3. a table game in which short cues are used to knock balls into holes that are guarded by wooden pegs; penalties are incurred if the pegs are knocked over
True, the audience thinned out a bit at intermission, and many of those remaining refused to play along when Mr. Schiff tried to run the Prelude and Fugue in B minor from Book I of Bach’s “Well-Tempered Clavier” into Brahms’s three “Klavierstücke” (Op. 119), interrupting at length with applause. It’s hard to blame anyone for enthusiasm or for wanting to relieve the tension of a long and intense musical evening.
… Mr. Schiff concluded with a brilliant performance of Beethoven’s Piano Sonata No. 26 (“Les Adieux”). Well, almost concluded. He also likes to surprise with generous encores. On Tuesday he added all of Bach’s Italian Concerto and Brahms’s posthumous “Albumblatt” in A minor. On Thursday he offered a Beethoven bagatelle (Op. 126, No. 6) and Bach’s “Capriccio on the Departure of a Beloved Brother.”
Do students at your school seem bored? Do you ever feel bored? If so, when? Why?
The authors of the essay you are about to read spent six years studying high schools across the United States. What did they find? They noted that “boredom was pervasive.” They also learned that debate, drama and other extracurriculars frequently provide the excitement many classrooms lack.
Do you agree with their findings? Or do you think school, for the most part, is actually interesting, rigorous and engaging?
When you ask American teenagers to pick a single word to describe how they feel in school, the most common choice is “bored.” The institutions where they spend many of their waking hours, they’ll tell you, are lacking in rigor, relevance, or both.
They aren’t wrong. Studies of American public schools from 1890 to the present suggest that most classrooms lack intellectual challenge. A 2015 Gallup Poll of nearly a million United States students revealed that while 75 percent of fifth-grade students feel engaged by school, only 32 percent of 11th graders feel similarly.
What would it take to transform high schools into more humanizing and intellectually vital places? The answer is right in front of us, if only we knew where to look.
The Op-Ed continues:
As we spent more time in schools, however, we noticed that powerful learning was happening most often at the periphery — in electives, clubs and extracurriculars. Intrigued, we turned our attention to these spaces. We followed a theater production. We shadowed a debate team. We observed elective courses in green engineering, gender studies, philosophical literature and more.
As different as these spaces were, we found they shared some essential qualities. Instead of feeling like training grounds or holding pens, they felt like design studios or research laboratories: lively, productive places where teachers and students engaged together in consequential work. It turned out that high schools — all of them, not just the “innovative” ones — already had a model of powerful learning. It just wasn’t where we thought it would be.
Consider the theater production that we observed at a large public high school in an affluent suburban community. Students who had slouched their way through regular classes suddenly became capable, curious and confident. The urgency of the approaching premiere lent the endeavor a sense of momentum. Students were no longer vessels to be filled with knowledge, but rather people trying to produce something of real value. Coaching replaced “professing” as the dominant mode of teaching. Apprenticeship was the primary mode of learning. Authority rested not with teachers or students but with what the show demanded.
The essay goes on:
How can we make what happens before the bell more like what happens after it?
Schools need to become much more deeply attached to the world beyond their walls. Extracurriculars gain much of their power from their connections to their associated professional domains. School subjects, in comparison, feel devoid of context. Promising schools tackle this dilemma in different ways: Some use project-based learning to engage students in their local communities; some collaborate with museums, employers and others who can give students experiences in professional domains; still others prioritize hiring teachers who have had experience working in (and not just teaching about) their fields. All of these choices bring meaning to work that is too often taught in a vacuum.
Students, read the entire essay, then tell us:
— What, if any, observations about schools and students made by the authors resonate with your experiences? Does anything in the Op-Ed differ from what you have observed about school? Explain.
— Is school really as boring as some students say it is? Explain.
— Do you participate in any extracurricular activities? If so, what do you like — or dislike — about them? Is there anything about that activity or activities that you recommend be replicated in your core classes? Explain.
— Whether you think school is boring or not, what suggestions do you have to make your classes more relevant, engaging or rigorous? Do you agree with the authors’ recommendations?
Students 13 and older are invited to comment. All comments are moderated by the Learning Network staff, but please keep in mind that once your comment is accepted, it will be made public.
