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Friday, November 15, 2019

NCSA Launches Center for Artificial Intelligence Innovation | HPCwire

The National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign is excited to announce the creation of the Center for Artificial Intelligence Innovation (CAII), which seeks to continue the groundbreaking work already being done in the domains of Deep Learning, Machine Learning, and Artificial Intelligence.

Announcing the Center for Artificial Intelligence Innovation

“We are truly excited to announce the formation of this Center, which brings together a number of efforts that have been underway for some time along with new areas of focus to propel AI research and application forward,” said NCSA’s Director, Bill Gropp...

By leveraging the expertise and resources that exist at NCSA and Illinois, the CAII seeks to become a hub for innovation on campus, regionally, nationally and internationally. The CAII at Illinois will complement existing initiatives across campus by operating as a central nexus for AI research with applications in both academia and industry...

For more information about the Center for Artificial Intelligence Innovation, visit the new website here

About NCSA

The National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign provides supercomputing and advanced digital resources for the nation’s science enterprise. At NCSA, University of Illinois faculty, staff, students, and collaborators from around the globe use advanced digital resources to address research grand challenges for the benefit of science and society. NCSA has been advancing one third of the Fortune 50® for more than 30 years by bringing industry, researchers, and students together to solve grand challenges at rapid speed and scale.

Source: HPCwire and NCSAatIllinois Channel (YouTube)

Do Schools Need a Robotics Program? | Innovation - EuroScientist

Robots earned their place in academia thanks to people like Nils Nilsson, argues Megan Ray Nichols, freelance STEM writer and blogger. 

Photo: EuroScientist
By today’s standards, Nilsson’s early work — a top-heavy robot called “SHAKEY” — is fairly primitive. Nilsson, who passed away in April 2019, nevertheless helped cement the idea that robotics will always inspire widespread interest and will always have a place in the world’s classrooms.

There are multiple reasons why schools, students and society all benefit when schools invest in robotics programs. Plus, getting such a program off the ground isn’t as difficult as it might sound.

Why Robotics and Why Now? 
Why now? Because robotics and artificial intelligence have a huge role to play in humanity’s future. The number of tasks and even whole careers which are ripe for automation is only growing larger. It’s not the end of work as we know it, but as reported by Oxford Economics, the worldwide manufacturing sector all by itself could shed as much as 8.5% of its workforce by 2030...

Here are some equally powerful reasons to add robotics programs to the world’s classrooms:
  • Robotics inspires creative problem-solving: Studying robotics is an activity that combines free-play and experimentation with the rigorous process of scientific trial and error. While learning about robotics, kids and older students get to see the direct impact of cause-and-effect in an activity that combines digital mastery with physical output.
  • Robotics lessons are highly engaging: The combination of digital, electrical and mechanical learning opportunities is highly stimulating and keeps students engaged with their studies. Experimenting with robotics is also a highly student-driven exercise and one that provides “active learning,” which conveys (and helps students retain) much more information than lectures and memorization.
  • Robotics makes students more code-aware: Robotics are soon to be everywhere and computers are already ubiquitous. Studying robotics early in their educational careers helps students become “code-aware,” which is another way of saying it helps them better understand the modern world and how it functions. Steve Jobs was a proponent of teaching coding to every student for this reason.
  • Robotics cement fundamental math and science concepts: You can’t have robotics without mathematics, physics and maybe even a touch of physical chemistry knowledge to understand how the elements can influence the performance of electronics, including robots that must perform in the intense cold of outer space.
Robotics presents students with a compelling problem (make this robot do “X”) that requires them to draw on each of these skills and fundamental knowledge areas. They’re learning lifelong concepts without being overly aware that they’re learning at all.

Source: EuroScientist 

A brief history of machine learning in cybersecurity | Cybersecurity - SecurityInfoWatch

How to connect all the dots in a complex threat landscape by David Barton, Chief Information Security Officer and Dr. Albert Zhichun Li, Chief Security Scientist at Stellar Cyber.

 Developers are showing more interest in using Machine Learning (ML) to automate threat-hunting.
Photo: Courtesy of Big
As the volume of cyberattacks grows, security analysts have become overwhelmed. To address this issue, developers are showing more interest in using Machine Learning (ML) to automate threat-hunting. In fact, researchers have tried to implement ML in cybersecurity solutions since the late 1980s, but progress has been slow. Today, ML is showing increasing promise with the advent of Big Data because the quality of information from which ML can learn is improving. However, there is much more to be done.

Anomaly Detection – The Early Days

When we talk about security, we want a system that can separate good from bad, normal from abnormal. Therefore, it is quite natural to apply anomaly detection to security...

The Rise of Big Data 
After 2000, developers and researchers began creating spam, phishing, and URL filtering systems based on supervised learning. In supervised learning, decisions are based on comparing a set of data (or labels) against a perceived threat. One such example is a URL blacklist, where incoming e-mail is matched against a list of undesirable URLs and rejected if it matches a label on the list. A supervised learning algorithm analyzes the data and produces an inferred function (i.e., this traffic behavior matches this input data, therefore it is bad), which can be used for mapping new examples.
Read more... 

