Kids may intuitively master Twitter, Snapchat and Angry Birds, but unless we teach them programming in school, they’ll never have the skills necessary to develop the next generation of software.
Coding as a career is not for everyone, nor should it be. Unlike 2012’s viral campaign called CodeYear, created by startup Codecademy, I am not suggesting that people drop what they’re doing and try to become programmers overnight. The point is, just as we do with broadly applicable subjects like math and science, we should introduce it and teach it early on so that kids have yet another valuable arrow in their knowledge quiver, whether they later pursue a career in programming or not.
As a visual experience, museums are finding new rivals in services like Netflix and YouTube. But many are incorporating new tech to enhance the museum-going experience.
Despite having one of the greatest collections of art and being one of the most visited museums in the world, the Met finds itself in the same boat as other museums: How does it compete in an age where our eyeballs are glued to our screens. Why spend the energy to visit a museum when you can do it virtually online?
“Our competition is Netflix and Candy Crush,” Sreenivasan says, not other museums.
Which is why the Met and other museums are investing in technologies to make the museum experience more interactive, even working with the smartphones that guests carry with them. The Met has a staff of 70 in the digital-media department, and 70 more handling tech hardware in general. Rather than fighting Facebook and YouTube, it’s acknowledging that services like Snapchat are the new culture. The Met’s mission is finding a way to fit in alongside them.
Thanks to the advances in deep machine learning, technology companies across the globe are teaching computers to think for themselves
Machine learning and deep learning have grown from the same roots within computer science, using many of the same concepts and techniques. Simply put, machine learning is an offshoot of artificial intelligence that enables a system to acquire knowledge through a supervised learning experience.
It’s a straightforward enough process, in theory: a human being provides data for analysis, and then gives error-correcting feedback that enables the system to improve itself. Depending upon the patterns in the data it’s exposed to, and which of those it recognises, the system will adjust its actions accordingly. It's this ability to self-develop without the need for explicit programming, but rather to change and adapt when exposed to new data, that makes machine learning such a powerful tool.
But how much control can we knowingly cede to a computer that has no morals, no ethics, only programming. If an autonomous car kills, who do we blame?
“Fundamental to Computer Science is 'computational thinking', but this term is slippery. It implies learning to think like a computer, but thinking is a human concept that computers are not actually capable of.”
‘Computational thinking’ is the understanding of how to construct problems so they can eventually be expressed in binary mathematics. This is a complex and multi stage process involving an understanding of the key concepts of algorithms, logical reasoning, decomposition and abstraction.
When the growth of robot capabilities begins in earnest, it will likely be explosive.
About half a billion years ago, life on earth experienced a short period of very rapid diversification called the “Cambrian Explosion.” Many theories have been proposed for the cause of the Cambrian Explosion, with one of the most provocative being the evolution of vision, which allowed animals to dramatically increase their ability to hunt and find mates (for discussion, seeParker 2003). Today, technological developments on several fronts are fomenting a similar explosion in the diversification and applicability of robotics. Many of the base hardware technologies on which robots depend—particularly computing, data storage, and communications—have been improving at exponential growth rates. Two newly blossoming technologies—“Cloud Robotics” and “Deep Learning”—could leverage these base technologies in a virtuous cycle of explosive growth.
Computer-generated topics use statistical methods to suggest themes that emerge from term coöccurence — how often certain words appear close to one another.
The topics on this page were generated by a computer program which automatically “read” all of the articles ever published in Vogue, and grouped together those words which were clustered more frequently than a normal distribution would predict.
Click on a the timeline displayed in each topic to see the highest-saturated articles for that year
The push to teach coding and computational thinking in schools is starting to attract widespread support, to the point where it’s only a matter of time before these subjects are introduced.
While some states have already made plans to introduce a digital technology course option into the curriculum, there’s a need to ensure that sufficient resources are in place to help teachers make the transition.
“Some schools have been selectively teaching coding for years and most teachers already incorporate some aspects of computational thinking and problem-solving in their lesson program so it shouldn’t be too hard to fill in the gaps,” she said. However, some of her peers aren’t so confident. A study by Macquarie University of 144 teachers late last year found that more than half (79) had never heard of computational thinking and most had very little idea of what it involved.
Whenever I speak to teachers about computational thinking it seems to place a layer of tension and confusion upon their shoulders, as most have preconceptions about this new ‘imposition placed upon them’.