1. After looking closely at the image above (or at the full-size image), think about these three questions:
• What is going on in this picture?
• What do you see that makes you say that?
• What more can you find?
2. Next, join the conversation by clicking on the comment button and posting in the box that opens on the right. (Students 13 and older are invited to comment, although teachers of younger students are welcome to post what their students have to say.)
3. After you have posted, try reading back to see what others have said, then respond to someone else by posting another comment. Use the “Reply” button or the @ symbol to address that student directly.
Each Monday, our collaborator, Visual Thinking Strategies, will facilitate a discussion from 9 a.m. to 2 p.m. Eastern Time by paraphrasing comments and linking to responses to help students’ understanding go deeper. You might use their responses as models for your own.
If you have been Rip Van Winkle then you are unlikely to be aware of that enormous amount of funds that have been raised in 2018 and early 2019. The numbers can be and are quite staggering.
Most folks would assume that every vendor (in order to do X) are seeking funding, but that just isn’t the case. There are plenty of vendors in e-learning who do not want nor seek the raising of capital.
When you raise capital, the folks giving you the funds (investors) are not doing it out of their kindest of the hearts. They expect returns. They expect growth and the vendor hitting targets. And they expect to get their money back at some point, often double at the minimum.
They take a percentage of the company. Maybe a few percentile points, maybe 50 percent or more.
In other words, raising capital comes with pluses ($$$) and minuses. There are vendors who raise a few million and the investor or investors take a strong interest in the firm to the point that the former person running the show is no longer the key player. Others are hands-off (at least for the time being).
Often times, the consumer is unaware of the numbers being raised by vendors. Nor are they aware that their favorite platform or vendor could be seeking to raise capital.
Some vendors raise huge amounts, only to burn thru them faster than the Superman racing a locomotive. Grovo, once a big darling in the space, is a fine example of a high burn rate.
Who is leading the charge?
Before the viewing, there are a couple of key data points.
a. The vast amount of funding is via EdTech – A lot of money is flowing thru the educational market.
b. A large amount of funding is from China, focusing specifically on Chinese companies.
c. Corporate – as in vendors targeting the corporate market are raising funds, but by no means, from a totality standpoint is it close to EdTech.
In recent news for example, GO1 raised 30 million dollars and people went “ooh, ahh.” A firm (EdTech) in China raised over 250 million dollars, and you heard only crickets.
Who has Received What in 2019 (as of March 31, 2019)
The list will be presented as the following
(Name of the vendor, Amount raised, type of funding (Series A, B, C, D, Angel/Seed). For this post, vendors who raised funds whereas the amount was not disclosed anywhere, they are excluded.
The list will be from recent to past, as in March is first, then February and then January 2019. Again, this is only for 2019. If the vendor targets EdTech it will be noted. If Corporate the same.
Investopediadoes an outstanding job explaining it for the every day human (i.e. none Wall Street type of person)
April Bonus
This post will be updated to announce a vendor who is being acquired (well, they are acquired, but will make it public on the 2nd of April) and who I agreed to keep their name confidential until it is made public. They are an LMS vendor whose system I have liked for a long time. So, check back if you are curious or read my Twitter feed. : )
Bottom Line
There you have it. As you can see EdTech is where the money is flowing, but corporate does have a couple of dips into the funding ice cream machine.
Big winners were EdTech marketplace platforms whereas a person can learn a skill, language thru purchasing content. Kognity is a SaaS digital publishing platform focused on textbooks, but I could see them easily doing digital workbooks on the corporate side, something some consumers are actively seeking (and which is non education oriented).
Another funding post will be made at the end of July.
Un nuevo síndrome hereditario de arritmia cardíaca ha sido descubierto por un equipo de cardiólogos del Hospital Universitario de Copenhague Rigshospitalet, con la ayuda de bioinformáticos de la Universidad Técnica de Dinamarca y de la Universidad de Copenhague (DTU y UCPH, por sus siglas en inglés).
El síndrome se diagnosticó por primera vez en una familia danesa y, más tarde, también en otras cuatro. En las cinco familias se observó un ritmo cardíaco anormal observado en los electrocardiogramas y problemas relacionados con la frecuencia cardíaca. De hecho, en algunos casos la enfermedad causó muerte súbita.
