Don’t trust AI until we build systems that earn trust — from economist.com
Progress in artificial intelligence belies a lack of transparency that is vital for its adoption, says Gary Marcus, coauthor of “Rebooting AI”

Excerpts:

Mr Marcus argues that it would be foolish of society to put too much stock in today’s AI techniques since they are so prone to failures and lack the transparency that researchers need to understand how algorithms reached their conclusions.

As part of The Economist’s Open Future initiative, we asked Mr Marcus about why AI can’t do more, how to regulate it and what teenagers should study to remain relevant in the workplace of the future.

Trustworthy AI has to start with good engineering practices, mandated by laws and industry standards, both of which are currently largely absent. Too much of AI thus far has consisted of short-term solutions, code that gets a system to work immediately, without a critical layer of engineering guarantees that are often taken for granted in other field. The kinds of stress tests that are standard in the development of an automobile (such as crash tests and climate challenges), for example, are rarely seen in AI. AI could learn a lot from how other engineers do business.

The assumption in AI has generally been that if it works often enough to be useful, then that’s good enough, but that casual attitude is not appropriate when the stakes are high. It’s fine if autotagging people in photos turns out to be only 90 percent reliable—if it is just about personal photos that people are posting to Instagram—but it better be much more reliable when the police start using it to find suspects in surveillance photos.

 

The Research is in: 2019 Education Research Highlights — edutopia.org by Youki Terada
Does doodling boost learning? Do attendance awards work? Do boys and girls process math the same way? Here’s a look at the big questions that researchers tackled this year.

Excerpt:

Every year brings new insights—and cautionary tales—about what works in education. 2019 is no different, as we learned that doodling may do more harm than good when it comes to remembering information. Attendance awards don’t work and can actually increase absences. And while we’ve known that school discipline tends to disproportionately harm students of color, a new study reveals a key reason why: Compared with their peers, black students tend to receive fewer warnings for misbehavior before being punished.

CUT THE ARTS AT YOUR OWN RISK, RESEARCHERS WARN
As arts programs continue to face the budget ax, a handful of new studies suggest that’s a grave mistake. The arts provide cognitive, academic, behavioral, and social benefits that go far beyond simply learning how to play music or perform scenes in a play.

In a major new study from Rice University involving 10,000 students in third through eighth grades, researchers determined that expanding a school’s arts programs improved writing scores, increased the students’ compassion for others, and reduced disciplinary infractions. The benefits of such programs may be especially pronounced for students who come from low-income families, according to a 10-year study of 30,000 students released in 2019.

Unexpectedly, another recent study found that artistic commitment—think of a budding violinist or passionate young thespian—can boost executive function skills like focus and working memory, linking the arts to a set of overlooked skills that are highly correlated to success in both academics and life.

Failing to identify and support students with learning disabilities early can have dire, long-term consequences. In a comprehensive 2019 analysis, researchers highlighted the need to provide interventions that align with critical phases of early brain development. In one startling example, reading interventions for children with learning disabilities were found to be twice as effective if delivered by the second grade instead of third grade.

 

The future of law and computational technologies: Two sides of the same coin — from law.mit.edu by Daniel Linna
Law and computation are often thought of as being two distinct fields. Increasingly, that is not the case. Dan Linna explores the ways a computational approach could help address some of the biggest challenges facing the legal industry.

Excerpt:

The rapid advancement of artificial intelligence (“AI”) introduces opportunities to improve legal processes and facilitate social progress. At the same time, AI presents an original set of inherent risks and potential harms. From a Law and Computational Technologies perspective, these circumstances can be broadly separated into two categories. First, we can consider the ethics, regulations, and laws that apply to technology. Second, we can consider the use of technology to improve the delivery of legal services, justice systems, and the law itself. Each category presents an unprecedented opportunity to use significant technological advancements to preserve and expand the rule of law.

For basic legal needs, access to legal services might come in the form of smartphones or other devices that are capable of providing users with an inventory of their legal rights and obligations, as well as providing insights and solutions to common legal problems. Better yet, AI and pattern matching technologies can help catalyze the development of proactive approaches to identify potential legal problems and prevent them from arising, or at least mitigate their risk.

We risk squandering abundant opportunities to improve society with computational technologies if we fail to proactively create frameworks to embed ethics, regulation, and law into our processes by design and default.

To move forward, technologists and lawyers must radically expand current notions of interdisciplinary collaboration. Lawyers must learn about technology, and technologists must learn about the law.

