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Wednesday, May 23, 2018

Retooled Courses Help Students Avoid a Remedial-Math Roadblock to College | Mathematics - Education Week

"Math is a notorious stumbling block that trips up students seeking college degrees. Every year, tens of thousands of young people fail to graduate because they can't earn enough math credits" reports Catherine Gewertz, associate editor for Education Week.

Calculus, Statistics, and the Future of High School Math 


The landscape is daunting: Two-thirds of the students at community colleges, and 4 in 10 of those at four-year institutions, take remedial courses. Math is a much bigger sand trap than English: Far more postsecondary students fall into remedial math than reading, and fewer move on to credit-bearing courses.

To help students across that bumpy terrain, math educators have been trying new approaches that are designed to capture high school skills and college-level content on a compressed timeline. They're teaching math through real-world problems, and reworking course content to better mesh with students' career goals.
Community colleges are using the courses to help students avoid the math pothole. But high schools are starting to embrace them, too, as a way to bolster students with shaky math skills—or low confidence in their overall academic power—and boost the chances they'll earn college degrees.

The new approach rocked Skyler Puckette's world. The Madison, Wis., student was homeschooled since early childhood. She didn't soar in her studies, and her most intense struggles were in math, she said. As she fell further behind, a high school diploma became impossible.

Making plans to get her GED, Puckette learned about a program at Madison Area Technical College that would help her earn her high school diploma and associate degree. Enrolling last fall, she placed into a course called "math reasoning," one of the new breed of math classes designed to help students like her.

The course was very different from her earlier math learning, which focused on procedures. It used real-world scenarios to engage students, asking them to apply math formulas to calculating the dosage of a baby's medication, or analyzing the racial disparities in prison populations. It required them to work in groups, a technique to eliminate the isolation struggling students can experience.
Read more...

Source: Education Week and Education Week Channel (YouTube)


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Machine Learning and Human Resources | Technology - BBN Times

Naveen Joshi, Tech Guru says, "The application of machine learning in the recruitment industry provides useful information to HR executives to shortlist ideal candidates, predict performance levels as well as improve organisational performances."

Photo: BBN Times

One of the core responsibilities for a recruiter is to bring in candidates that can add value to the organization and be a good investment for the company’s time and resources. With innovations in technology, HR executives are looking for different ways of hiring candidates. With HR firms leveraging different technologies, machine learning in recruitment can be the technology that assists recruitment agencies to come up with the best candidates.

Obstacles in the Recruitment 
Process A major obstacle faced by recruiters is that they have too many contacts, but they cannot make a move to connecting with a candidate. Often when an HR executive interviews a potential candidate, they are expected to hire an individual in the shortest time possible. This method of hastened hiring leads to the HR executive hiring a candidate that is unworthy of the position. Another common problem faced by a recruitment company is that communicating minute details with candidates and wasting crucial time that can be used to contact other prospects.

Outdated hiring patterns are also equally responsible for a company’s failing recruitment team. With changing times, organizations need to keep track of how they can transform their hiring process. To transform their recruitment process, authorities can keep track of current market trends. With the availability of such information, organizations can then decide on how they want to tweak their hiring process to lure better candidates.

With the technological innovations evolving at a groundbreaking speed, authorities also need to keep up with how different technologies can add relevance to their organization's hiring process. When a company fails to adapt to the technological changes, its performance is bound to take a hit.
Read more... 

Source: BBN Times


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Tuesday, May 22, 2018

Neural network? Machine Learning? Here's all you need to know about AI | Technology, In Other News - Deccan Chronicle

AI is an umbrella term for a range of computer algorithms and approaches that allow machines to sense, reason, act and adapt like humans, as Deccan Chronicle reports.

