GOOGL) combined.

Naman Shukla ends his article with the following conclusion: "Throughout the past few years, Microsoft has been performing amazingly well. Furthermore, its cloud platform looks well-poised to grow at a rapid pace in the years ahead. In the prior quarter, the revenue generated from Azure surged 95%."

With time, Microsoft appears set to to swiftly expand its portfolio of data and artificial intelligence abilities in Azure. The company has also made a smart move by launching Connected Vehicle Platform, as it will further strengthen its Azure platform.

Currently, Microsoft sits at a second position in the cloud market, and it is highly likely that its market share will continue surging in the years ahead.

Source: GuruFocus

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How to prepare for employment in the age of artificial intelligence | TNW

Follow on Twitter as @bendee983
"For centuries, humans have been fretting over “technological unemployment” or the loss of jobs caused by technological change. Never has this sentiment been accentuated more than it is today, at the cusp of the next industrial revolution." argues Ben Dickson, founder of TechTalks

Photo: TNW

With developments in artificial intelligence continuing at a chaotic pace, fears of robots ultimately replacing humans are increasing.

However, while AI continues to master an increasing number of tasks, we’re still decades away from human jobs going extinct. With AI finding its way into more and more domains, the demand for tech talent is growing.

There’s an unprecedented shortage of programmers, data scientists, cybersecurity experts and IT specialists, among others. And we can only bridge this widening gap if we help the workforce adapt to the jobs of the future. Interestingly, AI can play a crucial role in this regard.

Here is how we can smooth the transition to the age of Artificial Intelligence.


Photo: TNW
Teaching and learning has been the centerpiece of the human society’s evolution. Education in this day and age has to reflect the upheavals overcoming the socio-economic landscape.

This means we need more focus on computer science in schools and academic institutions. This will help prepare future generations to fill tech vacancies.

Governments and the private sector must also play a more active role in helping the workforce acquire tech skills. This includes people currently who are filling job roles that will likely become subject to automation in coming years.

The Obama Administration’s TechHire Initiative is an example of governmental effort to put more people into tech jobs. The program is meant to help people with academic and technical hurdles to shortcut their way to well-paying tech jobs.

Other notable developments include the establishment of learning centers such as Coursera, Codeacademy, Big Data University and Microsoft’s edX. These online platforms provide users with free tools and massively open online courses (MOOCs) to learn top-demand tech skills...

Assisting humans in tech jobs  
One of the main hurdles for entrance into tech jobs is the sophisticated level of skills, experiment and knowhow required. The same goes for other fields where talent and expertise is in high demand, such as medicine.

For instance, the cybersecurity industry is currently struggling with a shortage of one million skilled workers. Meanwhile the amount of time and effort required to train a security analyst is overwhelming.

Fortunately, AI-powered security tools can downsize the effort required by security experts in maintaining the integrity of IT systems. By learning to analyze and flag network events or process behavior, tools such as MIT’s AI2 and IBM’s Watson for Security enable security analysts to become more productive and efficient in fighting cyber attacks.

Source: TNW

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We have nothing to fear from artificial intelligence, says pioneer | The Times

Follow on Twitter as @olivernmoody
"Elon Musk and Bill Gates are among those who fear that artificial intelligence threatens mankind, but such critics have been rebuked by the head of Google’s project to make a machine that can learn and create like a person." summarizes Oliver Moody, Science Correspondent.

Demis Hassabis says the grim vision of the future posed by AI were largely based on ignorance.
Photo: Jung Yeon-Je/Getty Images

Deep Mind, a British business bought by Google three years ago, is taking on problems from diseases to inefficiencies in the National Grid, just as others say that a superhuman AI could be our deadliest invention.

Demis Hassabis, Deep Mind’s chief executive, said that apocalyptic visions of the future were mostly based on ignorance. “I don’t think it’s very helpful for other people who are incredible in their domains commenting on something they actually know very little about,” he said, “but because they are quite big celebrities now, more than just scientists or businessmen, it gets picked up a lot.”

Dr Hassabis told an event organised by the Cambridge Society for the Application of Research: “There are some valid worries and I think these are research questions of vulnerability and interpretability, but I think this general meme of fearfulness doesn’t help reasoned debate.