Para identificar el síndrome, el investigador español José María González-Izarzugaza y Søren Brunak, ambos expertos de las instituciones danesas, han analizado los datos genómicos de las familias para determinar la composición de su material genético.
Para ello, los científicos han utilizado una de las supercomputadoras más grandes del mundo, Computerome, que se encuentra en la DTU. Como informa la agencia SINC, la bioinformática está ganando mucho peso en la medicina personalizada. Los superordenadores permiten conocer las mutaciones que poseen los pacientes en sus células. “En un futuro no muy lejano, la secuenciación de pacientes será parte de la rutina clínica. Afortunadamente ya se está empezando a implementar en algunos países, como es el caso de Dinamarca”, apunta González-Izarzugaza.
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Regina Barzilay teaches one of the most popular computer science classes at the Massachusetts Institute of Technology.
And in her research — at least until five years ago — she looked at how a computer could use machine learning to read and decipher obscure ancient texts.
“This is clearly of no practical use,” she says with a laugh. “But it was really cool, and I was really obsessed about this topic, how machines could do it.”
But in 2014, Barzilay was diagnosed with breast cancer. And that not only disrupted her life, but it led her to rethink her research career. She has landed at the vanguard of a rapidly growing effort to revolutionize mammography and breast cancer management with the use of computer algorithms.
She started down that path after her disease put her into the deep end of the American medical system. She found it baffling.
“I was really surprised how primitive information technology is in the hospitals,” she says. “It almost felt that we were in a different century.”
Questions that seemed answerable were hopelessly out of reach, even though the hospital had plenty of data to work from.
“At every point of my treatment, there would be some point of uncertainty, and I would say, ‘Gosh, I wish we had the technology to solve it,’ ” she says. “So when I was done with the treatment, I started my long journey toward this goal.”
Getting started wasn’t so easy. Barzilay found that the National Cancer Institute wasn’t interested in funding her research on using artificial intelligence to improve breast cancer treatment. Likewise, she says she couldn’t get money out of the National Science Foundation, which funds computer studies. But private foundations ultimately stepped up to get the work rolling.
Barzilay struck up a collaboration with Connie Lehman, a Harvard University radiologist who is chief of breast imaging at Massachusetts General Hospital. We meet in a dim, hushed room where she shows me the progress that she and her colleagues have made in bringing artificial intelligence to one of the most common medical exams in the United States. More than 39 million mammograms are performed annually, according to data from the Food and Drug Administration.
Step one in reading a mammogram is to determine breast density. Lehman’s first collaboration with Barzilay was to develop what’s called a deep-learning algorithm to perform this essential task.
“We’re excited about this because we find there’s a lot of human variation in assessing breast density,” Lehman says, “and so we’ve trained our deep-learning model to assess the density in a much more consistent way.”
Lehman reads a mammogram and assesses the density; then she pushes a button to see what the algorithm concluded. The assessments match.
Next, she toggles back and forth between new breast images and those taken at the patient’s previous appointment. Doing this job is the next task she hopes computer models will take over.
“The optimist in me says in three years we can train this tool to read mammograms as well as an average radiologist,” says Connie Lehman, chief of breast imaging at Massachusetts General Hospital in Boston.Kayana Szymczak for NPR
“These are the sorts of things that we can also teach a model, but more importantly we allow the model to teach itself,” she says. That’s the power of artificial intelligence — it’s not simply automating rules that the researchers provide but also creating its own rules.
“The optimist in me says in three years we can train this tool to read mammograms as well as an average radiologist,” she says. “So we’ll see. That’s what we’re working on.”
This is an area that’s evolving rapidly. For example, researchers at Radboud University Medical Center in the Netherlands spun off a company, ScreenPoint Medical, that can read mammograms as well as the average radiologist now, says Ioannis Sechopoulos, a radiologist at the university who ran a study to evaluate the software.
“A very good breast radiologist is still better than the computer,” Sechopoulos says, but “there’s no theoretical reason for [the software] not to become as good as the best breast radiologists in the world.”
At least initially, Sechopoulos suggests, computers could identify mammograms that are clearly normal. “So we can get rid of the human reading a significant portion of normal mammograms,” he says. That could free up radiologists to perform more demanding tasks and could potentially save money.
