Anderson University to offer minor in coding through partnership with Apple | Anderson Independent Mail

Follow on Twitter as @gsilvarole"Through its partnership with Apple as an Apple Distinguished School, AU is adopting the tech giant's Everyone Can Code curriculum" inform Georgie Silvarole, Anderson Independent Mail.  

Anderson University is a is a comprehensive Christian university offering bachelors, masters and doctoral degrees on campus, in Greenville and online. A photo taken on Nov. 27, 2017 shows AU's Alumni Lawn awash in late-afternoon sunlight.
Photo: Georgie Silvarole
Beginning next semester, Anderson University will offer students the chance to pursue a minor in computer coding.
Through its partnership with Apple as an Apple Distinguished School, Anderson University is adopting the tech giant's "Everyone Can Code" curriculum, the university said in a news release. 
The minor, which is open to all students pursuing any major, will focus on iOS app design, web management, computer coding and product development, according to the release. 
The curriculum — designed by AU's College of Arts and Sciences, AU's Center for Innovation and Digital Learning and Apple engineers — is geared toward equipping students with skills in information technology and software development through coding and app creation experience, according to the release.
“We believe this opportunity will allow us to do something truly unique in liberal arts education,” AU President Evans Whitaker said in the release. “Our goal is to fully prepare our students for their careers, and having them learn a marketable and highly sought-after skill like coding will help them in whatever field they choose.”Read more... 
Source: Anderson Independent Mail

UNL is working to improve computer science education in schools | NTV

"The University of Nebraska-Lincoln's College of Education and Human Sciences is partnering with Code.org to improve computer science education for Nebraska K-12 students" reports KHGI Nebraska TV

Photo: MGN
The university is joining a nationwide network of regional partners that provide high-quality professional development to K-12 educators through local school district collaborations and work to build local communities of computer science educators statewide.

"The goal of our regional partnership with Code.org is to establish the university as a regional hub for K-12 computer science," said Guy Trainin, professor in the Department of Teaching, Learning and Teacher Education. "We will use our existing strong relationships with school districts and educators across the state to build the knowledge and capacity of K-12 teachers so they can better teach computer science to their students."

According a press release from UNL, the Code.org regional partnership will be part of the department's Tech EDGE program, which Trainin directs. Through Tech EDGE, Trainin has posted more than 300 video podcasts and hosted 20 conferences to help teachers bring technology instruction to their classrooms.

As a regional partner, Tech EDGE will leverage its expertise in hosting professional learning workshops for K-12 educators. Through the partnership, the university will host a number of daylong computer science workshops, a five-day summer experience for educators, and workshops for counselors and administrators of teachers participating or interested in Code.org professional learning programs.

Source: NTV

How schools prepare the next generation to enter a digital workforce | Seattle Times

Provided by Microsoft Philanthropies 

Just four in 10 schools in the U.S. offer a computer science course, according to Code.org.
When Sierra Acy was in high school she knew she was interested in computer science. Luckily, her high school was an exception in the U.S.— it offered computer science courses. “I took Intro Computer Science and the following year I took AP Computer Science,” Acy says.

Shuyi Ma works as a postdoctoral scientist at the Center for Infectious Disease Research in Seattle.
Photo: Courtesy of Center for Infectious Disease Research
Early on at Disney, Acy heard about a volunteer program where people working in the tech industry could go into high schools and help teach computer science courses, giving students a chance to interact with people who actually use the content they’re teaching every day and to help classroom teachers gain better understanding of the subject matter to improve the computer science offerings in the future. She leapt at the chance.

“During my high school experience, neither of the computer science courses I took were as well done or put together as they could have been,” she says. “As is the case a lot of the time, the teachers weren’t actually trained in the computer science industry or any of the technical aspects of the field, they were just kind of put into the position and told to go.”

Women who try AP Computer Science in high school are 10 times more likely to major in it in college, according to a 2007 research study by the College Board.

Acy is now volunteering approximately 10 hours a week, along with a small team of others at Disney, to co-teach the AP Computer Science course at Walla Walla High School in southeastern Washington through the TEALS (Technology Education and Literacy in Schools) program.

Kevin Wang founded TEALS in 2009. At the time, he had been volunteering part time as a computer science teacher at a Seattle high school in addition to his work as an engineer at Microsoft. “When I was sent to a College Board AP Computer Science workshop, I expected to meet a lot of other AP Computer Science teachers with a similar background to my own, but I was shocked to find that most of the other teachers were not computer science people at all. I was maybe the only computer science major there, the rest of these teachers had about four days to learn a college semester’s worth of computer science well enough to teach it, which is incredibly tough.”

Wang knew there were plenty of other computer science folks like him in the industry who could help these teachers, and students, have a more effective educational experience. So he decided to do something about it. Today, Wang runs TEALS full time, funded by Microsoft’s philanthropy arm. They’re operating in 348 schools in 29 different states and Washington, D.C. Here in Washington State, TEALS is partnered with 86 schools — that’s 10 percent of all Washington high schools.

Wang suggests that the reach is even further than those numbers suggest since the program is modeled to help get classroom teachers prepared to teach computer science on their own after two years of co-teaching with volunteers who know the material by heart.

