Hold on to your hats: we’ve figured out how coronavirus will (indirectly) affect the weather. Honest. CC-licensed photo by Dimitrio Lewis on Flickr.
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A selection of 10 links for you. Use them wisely. I’m @charlesarthur on Twitter. Observations and links welcome.
Clifton Collins (49), originally from Crumlin, Dublin, bought most of the bitcoin in late 2011 and early 2012 using cash he made growing crops of cannabis. The crypto currency has soared in value since then. Collins spent some of his money buying a two-seater gyro plane and learning how to fly it.
In early 2017 he had just over 6,000 bitcoin in one account but he feared it may be too easy for a hacker to access. He decided to spread his wealth across 12 new accounts and transferred exactly 500 bitcoin, worth almost €4.5 million, into each of them.
Collins then printed out the codes for the 12 accounts onto an A4 piece of paper. He hid the paper inside the aluminium cap of his case containing his rod which he kept at his rented home in Farnaught, Cornamona, Co Galway.
But when he was arrested with cannabis herb in 2017 in Co Wicklow and jailed for five years, there was a break-in at the house and it was also cleared on behalf of the landlord with many of Collins’s items being taken to a dump in Co Galway.
Workers at the dump told gardaí they remembered seeing discarded fishing gear. However, waste from the dump is sent to Germany and China to be incinerated and the fishing rod case has never been found.
Collins told gardaí he has had time to come to terms with the loss of the money and regarded it as punishment for his own stupidity.
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Kasparov: I always say I was the first knowledge worker whose job was threatened by a machine. But that helps me to communicate a message back to the public. Because, you know, nobody can suspect me of being pro-computers.
Will Knight: What message do you want to give people about the impact of AI?
Kasparov: I think it’s important that people recognize the element of inevitability. When I hear outcry that AI is rushing in and destroying our lives, that it’s so fast, I say no, no, it’s too slow.
Every technology destroys jobs before creating jobs. When you look at the statistics, only 4% of jobs in the US require human creativity. That means 96% of jobs, I call them zombie jobs. They’re dead, they just don’t know it.
For several decades we have been training people to act like computers, and now we are complaining that these jobs are in danger. Of course they are. We have to look for opportunities to create jobs that will emphasize our strengths. Technology is the main reason why so many of us are still alive to complain about technology. It’s a coin with two sides. I think it’s important that, instead of complaining, we look at how we can move forward faster.
When these jobs start disappearing, we need new industries, we need to build foundations that will help. Maybe it’s universal basic income, but we need to create a financial cushion for those who are left behind. Right now it’s a very defensive reaction, whether it comes from the general public or from big CEOs who are looking at AI and saying it can improve the bottom line but it’s a black box. I think it’s we still struggling to understand how AI will fit in.
Hmm. Does hairdressing require creativity? Yet there are plenty of jobs in it. I’m not so sure there are that many zombie jobs in reality.
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Using a machine-learning algorithm, MIT researchers have identified a powerful new antibiotic compound. In laboratory tests, the drug killed many of the world’s most problematic disease-causing bacteria, including some strains that are resistant to all known antibiotics. It also cleared infections in two different mouse models.
The computer model, which can screen more than a hundred million chemical compounds in a matter of days, is designed to pick out potential antibiotics that kill bacteria using different mechanisms than those of existing drugs.
“We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery,” says James Collins, the Termeer Professor of Medical Engineering and Science in MIT’s Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering. “Our approach revealed this amazing molecule which is arguably one of the more powerful antibiotics that has been discovered.”
In their new study, the researchers also identified several other promising antibiotic candidates, which they plan to test further. They believe the model could also be used to design new drugs, based on what it has learned about chemical structures that enable drugs to kill bacteria.
“The machine learning model can explore, in silico, large chemical spaces that can be prohibitively expensive for traditional experimental approaches,” says Regina Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).
A reminder that pharmaceutical companies haven’t invested in antibiotics for years, not particularly because they couldn’t, but because they’re not sufficiently profitable.
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Quinn Norton, who was hired and then rapidly fired by the New York Times to be its lead opinion writer on technology back in February 2018:
I’ve spent a lot of my career weaving in elements of satirical bait-and-switch into my commentary and articles, and plenty of the bait without the switch was on display that day. I realized I couldn’t counter it, not all of it, and really not even a bit of it. No one was listening.
Online crowd stomping someone is like a sealioning of mythic proportions, where the crowd tempts you to think if you could just explain it would be OK, but it’s not true, it’s a lie that fucks with your head, a crowd screaming why are you hitting yourself while also telling you to kill yourself.
It’s not that the crowd used my weaknesses against me, it’s that they used my strengths. My pacifism, my work with weird and marginalized communities, my love of flawed people, my humor, my long thoughts and hopes about complicated moral topics, these were all used to reduce me to nazi sympathizer, a homophobe, a white supremacist.