Sechopoulos says the biggest challenge now isn’t technology but ethics. When the algorithm makes a mistake, “then who’s responsible, and who do we sue?” he asks. “That medical-legal aspect has to be solved first.”
Lehman sees other challenges. One question she’s starting to explore is whether women will be comfortable having this potentially life-or-death task turned over to a computer algorithm.
“I know a lot of people say … ‘I’m intrigued by [artificial intelligence], but I’m not sure I’m ready to get in the back of the car and let the computer drive me around, unless there’s a human being there to take the wheel when necessary,’ ” Lehman says.
She asks a patient, Susan Biener Bergman, a 62-year-old physician from a nearby suburb, how she feels about it.
Bergman agrees that giving that much control to a computer is “creepy,” but she also sees the value in automation. “Computers remember facts better than humans do,” she says. And as long as a trustworthy human being is still in the loop, she’s OK with empowering an algorithm to read her mammogram.
Lehman is happy to hear that. But she’s also mindful that trusted technologies haven’t always been trustworthy. Twenty years ago, radiologists adopted a technology called CAD, short for computer-aided detection, which was supposed to help them find tumors on mammograms.
“The CAD story is a pretty uncomfortable one for us in mammography,” Lehman says.
The technology became ubiquitous due to the efforts of its commercial developers. “They lobbied to have CAD paid for,” she says, “and they convinced Congress this is better for women — and if you want your women constituents to know that you support women, you should support this.”
Once Medicare agreed to pay for it, CAD became widely adopted, despite misgivings among many radiologists.
A few years ago, Lehman and her colleagues decided to see if CAD was actually beneficial. They compared doctors at centers that used the software with doctors at those that didn’t to see who was more adept at finding suspicious spots.
Radiologists “actually did better at centers without CAD,” Lehman and her colleagues concluded in a study. Doctors may have been distracted by so many false indications that popped up on the mammograms, or perhaps they became complacent, figuring the computer was doing a perfect job.
Whatever the reason, Lehman says, “we want to make sure as we’re developing and evaluating and implementing artificial intelligence and deep learning, we don’t repeat the mistakes of our past.”
That’s certainly on the mind of Joshua Fenton, a family practice doctor at the University of California, Davis’ Center for Healthcare Policy and Research. He has written about the evidence that led the FDA to let companies market CAD technology.
“It was, quote, ‘promising’ data, but definitely not blockbuster data — definitely not large population studies or randomized trials,” Fenton says.
The agency didn’t foresee how doctors would change their behavior — evidently not for the better — when using computers equipped with the software.
“We can’t always anticipate how a technology will be used in practice,” Fenton says, so he would like the FDA to monitor software like this after it has been on the market to see if its use is actually improving medical care.
Those challenges will grow as algorithms take on ever more tasks. And that’s on the not-so-distant horizon.
Lehman and Barzilay are already thinking beyond the initial reading of mammograms and are looking for algorithms to pick up tasks that humans currently can’t perform well or at all.
One project is an algorithm that can examine a high-risk spot on a mammogram and provide advice about whether a biopsy is necessary. Reducing the number of unnecessary biopsies would reduce costs and help women avoid the procedure.
They have also developed a computer program that analyzes a lot of information about a patient to predict future risk of breast cancer.
The first time you go and do your screening, Barzilay says, the algorithm doesn’t just look for cancer on your mammogram — “the model tells you what is the likelihood that you develop cancer within two years, three years, 10 years.”
That projection can help women and doctors decide how frequently to screen for breast cancer.
“We’re so excited about it because it is a stronger predictor than anything else that we’ve found out there,” Lehman says. Unlike other tools like this, which were developed by examining predominantly white European women, it works well among women of all races and ages, she says.
Lehman is mindful that an algorithm developed at one hospital or among one demographic might fail when tried elsewhere, so her research addresses that issue. But potential pitfalls aren’t what keep her up at night.
“What keeps me up at night is 500,000 women [worldwide] die every year of breast cancer,” she says. She would like to find ways to accelerate progress so that innovations can help people sooner.
And that imperative calls for more than new technology, she says — it calls for a new philosophy.