Source: Seattle Times  

Parents search for toys that make learning math, science fun | KGO-TV - Technology

Photo: Michael FinneyToys that combine a STEM education with a child's fascination and imagination are gaining in popularity. As Michael Finney, 7 On Your Side Reporter and KGO Radio Host, reports, toymakers are counting on parents shelling out big bucks for their child's education.

Photo: Storyblocks.com

Playing with robots is only half the fun for UB Tech.

Its creators hope kids find building them is just as exciting.

"They can actually learn to assemble and build their own robot using 3D instructions," said Max Mai of UB Tech.

This computer kit from San Francisco-based Piper comes with hours worth of games.

But those games are just a means to an end.

"This is a computer kit that kids build themselves to learn about programming," said Tommy Gibbons of Piper.

Games such as these are gaining in popularity largely due to demand from parents.

"Right now everyone wants their kids to learn how to know how to build hardware," said Jeff Lee of Tech Bargains. "They want their kids to know how to build products, code. All in the hopes of getting their kids a better paying job."

Kids learn how to build robots by first snapping pieces into place on an animated model.

They then duplicate their efforts with the actual robot parts.
These robots can also be programmed to mimic various human emotions.
That's the expression of being angry, " said Mai as the Robot lowers his head and snaps its jaws.

Source: KGO-TV

What can Sharepoint do for you

Project documents and attachments are kept in one central library, and version control issues are eliminated as there is only ever one version of a project. All project information is stored in SharePoint, and shared calendars mean any confusion over due dates or meetings is a thing of the past.

Learning to be a mentor | Science - From the Magazine

"My early experiences mentoring undergraduate students didn't go well. My first attempt came during the second year of my Ph.D." says Aditi Deshpande, scientist at Allena Pharmaceuticals in Newton, Massachusetts.
Photo: Robert Neubecker
I was still trying to learn some lab techniques myself, and I wasn't sure whether I would be able to invest the time needed to train a student. But I was interested in developing my mentoring skills, and my adviser encouraged me to give it a try. The student required hand-holding and close monitoring, and it quickly became evident that the collaboration wasn't working. After similar false starts with a few more students, I ended up being reluctant to work with undergraduate researchers at all—until a new student helped me realize what is required to mentor undergraduates, and the rewards it can bring.
“It has been a joy to watch Karina mature as an able scientist.” I met Karina when she was a sophomore, and I ended up working with her until she defended her senior thesis. She was smart and eager to learn, asking all the right questions, and I felt she might finally be the right fit. Moreover, the timing was right. My experiences with previous undergraduates had prepared me to set appropriate expectations and gradually build on them. As a third-year student, I was also ready to delegate and give her room to grow. Here are the lessons that working with Karina taught me.
SHOW THE BIG PICTURE. Most undergraduates are completely new to research, so it is crucial to explain the broader context for the work and justify its importance. This will provide an overall goal, which will help get students interested and keep them focused as they learn the ropes. In my first meeting with Karina, I talked through a highly simplified slide deck about the project, explaining its goals and getting her excited about working on it. Describing my research in this simplified manner also helped me develop my own communication and storytelling skills. During my job interview at my current company, I used a similar approach.
INTRODUCE THE LITERATURE. Keeping up with the scientific literature is crucial for any researcher. But undergraduate students may not know this. Even if they do, they may feel overwhelmed by the volume and technical language. To help Karina start building her literature knowledge and confidence, I sent her relevant papers and followed up with discussions. These conversations also helped me deepen my understanding of my research and think about it in new ways.Read more... 

Additional resources 
Science  24 Nov 2017:
Vol. 358, Issue 6366, pp. 1098
DOI: 10.1126/science.358.6366.1098 
Source: Science

Science education: It's not just for kids at the Michigan Science Center | Model D

Photo: David Sands
"STEM jobs are being touted as critically important in the U.S., which is one reason why the Michigan Science Center offers programs targeted at audiences of all ages" according to David Sands, Detroit-based freelance writer.
An After Dark gaming event
Photo: courtesy of the Michigan Science Center.
Ever notice how young children are fascinated by dinosaurs, rocket ships and nature hikes? There's a reason for that; kids possess a natural curiosity about the world around them that makes science and technology appealing fields of exploration. Unfortunately, many people lose their sense of wonder for these topics as they grow older.

According to the National Center for STEM (Science, Technology, Engineering and Math) Elementary Education at St. Catherine University in St. Paul, Minnesota, a third of all children have lost their interest in science by the time they reach the fourth grade. It's even worse by eighth grade, when research shows a staggering 50 percent of students have lost interest in science or consider it irrelevant to their education or future plans. 
Visitors look at an exhibit from "1001 Inventions"
Photo: courtesy of the Michigan Science Center.
With STEM jobs being touted as critically important in the U.S. for the foreseeable future, that's bad news. But at the Michigan Science Center, programs targeted at audiences of all ages are changing the conversation in Metro Detroit.

"People tend to think that we focus on elementary school programs," says Charles Gibson, Director of Innovation and Outreach with the Detroit-based institution. "But we also look for interactive ways to engage middle school students, teens and young adults."