 

 

Colleges see equity success with adaptive learning systems — from edtechmagazine.com by Shailaja Neelakantan
Powered by advanced algorithms, adaptive learning technologies boost completion rates and give students confidence.

“I used to teach one class of 100 students, but now I teach 100 classes of one student each,” said Doug Williams, the adaptive learning coordinator at Arizona State University, in the white paper, describing the effect of using such a technology-driven system to improve learning outcomes.

 

From DSC:
I post this item because I believe that this is the type of thing that will be a piece of our future learning ecosystems. Learning agents. Systems that accommodate each individual’s learning preferences. Real-time formative assessments…that impact what you see and experience next.  Intelligent systems. Intelligent tutoring.

People demonstrate mastery at different times — let that be part of our futures — versus this one-size fits all, hop-on-board-or-you-miss-the-train…a train that stops for no one.

 

 

Top ten podcasts every teacher needs to hear — from wiley.com; with thanks to Emily Liebtag for her posting on Twitter for this resource

Excerpt:

Listening to podcasts is an easy way to dive into a topic that interests you and learn something new from others who share your passion for education.

We’re highlighting the following ten podcast episodes featuring Jossey-Bass authors that you can listen to whenever, wherever to help you master your craft or reignite your love of teaching.

So, take some time for yourself, grab your earbuds, and press play on these…

 

Students nationwide to join coding boot camp phase of 2019 National Cyber Robotics Coding Competition — from gocoderz.com

Excerpts:

During the first phase, a two-week boot camp, students and educators begin learning about coding and robotics in a virtual, highly scaffolded “sandbox” on the competition platform, the award-winning CoderZ Cyber Robotics Learning Environment. The cloud-based platform features a graphical simulation of LEGO Mindstorms EV3 robots; users activate the virtual robot, or “cyber-robot,” in game-like “missions” and watch the results in a real-time simulation.

Organized by ISCEF, the Intelitek STEM and CTE Education Foundation, the national CRCC is the first-of-its-kind, online coding and robotics tournament for students in grades 5-8 that enables schools, districts, after-school programs and clubs to engage students in STEM learning.

 

Also see:

Cyber Robotics 101 Course

Bring Cyber Robotics into your classroom. Use the appeal of robotics and gaming to introduce all your students to coding

The solution empowers all students to learn STEM.
Students learn how to code and operate virtual robots guided by a step-by-step instruction and gamified missions completely online. No need for expensive hardware or specialized training.

CoderZ is classroom ready, designed for teachers, and school friendly. The courseware can be teacher-led, self-paced or used in flipped classroom.

Level: Middle School (5 – 8th Grade). No previous knowledge is needed.
Length: 15 hours of courseware and programming exercises

Give students an in depth look at STEM and cyber robotics using all the available teacher resources…

Coding Robots

Introduce students to the concepts of Robots and Code with CoderZ, an online learning environment for programming real and virtual robots.

The Robotics & Coding STEM Curriculum brings your students up to speed with code and robotics in no time. This 45 hour program will teach your students to solve STEM problems through code, using math and engineering to overcome challenges. CoderZ uses engaging simulation so students will have immediate life-like feedback and can work from any computer, even from home, making sure all students get to code their robot even when time and resources are limited.

The Coding Robots STEM Curriculum brings your students up to speed with code and robotics in no time. This 45 hour program will teach your students to solve STEM problems through code, using math and engineering to overcome challenges. CoderZ helps get teachers started with robotics and bring the interdisciplinary value of STEM into the classroom. CoderZ uses engaging simulation so students will have immediate life-like feedback and can work from any computer, in class or at home, making sure all students get to code their robot even when time and resources are limited.

Learning Robotics and Coding with CoderZ

CoderZ is an online STEM learning environment where students worldwide engage in Robotics and Computer Science Education (CSEd) by coding virtual 3D robots.

 

China has started a grand experiment in AI education. It could reshape how the world learns. — from technologyreview.com by Karen Hao
In recent years, the country has rushed to pursue “intelligent education.” Now its billion-dollar ed-tech companies are planning to export their vision overseas.

Excerpt:

Zhou Yi was terrible at math. He risked never getting into college. Then a company called Squirrel AI came to his middle school in Hangzhou, China, promising personalized tutoring. He had tried tutoring services before, but this one was different: instead of a human teacher, an AI algorithm would curate his lessons. The 13-year-old decided to give it a try. By the end of the semester, his test scores had risen from 50% to 62.5%. Two years later, he scored an 85% on his final middle school exam.

“I used to think math was terrifying,” he says. “But through tutoring, I realized it really isn’t that hard. It helped me take the first step down a different path.”