The human-like capabilities include things like apps that recognise your face in photos, robots that can navigate hotels and factory floors, and devices capable of having (somewhat) natural conversations with you.
Photo: Pixabay

Artificial intelligence encapsulates a broad set of computer science for perception, logic and learning. One method of AI is machine learning – programs that perform better over time and with more data input. Deep learning is among the most promising approaches to machine learning. It uses algorithms based on neural networks – a way to connect inputs and outputs based on a model of how we think the brain works – that find the best way to solve problems by themselves, as opposed to by the programmer or scientist writing them. Training is how deep learning applications are “programmed” – feeding them more input and tuning them. Inference is how they run, to perform analysis or make decisions...

Training and Inference
There are two more quick concepts worth noting: training and inference. Training is the part of machine learning in which you’re building your algorithm, shaping it with data to do what you want it to do. “Training is the process by which our system finds patterns in data,” wrote the Intel AI team. “During training, we pass data through the neural network, error-correct after each sample and iterate until the best network parametrization is achieved. After the network has been trained, the resulting architecture can be used for inference.”

And then there’s inference, which fits its dictionary definition to the letter: “The act or process of deriving logical conclusions from premises known or assumed to be true.” In the software analogy, training is writing the program, while inference is using it.

“Inference is the process of using the trained model to make predictions about data we have not previously seen,” wrote those savvy Intel folks. This is where the function that a consumer might see – Aier’s camera assessing the health of your eyes, Bing answering your questions or a drone that auto-magically steers around an obstacle – actually occurs.
Read more...  

Source: Deccan Chronicle 


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AI 101, Because We Can’t Escape the Inevitable (It’s Free Too) | Artificial Intelligence - Futurism

Helsinki University in Finland is offering the world’s first online artificial intelligence course geared towards beginners, as Engadget reports. 

Photo: Getty Images / Emily Cho

Artificial intelligence plays a role in nearly everyone’s life now, so it only seems fair that everyone should also have the opportunity to learn exactly what it is and how it functions. At least, that’s what Helsinki University in Finland thinks.

Not only can anyone with web access enroll, but it’s also free. Because the course only takes about 30 hours to complete, it’s possible it might help people get to know—and form opinions on—artificial intelligence. And that knowledge will be useful, since the technology is becoming more increasingly widespread.

Heslinki’s AI class is different from the program Carnegie Mellon University announced they’d be offering a few weeks ago. The Pennsylvania-based school, which already hosts one of the premiere robotics labs in the nation, will gear their program towards students who want to make a career out of AI development and research. The program will only accept four percent of newly-enrolled students, and will involve four years of challenging AI-based classes.

Helsinki University, on the other hand, will start their class with something simpler: What is artificial intelligence? From there, the lessons will branch out and explain what problems AI can help solve, how it’s being used now, and what qualifies as machine learning. These are the kinds of core concepts that will give Helsinki students background knowledge about, say, the AI Facebook uses for facial recognition, or the machine learning backing Uber’s pricing surges.

While students enrolling in Carnegie Mellon’s program will likely already have a decent grasp of AI’s founding principles, most lay-people do not. In March 2017, a worldwide survey of internet users found that three out of 10 people polled had heard of AI, but didn’t know much about it. With so few people understanding the tech, it make sense that they’re also wary of it: 41 percent of respondents in a 2017 Forbes poll said they couldn’t cite an example of AI that they trust.

As people enroll in Helsinki’s course, or courses like it, artificial intelligence can lose some of that ambiguity. And that’s a good thing because, as the technology develops, regulators are going to decide what practices will and won’t be allowed. Congress introduced a bipartisan bill for putting parameters on AI in December of last year, and based on what those two surveys show, not many Americans understood what their representatives were proposing.
Read more... 

Source: Futurism


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MIT's Feature Labs Helps Companies Develop Faster Machine Learning Algorithms | Intelligent Machines - Evolving Science

Photo: Nikos Dimitris Fakotakis
Nikos Dimitris Fakotakis, PhD  Researcher summarizes, "Feature Labs, a startup that began at MIT in 2015, was initiated with the primary purpose of helping data scientists build machine learning (ML) algorithms that run much faster."