The center's current special exhibit, "1001 Inventions: Untold Stories from a Golden Age of Innovation," is a wonderful example of this hands-on approach. The exhibit educates visitors about an exciting period of technological innovation in the 7th through 17th centuries with a combination of films, video games, hands-on activities and live actors. Open through January 7, the award-winning exhibit is free with paid general admission.

Source: Model D

What Can Science Gain From Computers That Learn? | Newswise

The DOE Science News Source is a Newswise initiative to promote research news from the Office of Science of the DOE to the public and news media.
Photo: Shannon Brescher Shea"Machine learning and deep learning programs provide a helping hand to scientists analyzing images" reports Shannon Brescher Shea, Senior Writer/Editor in the Office of Science

Photo: Image courtesy of Greg Stewart/SLAC National Accelerator Laboratory
Physicists on the MINERvA neutrino experiments at the Department of Energy’s Fermilab faced a conundrum. Their particle detector was swamping them with images. The detector lights up every time a neutrino, a tiny elementary particle, breaks into other particles. The machine then takes a digital photo of all of the new particles’ movements. As the relevant interactions occur very rarely, having a huge amount of data should have been a good thing. But there were simply too many pictures for the scientists to be able to analyze them as thoroughly as they would have liked to.

Enter a new student eager to help. In some ways, it was an ideal student: always attentive, perfect recall, curious to learn. But unlike the graduate students who usually end up analyzing physics photos, this one was a bit more – electronic. In fact, it wasn’t a person at all. It was a computer program using machine learning. Computer scientists at DOE’s Oak Ridge National Laboratory (ORNL) brought this new student to the table as part of a cross-laboratory collaboration. Now, ORNL researchers and Fermilab physicists are using machine learning together to better identify how neutrinos interact with normal matter.  

“Most of the scientific work that’s being done today produces a tremendous amount of data where basically, you can’t get human eyes on all of it,” said Catherine Schuman, an ORNL computer scientist. “Machine learning will help us discover things in the data that we’re collecting that we would not otherwise be able to discover.”

Fermilab scientists aren’t the only ones using this technique to power scientific research. A number of scientists in a variety of fields supported by DOE’s Office of Science are applying machine learning techniques to improve their analysis of images and other types of scientific data.

Teaching a Computer to Think
In traditional software, a computer only does what it’s told. But in machine learning, tools built into the software enable it to learn through practice. Like a student reading books in a library, the more studying it does, the better it gets at finding patterns that can help it solve a big-picture problem.

“Machine learning gives us the ability to solve complex problems that humans can’t solve ourselves, or complex problems that humans solve well but don’t really know why,” said Drew Levin, a researcher who works with DOE’s Sandia National Laboratories.

Recognizing images, like those from experiments like MINERvA, is one such major problem. While humans are great at identifying and grouping photos, it’s difficult to translate that knowledge into equations for computer programs...

What Machine Learning Can Do For You
Grouping and identifying images is one of the most promising uses for machine learning. Back in 2012, a deep-learning program could identify photos in a specific database of images with a 20 percent error rate. Over the course of only three years, scientists improved deep-learning programs so much that a similar program in 2015 beat the average human error rate of 5 percent.

“There’s a lot of image-based science that can benefit from deep learning,” said Tom Potok, leader of ORNL’s Computational Data Analytics group.

For image recognition that requires special expertise, machine learning can provide even bigger benefits. “These techniques are extremely efficient at finding subtle signals” like small shifts in particle tracks, said Gabe Perdue, a Fermilab physicist on the MINERvA experiment.

While Fermilab physicists are using deep learning to understand neutrinos, other scientists are using it to understand images from sources as diverse as telescopes and light sources...

Whether in neutrino experiments or cancer research, machine learning offers a new way for both researchers and their electronic students to better understand our world and beyond.

Photo: Prasanna Balaprakash, Computer Scientist.As Prasanna Balaprakash, a computer scientist at DOE’s Argonne National Laboratory, said, “Machine learning has applications all the way from subatomic levels up to the universe. Wherever we have data, machine learning is going to play a big role.”

Source: Newswise (press release)

Teachers’ meet in December to bridge learning gaps | Times of India - Schools & Colleges

"Teaching of science and mathematics has been a problem in the state with various assessments by both the government and private bodies reflecting low levels of understanding of both subjects among students" continues Times of India.

Photo: Storyblocks.comAbout 75 teachers from the state will participate and imbibe innovative methods for evaluation of science learning in classroom.

Apurva Barve, centre coordinator of the Centre of Excellence in Science and Mathematics Education (COESME) at IISER, Pune, said three or four teachers who are selected will get a month's internship and exposure to new-age technologies. 

"The state government also organises conferences but in those, a few speak and the rest merely listen . At this congress in IISER, we give a platform to science and mathematics teachers to communicate their ideas, and share new experiments in teaching methodology and science education," A P Deshpande, secretary of the parishad, said.  
Read more... 

Source: Times of India 

Four strategies for remembering everything you learn | The Independent

Drake Baer, Correspondent, Business Inside summarizes, "Because there's learning and there's knowing how to learn."