So many of the things people brought up and threw at me weren’t my mistakes at all, but things I’m proud of, like trying to argue an anon out of making rape jokes at a feminist on Twitter.
And then my colleagues in American journalism did me dirty. They ran with the crowd, releasing fast articles without any more context than Twitter and Facebook, without talking to me or trying to understand what was happening. Not all, but most. Enough that I knew I wouldn’t get work again, that anyone who googled me would not speak to me again.
Riku Merikoski on how Germany’s renewables incentives are all out of kilter:
It is a well-known fact that we need to electrify our use of energy, or in other words, replace burning of fuels with clean electricity, if we are to decarbonize our energy systems. And here lies the final problem. Germany taxes natural gas very lightly. Natural gas costs a household in Berlin about 62 €/MWh (6.2 c/kWh), as Household Energy Price Index tells us. This deserves another deep contemplation:
Even if a German household had a heat-pump installed with a COP of 3 (meaning one kWh of electricity is used to create 3 kWh of heat), and even if the market cost of electricity was free, the German household would be economically better off burning natural gas for heating.
And this dynamic can clearly be seen from image 2 below on how German houses are heated. Half of households are heated with natural gas and a quarter with fuel oil (Heizöl). Around 14 % are heated with district heating (Fernwärme), while electricity and heat pumps have a share of less than 5 %. And this is not due to old houses being heated with oil and gas. In 2019, the most popular heating method in new houses was still natural gas, and its popularity is still growing.
Image 2. Heating sources of German houses. Source: BDEW.
It is now the year 2020, and Germany has not even started to decarbonize their building heating. What is worse, as we learned above, the German tax-policy not only does not support this decarbonization of heating, but actively discourages it. Even “free” electricity is much too expensive for Germans to use for space heating because how electricity is taxed. If Germany wants to decarbonize its heating sector, this needs to change.
A first step would be to fix electricity taxation so that “free” electricity would be much lower cost for households than it is today, as when this happens, there is usually an oversupply of wind or solar and the electricity grid is somewhat clean (although Germany’s coal-plants often keep operating as it is too expensive to ramp them down just for a couple hours). This would also create additional demand for those low-cost periods which would increase the prices a bit and fix the broken market a little bit.
Electricity demand and industrial output remain far below their usual levels across a range of indicators, many of which are at their lowest two-week average in several years. These include:
• Coal use at power stations reporting daily data at a four-year low
• Oil refinery operating rates in Shandong province at the lowest level since 2015
• Output of key steel product lines at the lowest level for five years
• Levels of NO2 air pollution over China down 36% on the same period last year
• Domestic flights are down up to 70% compared to last month.
All told, the measures to contain coronavirus have resulted in reductions of 15% to 40% in output across key industrial sectors. This is likely to have wiped out a quarter or more of the country’s CO2 emissions over the past two weeks, the period when activity would normally have resumed after the Chinese new-year holiday. (See methodology below.)
Over the same period in 2019, China released around 400m tonnes of CO2 (MtCO2), meaning the virus could have cut global emissions by 100MtCO2 to date. The key question is whether the impacts are sustained, or if they will be offset – or even reversed – by the government response to the crisis.
Via Paul Kedrosky’s excellent (if occasional) newsletter, in which he also points to a paper which suggests that the reduction in airplane flights will probably mean less cloud cover and so greater temperature variation and thus more extreme weather on the US west coast. (And you didn’t think there was any way to connect the climate crisis and coronavirus.)
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Take Japan: The world’s third-largest economy shrank 1.6% in the fourth quarter of 2019 as the country absorbed the effects of a sales tax hike and a powerful typhoon. It was biggest contraction compared to the previous quarter since 2014.
Then there’s Germany. The biggest economy in Europe ground to a halt right before the coronavirus outbreak set in, dragged down by the country’s struggling factories. The closely-watched ZEW Indicator of Economic Sentiment in Germany decreased sharply for February, reflecting fears that the virus could hit world trade.
Bank of America economist Ethan Harris points to the number of smaller economies that are hurting, too. Hong Kong is in recession and Singapore could soon suffer a similar fate. Fourth quarter GDP data from Indonesia hit a three-year low, while Malaysia had its worst reading in a decade, he noted to clients on Friday.
Meanwhile, engines of growth like China and India slowed in 2019. Fourth quarter GDP data for the latter comes out this week.
All of this brings to the fore concerns about the global economy’s ability to withstand a shock from the coronavirus. Harris says the weak quarter was likely a result of lingering damage from the trade war between China and the United States. The coronavirus is poised to make matters worse.
“Global equities have rebounded as the US and China have converged to a ceasefire, but companies with global supply chains remain deeply uncertain,” he said.
Even the United States may not be in as strong a position as previously thought. IHS Markit said Friday that US services sector contracted in February, with the reading hitting a 76-month low. It’s the first time the sector has contracted in four years.