Computational thinking is typically associated with coding and computer programming, but it’s also more than that, involving “solving problems, designing systems, and understanding human behavior,” according to Carnegie Mellon University.
These are important skills in a technology-driven world, whether you want to become a programmer or not. Many schools around the country offer after-school programs or electives for students interested in computational thinking. In South Fayette, a suburban and rural district of 2,700 students near Pittsburgh, it’s woven into the district culture, as well as the core curriculum at every grade level.
The exhibition brings the Pixar production pipeline to life—from storyboard and concept art all the way to a final rendered frame. Throughout the exhibition, visitors can engage in hands-on, screen-based, and physical activities that let them explore the computational thinking skills integral to the Pixar process. Visitors can explore the creativity and artistry of Pixar filmmakers, and learn how computers are used as a filmmaking tool. In this exhibition, students and adults will learn about the STEM (science, technology, engineering, and math) skills needed in the 21st century workforce.
The Museum has been working in collaboration with Pixar on this exhibition for five years. The opportunity to collaborate with the artists and computer scientists at Pixar has been a great experience for our institution. What made the collaboration work so well is the fact that Pixar and the Museum of Science share a similar culture: We both value the roles interdisciplinary teams and iterative design play in the creative process.
Our initial plans called for a 5,000-square-foot exhibition. But that wasn’t big enough. We needed a bigger platform to show how computational thinking must become an essential part of the way schoolchildren learn how to breakdown complex problems into smaller steps, how to handle data, and how to test the design of their solutions.
Many passionate computer science educators believe computational thinking, problem solving, persistence and analysis learned in computer science helps students do better in other courses, especially math.
There’s a clear correlation
According to College Board data, students who take the AP Computer Science exam earn higher AP Calculus and Statistics scores relative to peers who previously performed at a similar level in math.
Secret Coders is a project that’s been on my mind for a long, long time. I’m a cartoonist. I write and draw comic books and graphic novels. I’m also a coder. I majored in Computer Science at U.C. Berkeley and worked as a software developer for a couple of years. Then I taught high school computer science for over a decade and a half in Oakland, California. For the most part, these were two separate worlds for me. I taught programming by day and made comics by night. But I’ve always wanted to bring them together. I wanted to make an explicitly educational comic that taught readers the concepts I covered in my introductory programming class. That’s what Secret Coders is. It’s both a fun story about a group of tweens who discover a secret coding school, and an explanation of some foundational ideas in computer science.
My characters are inspired by real people. Hopper and Eni’s mentor is a grumpy old janitor named Mr. Bee who has a secret past. Bee is an embodiment of the ideals espoused by a computer scientist named Seymour Papert. Papert is something of a genius. He helped invent a computer programming language for kids called Logo, which was how I learned to code. Papert also worked on the Lego Mindstorms toy line. By including a wide array of inspirations, I’m hoping to reflect at least some of the diversity of the tech community.
Computers and the algorithms they run are precise, perfect, meticulously programmed, and austere. That’s the idea, anyway But there’s a burgeoning, alternative model of programming and computation that sidesteps the limitations of the classic model, embracing uncertainty, variability, self-correction, and overall messiness. It’s called machine learning, and it’s impacted fields as diverse as facial recognition, movie recommendations, real-time trading, and cancer research—as well as all manner of zany experiments, like Google’s image-warping Deep Dream.
The question, then, is why one would want to generate opaque and unpredictable networks rather than writing strict, effective programs oneself. The answer, as Domingos told me, is that “complete control over the details of the algorithm doesn’t scale.”
There is merit in school students learning coding. We live in a digital world where computer programs underlie everything from business, marketing, aviation, science and medicine, to name several disciplines. During a recent presentation at a radio station, one of our hosts said that IT would have been better background for his career in radio than journalism.
There is also a strong case to be made that Australia’s future prosperity will depend on delivering advanced services and digital technology, and that programming will be essential to this end. Computer programs and software are known to be a strong driver of productivity improvements in many fields.
Although we've yet to settle on a term for it, the convergence of HPC and a new generation of big data technologies is set to transform science. ConFlux will enhance traditional physics-based computer models with big data techniques. The design strategy calls for specialized supercomputing nodes matched to the needs of data-intensive operations. Enabling technologies include next-generation processors, GPUs, large memories, ultra-fast interconnects, and a three-petabyte hard drive.