“ It actually drives that debate away. I’ve told all of those people you mentioned [Mr Musk and Mr Gates] that it’s not very helpful. Some of them have moderated their comments, but others haven’t.”

Source: The Times

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Sunday, March 26, 2017

Learning not to ‘over think’ predictive analytics | Information Management

"Most firms are more ready than they realize to implement the technology, and they fail to see the countless opportunities at hand." according to David Weldon, editor-in-chief of Information Management.   

Photo: Information Management

The predictive analytics and machine learning markets are projected to grow at a rate of 15 percent annually through 2021, yet many organizations fail to reap full benefits from their investments. The problem is often that the organization makes the process too complicated.

The irony is that most large organizations are more prepared to implement and use predictive analytics and machine learning than they think, says Mike Gualtieri, a research analyst with Forrester Research. 

Gualtieri has just published a Forrester Wave report on predictive analytics and machine learning, “The Forrester Wave: Predictive Analytics and Machine Learning Solutions, Q1 2017.” In it, Gualtieri notes that organizations that want to leverage artificial intelligence need to start with a predictive analytics and machine learning solution.

“Predictive models created using machine learning are already commonly used for marketing, customer intelligence, and risk models,” Gualtieri says. “The teams of data scientists that create these models are in the know. But, often the employees such as enterprise architects who are charged with investigating AI don't understand that machine learning models are fundamental building blocks of AI.”

That is unfortunate, Gualtieri says. “There are hundreds, if not thousands, of opportunities to use machine learning models in business processes and customer experiences. This is not day one, but it is still only day 2. There is tremendous opportunity today, but most enterprises struggle about how to think about AI. They are thinking too big. Successful machine learning models is about predicting one simple thing that can have a big impact on the business such as the next best product to recommend for an individual customer.”

Forrester forecasts a 15 percent compound annual growth rate (CAGR) for the PAML market through 2021. That’s a conservative estimate, given that the PAML category includes and overlaps with AI and deep learning. Gualtieri says the category continues to be hot since most large enterprises want the power to predict and have only scratched the surface of what is possible. 
Read more... 

Source: Information Management  

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Smiths Detection Inc. Partners with Duke University in Deep Learning Research for Airport Screening | Business Wire

Smiths Detection Inc. (SDI) announced that it is partnering with the Duke University Edmund T. Pratt Jr. School of Engineering, Department of Electrical and Computer Engineering, in a “deep learning” digital solution project to advance airport checkpoint x-ray system screening capabilities.   

The U.S. Transportation Security Administration (TSA) has entered into a contract with Duke University for this deep learning initiative to refine and apply state-of-the-art machine learning techniques in the security space. In this case, Duke and SDI will partner to apply the deep learning methodology to enhance the capabilities of checkpoint x-ray systems.   

Dan Gelston, President of SDI, said, “We must continue to invest in digital solutions to remain at the forefront of technology. This partnership, combined with our focus on innovation and experience in threat detection, leads the security industry in the development of state-of-the-art methods to help make the world a safer place.”

The principal investigators for this effort will be Professor Lawrence Carin at Duke University and Dr. Kristofer Roe of Smiths Detection Inc. Professor Carin has more than 27 years of experience and is also Vice Provost of Research for Duke. Dr. Roe, currently Director, R&D – Imaging for SDI, is responsible for imaging technology research and development in the areas of screening and aviation security. Dr. Roe was also the principal investigator of the NextGen Checked Baggage Program (Manhattan II) program with TSA.

Smiths Detection, part of Smiths Group, is a global leader in threat detection and screening technologies for military, air transportation, homeland security and emergency response markets. Our experience and history across more than 40 years at the frontline, enables us to provide unrivalled levels of expertise to detect and identify constantly changing chemical, radiological, nuclear and explosive threats, as well as weapons, dangerous goods, contraband and narcotics.

Our goal is simple – to provide security, peace of mind and freedom of movement upon which the world depends. 
For more information visit

Source: Business Wire (press release)

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What Is The Best Way To Learn Machine Learning Without Taking Any Online Courses? | Forbes

"What is the best way to start learning machine learning and deep learning without taking any online courses? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world." Quora, Contributor.