Many struggle to retain facts they learn
Photo:  Getty Images/iStockphoto
If you're going to learn anything, you need two kinds of prior knowledge:
  • knowledge about the subject at hand, like math, history, or programming
  • knowledge about how learning actually works
The bad news: Our education system kinda skips one of them, which is terrifying, given that your ability to learn is such a huge predictor of success in life, from achieving in academics to getting ahead at work. It all requires mastering skill after skill.

“Parents and educators are pretty good at imparting the first kind of knowledge,” shares psych writer Annie Murphy Paul. “We're comfortable talking about concrete information: names, dates, numbers, facts. But the guidance we offer on the act of learning itself - the 'meta-cognitive' aspects of learning - is more hit-or-miss, and it shows.”

To wit, new education research shows that low-achieving students have “substantial deficits” in their understanding of the cognitive strategies that allow people to learn well. This, Paul says, suggests that part of the reason students perform poorly is that they don't know a lot about how learning actually works.

It's a culture-wide issue.

Henry Roediger and Mark McDaniel, psychologists at Washington University in St. Louis  and coauthors of Make It Stick: The Science Of Successful Learning, say that “how we teach and study is largely a mix of theory, lore, and intuition.”

So let's cut through that lore. Here are learning strategies that really work.

Force yourself to recall 
The least-fun part of effective learning is that it's hard. In fact, the “Make It Stick” authors contend that when learning if difficult, you're doing your best learning, in the same way that lifting a weight at the limit of your capacity makes you strongest.

It's simple, though not easy, to take advantage of this: force yourself to recall a fact. Flashcards are a great ally in this, since they force you to supply answers.

Don't fall for fluency
When you're reading something and it feels easy, what you're experiencing is fluency.

It'll only get you in trouble. 

Example: Say, for instance, you're at the airport and you're trying to remember which gate your flight to Chicago is waiting for you at. You look at the terminal monitors — it's B44. You think to yourself, oh, B44, that's easy. Then you walk away, idly check your phone, and instantly forget where you're going. 
Read more... 

Source: The Independent

Students learn the science behind Star Wars | Warrington Guardian

"HOW much of the action in Star Wars could happen in the real world?" continues Warrington Guardian.

Students from St Gregory's RC learned about Star Wars.
Well students from St Gregory's High School in Warrington found out during an event today.

Chapelford resident Professor Carsten Welsch, head of physics at the University of Liverpool and head of communication for the Cockcroft Institute, explored the 'Physics of Star Wars' in an event on Monday designed to introduce cutting-edge science to hundreds of secondary school children, undergraduate and PhD students, as well as university staff. 

Professor Welsch said: “I selected iconic scenes from the movies that everybody will immediately recognise, and used real world physics to explain what is possible and what is fiction.

"For example, a lightsabre, as shown in the film, wouldn’t be possible according to the laws of physics, but there are many exciting applications that are possible, such as laser knives for high precision surgery controlled by robot arms and adaptive manufacturing using lasers for creating complex structures in metals.

“A short scene from Star Wars was just the introduction, the appetizer, to make the participants curious, but then I linked what I had just shown in the film to ongoing research here in the department and in particular our accelerator science projects at the Cockcroft Institute.

“In the first movie from 1977, the rebels have used proton torpedoes that make the Death Star explode as their lasers wouldn’t penetrate the shields. I linked that to our use of ‘proton torpedoes’ in cancer therapy. Within the pan-European OMA project we are using proton beams to target something that is hidden very deep inside the body and very difficult to target and destroy...

Professor Welsch and members of his QUASAR Group had the permission of Lucasfilm to use film excerpts; these were complemented by Lego Star Wars models, a real cantina as found in the movie, storm troopers and even Darth Vader himself. 

Source: Warrington Guardian 

Robotics offers multiple learning experiences for students | Alpena News

"Local schools are becoming more involved with robotics programs. Being in robotics helps students in their classes, helps them create leadership roles and helps build a student’s work ethic" notes Julie Goldberg, News Staff Writer.

Alpena High School sophomore Aaron West works on fixing his robot. West said that being in robotics has helped him put more focus into his classes and has also helped him learn how to manage his time outside of the classroom. 
Photo: Julie Goldberg 
STEM education — science, technology, engineering and mathematics — is a new movement in American education that helps prepare students for the workforce. Being involved in STEM education classes helps a student prepare for the job openings that will be available.

“There are so many job openings right now, especially in Michigan,” teacher Melissa Doubek said.  

“There are job openings in stem-related careers and there are possibilities for jobs here in Alpena.”

One benefit of STEM education is that what students learn in their classes helps them in robotics and they can apply what they learn in class to the robots they are working on.

“We have our math and science classes and together with our work ethic, we can put those together to build the robots,” AHS sophomore Aaron West said.

Along with applying their school work to robotics, students learn about time management because they learn how to balance their time outside of the classroom. West said being in robotics helps students focus in their classes.
“We make sure that our classes are in top order because if our grades drop, we are not allowed on the team,” he said.

When Alpena goes to tournaments, West said students make sure that their homework is done before the tournament by using their time efficiently so they are able to compete.
“Robotics makes you realize how important hard work is and also realizing the value of cooperating with other people,” sophomore Emeline Hanna said. “Even if something goes wrong, you should still be determined to keep going because there are things that will go wrong in robotics.”

