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The course Coronavirus 101 is, really, ongoing. Our grading of Sweden’s performance is meant to be suggestive.
We might say that there are three principal components of the overall grade in Coronavirus 101: The midterm exam is the entire polity’s handling of the elderly care challenge. The term paper is the government’s pandemic policy; that is, the extent and duration of locking down the life of society. The final exam is the government’s policies dealing with the social and economic fallout; policies such as bailouts, extended unemployment benefits, “stimulus,” and so on. Other actions, too, matter, such as waiving obstructive restrictions, avoiding “price gouging” folly, smartly pursuing therapies and vaccine development, and so on.
For the three main components we give, if somewhat tentatively, Sweden a D on elderly care, an A on lockdown, and a C on economic-fallout policies. Overall, Sweden gets a B- in the course. There won’t be many polities with an overall course grade higher than Sweden’s.

Source: Give Sweden a B- in Coronavirus 101 – AIER

There’s a growing body of evidence that suggests it’s society, not night owls like Carter, that is wrong. The field of chronobiology seeks to understand how individuals are driven by an internal clock – their “chronotype” – one that is set by genetics, not willpower. The term night owl is shorthand for the chronotype that drives people to go to bed later and rise later. This contrasts with morning larks, who naturally want to go to bed early and wake up early. Most people fall somewhere between the two, with an average sleep cycle running from around 11.30pm until 7.30am. People tend to change over their lifetime. They are larks in childhood, night owls as teens, and more lark-like again as they get older.

Source: Extreme night owls: ‘I can’t tell anyone what time I go to bed’ | Life and style | The Guardian

According to Baidu, PaddlePaddle has been adopted by more than 1.9 million developers, with more than 84,000 businesses using the deep learning platform to create more than 230,000 models. The company said it also worked with several global hardware manufacturers including Intel, Huawei, MediaTek, and Inspur, on the PaddlePaddle ecosystem.
Baidu CTO Wang Haifeng said: “Now is an unprecedented opportunity for the development of PaddlePaddle given the rise of industrial intelligence and the acceleration of AI-powered infrastructure. We will continue to embrace the open-source spirit, drive technological innovation, and partner with developers to advance deep learning and AI technologies and speed up the process of industrial intelligence.”
Alibaba last November also published the core codes of its machine learning platform Alink on GitHub, uploading a rang of algorithm libraries that it said supported batch and stream processing. These were essential to support machine learning tasks such as online product recommendations and smart customer services.

Source: Baidu releases quantum machine learning toolkit on GitHub | ZDNet

No, my worry is that, especially now that you’re out of college, you won’t put enough really excellent stuff into your brain. I’m talking about what you might call the “theory of maximum taste.” This theory is based on the idea that exposure to genius has the power to expand your consciousness. If you spend a lot of time with genius, your mind will end up bigger and broader than if you spend your time only with run-of-the-mill stuff.
The theory of maximum taste says that each person’s mind is defined by its upper limit—the best that it habitually consumes and is capable of consuming

Source: A Commencement Address Too Honest to Deliver in Person – The Atlantic

This is the story of a debugging case with a happy ending. TL;DR This is a bug in the glibc malloc(). It mainly affects 64 bits multi-threading applications. With a special mention to Java applications because the JVM seems to always trigger the worst case. The write up is quite long, skip to third section glibc malloc() if you just want the ending.

Source: Major Bug in glibc is Killing Applications With a Memory Limit – The HFT Guy

What Is Big Data? Big data is the collection and analysis of information from various sources. It has two types: structured and unstructured. Structured data includes SQL databases, while unstructured data includes document files and raw streaming data from sensors. The industry describes big data in three major Vs:

1. Volume: A business can have multiple sources for its data. Technologies today have allowed business to store more data than has ever been possible.
2. Velocity: In reality, data is coming in at breakneck speed — and in real-time, or as close to real-time as possible. Velocity also describes how fast data is processed and analyzed.
3. Variety: In addition to the amount and speed of data that goes into your system, it also comes in different formats. From business sale records to database information, it’s all big data.

Source: A Beginner’s Guide to Big Data – DZone Big Data

The “Phoenix Checklist” is a set of questions developed by the CIA to define and think about a problem, and how to develop a solution.

