Is online education right for you? 5 questions answered | Online education - The Conversation Africa

Editor’s Note: 
When U.S. News & World Report released its best online education program rankings this year, many schools that fared well in the rankings were quick to call attention to their success. Here, Vanessa Dennen, a researcher of online learning and co-editor-in-chief of the journal The Internet and Higher Education, offers insights into what college students should consider before they enroll in an online degree program.

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While online education may seem like a convenient way to earn a degree in the comfort of your own home or office, warns Vanessa Dennen, researcher of online learning reports of pitfalls that can seriously set a student behind.

Online education presents many conveniences but requires serious time management skills.
Photo: fizkes/Shutterstock.com

1. Is online education as easy and convenient as it seems? 
Online learning may give students a choice about when and where to study, but this flexibility should not be confused with being easy or fast. Learning is a process and it takes time. By studying online, you might be able to eliminate commute time and the dreaded hunt for parking on campus, but you still have to put in the time and effort to learn.

Students with good study habits and time management skills tend to perform better online than students who procrastinate. It’s easy to put off schoolwork when there isn’t a specific class meeting time, and students who regularly choose to put other activities first may find themselves quickly falling behind in an online course. 
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Source: The Conversation Africa

What the best mentors do | Career - Nature.com

The 2019 Nature Awards for Mentoring in Science honour two scientists from India who prioritize people over competition and publications, inform Amber Dance, freelance writer in Los Angeles, California.
 

Two scientists, who won kudos from their protegees for being attentive to students’ well-being, ideas and accomplishments, have received the 2019 Nature Awards for Mentoring in Science .

Vidita Vaidya, winner of one of
the 2019 Nature Awards for Mentoring in Science.
Photo: Bhavisha Kaku Shah

Vidita Vaidya, a neuroscientist at the Tata Institute of Fundamental Research in Mumbai, India, received the mid-career achievement award. Roddam Narasimha, who collected the lifetime-achievement award, is a fluid dynamicist at the Jawaharlal Nehru Centre for Advanced Scientific 

Research in Bengaluru, India. Both earned praise from former trainees for prioritizing the success of their laboratory members over competition or a publish-or-perish mentality, and for the joy they find in science.

Nature’s mentoring-award programme, which in 2019 marked its 15th year, annually confers two prizes: one for a mid-career mentor, and the other for a lifetime of achievement in mentoring. Each year, the awards recognize mentors from a different country or region. The 2019 awards sought nominations from India, a country that produced 24,300 PhD graduates in 2014, the fourth-highest number in the world after the United States, the United Kingdom and Germany. The nominations were judged by a panel that included Indian scientists working in the nation and abroad; each award had a prize of 700,000 rupees (US$9,800)...

India’s talent pool 

Roddam Narasimha, winner of 
the lifetime-achievement award.
Photo: Prof Roddam Narasimha

Those who nominated Narasimha lauded his accessibility, openness to students’ thoughts and dissenting opinions, and insistence on giving credit for their work and ideas. One noted that Narasimha makes no judgements on the basis of gender, caste or background — and took this approach well before ‘diversity’ was a buzzword.

Narasimha, too, benefited from strong mentorship early in his career. As an undergraduate at the Indian Institute of Science in Bengaluru, he studied with mathematician and aerospace engineer Satish Dhawan, who could have obtained a position abroad but chose to work in India, Narasimha says. “I learnt a great deal from him about how you can get things done without actually having to be strict,” he recalls. Dhawan also taught Narasimha to tackle research that would have applications that benefit India. During his career, Narasimha has worked on aerospace-technology development, a computing initiative and university science education.  
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Additional resources 
doi: 10.1038/d41586-020-00351-7

Source: Nature.com

Smarter Robotics Are Changing Security | Security - Robotics Tomorrow

While robots already play a prominent role in logistics and manufacturing, industry leaders are just beginning to realize the potential of this technology in security measures by Scott Huntington, creator and editor of Off The Throttle.

Smarter Robotics Are Changing Security

Businesses across the globe are using robotics technology to pick and pack orders, weld metal parts, apply paints and sealants, complete accurate inspections and much more. In 2018 alone, a record 381,000 industrial robotics units were shipped — an increase of 30% compared to the year before.