Answer by Eric Jang, Research engineer at Google Brain, on Quora:

Photo: Shutterstock

Let me first start off by saying that there is no single “best way” to learn machine learning, and you should find a system that works well for you. Some people prefer the structure of courses, others like reading books at their own pace, and some want to dive right into code.

I started with Andrew Ng’s Machine Learning Coursera course in 2012, knowing almost zero linear algebra and nothing about statistics or machine learning. Note that although the class covered neural networks, it was not a course on Deep Learning. I really enjoyed how the course formulated “machine learning” as nothing more than numerical optimization.

Deep Learning book
If online courses are too slow for you, the best consolidated resource is probably Deep Learning book by Goodfellow, Bengio, and Courville. It has a few chapters dedicated to the basics (sort of like what is covered in Ng’s class) and then jumps into practical DNNs.

Murphy’s Probabilistic
Machine Learning textbook
A statistical/mathematically rigorous background is not required to do useful Deep Learning work, but it really helps to formulate hypotheses about why models are/are not working, and what might help. Murphy’s Probabilistic Machine Learning textbook is a great foundation for mathematically rigorous ML (and has great diagrams too!) 

After you finish the DL book, you can “specialize” into one of the subfields/sub-subfields of Deep Learning, by implementing some of the papers yourself. Some example topics:
  • Bayesian Deep Learning (combining neural nets with graphical models)
  • Deep Reinforcement Learning (AlphaGo, Atari-playing AI, Robotics)
  • Generative Models (GANs, PixelCNN, VAEs)
  • Adversarial Methods (GANs, Actor-Critic)
  • Theory of Deep Learning
  • Computer Vision
  • NLP/Speech (translation, captioning, seq2seq models)
  • Symbolic reasoning (e.g. proof-solving)
  • Recurrent Neural Networks (e.g. LSTMs, external memory, attention)
  • Applications (solving domain-specific problems like classifying cancer, protein folding, lip reading from video)
  • Meta-learning / learning-to-learn (Synthetic Gradients, Pathnet)
The Deep Learning field has dramatically expanded in the last few years, to the point where it’s not realistic to grok all the subfields of Deep Learning in a short amount of time.
Read more... 

Source: Forbes

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Machine learning: Should we be excited or fearful for our jobs? |

How machine learning can drive efficiency rather than drive people out of their jobs, insist Nicola Mortimer, head of business products, marketing and operations at Three Ireland

Photo: vectorfusionart/Shutterstock

2017 will be a year of dramatic acceleration in the pace of development of artificial intelligence (AI) and the internet of things (IoT). Machine learning is predicted to be an integral part of more than 300m new smartphones sold this year. So, should we be excited or fearful for our jobs?

It has been predicted that machine learning capabilities will be present in more than 20pc of smartphones sold globally in 2017. With few devices more ubiquitous in the developed world than the smartphone, machines that learn will now be at the fingertips of a large percentage of the population.

What will the increasing development of machine learning, AI, machine-to-machine (M2M) communication and IoT mean for business and industry, and the people who work within them?

One of the important things to realise about the way machines learn, and therefore develop intelligence, is that it is not a mysterious, science-fiction process. Machine learning produces, in effect, nothing more than glorified data crunchers.

Machines that learn can learn only from the data they receive and analyse. What makes them such quick learners and so apparently intelligent is that – unlike humans – they can receive, absorb and analyse all the relevant data in the world at incredibly high speed, and then use it to inform the decisions they make.

Importantly, for the development of true AI, these machines are now also beginning to learn from the data and adapt their behaviour accordingly. For example, at the simplest level, Google Translate now adapts as it learns, to make its translations more accurate.

At the other extreme, data gathered from the journeys of Tesla test vehicles is uploaded to the cloud and made available to all Tesla driverless cars. This means if a test vehicle has driven a stretch of road, when another Tesla vehicle travels it for the first time, it will know how to brake for a specific corner, which lane to take for a turn, even what driving line to take to avoid a large pothole.


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