 

The strategy has fueled mind-boggling growth. In the five years since it was founded, the company has opened 2,000 learning centers in 200 cities and registered over a million students—equal to New York City’s entire public school system. It plans to expand to 2,000 more centers domestically within a year. To date, the company has also raised over $180 million in funding. At the end of last year, it gained unicorn status, surpassing $1 billion in valuation.

 

Research Posters Are a Staple of Academic Conferences. Could a New Design Speed Discovery? — from edsurge.com by Jeff Young

Excerpts:

Scholars around the world share their latest research findings with a decidedly low-tech ritual: printing a 48-inch by 36-inch poster densely packed with charts, graphs and blocks of text describing their research hypothesis, methods and findings. Then they stand with the poster in an exhibit hall for an hour, surrounded by rows of other researchers presenting similar posters, while hundreds of colleagues from around the world walk by trying to skim the displays.

Not only does the exercise deflate the morale of the scholars sharing posters, the ritual is incredibly inefficient at communicating science, Morrison argues.

Morrison says he has a solution: A better design for those posters, plus a dash of tech.

 

 

To make up for all the nuance and detail lost in this approach, the template includes a QR code that viewers can scan to get to the full research paper.

 

From DSC:
Wouldn’t this be great if more journal articles would do the same thing?  That is, give us the key findings, conclusions (with some backbone to them), and recommendations right away! Abstracts don’t go far enough, and often scholars/specialists are talking amongst themselves…not to the world. They could have a far greater reach/impact with this kind of approach.

(The QR code doesn’t make as much sense if one is already reading the full journal article…but the other items make a great deal of sense!)

 

 

Bad bargain: Why we still ask kids to factor polynomials and how we fix it — from gettingsmart.com by Tom Vander Ark

Excerpt:

OK, we cut a bad deal 20 years ago and it’s time to fix it.

Kids are still factoring polynomials and that’s just dumb. Requiring every student to pass a course on regurgitated symbol manipulation (Algebra 2) is torturous for many students and why some dropout. It’s an inequitable barrier to college and careers.

“The tragedy of high school math,” said venture investor and education advocate Ted Dintersmith (who has a Ph.D. in math modeling), “is that less than 20% of adults ever use algebra. No adult in America still does integrals and derivatives by hand – the calculus that blocks so many from career paths. It remains in the curriculum because it’s easy to test, not important to learn.”

Math educator Dan Meyer told the We’re Doing It All Wrong Podcast that algebra 2 is “arcane gibberish…not useful knowledge”…

Now, rather than the plug and crank of symbol manipulation, we should be teaching computational thinking. As mathematician Conrad Wolfram said, we should be teaching math as if computers existed.

Rather than a separate symbol language, Wolfram argues, math should be taught as computational thinking and integrated across the curriculum. That starts with problem finding–spotting big tough problems worth working on. Next comes understanding the problems and valuables associated–that’s algebraic reasoning. But rather than focusing on computation (including factoring those nasty polynomials), students should be building data sets and using computers to do what they’re good at–calculations.

To fix the problem, states that require Algebra 2 should swap it out for a course in coding and computational thinking. Colleges and college entrance exams should drop Algebra 2 requirements. They should start by asking young people about their contributions to solving big problems.

 

From DSC:
This posting reminded me that, just the other day, I took the picture below…it’s outside a local mall. The annotated picture below gives you some of my thoughts on this ridiculous setup. 

 

Are some of our educational systems setup like this stop sign outside an abandoned, old store that's no longer being used?!

 

 

 

Math Visuals — from mathvisuals.wordpress.com

Examples:

 

 

 

 

 

Training the workforce of the future: Education in America will need to adapt to prepare students for the next generation of jobs – including ‘data trash engineer’ and ‘head of machine personality design’– from dailymail.co.uk by Valerie Bauman

Excerpts:

  • Careers that used to safely dodge the high-tech bullet will soon require at least a basic grasp of things like web design, computer programming and robotics – presenting a new challenge for colleges and universities
  • A projected 85 percent of the jobs that today’s college students will have in 2030 haven’t been invented yet
  • The coming high-tech changes are expected to touch a wider variety of career paths than ever before
  • Many experts say American universities aren’t ready for the change because the high-tech skills most workers will need are currently focused just on people specializing in science, technology, engineering and math

.

