Homeless

The word is that John Bolton is not going quietly after President Trump’s ostentatious slam-dunking of him on Twitter. Maybe he won’t. But there’s a part of this equation I doubt we’ll see discussed much in the press coverage of this story. Bolton isn’t really a foreign policy guy and hasn’t been for more than a decade. Yes, he still discusses foreign policy and for the last year or so he had what is basically the top foreign policy job in the U.S. government. But since the end of the Bush years Bolton has really been a public politics guy and a consummate player in the GOP buck-raking industrial complex.

Bolton had a $500,000 a year gig with Fox News. But he also had a slew of PACs and fundraising entities dedicated to sounding the alarm about bad acting regimes and sending money into John Bolton’s pocket. He became one of the GOP’s many professional yakkers and scaremongers who make big dollars raising money off the folks who watch Fox News.

Just for kicks, here’s some of the fundraising emails I pulled up in my inbox, each with links to give money by this or that deadline.

Just one sample of the sort of stuff you’d find in those emails:

But here’s the thing. Donald Trump owns the Republican party. Just ask Justin Amash and Mark Sanford and Bob Corker and a number of others. Trump is the first, second and third rail of Republican politics. You can’t be anti-Trump and be anywhere in the GOP/Fox News funding system, let alone in elected office. If you want to stay in, you have to do what Sen. Ben Sasse did and give Trump full custody of your dignity with maybe the hope of occasional visitation rights.

I have no doubt that Bolton wants to roast Trump alive. Partly it’s just payback for canning and humiliating him. But Bolton must also be horrified by what Trump appears to want to do in Afghanistan, Iran, North Korea and various other places. But if Bolton goes full Trump critic, it’s very hard to see how he’s ever going to make the massive paydays he was before Trump picked him.

Not that that’s the end of the world. I’m sure he’s a wealthy man and he could find other ways to make money. But that’s a Fox News world. And if he goes anti-Trump, that world will be closed to John Bolton. And that big money is going to be really hard to forego.

by Josh Marshall, TPM |  Read more:

Images: TPM
[ed. See also: Trump Finally Fired John Bolton, but Does It Really Matter? (New Yorker).]

Seeing What the Fighting Is All About on Alaska’s Coastal Plain

Seeing What the Fighting Is All About on Alaska’s Coastal Plain (NY Times)

Image: Christopher Miller

Mud Maker: The Man Behind MLB’s Essential Secret Sauce

Jim Bintliff’s collection of lies is small and sharply curated, each one loose enough to be plausible and mundane enough to limit interest in verifying it. They work like this: Bintliff will be out on the banks of a tributary of the Delaware River, in his personal uniform of denim cutoffs and disintegrating sneakers, using a shovel to harvest buckets of mud. Someone will come along and ask what he’s doing. Bintliff sizes up the questioner, usually a boater or swimmer or fisherman, then picks from his collection. I’ve been sent by the Environmental Protection Agency, and I’m surveying the soil. Or: I’m helping the Port Authority, looking into pollution. Or, if it’s a group of young folks who look like they’ve only come out on the water for a good time: I take this mud, and I put it on my pot plants. They grow like trees.

This always does the trick. It prevents anyone from exploring what he’s actually doing, which is what he’s done for decades, what his father did before him, and his grandfather before him: Bintliff is collecting the mud that is used to treat every single regulation major league baseball, roughly 240,000 per season.

Mud is a family business; it has been for more than half a century. For decades, baseball’s official rule book has required that every ball be rubbed before being used in a game. Bintliff’s mud is the only substance allowed. Originally marketed as “magic,” it’s just a little thicker than chocolate pudding—a tiny dab is enough to remove the factory gloss from a new ball without mucking up the seams or getting the cover too filthy. Equipment managers rub it on before every game, allowing pitchers to get a dependable grip. The mud is found only along a short stretch of that tributary of the Delaware, with the precise location kept secret from everyone, including MLB.