“We’re too fearful of change,” she says. “We’re too fearful of losing our jobs. We’re too fearful of things not staying the way they’ve always been. We’re going to have to think differently.”
For some older people, the brain boosts from exercise can be almost immediate. Improvements in their thinking abilities after a single 20-minute bout of pedaling a stationary bike mirrored those produced by three months of regular exercise, according to a preliminary study presented March 24 at the annual meeting of the Cognitive Neuroscience Society.
The similarity between a single bout of exercise and months of training “suggests we don’t have to wait three months to see an improvement,” cognitive neuroscientist Michelle Voss of the University of Iowa in Iowa City said. “We can get a day-by-day boost.”
Voss and her colleagues enlisted 34 people with an average age of 67 to undergo brain scans, memory tests and exercise. In the first part of the study, she and her colleagues were looking for effects of a single 20-minute stint on a stationary bike, designed to be rigorous enough to make people sweat. Participants were huffing and puffing, but could still talk during the workout.
Before and after exercising, participants underwent functional MRI brain scans and took memory tests that involved remembering previously seen faces. The team did similar brain tests on a different day, after participants spent 20 minutes on a bike that pedaled for them.
On average, after 20 minutes of intense exercise, people were better at remembering the faces, especially when the task was hard, than after sitting on the self-pedaling bike. And certain connections between brain areas got stronger, too, the fMRI scans showed.
Participants then were divided into two groups — one that spent the next three months exercising three times a week for 50 minutes, and one that spent just four minutes exercising three times a week. When studied as a group, people’s results after this longer-term exercise were similar to their results after the 20-minute bout of exercise, with an overall improvement on the face task for people with the longer workouts compared with those who exercised for 12 minutes a week.
But within that average, people’s responses varied. To Voss’ surprise, the people who improved a lot after 20 minutes had similar memory improvements, and similar brain changes, after the three months. And those who didn’t improve after the 20 minutes were less likely to have improved after three months.
“If it’s not working for some people, that’s good to know,” Voss says. “But you can go one step further and ask, ‘Are the reasons it’s not working modifiable? And can we learn that quickly? Can we fail fast?’”
Teasing apart the individual variation among exercise effects is “really exciting,” says cognitive neuroscientist Wendy Suzuki of New York University in New York City. She cautions that the study is preliminary. Still, she says, “this is exactly the right question to ask.”
Suzuki thinks of exercise as medicine. “The key word is ‘personalized’ medicine,” she says. “Can it be designed for you at your age and fitness level and gender and genetic background?” The answer, she says, is theoretically yes, though scientists have much more work to do to understand how exercise affects people differently.
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ou’ve had your laptop for a few years and it’s starting to feel sluggish when you’re running demanding tasks. Maybe your RAM is just not up to snuff, or your old spinning-disk hard drive is starting to falter. It’s time to upgrade! But instead of replacing the entire laptop, you might be able to swap out some bits inside to breath in some new life.
Products used in this guide
Check if you can upgrade with Crucial
Unfortunately, unlike desktop PCs which you can normally upgrade, laptops are increasingly sealed units that may have certain limitations when it comes to accessing the insides and tinkering with what’s contained within the chassis. Actually gazing at specific components is one thing, being able to remove said chips and boards and replace with enhanced replacements is a completely different ball game.
The most common upgrades these days in laptops are RAM and storage drives. The latter is a recommended task on machines that sport a mechanical drive, which can be upgraded to a vastly superior SSD solution. The same goes for RAM when the total amount available to Windows and applications is 4GB. Moving up to 8GB or even 16GB can really boost productivity and multi-tasking.
We’d avoid touching anything else inside most laptops, such as the Wi-Fi card or CPU, unless you absolutely know what you’re doing and are sure everything is compatible. The easiest way to see just what you’ll be able to do with the laptop is to look on the manufacturer’s website or open up the PC and take a look inside. The latter usually involves removing a number of screws on the underside, but be sure to check with the manual (or online guides) for further details.
If you want to be certain you’ll be upgrading using the correct parts, Crucial — which sells branded RAM and SSDs — has a handy tool available that can quickly check if you’re able to upgrade the memory or storage in thousands of laptop models.