The electronics, automotive and metal sectors rely on this technology to automate production and drive growth. With it, manufacturers can boost performance and productivity while maintaining better quality and consistency. Previously, robots were limited in their abilities, rooted in one location and separated from their human counterparts. Today, however, these machines can assess their surroundings and work in tandem with employees...

The Future of Robotics in Security

Today's smart security robotics can track people and assets, patrol physical areas, record data and much more. They have the power to pinpoint problems that may be undetectable to humans — plus, they never sleep. 

One of the most significant benefits of modern technology is the ability to amplify current security procedures with additional coverage. How will robots take over the field in the next five to 10 years, however?
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Source: Robotics Tomorrow

What the world can learn from Japan’s robots | Editor’s Picks - BBC News

Japan is changing: a rapidly ageing society, a record-breaking influx of visitors from overseas, and more robots than ever. That's where the country's young people come in. Gen J, a new series by BBC Worklife, keeps you up to speed on how the nation's next generation is shaping the Japan of tomorrow. 

Additional reporting by Yoko Ishitani and Mari Murakami.

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Japan is rolling out robots in nursing homes, offices and schools as its population ages and workforce shrinks. What can it teach other countries facing the same problems? 

This police robot acts as eyes and ears for human officers in a different room. Countries like China and South Korea have expressed interest in such tech
Photo: Bryan Lufkin

At a sleek office building in Shinagawa, Tokyo, workers are strolling in and out for lunch. As they walk through the glass doors, they pass two security guards, each dutifully flanking the passage in stern silence. It all seems pretty unremarkable, until you realise one of those security guards is a robot.

Standing up to five feet tall with wheels and a blue police cap, “his” name is Ugo. His battery lasts up to half a work day, and every two hours he’ll do a routine patrol around the building – even summoning lifts by pushing the call button himself. His digital “face” – which usually displays two big blue cartoony eyes – is replaced with the Japanese text for “on duty”.

“It’s important for the robot to feel cute, so you’re not intimidated,” says Ken Matsui, CEO of Mira Robotics, the start-up behind Ugo. Right now, the police bot – whose built-in camera allows guards downstairs to see things from the robot’s point of view – is one of only two prototypes in the country. But Matsui says companies in China and South Korea are interested in his company's work, which also includes cleaning robots for use in houses and schools...

Hiroshi Ishiguro, the Osaka University roboticist, says we’ll see other cute, communicative robots in places like hotel rooms or restaurants (where touch-screen menus are already commonplace in Japan) to assist guests in other languages. Meanwhile, government initiatives continue: last year, robots began to be rolled out in 500 classrooms across Japan to help teach English after a 250m yen ($2.3m) investment from the Education Ministry.

That could, in turn, help Japan’s younger generation grow up at ease with robots in a variety of environments. Ishiguro believes that they will integrate into our lives the same way smartphones did a decade ago. “Not just Japan will have more robots, but the whole world,” he predicts.
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Source: BBC News  

The 9 Best Deep Learning Courses 2020 | Courses - Benzinga

Allison Martin, Contributor, Benzinga recommends, Get up to 90% off Deep Learning Courses on Udemy.
 

Benzinga's exclusive offer gives readers access to a collection of technology courses, perfect for any skill level from beginners to advanced. Along with live examples and personalized resources, you will also get lifetime access to the course information which is constantly updated.

Are you a student or software developer seeking to advance your knowledge of deep learning? By becoming a subject-matter expert, you can position yourself for lucrative opportunities and rapid career growth. 
But how can you acquire the knowledge you need? Consider an online deep learning course. They’re available at all skill levels and price points. So you shouldn’t have trouble finding an option that works for you...

Choose the Right Deep Learning Course for 
You No need to spend countless hours searching for the perfect deep learning course. Consider giving one of our free beginner recommendations a try if you’re just starting out. An intermediate or advanced course from our top picks would be your best bet if you’ve moved beyond the beginner level. It’s a good idea to read reviews to gauge whether the course is interactive and led by an experienced instructor. It should also offer a refresher on the basics of machine learning to set the tone for an effective learning experience. 
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Source: Benzinga

Machine Learning… Everywhere | Machine Learning - SemiEngineering

How ML can enable self-optimizing tools that look for DRC hotspots, EM/IR distribution, and more, notes Stelios Diamantidis, director of AI products and research at Synopsys.