The business is small and fundamentally unglamorous. Bintliff harvests the mud himself, using only a shovel and a few buckets, as he has for his entire adult life. The 62-year-old has recently begun bringing a trusted assistant to help him carry the load, but other than that, the process is the same as it has always been. After he collects the mud, he hauls it back to his yard in southern New Jersey, where it sits until he’s ready to pack it up in his garage and ship it out to teams. His wife, Joanne, takes orders and does invoicing. That’s it. There’s no one and nothing else to the operation. It’s increasingly out of place in a hyper-controlled, ultra-competitive, high-tech league, where every detail is calibrated for peak efficiency.

So it shouldn’t be surprising that MLB has recently tried to eliminate Bintliff, teaming with Rawlings to develop a ball that doesn’t need to be enhanced by mud. But baseball is realizing that it isn’t so easy to replace him, and, in fact, it might not be possible at all.

by Emma Baccellieri, Sports Illustrated |  Read more:

Image: LEBRECHTMEDIA

Face Recognition, Bad People and Bad Data

• We worry about face recognition just as we worried about databases - we worry what happens if they contain bad data and we worry what bad people might do with them

• It’s easy to point at China, but there are large grey areas where we don't yet have a clear consensus of what ‘bad’ would actually mean, and how far we worry because this is different rather than just because it’s just new and unfamiliar

• Like much of machine learning, face recognition is quickly becoming a commodity tech that many people can and will use to build all sorts of things. ‘AI Ethics’ boards can go a certain way but can’t be a complete solution, and regulation (which will take many forms) will go further. But Chinese companies have their own ethics boards and are already exporting their products.

Way back in the 1970s and early 1980s, the tech industry created a transformative new technology that gave governments and corporations an unprecedented ability to track, analyse and understand all of us. Relational databases meant that for the first time things that had always been theoretically possible on a small scale became practically possible on a massive scale. People worried about this, a lot, and wrote books about it, a lot.

Specifically, we worried about two kinds of problem:

• We worried that these databases would contain bad data or bad assumptions, and in particular that they might inadvertently and unconsciously encode the existing prejudices and biases of our societies and fix them into machinery. We worried people would screw up.

• And, we worried about people deliberately building and using these systems to do bad things

That is, we worried what would happen if these systems didn’t work and we worried what would happen if they did work.

We’re now having much the same conversation about AI in general (or more properly machine learning) and especially about face recognition, which has only become practical because of machine learning. And, we’re worrying about the same things - we worry what happens if it doesn’t work and we worry what happens if it does work. We’re also, I think, trying to work out how much of this is a new problem, and how much of it we’re worried about, and why we’re worried.

First, ‘when people screw up’.

When good people use bad data

People make mistakes with databases. We’ve probably all heard some variant of the old joke that the tax office has misspelled your name and it’s easier to change your name than to get the mistake fixed. There’s also the not-at-all-a-joke problem that you have the same name as a wanted criminal and the police keep stopping you, or indeed that you have the same name as a suspected terrorist and find yourself on a no-fly list or worse. Meanwhile, this spring a security researcher claimed that he’d registered ‘NULL’ as his custom licence place and now gets hundreds of random misdirected parking tickets.

These kinds of stories capture three distinct issues:

• The system might have bad data (the name is misspelled)…

• Or have a bug or bad assumption in how it processes data (it can’t handle ‘Null’ as a name, or ‘Scunthorpe’ triggers an obscenity filter)

• And, the system is being used by people who don’t have the training, processes, institutional structure or individual empowerment to recognise such a mistake and react appropriately.

Of course, all bureaucratic processes are subject to this set of problems, going back a few thousand years before anyone made the first punch card. Databases gave us a new way to express it on a different scale, and so now does machine learning. But ML brings different kinds of ways to screw up, and these are inherent in how it works.