The tool should return results as to what type of RAM your laptop supports and whether an SSD upgrade is in on the cards. Crucial will, of course, recommend its own products for you to use (we highly recommend the brand), but you can use alternatives from other companies, so long as you match up specifications. Be wary when it comes to SSDs as there are multiple available. The same goes for RAM with regards to DDR2, DDR3 and DDR4.
It’s also worth noting that you’ll need to clone the old drive to continue using your Windows installation on the new storage solution. That or you can re-install Windows on the new storage drive to start fresh.
Our top equipment picks
For upgrades, take a look at our recommended options. Be sure to double check for compatibility, but this is what you should be aiming for in terms of capacity and speeds — so long as your laptop can support such component upgrades.
Samsung has long been at the forefront of the SSD market and its latest is certainly the greatest, with an incredible performance backed up with a great warranty and reliability.
Samsung did something incredible with the 970 EVO Plus, offering performance that matches (even supersedes) the 970 PRO but without the insane price tag. The 970 EVO Plus is based on Samsung’s latest 96-layer V NAND memory, and with prices starting at less than $100 for the 256GB storage capacity, this is an extremely enticing SSD.
WD took full advantage of 3D NAND technology and came up with the latest iteration of Blue SSDs.
Sporting 560 MB/s and 530 MB/s for read and write speeds, the 1TB version of the Western Digital Blue SSD offers great value. Not only do you have a choice of capacity for 2.5-inch drives, but there are also M.2 modules to choose from.
Crucial memory is both reliable and affordable. This 16GB kit will ensure your laptop has more than enough RAM to run multiple apps simultaneously and even tackle some of those more demanding PC games.
Upgrading from 4GB or 8GB of RAM, your laptop will feel quicker and more powerful with a 16GB kit like this example from Crucial installed. You’ll be able to run numerous tabs on your favorite browser, play even more demanding games (so long as you have a decent GPU) and avoid sluggish performance.
Additional Equipment
You’ll need a toolkit to take apart your laptop to replace components. We’ve got you covered with this handy recommendation.
Sometimes you don’t need countless screwdriver tips, a brush, cable cutter, among other tools. This is what makes the Yougai toolkit more appealing for someone who won’t be tinkering with hardware too often.
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We launched 97 new courses in February and March of this year, including 13 courses in Arabic, 10 in Spanish, and 11 in Russian.
Here are our top ten courses in English from the past two months:
AI For Everyone, deeplearning.ai – AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone–especially your non-technical colleagues–to take.
Innovation and emerging technology: Be disruptive, Macquarie University – ‘Disruption’ has become a buzz word in the business world. But what is a disruptive change-maker? In this course you will learn how to deploy disruptive strategic thinking to develop or protect your organisation’s competitive advantage.
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning, deeplearning.ai – If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
State Estimation and Localization for Self-Driving Cars, University of Toronto – This course will introduce you to the different sensors and how we can use them for state estimation and localization in a self-driving car.
Decision Criteria & Applications, University of Michigan – This course is an introduction to decision-making criteria widely used in the real world and will help you understand the foundational principles of how most organizations make decisions.
Visual Perception for Self-Driving Cars, University of Toronto – This course will introduce you to the main perception tasks in autonomous driving, static and dynamic object detection, and will survey common computer vision methods for robotic perception.
Time Value of Money, University of Michigan – This course is an introduction to time value of money (TVM) and decision-making to help you understand the basics of finance.
Introduction to Supply Chain Finance & Blockchain Technology, New York Institute of Finance – What is Supply Chain Finance? How does Blockchain apply? In this course, you’ll learn about an emerging set of solutions within trade finance implemented by financial institutions, leading corporate buyers and their trading partners all over the world known as Supply Chain Finance.
Security and Privacy for Big Data – Part 1, EIT Digital – You will discover cryptographic principles, mechanisms to manage access controls in your Big Data system. By the end of the course, you will be ready to plan your next Big Data project successfully, ensuring that all security related issues are under control.
Intel® Network Academy – Network Transformation 102, Intel – Welcome to the Intel® Network Academy – a comprehensive training program on network transformation. In this program, we will be covering the topic areas of software defined infrastructure (SDI) network functions virtualization (NFV), software-defined networking (SDN) and beyond.