 

 

The five most important new jobs in AI, according to KPMG — from qz.com by Cassie Werber

Excerpt:

Perhaps as a counter to the panic that artificial intelligence will destroy jobs, consulting firm KPMG published a list (on 1/8/19) of what it predicts will soon become the five most sought-after AI roles. The predictions are based on the company’s own projects and those on which it advises. They are:

  • AI Architect – Responsible for working out where AI can help a business, measuring performance and—crucially— “sustaining the AI model over time.” Lack of architects “is a big reason why companies cannot successfully sustain AI initiatives,” KMPG notes.
  • AI Product Manager – Liaises between teams, making sure ideas can be implemented, especially at scale. Works closely with architects, and with human resources departments to make sure humans and machines can all work effectively.
  • Data Scientist – Manages the huge amounts of available data and designs algorithms to make it meaningful.
  • AI Technology Software Engineer – “One of the biggest problems facing businesses is getting AI from pilot phase to scalable deployment,” KMPG writes. Software engineers need to be able both to build scalable technology and understand how AI actually works.
  • AI Ethicist – AI presents a host of ethical challenges which will continue to unfold as the technology develops. Creating guidelines and ensuring they’re upheld will increasingly become a full-time job.

 

While it’s all very well to list the jobs people should be training and hiring for, it’s another matter to actually create a pipeline of people ready to enter those roles. Brad Fisher, KPMG’s US lead on data and analytics and the lead author of the predictions, tells Quartz there aren’t enough people getting ready for these roles.

 

Fisher has a steer for those who are eyeing AI jobs but have yet to choose an academic path: business process skills can be “trained,” he said, but “there is no substitute for the deep technical skillsets, such as mathematics, econometrics, or computer science, which would prepare someone to be a data scientist or a big-data software engineer.”

 

From DSC:
I don’t think institutions of higher education (as well as several other types of institutions in our society) are recognizing that the pace of technological change has changed, and that there are significant ramifications to those changes upon society. And if these institutions have picked up on it, you can hardly tell. We simply aren’t used to this pace of change.

Technologies change quickly. People change slowly. And, by the way, that is not a comment on how old someone is…change is hard at almost any age.

 

 

 

 

 

The information below is from Heather Campbell at Chegg
(emphasis DSC)


 

Chegg Math Solver is an AI-driven tool to help the student understand math. It is more than just a calculator – it explains the approach to solving the problem. So, students won’t just copy the answer but understand and can solve similar problems at the same time. Most importantly,students can dig deeper into a problem and see why it’s solved that way. Chegg Math Solver.

In every subject, there are many key concepts and terms that are crucial for students to know and understand. Often it can be hard to determine what the most important concepts and terms are for a given subject, and even once you’ve identified them you still need to understand what they mean. To help you learn and understand these terms and concepts, we’ve provided thousands of definitions, written and compiled by Chegg experts. Chegg Definition.

 

 

 

 

 


From DSC:
I see this type of functionality as a piece of a next generation learning platform — a piece of the Living from the Living [Class] Room type of vision. Great work here by Chegg!

Likely, students will also be able to take pictures of their homework, submit it online, and have that image/problem analyzed for correctness and/or where things went wrong with it.

 

 


 

 
 

Smart Machines & Human Expertise: Challenges for Higher Education — from er.educause.edu by Diana Oblinger

Excerpts:

What does this mean for higher education? One answer is that AI, robotics, and analytics become disciplines in themselves. They are emerging as majors, minors, areas of emphasis, certificate programs, and courses in many colleges and universities. But smart machines will catalyze even bigger changes in higher education. Consider the implications in three areas: data; the new division of labor; and ethics.

 

Colleges and universities are challenged to move beyond the use of technology to deliver education. Higher education leaders must consider how AI, big data, analytics, robotics, and wide-scale collaboration might change the substance of education.

 

Higher education leaders should ask questions such as the following:

  • What place does data have in our courses?
  • Do students have the appropriate mix of mathematics, statistics, and coding to understand how data is manipulated and how algorithms work?
  • Should students be required to become “data literate” (i.e., able to effectively use and critically evaluate data and its sources)?

Higher education leaders should ask questions such as the following:

  • How might problem-solving and discovery change with AI?
  • How do we optimize the division of labor and best allocate tasks between humans and machines?
  • What role do collaborative platforms and collective intelligence have in how we develop and deploy expertise?


Higher education leaders should ask questions such as the following:

  • Even though something is possible, does that mean it is morally responsible?
  • How do we achieve a balance between technological possibilities and policies that enable—or stifle—their use?
  • An algorithm may represent a “trade secret,” but it might also reinforce dangerous assumptions or result in unconscious bias. What kind of transparency should we strive for in the use of algorithms?

 

 

 

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