Faster machine learning algorithms created by MIT’s Feature Labs.
Photo: Public Domain

This is important for the future of artificial intelligence because of the impact it will have on many scientific areas. Dedicated collaborators, with strong roots in research, have systematically tried to create algorithms that warrant major changes. These changes could be with respect to how organizations build and integrate artificial intelligence models into their new services and products.

With machine learning, there is high storage of training data that needs to be decoded at an initial stage because of the complexity of content.

For this purpose, the scientists at Feature Labs have developed tools to have a better acceleration of the algorithms. The main goal of the model is the transformation of big data into valuable knowledge, which can be applied to real-world scenarios with the help of sophisticated training algorithms.

In various fields of study, we have significant amounts of data for the right training of systems. With machine learning, we build models, which lead us to our common goal - the extraction of valuable information.

A vital characteristic of ML's success is feature engineering, the process of transforming raw data into features that better represent the problem, compared to predictive models. This is, obviously, depending on how the data can be presented.

Feature Labs contributes to the quality of the main model by developing an automatic feature selection and preparation. The aim is to provide great features, describing the structure, that is inherent in the data.
Read more... 

Source: Evolving Science


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Sunday, May 20, 2018

Suggested Books of the Week 20, 2018

Check out these books below by Cambridge University Press and Oxford University Press

Photo: Storyblocks.com

Calendrical Calculations

Calendrical Calculations:
The Ultimate Edition
An invaluable resource for working programmers, as well as a fount of useful algorithmic tools for computer scientists, astronomers, and other calendar enthusiasts, The Ultimate Edition updates and expands the previous edition to achieve more accurate results and present new calendar variants. The book now includes coverage of Unix dates, Italian time, the Akan, Icelandic, Saudi Arabian Umm al-Qura, and Babylonian calendars. 

There are also expanded treatments of the observational Islamic and Hebrew calendars and brief discussions of the Samaritan and Nepalese calendars. Several of the astronomical functions have been rewritten to produce more accurate results and to include calculations of moonrise and moonset. The authors frame the calendars of the world in a completely algorithmic form, allowing easy conversion among these calendars and the determination of secular and religious holidays. LISP code for all the algorithms is available in machine-readable form.  
Read more...

Adversarial Machine Learning 

Adversarial Machine Learning
Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks...
Read more...

Cambridge Series in Statistical and Probabilistic thematics


Cambridge Series in Statistical
and Probabilistic Mathematics


This series of high quality upper-division textbooks and expository monographs covers all areas of stochastic applicable mathematics. The topics range from pure and applied statistics to probability theory, operations research, mathematical programming, and optimisation. The books contain clear presentations of new developments in the field and also of the state of the art in classical methods. While emphasising rigorous treatment of theoretical methods, the books also contain applications and discussions of new techniques made possible by advances in computational practice.
Read more... 

Society and the Internet
 
Society and the Internet
The book describes how society is being shaped by the diffusion and increasing centrality of the Internet in everyday life and work. It introduces students and those interested in the factors shaping the Internet and its impact on society to a core set of readings that address this question in specific social and institutional contexts.
  • Interdisciplinary appeal across the social sciences
  • Chapters focus on showing how research can inform and stimulate debate on theory, policy, and practice
  • Accessibly written and clearly structured introduction to the social shaping of the Internet and its societal implications
  • Ideal for advanced courses on the Internet and ICT
Read more

Artificial Intelligence: A Very Short Introducion 

Artificial Intelligence:  
A Very Short Introducion
This concise guide explains the history, theory, potential, application, and limitations of Artificial Intelligence. Boden shows how research into AI has shed light on the working of human and animal minds, and she considers the philosophical challenges AI raises: could programs ever be really intelligent, creative or even conscious?
  • Presents a rounded view of Artificial Intelligence, its history, its successes, its limitations, and its future goals
  • Considers the realistic and unrealistic expectations we have placed on AI


  • Shows how the results of Artificial Intelligence have been valuable in helping to understand the mental processes of memory, learning, and language for living creatures
  • Explores the issues AI raises about what it means to be creative, intelligent, conscious - and human
  • Part of the Very Short Introductions series - over nine million copies sold worldwide
  • Read more...