Photo: JumpStory

AI is transforming the world around us, creating an avenue to innovation across all sectors of the global economy. Today, AI can interact with humans through natural language; identify bank fraud and protect computer networks; drive cars around city streets; and play complex games like chess and Go. Machine-learning is offering solutions to many complex problems around us where analytical solutions may be too expensive or practically impossible. How about chip design? Can ML offer solutions to key problems in semiconductor engineering?

A deluge of design challenges
Over the years, the EDA industry has offered many solutions in the modeling and design creation of complex systems. Most design problems in EDA are NP-hard; there are simply no polynomial-time algorithms to solve these problems and hence an optimal solution cannot be identified analytically. Today’s EDA systems are finding it difficult to keep up with advanced process node requirements due to a deluge of new design challenges (figure 1).

Figure 1

To make things worse, these requirements are interdependent and need to be considered concurrently across multiple planes of design optimization. The actual application and techniques used depend highly on each specific problem space. How does one prepare a general solution for a specific problem when there is limited access to the design environment?
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Source: SemiEngineering

Is It Possible for Artificial Intelligence to Rival Human Creativity? | Technology - The National Interest Online

This article is republished from The Conversation under a Creative Commons license. 
Read the original article. 

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Wonders of the world, says Tim Schweisfurth, Associate Professor and René Chester Goduscheit, Professor.

Photo: Reuters

The European Patent Office recently turned down an application for a patent that described a food container. This was not because the invention was not novel or useful, but because it was created by artificial intelligence (AI). By law, inventors need to be actual people. This isn’t the first invention by AI – machines have produced innovations ranging from scientific papers and books to new materials and music.

That said, being creative is clearly one of the most remarkable human traits. Without it, there would be no poetry, no internet and no space travel. But could AI ever match or even surpass us? Let’s have a look at the research.

From a theoretical perspective, creativity and innovation is a process of search and combination. We start from one piece of knowledge and connect it with another piece of knowledge into something that is new and useful. In principle, this is also something that can be done by machines – in fact, they excel at storing, processing and making connections within data...

In addition, our analysis is based on the fact that machines mostly innovate on narrow datasets. AI could become much more creative if it could combine big, rich and otherwise disconnected data.

Also, machines may get better at creativity when they get better at the kind of broad intelligence humans possess – something we call “general intelligence”. And this might not be too far in the future – some experts assess that there is a 50% chance that machines reach human-level intelligence within the next 50 years.
Read more... 

Source: The National Interest Online

Using math to understand inequity | Social Sciences - OUPblog

What can math tell us about unfairness? by Cailin O'Connor, associate professor of logic and philosophy of science and a member of the Institute for Mathematical Behavioral Science at the University of California, Irvine.

Photo: Markus Spiske on Unsplash

Bias, discrimination, and inequity are phenomena that are deeply complex, context sensitive, personal, and intersectional. The mathematical modeling of social scenarios, on the other hand, is a practice that necessitates simplification. Using models to understand what happens in our social realm means representing the complex with something much less complex in order to study it.

Nonetheless, when used carefully models can help us learn about deep patterns related to inequity, including those that govern how unfair norms and patterns of behavior emerge in groups of people.   They are especially useful in highlighting spaces of possibility.   What might happen in the complex dynamics of the social realm?   Once we answer this question, we can use experiments and experimental studies to learn more about these possibilities.

In recent work by fellow modelers and myself, we’ve looked in particular at what minority status means for patterns of unfair behavior.   Suppose you are part of a company where people work together, but are still figuring out some of the social rules for how this is done.  Suppose further that men make up the majority of workers and women the minority.  And lastly suppose, as argued by Cecilia L. Ridgeway in Framed by Gender, that like most humans your coworkers notice gender, and use it to shape their interactions with others in ways that can be subtle (and sometimes not-so-subtle).  In a case like this, even without gender biases coming into play, women can end up disadvantaged...

Evolutionary biologists have shown that when it comes to mutualistic interactions between species, sometimes the species that evolves more quickly ends up at a disadvantage simply by dint of this quick evolution.   This is called The Red King Effect.   Surprisingly, starting with the work of Justin Bruner, it has become clear that a similar thing might happen in the cultural realm.  How?
Read more...

Recommended Reading

The Origins of Unfairness:
Social Categories and Cultural Evolution

Source: OUPblog

French mathematician and spider aficionado Cédric Villani honoured with a new orb-weaver | Pensoft blog

With their astonishingly precise spiral webs, orb-weaving spiders are the arachnid analogy of first-class mathematicians, note the researchers behind the study by Pensoft Editorial Team.