Robotics gives students experience in the science field and also gets the students interested and excited about learning new things. Doubek said some students never know that they are interested in engineering before joining robotics, but once they join, they get excited.

“They realize that what they are learning in math, physics, business and advertising, and public relations during the school day is important in robotics,” she said.

Students realize they like certain fields of science and there are different fields that students can pursue careers in. Doubek said there are some students who aren’t interested in engineering, but are instead interested in medicine. She said some students realize they want to learn leadership skills when in robotics.

“That is just as important as the students realizing that they want to go into engineering,” 

Doubek said. “We put a lot of students in leadership roles because we are trying to grow the leaders of tomorrow.”

When students are a part of a robotics program, they learn more than just building the robots with their teammates. Mentor Gary Stevens said robotics teaches students how to work in a group and as a team.

Source: Alpena News

Artificial Intelligence Job Titles: What Is A Machine Learning Engineer? | Forbes - Tech

Photo: Adelyn ZhouIn this article Adelyn Zhou, Forbes Blog Contributor inform, "If you’re looking to embark on an AI project, the first step is to recruit the right team. You must also have Machine Learning Engineers. But who are they and what do they actually do?" 

Photo: Shutterstock
If you’re looking to embark on an AI project, the first step is to recruit the right team. This can be the most challenging part of the process as specialized AI talent is difficult to find. According to the NYTimes, there are fewer than 10,000 qualified people in the world and universities are only graduating about 100 new candidates each year with the requisite skills. Further complicating matters are the myriad of job descriptions, titles, roles, skills and technologies used in the industry. What does all the terminology mean? And how do they fit into your recruiting strategies for hiring AI talent?

Machine Learning Engineers
At the center of any machine learning project lie the machine learning engineers. With backgrounds and skills in data science, applied research and heavy-duty coding, they run the operations of a machine learning project and are responsible for managing the infrastructure and data pipelines needed to bring code to production. Explains eBay VP of engineering Japjit Tulsi, machine learning engineers must be able to “straddle the line between knowing the mathematics and coding the mathematics.”

Data Scientists
Supporting the machine learning engineers are data scientists who do not typically ship production code, but rather tackle discrete problems using preexisting data to validate models. They have PhDs in data science or statistics, or backgrounds in computer science, math and physics. According to Greg Benson, professor of computer science and chief scientist at AI firm SnapLogic, “data science people are focused on the algorithm and the analysis; they’re not operating on the software side.” In the process of developing algorithms and analyses, data scientists also perform the critical task of collecting, cleaning, and preparing data correctly which can be the most time-consuming portion of their work. Abhi Jha, director of advanced analytics at McKesson, admits that "the hard work is cleaning data, the model selection is easy."

Research Scientists / Applied Research Scientists
Research scientists often build on promising data leads uncovered by data scientists or experiment with novel approaches, some of which may have originated from academic or industry research facilities. They are more focused on driving scientific discovery and less concerned with pursuing industrial applications of their findings. 
Read more... 

Source: Forbes

Suggested Books of the Week 47, 2017

Check out these books below by Cambridge University Press, Oxford University Press, TradePub and Ancient Origins.

Photo: Storyblocks.com
Adversarial Machine Learning

Adversarial Machine
LearningWritten 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...

Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning, and those wanting to learn about the next stage of the cybersecurity arms race.
  • The first book to provide a state-of-the-art review of adversarial machine learning
  • Covers availability and integrity attacks, privacy-preserving mechanisms, near-optimal evasion of classifiers, and future directions for adversarial machine learning
  • Includes in-depth case studies on email spam and network security

Machine Learning Refined - Foundations, Algorithms, and Applications

Machine Learning
RefinedProviding a unique approach to machine learning, this text contains fresh and intuitive, yet rigorous, descriptions of all fundamental concepts necessary to conduct research, build products, tinker, and play...

With in-depth Python and MATLAB/OCTAVE-based computational exercises and a complete treatment of cutting edge numerical optimization techniques, this is an essential resource for students and an ideal reference for researchers and practitioners working in machine learning, computer science, electrical engineering, signal processing, and numerical optimization.
  • Provides MATLAB-based coding exercises, real-world examples, and practical applications 
  • Takes a unique approach, enabling a more coherent, intuitive, and interactive way of learning 
  • Includes over 150 illustrations, many of which are in full colour 

AI - Its Nature and Future

AI - Its Nature and FutureThe applications of Artificial Intelligence lie all around us; in our homes, schools and offices, in our cinemas, in art galleries and - not least - on the Internet. The results of Artificial Intelligence have been invaluable to biologists, psychologists, and linguists in helping to understand the processes of memory, learning, and language from a fresh angle.
As a concept, Artificial Intelligence has fuelled and sharpened the philosophical debates concerning the nature of the mind, intelligence, and the uniqueness of human beings... 

Artificial Intelligence - What Everyone Needs to Know 

Artificial Intelligence 
What Everyone Needs to Know Over the coming decades, Artificial Intelligence will profoundly impact the way we live, work, wage war, play, seek a mate, educate our young, and care for our elderly. It is likely to greatly increase our aggregate wealth, but it will also upend our labor markets, reshuffle our social order, and strain our private and public institutions. Eventually it may alter how we see our place in the universe, as machines pursue goals independent of their creators and outperform us in domains previously believed to be the sole dominion of humans. Whether we regard them as conscious or unwitting, revere them as a new form of life or dismiss them as mere clever appliances, is beside the point. They are likely to play an increasingly critical and intimate role in many aspects of our lives...