    The Oxford Handbook of Technology and Music Education

    The Oxford Handbook
    of Technology and Music Education
    Few aspects of daily existence are untouched by technology. The learning and teaching of music is no exception and arguably has been impacted as much or more than other areas of life. Digital technologies have come to affect music learning and teaching in profound ways, influencing everything from how we create, listen, share, consume, interact, and conceptualize musical practices and the musical experience. For a discipline as entrenched in tradition as music education, this has brought forth myriad views on what does and should constitute music learning and teaching...
    Read more...

    Enjoy your reading! 

    Source: Cambridge University Press and Oxford University Press


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    85 books for summer reading | Books - Milwaukee Journal Sentinel

    "Whether you're on the road or staying on the porch this summer, a book can be your traveling companion" says Jim Higgins, writes about books and the performing arts.

    A selection of summer reading choices for 2018
    Photo: Submitted photos

    Here are some suggestions of new and newish books for summer reading, including ones with a Wisconsin pedigree. While these selections keep pleasure reading foremost in mind, some hard-hitting books are included. 

    The Milwaukee Public Library encourages your children to join its Super Reader Squad for children 12 and younger, and its teen reader program for youth ages 13 through 18. In addition to the pure pleasure of reading, children and teens can earn prizes. 

    If you live in a different community, check with your local library. It probably has a summer reading program, too.

    Thanks to my colleague Chris Foran for contributing the pop-culture and baseball sections, and to contributor Mike Fischer,whose previous reviews inspired some of these picks.
    Read more...

    Source: Milwaukee Journal Sentinel


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    Saturday, May 19, 2018

    UI College of Education brings advanced science education to rural areas | Iowa Now

    Photo: Lynn Anderson Davy
    "It’s just after 3:30 p.m. on a recent Tuesday and Atlantic Middle School, and science teacher Kara Martin is passing out Pringles potato chips" reports Lynn Anderson Davy, Office of Strategic Communication. 

    Atlantic Middle School students (from left) Claire Pellett, Dante Hedrington, Mary McCurdy, Nicole Middents work on an exercise that involves engineering and science concepts...
    Photo: Lynn Anderson Davy.

    But instead of popping the chips into their mouths, the sixth- and seventh-graders cradle them in open palms, waiting for instructions.

    This isn’t snack time. This is science.

    With the chips distributed, Martin explains that students will work in small groups to engineer the perfect chip package, one that will measure a maximum of 3-by-5 inches and will protect their chip as it travels through the U.S. Postal Service system.

    “And you can’t write ‘fragile’ or ‘handle with care’ on your package,” Martin says just before students leap into action.

    For the past three years, a small number of science-savvy students at Atlantic Middle School, located 80 miles west of Des Moines, have benefited from additional classroom work in STEM subjects. Sixth- and seventh-grade students meet with Martin for an hour every Thursday to conduct experiments that incorporate advanced engineering and science concepts. Eighth-graders meet on Saturday mornings with another teacher.

    The science tutorials are part of a program offered by the Belin-Blank Center, part of the University of Iowa College of Education. With funding from the National Science Foundation and the Jack Kent Cooke Foundation, the STEM Excellence and Leadership program aims to encourage gifted students in rural areas to take on rigorous science and technology classes and urges them to pursue STEM fields in college. Although the program is still fairly new, feedback from participating middle school teachers has been positive.

    “I’ve seen some students make 180-degree turnarounds in terms of their classroom attitude and behavior,” says Andrea Reilly, a UI College of Education alumna who is a science teacher at Atlantic Middle School and the talented and gifted coordinator for the Atlantic Community School District. “I have parents ask me how they can get their kids into the program. It’s seen as a tremendous asset.”