The newfound Araniella villanii is an orb-weaver spider, a group that uses math to spin their webs.

Considered as one of the best studied spiders, the orb-weavers remain poorly known in the central parts of the Palearctic ecozone. Hence, an international research team took to the Caucasus, Middle East and Central Asia. Their article in the open-access peer-reviewed journal ZooKeys documents three new-to- science species, where one is named after the Indo-Iranian god of light Mithra. Another carries the name of the flamboyant French mathematician and spider aficionado Cédric Villani.

Despite being considered as one of the best-studied spiders in the Palearctic, the orb-weaver spiders (family Araneidae) remain poorly known in the central parts of the ecozone. To bridge the knowledge gaps, an international research team of researchers took to the Caucasus, Middle East and Central Asia to study two of those genera: Araniella and Neoscona. 

As a result, in their article, recently published in the open-access scientific journal ZooKeys, arachnologists Alireza Zamani (University of Turku, Finland), Yuri M. Marusik (Institute for Biological Problems of the North RAS, Russia) and Anna Šestáková (The Western Slovakian Museum, Slovakia) describe three new-to-science species, where one: Araniella villanii – carries the name of the flamboyant French mathematician and spider aficionado Cédric Villani, who has been dubbed the “Lady Gaga of Mathematics”. Even if unknown until now, the species turned out to have a wide distribution, ranging from south-western Iran to eastern Kazakhstan and northern India.

French mathematician and spider aficionado Cédric Villani (left) with lead author and discoverer of the three new-to-science orb-weaver spiders Alireza Zamani (right) in Iran (2015).
Photo: Alireza Zamani

"It's a well-known fact within the arachnological community that spiders are masters of mathematics and architecture", explains lead author of the study Alireza Zamani. "Orb-web spiders, in particular, tend to build beautiful and architecturally aesthetic webs, some of which are formed in spirals in line with the repetitive pattern of the golden ratio."...

"Araniella villanii is known from a few localities in southwestern Iran, eastern Kazakhstan and northern India, a distribution range covering at least ten countries, and yet the species was unknown to science until now. I think that the message that this particular discovery implies is that while there are such widely-distributed undescribed species out there, we need more and more taxonomic research, both in the field and in the natural history museum collections, which house a considerable number of undescribed species, in order to preserve the remaining biodiversity on earth, before it's too late.
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Additional resources 
Zamani A, Marusik YM, Šestáková A (2020) On Araniella and Neoscona (Araneae, Araneidae) of the Caucasus, Middle East and Central Asia. ZooKeys 906: 13-40.  
https://doi.org/10.3897/zookeys.906.47978

Source: Pensoft blog

The Theorem that Made a Mathematician | Math - Scientific American

Photo: Evelyn Lamb

Evelyn Lamb, Freelance math and science writer based in Salt Lake City, Utah explains, Sometimes unimportant math can change your life.

Photo: Getty Images

In the most recent episode of our podcast My Favorite Theorem, Kevin Knudson and I talked with aBa Mbirika (who goes by aBa), a math professor at the University of Wisconsin Eau Claire. You can listen to the episode here or at kpknudson.com, where there is also a transcript.

If you'd like to see aBa's talk from the Joint Mathematics Meetings, which he mentioned in the episode, it was recorded, and you can watch it here.


As is the case for many of our guests, aBa had trouble choosing a favorite theorem. He took us on a tour of a few of his favorites before settling on one to tell us more about. The theorem he settled on is not an important theorem. You won’t find it in any textbooks. But it was important to aBa when he was a beginning math student.

Many people are familiar with divisibility tests for 3 and 9: if the (base ten) digits of a number add up to a multiple of 3, the number is divisible by 3. If they add up to a multiple of 9, the number is a multiple of 9. There are also easy ways to tell whether a number is divisible by 2 or 5 or 11. But you probably didn’t learn a divisibility test for 7. (There is one, but it’s not as easy to use as the rules for 3 and 9.)...

I suspect many other mathematicians have similar stories of the simple, often unimpressive theorems and exercises that first made them feel like they had the power to create in mathematics. I certainly felt this way in my very first proof writing class. So today I salute the tiny theorems that helped us become mathematicians.
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Source: Scientific American