Andrea Berlin, a College of Arts & Sciences professor of archaeology, who specializes in Middle Eastern pottery made during the period from five centuries before Christ to 640 C.E., is one of them. Berlin says archaeological digs and other historical research have uncovered such a mountain of data that “most archaeologists cannot wrap their arms around it.” She’s hoping to develop a website and app that would allow scholars who aren’t computer scientists to gather, mix, and match that information—by time period, region, and other traits. Then a political scientist, for instance, could “compare the patterns, intensity, and direction of trade under earlier political regimes as revealed by archaeological evidence, and gain hard data and real insight into the relationship between economies and various imperial systems,” she says. Currently, no one can do that, because “there’s no venue by which somebody could access the data.”
Computers and computational thinking have revolutionized the way she regards information and its uses. “I used to think of archaeological data as a great mass comprised of many separate items—whole things,” she says. But like an atom, each individual datum can be split into different attributes, and “different users might want to deploy selections of those attributes for different and various questions,” like that hypothetical political scientist mapping ancient trade routes.
.Located at an intersection of the School of Humanities, Arts and Social Sciencesand the Comparative Media Studies Program at MIT, HyperStudio is at once a group of scholars and developers, a laboratory, and an idea: to bring cutting edge digital technology to the humanities. HyperStudio works with innovative educators at MIT to integrate technology into their research and teaching with original content and media delivery platforms, or to imagine new, dynamic ways to innovate humanities learning with contemporary tools in tech. In addition to the project-based approach that is meant to conceptualize, support, produce, and otherwise shepherd digital humanities ideas brought to them for development by other members of the MIT community, HyperStudio continually works on in-house projects that offer knowledge, services, and tools for public consumption, increasing awareness in the new ways modern humanities scholars can interrogate their fields.
Starting this autumn, Finnish children will start learning computer programming from their very first year in school. Under the new curriculum, programming will not be taught as a separate subject in elementary schools, but will be integrated with other subjects such as math lessons.While all children will be taught coding, not all will become professional coders. According to Mika Ruikka, the intention is not to prepare children to enter the field.
"However, in almost all professions today, computer technologies are needed and so it is useful to know what the principles involved are. Programming also teaches logical thinking which helps a lot, for example with math," he points out. "We can't say for sure what all the skills are that these pupils will need in the future. But, logical thinking is required in more than just programming."
Parents are eager for their children to learn coding skills, but their message hasn't yet hit the in-box of school administrators.That’s the finding of a new Gallup study commissioned by Google that spotlights a potentially perilous economic disconnect as tech companies struggle to enlarge their engineering talent pools.
Among key and contrasting findings are the facts while 90 percent of parents see computer science, or CS, as “a good use of school resources” (and 66 percent say CS should be required learning alongside other core classes), fewer than 8 percent of administrators believe parent demand is high. They also cite a lack of trained teachers as a top barrier to offering CS courses. Three quarters of principals report no CS programs in their school.
No historical record may capture the nation's changing political consciousness better than the president's State of the Union address, delivered each year except one since 1790.
Now, a computer analysis of this unique archive puts the start of the modern era at America's entry into World War I, challenging histories placing it after Reconstruction, the New Deal or World War II. A team of researchers at Columbia University and University of Paris published their results this week in the Proceedings of the National Academy of Sciences.
Though discussion of industry, finance and foreign policy dominate the record year after year, the study shows that modern political thought, defined by nation building, the regulation of business and the financing of public infrastructure, emerges with a sharp line after WWI.
David Blei, a statistician and computer scientist at Columbia's Data Science Institute who was not involved in the research, says the study pushes the boundaries for statistical machine learning of language.
“We’re watching you.” This was the warning that the Chicago Police Department gave to more than 400 people on its “Heat List.”
The list, an attempt to identify the people most likely to commit violent crime in the city, was created with a predictive algorithm that focused on factors including, per the Chicago Tribune, “his or her acquaintances and their arrest histories – and whether any of those associates have been shot in the past.”
Algorithms like this obviously raise some uncomfortable questions. Who is on this list and why? Does it take race, gender, education and other personal factors into account? When the prison population of America is overwhelmingly Black and Latino males, would an algorithm based on relationships disproportionately target young men of color?
Transparency in the inputs to such algorithms and how their outputs are used is likely to be an important component of such efforts. Ethical considerations like these have recently been recognized as important problems by the academic community: new courses are being created and meetings like FAT-ML are providing venues for papers and discussions on the topic.
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