    Atlantic Middle School is one of 10 schools across Iowa that are part of the STEM Excellence and Leadership program. Teachers at participating schools receive additional funding to purchase science and technology resources, including supplies for experiments and classroom projects, and are invited once a year to tour the UI campus with their students. As part of the tour, students visit science and engineering labs on campus and meet with college students. Teachers also visit campus during the summer for professional development and discuss new ways to boost STEM education at their schools.
    Read more... 

    Source: Iowa Now


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    Difference Between Machine Learning and Deep Learning | Technology - BBN Times

    Photo: Naveen Joshi
    "Machine learning and deep learning are often confused to be the same. The following article highlights their main differences and how both technologies will change the world" according to Naveen Joshi, Tech Guru.

    Photo: BBN Times

    Over the past few years, the growth in technologies like AI, big data, and blockchain has offered incredible benefits to both, the industries and the end users. As a result, AI is gaining a lot of attention due to its ability to create machines that can behave intelligently and smartly, like humans. However, AI is broadly classified into two major concepts: machine learning and deep learning. These two terms are often used interchangeably and the difference between machine learning and deep learning remains unknown to most. Machine learning and deep learning are entirely different from one another.

    To simplify, deep learning is a part of machine learning. By definition, machine learning is an approach of AI that is explicitly programmed to make devices understand, analyze, learn, and adapt to their work environment. For instance, Netflix offers us a list of movie recommendations based on our past preferences. Machine learning algorithm parses the assimilated data and its algorithms analyze it to act accordingly. Furthermore, we have come across Google’s ‘did you mean’ section, right?, which pops up when a typo error occurs. Google uses its machine learning algorithm to learn from our mistakes and recommend the correct word to us.

    On the other hand, deep learning is a part of machine learning that parses massive volume of data using neural networks to obtain a more profound outcome that machine learning fails to achieve. In simple words, deep learning algorithm works similar to how humans interpret and understand a situation. For example, when it comes to identifying a dog, deep learning technique applies its algorithm that finds out that a dog image has been provided to them, whereas machine learning just parses the data and outputs that it is an animal.
    Read more...

    Source: BBN Times 


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    To Build Truly Intelligent Machines, Teach Them Cause and Effect | Quanta Magazine

    Judea Pearl, a pioneering figure in artificial intelligence, argues that AI has been stuck in a decades-long rut. His prescription for progress? Teach machines to understand the question why.  

    Photo: Monica Almeida for Quanta Magazine

    Artificial intelligence owes a lot of its smarts to Judea Pearl. In the 1980s he led efforts that allowed machines to reason probabilistically. Now he’s one of the field’s sharpest critics. In his latest book, The Book of Why: The New Science of Cause and Effect, he argues that artificial intelligence has been handicapped by an incomplete understanding of what intelligence really is.

    Artificial intelligence owes a lot of its smarts to Judea Pearl. In the 1980s he led efforts that allowed machines to reason probabilistically. Now he’s one of the field’s sharpest critics. In his latest book, “The Book of Why: The New Science of Cause and Effect,” he argues that artificial intelligence has been handicapped by an incomplete understanding of what intelligence really is. 

    Three decades ago, a prime challenge in artificial intelligence research was to program machines to associate a potential cause to a set of observable conditions. Pearl figured out how to do that using a scheme called Bayesian networks. Bayesian networks made it practical for machines to say that, given a patient who returned from Africa with a fever and body aches, the most likely explanation was malaria. In 2011 Pearl won the Turing Award, computer science’s highest honor, in large part for this work.


    But as Pearl sees it, the field of AI got mired in probabilistic associations. These days, headlines tout the latest breakthroughs in machine learning and neural networks. We read about computers that can master ancient games and drive cars. Pearl is underwhelmed. As he sees it, the state of the art in artificial intelligence today is merely a souped-up version of what machines could already do a generation ago: find hidden regularities in a large set of data. “All the impressive achievements of deep learning amount to just curve fitting,” he said recently.
    Read more... 

    Recommended Reading

    The Book of Why:
    The New Science of Cause and Effect
    "A Turing Prize-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence" 

    Source: Quanta Magazine


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