Source: SecurityInfoWatch

Thursday, November 14, 2019

AI Is Not Real: How Intelligent Is Artificial Intelligence? | Technology - International Business Times

Hercules Reyes, International Business Times summarizes, Despite its popularity both in consumer technology and in popular fiction, experts believe that AI is not real.

Robots are already widely used in Japan -- from cooking noodles to helping patients with physiotherapy
This is the bold statement that Scientific American gave regarding the matter. In their recent statement, they claimed that what people call nowadays as Artificial Intelligence (AI) is not actually AI, but something else entirely.

Sure, we hear the description of AI branded on almost every new tech nowadays. Even on smartphones, companies now love to call their camera technology as something powered by AI. 

But experts are agreeing that there is no real AI yet, or at least not in the sense that most of us know and in the sense that popular fiction has portrayed so far...

Experts believe that AI, as we know it now, is simply automation. It is simply a set preprocess that are programmed to be executed in a specific time or a specific trigger. Imagine a factory full of workers with different tasks. Now, an automated system will have machines do all these supposed human tasks in the same manner and maybe even faster. This is not AI, but rather, just automation.

Source: International Business Times

What Does an Actuary Do? (+Salary and Skills Required) | Management - G2

When it comes to finance, there’s a common mentality of “no risk, no reward.” explains Derek Doeing, specializing in HR and Recruitment trends and insights.  

Photo: G2

Risk and reward play a crucial role in the financial success of all sorts of organizations. There are several businesses that desperately need help managing and measuring the financial risks to their organization. This is where actuaries come in. 

What is an actuary? 
Becoming an actuary can be a lucrative career for those with an understanding of business and mathematics. If this seems like something you could be interested in, read on or jump ahead to get the information you need:...

What does an actuary do?  
An actuary is responsible for analyzing and managing the financial risks of a business. Working in actuarial science is a deeply sought-after profession that plays an important role in the success of a company. Actuaries help organizational leaders make strategic decisions and communicate solutions for deeply complex financial issues...

Actuarial education  
Becoming an actuary requires you to receive a Bachelor’s degree to start. Many institutions offer actuarial science programs, but you could also study curriculum in business, math, or economics. There are also a number of required actuarial exams that cover important skills like mathematics, probability, economics, and business.

Additional resources

Photo: G2
What Is Actuarial Science? (+Exams, Jobs, and Salaries) by Mara Calvello, Senior Content Marketing Specialist at G2.

Source: G2

41 New Skills You Can Now Learn on LinkedIn Learning | New Courses - The Learning Blog

Each week presents a new opportunity for you and your team to learn the skills necessary to take on the next big challenge recommends Zoë Kelsey, Learning Supporter at Linked.

Photo: JumpStory
Did you know Veterans are 39% more likely to move into leadership positions? They’re also more likely to stay with a company than average civilian professionals.

In honor of Veteran’s Day share a brand new course (we added 41 new courses this week!) with a Veteran friend or colleague to celebrate them. To learn more about Veteran employment, check out LinkedIn’s Veteran Opportunity Report

The new courses now available on LinkedIn Learning are:

Source: The Learning Blog

PhDs: the tortuous truth | Careers -

Chris Woolston, freelance writer in Billings, Montana reports, Nature’s survey of more than 6,000 graduate students reveals the turbulent nature of doctoral research. 

Photo: JumpStory
Getting a PhD is never easy, but it’s fair to say that Marina Kovačević had it especially hard. A third-year chemistry student at the University of Novi Sad in Serbia, she started her PhD programme with no funding, which forced her to get side jobs bartending and waitressing. When a funded position came up in another laboratory two years later, she made an abrupt switch from medicinal chemistry to computational chemistry. With the additional side jobs, long hours in the lab, and the total overhaul of her research and area of focus, Kovačević epitomizes the overworked, overextended PhD student with an uncertain future.

And yet she could hardly be happier. “I think I’m exactly where I need to be,” she says. “I love going to work each day. I have lots of things to do, but I’m not stressed. I can’t imagine anything else that would bring me this much joy.”

The results of Nature’s fifth survey of PhD students bear out Kovačević’s experience, telling a story of personal reward and resilience against a backdrop of stress, uncertainty and struggles with depression and anxiety. The survey drew self-selecting responses from more than 6,300 early-career researchers — the most in the survey’s ten-year history. The respondents hail from every part of the globe and represent the full spectrum of scientific fields...

Institutions also have much to learn. This survey and others like it should point the way for institutions trying to adapt to the needs of their students, Gotian says. Even though a majority of students are satisfied with their programmes, she says, their complaints and frustrations deserve close attention. “We don’t want to run programmes the way we did 20 years ago,” she says. “People have changed, technology has changed, the job market has changed. We need to constantly evolve.”
Read more... 