Weckert is the artist who created a traffic jam by pulling 99 Android phones around in a handcart; this is another of his projects, which looks at how a number of countries’ borders vary, depending on which country you’re viewing them from:
Maps have always been used to push influences or represent interests. With maps it is possible to enforce truth claims of knowledge under specific conditions, which are closely interwoven with power. They are a substitute for political and military power that is under the control of territories of state borders. Through the advent of online services such as GoogleMap, a production method has been developed, which allows to update and react in real-time without high financial effort. As a result, geographic atlases flipping side by side have been replaced by maps that look like a seemingly invisible skin over the surface of the earth and can be accessed by smartphones from anywhere in the world.
In these services, it can be noted that the national boundaries are represented differently by some countries, depending on which “google” the respective country is observed.This shows how the company “Google” supports the view of the respective regional government in order not to lose the local market for online map services. This raises the question of the influence of the map producer on the one hand. And on the other hand, that maps can create reality and thus reflect authoritarian images without us being aware of it. Thus, maps are not only projections of spatial knowledge, but also images of the world and, above all, intentions that we follow with mapping.
He says that this now doesn’t work (he created it in 2019), possibly due to a change in Google’s API. (It might be the difference in borders is still there though.) There’s a fascinating interview with him over at Android Authority by Tristan Rayner.
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App reverse-engineer Jane Wong added in a tweet that Google has around 470,000 results for a simple search of “chat.whatsapp.com,” part of the URL that makes up invites to WhatsApp groups.
Motherboard used a number of specific Google searches to find invite links to WhatsApp groups. Some of the groups appear to not be overly sensitive or for a particular audience. Many of the links on Google lead to groups for sharing porn.
But others appear to be catered to specific groups. Motherboard entered one WhatsApp group chat that described itself as being for NGOs accredited by the United Nations. After joining, Motherboard was able to see a list of all 48 participants and their phone numbers.
Danny Sullivan, Google’s public search liaison, tweeted “Search engines like Google & others list pages from the open web. That’s what’s happening here. It’s no different than any case where a site allows URLs to be publicly listed. We do offer tools allowing sites to block content being listed in our results.”
A WhatsApp spokesperson said in a statement, “Group admins in WhatsApp groups are able to invite any WhatsApp user to join that group by sharing a link that they have generated. Like all content that is shared in searchable, public channels, invite links that are posted publicly on the internet can be found by other WhatsApp users. Links that users wish to share privately with people they know and trust should not be posted on a publicly accessible website.”
So more a case of pilot error than Google in any way being malicious, but still a problem: there are fewer and fewer “private” channels in which to share things.
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An operator [of a telephone exchange] did more than simply connect a customer to his or her desired number, however. In the early decades of the industry, telephone companies regarded their business less as a utility and more as a personal service. The telephone operator was central to this idea, acting as an early version of an intelligent assistant with voice recognition capabilities. She got to know her 50 to 100 assigned customers by name and knew their needs. If a party didn’t answer, she would try to find him or her around town. If that didn’t succeed, she took a message and called the party again later to pass the message along. She made wake-up calls and gave the time, weather, and sports scores. During crimes in progress or medical emergencies, a subscriber needed only to pick up the handset and the operator would summon the police or doctors.
While operators were not highly paid, the need to attract and retain capable women from the middle classes led telephone companies to be benevolent employers by the standards of the day — and in some respects, of any day. Around the turn of the century, the companies catered to their operators with libraries, athletic clubs, free lunches, and disability plans. Operators took their breaks in tastefully appointed, parlor-like break rooms, some with armchairs, couches, magazines, and newspapers. At some exchanges, the companies provided the operators with a community garden in which they could grow flowers or vegetables. In large cities, company-owned dormitories were offered to night-shift operators…
…As the Bell System made its slow transition to an automated network, women operators kept making connections — not only for phone company customers but for themselves. Laura Smith, an employee of AT&T, reported in the system’s magazine, the Bell Telephone Quarterly, in 1932 that operators had been moving up through the ranks, not only into higher-level positions as chief operators, but also into roles in other parts of the company, such as the “employment department,” the accounting and financial departments, and engineering.
Growing demand for telephone service led the number of operators to increase for a while, from around 178,000 in 1920 to about 342,000 in the middle of the century — then it declined to less than 250,000 in 1960. Surveying the landscape in 1964, Barnard economist Elizabeth Faulkner Baker felt optimistic that the profession might have finally stabilized. “It is possible that the decline in the relative importance of telephone operators may be nearing an end,” she wrote. She suggested that “in the foreseeable future no machines will be devised” that could handle the array of different types of calls handled by operators, from credit card calls to directory information calls to conference calls to telephone-booth long-distance calls.
Don’t bet against technology in the long term.
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Errata, corrigenda and ai no corrida: none notified