THE PROBLEM
Why is it necessary to solve the problem?
What benefits will you receive by solving the problem?
What is the unknown?
What is it you don’t yet understand?
What is the information you have?
What isn’t the problem?
Is the information sufficient? Or is it insufficient? Or redundant? Or contradictory?
Should you draw a diagram of the problem? A figure?
Where are the boundaries of the problem?
Can you separate the various parts of the problem? Can you write them down? What are the relationships of the parts of the problem? What are the constants of the problem?
Have you seen this problem before?
Have you seen this problem in a slightly different form? Do you know a related problem?
Try to think of a familiar problem having the same or a similar unknown
Suppose you find a problem related to yours that has already been solved. Can you use it? Can you use its method?
Can you restate your problem? How many different ways can you restate it? More general? More specific? Can the rules be changed?
What are the best, worst and most probable cases you can imagine?
=====
THE PLAN
Can you solve the whole problem? Part of the problem?
What would you like the resolution to be? Can you picture it?
How much of the unknown can you determine?
Can you derive something useful from the information you have?
Have you used all the information?
Have you taken into account all essential notions in the problem?
Can you separate the steps in the problem-solving process? Can you determine the correctness of each step?
What creative thinking techniques can you use to generate ideas? How many different techniques?
Can you see the result? How many different kinds of results can you see?
How many different ways have you tried to solve the problem?
What have others done?
Can you intuit the solution? Can you check the result?
What should be done? How should it be done?
Where should it be done?
When should it be done?
Who should do it?
What do you need to do at this time?
Who will be responsible for what?
Can you use this problem to solve some other problem?
What is the unique set of qualities that makes this problem what it is and none other?
What milestones can best mark your progress?
How will you know when you are successful?
Source: Here’s the CIA’s “Phoenix Checklist” for thinking about problems / Boing Boing

In each case, we face real and hard choices between investing in humans and investing in technology. Because the brutal truth is that, as it stands, we are very unlikely to do both. The refusal to transfer anything like the needed resources to states and cities in successive federal bailouts means that the coronavirus health crisis is now slamming headlong into a manufactured austerity crisis. Public schools, universities, hospitals, and transit are facing existential questions about their futures. If tech companies win their ferocious lobbying campaign for remote learning, telehealth, 5G, and driverless vehicles — their Screen New Deal — there simply won’t be any money left over for urgent public priorities, never mind the Green New Deal that our planet urgently needs.
On the contrary: The price tag for all the shiny gadgets will be mass teacher layoffs and hospital closures.
Tech provides us with powerful tools, but not every solution is technological. And the trouble with outsourcing key decisions about how to “reimagine” our states and cities to men like Bill Gates and Eric Schmidt is that they have spent their lives demonstrating the belief that there is no problem that technology cannot fix.
For them, and many others in Silicon Valley, the pandemic is a golden opportunity to receive not just the gratitude, but the deference and power that they feel has been unjustly denied. And Andrew Cuomo, by putting the former Google chair in charge of the body that will shape the state’s reopening, appears to have just given him something close to free reign.

Source: A High-Tech Coronavirus Dystopia

or centuries western culture has been permeated by the idea that humans are selfish creatures. That cynical image of humanity has been proclaimed in films and novels, history books and scientific research. But in the last 20 years, something extraordinary has happened. Scientists from all over the world have switched to a more hopeful view of mankind. This development is still so young that researchers in different fields often don’t even know about each other.

Source: The real Lord of the Flies: what happened when six boys were shipwrecked for 15 months | Books | The Guardian

Here’s what’s coming for us now:
1. GLOBALIZATION OF WHITE SUPREMACY
2. ATTACKS ON TRUST AND TRUTH
3. BIOSECURITY
4. TECHNOLOGICAL DISRUPTION
5. NUKES
6. CLIMATE CHANGE
7. COVID-19’S NEXT LEVEL IMPACT
8. CATASTROPHIC EARTHQUAKES
9. UNKNOWN UNKNOWNS

Source: Experts Knew a Pandemic Was Coming. Here’s What They’re Worried About Next. – POLITICO