Additional resources
Nature 575, 403-406 (2019) 
doi: 10.1038/d41586-019-03459-7  


A personality quiz for fans of math and history: Are you a Newton or a Leibniz? | Science - Ars Technica

Math teacher Ben Orlin writes and draws the (aptly named) blog Math With Drawings and is the author of a new book, Change Is the Only Constant: The Wisdom of Calculus in a Madcap World. To mark its publication, he devised this entertaining accompanying quiz. You can read the Ars interview with Orlin here.

Photo: Isaac Newton
Photo: Gottfried Leibniz
Ars Technica's Jennifer Ouellette, senior reporter says, Isaac Newton and Gottfried Leibniz 
are like night and day, or derivatives and integrals.
Arch-rivals Isaac Newton and Gottfried Leibniz famously fought over the credit for inventing calculus.
Isaac Newton and Gottfried Leibniz have a lot in common. Birthdates in the 1640s. Fatherless childhoods. Colossal egos. Show-stopping wigs. Most of all, each had the honor of bringing calculus into the world. But when it comes to personalities, Newton and Leibniz are like night and day, or England and France, or derivatives and integrals. They’re rivals. Opposites, even. Do you belong on #TeamNewton or #TeamLeibniz? 
Take this quiz to find out!

Recommended Reading

Change Is the Only Constant:
The Wisdom of Calculus in a Madcap World
Source: Ars Technica 

Wednesday, November 13, 2019

Math Research: How Does it Work? | Campus - The UTD Mercury

Take an inside look at how UTD's mathematical scientists operate, solve problems using high dimension math, explains, according to Breanna Shen, Mercury Staff.  

Photo: Shubechhya Mukherjee, Mercury Staff
When people think of research, they imagine a scientist in a white lab coat pipetting chemicals or culturing cells on a petri dish. But when asked to consider “math research,” what comes to mind? What does pure math research entail?

This fall, the School of Natural Sciences and Mathematics inducted seven new tenured and tenure-track faculty. Among them, Baris Coskunuzer and Stephen McKeown conduct research in math, while Qiwei Li conducts statistics research.

“Pure mathematicians are not really interested in the real-life applications,” Coskunuzer said. “They are trying to solve nice puzzles, which give you interesting relationships between objects.” 

Coskunuzer got his first taste of math as a high schooler in Turkey, where he enjoyed classes in abstract math. He majored in math in college, and as a Ph.D. student at Caltech, his advisor introduced him to geometric topology... 

In contrast with pure math research, Li said, statistics research centers on application, especially in medicine or biology.

“Statistics is the science about data, because the data can reveal lots of interesting things about the body and the world,” Li said...

Li uses Bayesian statistical tools to draw conclusions in two areas of application, digital pathological images and microbiomes, which are collections of microorganisms, using both data and prior knowledge. High resolution images of pathological tissues can be analyzed by a deep learning AI to identify different types of cells, Li said. The patterns of cells are statistically quantified and used to predict patient survival outcome.

Source: The UTD Mercury

Mathematician discovers method to simplify polymer growth modelling | Mathematics - Phys.Org

A mathematician from RUDN University has proven that there are no solutions to functional differential inequalities associated with the Kardar-Parisi-Zhang (KPZ)-type equations, nonlinear stochastic partial differential equations that arise when describing surface growth by Phys.Org.

Photo: RUDN University
The obtained conditions for the absence of solutions will help in studies of polymer growth, the theory of neural networks, and chemical reactions. The article was published in Complex Variables and Elliptic Equations

The main difficulty with nonlinear partial differential equations is that many of them are not solved exactly. For practical purposes, such equations are solved numerically, and the questions of the existence and uniqueness of their solutions become problems over which scientists have been struggling for decades, and sometimes centuries. One of these problems—Navier-Stokes existence and smoothness—was included in the famous list of Millennium Prize problems: The Clay Mathematical Institute in the U.S. offers a prize of $1 million for solving any of these problems.

Any partial differential is defined in a certain area, e.g., on a plane or in a sphere, or in space. Usually, it is possible to find a solution to such equations in a small neighborhood of a point, i.e., a local solution. But it may remain unclear.

RUDN University Mathematical Institute mathematician Andrei Muravnik used the method of inequalities. He generalized the existing theorems to the quasilinear case that arises in the study of the KPZ-type equations. The conditions obtained not only limit the set of possible solutions to the KPZ-type equations, but are also are necessary for the solvability of problems that arise in practice. In particular, these results help in solving the problems of surface growth when modeling the behavior of polymers, and can also be used in the theory of neural networks.

Additional resources
A. B. Muravnik. On absence of global solutions of quasilinear differential-convolutional inequalities, Complex Variables and Elliptic Equations (2019).  
DOI: 10.1080/17476933.2019.1639049

Source: Phys.Org