The 50 Greatest Motivational Quotes Of All Time - And Why 

Download nowEveryone loves to sink their teeth into a great quote - but why?

Quotes in isolation have an impact. But quotes that are deeply explained resonate on a whole new level.

Feel free to read it all at once or dip into them from time to time. If you have a day when things aren't going quite according to plan or you feel less than inspired, open this up and you'll spot a quote in no time that will get you back on track.
Explore your curiosity and brighten your day with these motivational quotes!

Ancient Worlds: The Search for the Origins of Western Civilization 

Ancient Worlds: 
 The Search for the Origins
of Western Civilization Across the Middle East, the Mediterranean and the Nile Delta, awe-inspiring, monstrous ruins are scattered across the landscape - vast palaces, temples, fortresses, shattered statues of ancient gods, carvings praising the eternal power of long-forgotten dynasties. These ruins - the remainder of thousands of years of human civilization - are both inspirational in their grandeur, and terrible in that their once teeming centres of population were all ultimately destroyed and abandoned.
In this major book, Richard Miles recreates these extraordinary cities, ranging from the Euphrates to the Roman Empire, to understand the roots of human civilization...

Enjoy your reading!  

Source: Cambridge University Press, Oxford University Press, TradePub and Ancient Origins.

AI, machine learning new tools to fight cyber attacks | Deccan Chronicle - Technology

As the world is fast moving towards IoT and connected devices, deployment of AI has become inevitable for cyber security firms.
Cyber security companies are turning to artificial intelligence and machine learning tools to ward off growing number of attacks on networks, Finland-based internet security firm F-Secure said.

Networks are persistently exposed to threats like malware, phishing, password breaches and denial of service attacks...
Photo: Deccan Chronicle
As the world is fast moving towards Internet of Things and connected devices, deployment of artificial intelligence (AI) has become inevitable for cyber security firms to analyse huge amount of data to save networks from infiltration attempts, F-Secure's Security Advisor Sean Sullivan said.

Networks are persistently exposed to threats like malware, phishing, password breaches and denial of service attacks. On a daily basis, F-Secure Labs on an average receives sample data of 500,000 files from its customers that include 10,000 malware variants and 60,000 malicious URLs for analysis and protection, Sullivan said.

For humans, it is a big task to go through such huge amount of data and machine learning tools and AI are lending a helping hand at this stage, he said.

Machine learning can be used to train logic designed to detect suspiciousness based on the structure of a file or its behaviour or both, another Security Advisor Andy Patel said.

Sullivan said any abnormal behaviour of a file is flagged by AI which helps in detecting threats at an early stage without much damage being done to the network.
Read more... 

Source: Deccan Chronicle

Op-Ed: Many are pessimistic about the consequences of AI | Digital Journal - Tech & Science

"Charles Modeas, EU Research Commissioner claims that the development of AI and thinking machines pose a threat to our existence risk confusing science with science fiction" according to Ken Hanly, retired philosophy professor.

Softbank's "Pepper" humanoid robots 'rest' before the International Robot Exhibition in Tokyo, on December 2, 2015.
Photo: Yoshikazu Tsuno, AFP

Negative views of AI development predominate
In a speech to the European Parliament's Science and Technology Options Assessment Group Modeas said:“If you do any research on artificial intelligence these days, the results are astonishingly pessimistic. Nine articles out of ten on AI are negative. Not just negative. Alarmist and panicked, sometimes even hysterical. For me, a techno-optimist, it's shocking. And very disappointing."

Commissioner Modeas is betting that AI research will be a positive force even though he admits that public fear of the technology appears to be deep. He thinks the public fears what is the most exciting new technology for our generation. To deny the amazing benefits it can bring is not the answer he claims.  

Possible negative effects of AI development 
Warnings about AI development have come from notables such as Bill Gates, Stephen Hawking, and Elon Musk who argue that it could evolve to a point where it is beyond human control. Many critics also worry that robots with AI will take over jobs creating unemployment.  

Elon Musk's worries 
Musk who himself has been a prime contributor to technology both in electric vehicles and space rockets worries that competition for AI technology could lead to war as governments compete for superiority in weaponry using AI. However, it has surely always been the case that countries compete for technology especially technology that can be triumphant in warfare. This would be so whether AI developed or not. However, the development of AI will make this competition more dangerous.. 

Technology in the form of chemical weapons and nuclear bombs already show that it is imperative that we do everything we can to ensure that new AI technology is controlled. Musk's warning are very much based on reality. 

Will AI technology result in lost jobs? 
Another of Musk's worries was the loss of jobs. 

Economists Daron Acemoglu and Pascual Restrepo of the National Bureau of Economic Research looked at the historical effects of robots on employment in the US between 1990 and 2007 controlling for the influence of other factors. 

Their study showed that each new robot led to the loss of between 3 and 5.6 jobs in the local area. For each new robot added for 1,000 workers wages would also decline between 0.25 and 0.5 percent. 

The two researchers write: “Predictably, the major categories experiencing substantial declines are routine manual occupations, blue-collar workers, operators and assembly workers, and machinists and transport workers.” 

Steven Mnuchin, the US treasury secretary said that he was not worried about the effects of AI and automation on employment. Mnuchin said: “Quite frankly, I'm optimistic. I mean, that's what creates productivity." The productivity is increased because more is produced by each worker in conjunction with AI. However, the remaining workers will actually see their wages decline. The AI makes the firm more productive and usually more profitable as well. 

As more and more AI is used in production and elsewhere its negative effects on employment will likely be greater. 

Hawking's fears 
Stephen Hawking is a famous theoretical physicist and cosmologist. He suffers from a slow progressing but early onset type of ALS sometimes called Lou Gehrig's disease. Wikipedia notes: "Hawking has a rare early-onset, slow-progressing form of amyotrophic lateral sclerosis (ALS) that has gradually paralysed him over the decades. He now communicates using a single cheek muscle attached to a speech-generating device." 

Hawking is not averse to advanced technology. He uses such technology to communicate and could not talk without it.

Hawking's warning is dramatic. He has even said that the development of full AI could result in the end of the human race.
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Additional resources  
CYBERSEC INSIGHTS 2017 - Janis Sarts on tackling fake news and propaganda

Source: Digital Journal and EUROPEAN CYBERSECURITY FORUM - CYBERSEC Channel  (YouTube)

Why all the Focus on Artificial Intelligence? | TG Daily - Technology

In an article for TG Daily, Michael Manning says, "Artificial Intelligence is dominating all Industries, primarily Mobile App development. Why tech news is all about AI and why Google’s focus on AI-infused Apps."

Photo: TG DailyArtificial Intelligence means Intelligence for machines. The effort to make better and more efficient technology has been going on since the moment we discovered electricity. We have always thought of creating better and more amazing machines"

Creating machines that can process faster and more accurate than humans could ever do is no longer enough. We need better machines, smarter machines. How do we do that? By making them artificially intelligent. By devising, some way that would enable the machines so, they could 'think' for themselves, without any human intervention. That is what Artificial Intelligence is, and it's flooding the markets like an epidemic. Doubtful? Take a look at what the experts think.

Believe in the saying 'There is strength in numbers?' Well, AI does. AI did not progress on its own. Initially when it was just AI, or now termed as 'Narrow AI' we accomplished specific tasks that were as good as humans or better. Things like facial recognition on Facebook or cataloging images, done by Pinterest. With the help of Machine Learning and Deep learning, we might be able to bridge reality and fiction.
I visualize a time when we will be to robots what dogs are to humans, and I'm rooting for the machines - Claude Shanon.

Machine Learning, ML, and Deep Leaning, DL, is not so much different from AI. They are just more advanced techniques of achieving AI. Machine learning is basically, parsing data and reading it through a set of algorithms and learning something about the world. That beats hard-coding specific tasks for the machine. This way the machine LEARNS to perform a given function instead of some programmer 'hard-coding 'it. ML allowed performing more valuable and accurate tasks.

ML dominated computer vision for quite some time. But the level of accuracy and errors wasn't impressive. For instance, reading a board sign saying 'S-T-O-P' was great, but only in clear weather and no obscuring objects in between.

Don't give up yet, after machine learning came Deep Learning. In our quest to machine-travel as close as possible to the human brains, Deep learning brought us, Neural Networks. They are inspired by the biology of a human brain. Working in layers and layers of neurons...

...Better start getting ready for an all-out AI attack on our industry. All major organizations and companies recognize the golden opportunity to invest in AI and so should you!
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Recommended Reading
E-Learning Future: How Computerized Analysis Will Enhance Our Development by Michael Manning.
Photo: TG Daily"E-Learning has become one of the newest trends in big corporations as well as in small companies."

Source: TG Daily (blog)

How will AI and deep learning technologies impact the audit? | Siliconrepublic.com

Photo: Felice PersicoPhoto: Jeanne BoilletWe are at an inflection point in the debate about what AI means for industries and professions. With the critical mass of data now enough to feed the AI engine, its early applications are yielding some very interesting results. Felice Persico, global vice-chair for assurance at EY and Jeanne Boillet, global assurance innovation leader at EY, investigate.

Photo: Indypendenz/Shutterstock
There are many truths and half-truths out there concerning the impact that artificial intelligence (AI) will have across a range of industries and professions.

Some industries have adopted elements of the technology faster than others, with varying degrees of success. And, while there is undoubtedly a great deal of hype around AI, it seems certain that it will have a dramatic impact for many areas of business and the wider world over the coming years.

In fact, figures from a recent study found that 88pc of more than 230 business and technology executives surveyed are now using technologies that rely on AI. And, of those who haven’t yet deployed AI, 56pc plan to do so in the next two years.

This is because its progress has accelerated sharply in the last five to 10 years due to advances in low-cost parallel processing, learning algorithms and a rise in the sheer volumes of data that we now have at our disposal.

These advances give AI-powered or infused systems a huge volume of material to analyse, interpret and learn from – infinitely faster than humans ever could – with a view to gathering insights, solving problems and increasing productivity.

Most current uses of AI fit into what can be considered ‘understanding of the world’. That means harnessing AI’s abilities to handle, organise and analyse a range of inputs – most critically, huge amounts of structured and unstructured data, which can range from social media sentiment to a company’s business data.

To give an example, the EY Global Artificial Intelligence Lab has recently applied AI and deep learning technologies to the lease accounting process.

AI is used to streamline data capture from contracts by identifying the relevant clauses for accounting treatment, such as lease commencement date, payment amounts, and renewal or termination options.
Read more... 

Source: Siliconrepublic.com

The vital role of humans in machine learning | Media Update - Marketing

"Machine learning allows the algorithms that software and systems use to evolve as they process new data." by Media Update.

Photo: Media Update
Contrary to what many people think, humans play an integral role in this process and in applying machine learning-driven solutions to real-world problems. 

Machine learning allows systems and AI engines to automatically learn and improve from experience. It is used to improve existing data processing systems or to create new software solutions.

Humans are closely involved in every step of producing technology that relies on machine learning. media update unpacks the end-to-end role that humans play in machine learning, and how humans and AI complement one another.

Here’s a look at the numerous departments within a company that contribute to developing solutions powered by machine learning:

Sales and customer care teams 
AI is currently a buzzword, with many companies experimenting with the capabilities of this ever-evolving technology. But, AI-powered solutions will only survive in the marketplace if they are purpose-built for a customer need, and that is why sales and customer care teams have to be involved in the development of the product.
"AI-powered solutions will only survive in the marketplace if they are purpose-built for a customer need."Salespeople and customer support teams deal with customers and clients, which means they are ideally positioned to identify specific consumer needs. This information is key to developing viable products, services, or solutions. This is especially true for developers of machine learning-driven solutions.

The role of these teams does not end at this point. Customers provide feedback on the products or solutions, and these insights can be used to improve the company's offerings.  

Developers, experts, and knowledge engineers
Once the clients’ needs have been established, IT development teams create the algorithms that form the basis of the machine learning system. 

They gather relevant data from a number of sources inside and outside their company.Information and knowledge about the data are acquired from a subject matter expert, who is a specialist in the field related to the solution. Knowledge engineers enter this information into a knowledge base.
"Information and knowledge about the data are acquired from a subject matter expert, who is a specialist in the field related to the solution."An engine then creates models based on the data in the knowledge base. The development team evaluates the model that the engine has created and the results of new data it processes. They make necessary adjustments to the engine so that it processes the data correctly.

With this done, the technology is ready to be applied. It could be used in new software aimed at customers, such as automatic sentiment analysis of social media posts for media intelligence. Companies might apply the engine to their own internal systems to improve their data processing capabilities. 
Read more... 

Source: Media Update

How machine learning is helping neuroscientists understand the brain | Massive

Photo: Daniel Bear "An expert argues that neuroscience is using the wrong metaphors" according to Daniel Bear, postdoc at Stanford University.Photo: Massive
The workings of the brain are the greatest mystery in science. Unlike our models of physics, strong enough to predict gravitational waves and unseen particles, our brain models explain only the most basic forms of perception, cognition, and behavior. We know plenty about the biology of neurons and glia, the cells that make up the brain. And we know enough about how they interact with each other to account for some reflexes and sensory phenomena, such as optical illusions. But even slightly more complex levels of mental experience have evaded our theories.

We are quickly approaching the point when our traditional reasons for pleading ignorance – that we don’t have the right tools, that we need more data, that brains are complex and chaotic – will not account for our lack of explanations. Our techniques for seeing what the brain and its neurons are doing, at any given moment, get stronger every year.

But we are using the wrong set of metaphors to describe the entire field, basing our understanding of the brain on comparisons to communications fields, like signal processing and information theory. Going forward, we should leave that flawed language choice behind. Instead, the words and ideas needed to unlock our brains come from a computational field much nearer to real biology: the expanding world of machine learning.

Homines ex machina? 
For most of its history, “systems” neuroscience – the study of brains as large groups of interacting neurons – has tried to frame perception, action, and even cognition in terms taken from fields like signal processing, information theory, and statistical inference. Because these frameworks were essential for developing communications technology and data-processing algorithms, they suggested testable analogies for how neurons might communicate with each other or encode what we perceive with our senses. Many discussions in neuroscience would sound familiar to an audio engineer designing an amplifier: a certain region of the brain “filters” the sensory stimulus, “passing information” to the next “processing stage.”

Words of this sort preclude certain assumptions about how we expect to understand the brain. For instance, talking about different stages of processing implies that what goes on at one physical location in the brain can be distinguished from what goes on at another spot. Focusing on information, which has both a lay meaning and a precise mathematical definition, often conflates the two and postpones the question of what an animal actually needs to know to perform a certain behavior.

These borrowed descriptions proved fruitful for a time. Our computer algorithms for processing visual and auditory stimuli really do resemble the function of neurons in some parts of the brain, typically those closest to the sensory organs. This discovery was one of the earliest indications that we might understand the brain through simple, physics-like theories. If neurons really could be said to detect the edges in an image or break sounds down into their component frequencies, why shouldn’t the signal processing analogy extend to higher-level phenomena?
Read more... 

Source: Massive


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