So: imagine you want a software system that can recognise photos of cats. The old way to do this would be to build logical steps - you’d make something that could detect edges, something that could detect pointed ears, an eye detector, a leg counter and so on… and you’d end up with several hundred steps all bolted together and it would never quite work. Really, this was like trying to make a mechanical horse - perfectly possible in theory, but in practice the complexity was too great. There’s a whole class of computer science problems like this - thing that are easy for us to do but hard or impossible for us to explain how we do. Machine learning changes these from logic problems to statistics problems. Instead of writing down how you recognise a photo of X, you take a hundred thousand examples of X and a hundred thousand examples of not-X and use a statistical engine to generate (‘train’) a model that can tell the difference to a given degree of certainty. Then you give it a photo and it tells you whether it matched X or not-X and by what degree. Instead of telling the computer the rules, the computer works out the rules based on the data and the answers (‘this is X, that is not-X) that you give it. (...)

This works fantastically well for a whole class of problem, including face recognition, but it introduces two areas for error.

First, what exactly is in the training data - in your examples of X and Not-X? Are you sure? What ELSE is in those example sets?

My favourite example of what can go wrong here comes from a project for recognising cancer in photos of skin. The obvious problem is that you might not have an appropriate distribution of samples of skin in different tones. But another problem that can arise is that dermatologists tend to put rulers in the photo of cancer, for scale - so if all the examples of ‘cancer’ have a ruler and all the examples of ‘not-cancer’ do not, that might be a lot more statistically prominent than those small blemishes. You inadvertently built a ruler-recogniser instead of a cancer-recogniser.

The structural thing to understand here is that the system has no understanding of what it’s looking at - it has no concept of skin or cancer or colour or gender or people or even images. It doesn’t know what these things are any more than a washing machine knows what clothes are. It’s just doing a statistical comparison of data sets. So, again - what is your data set? How is it selected? What might be in it that you don’t notice - even if you’re looking? How might different human groups be represented in misleading ways? And what might be in your data that has nothing to do with people and no predictive value, yet affects the result? Are all your ‘healthy’ photos taken under incandescent light and all your ‘unhealthy’ pictures taken under LED light? You might not be able to tell, but the computer will be using that as a signal.

Second, a subtler point - what does ‘match’ mean? The computers and databases that we’re all familiar with generally give ‘yes/no’ answers. Is this licence plate reported stolen? Is this credit card valid? Does it have available balance? Is this flight booking confirmed? How many orders are there for this customer number? But machine learning doesn’t give yes/no answers. It gives ‘maybe’, ‘maybe not’ and ‘probably’ answers. It gives probabilities. So, if your user interface presents a ‘probably’ as a ‘yes’, this can create problems.

You can see both of these issues coming together in a couple of recent publicity stunts: train a face recognition system on mugshots of criminals (and only criminals), and then take a photo of an honest and decent person (normally a politician) and ask if there are any matches, taking care to use a fairly low confidence level, and the system says YES! - and this politician is ‘matched’ against a bank robber.

To a computer scientist, this can look like sabotage - you deliberately use a skewed data set, deliberately set the accuracy too low for the use case and then (mis)represent a probabilistic result as YES WE HAVE A MATCH. You could have run the same exercise with photos of kittens instead of criminals, or indeed photos of cabbages - if you tell the computer ‘find the closest match for this photo of a face amongst these photos of cabbages’, it will say ‘well, this cabbage is the closest.’ You’ve set the system up to fail - like driving a car into a wall and then saying ‘Look! It crashed!’ as though you’ve proved something.

But of course, you have proved something - you’ve proved that cars can be crashed. And these kinds of exercises have value because people hear ‘artificial intelligence’ and think that it’s, well, intelligence - that it’s ‘AI’ and ‘maths’ and a computer and ‘maths can’t be biased’. The maths can’t be biased but the data can be. There’s a lot of value to demonstrating that actually, this technology can be screwed up, just as databases can be screwed up, and they will be. People will build face recognition systems in exactly this way and not understand why they won’t produce reliable results, and then sell those products to small police departments and say ‘it’s AI - it can never be wrong’.

These issues are fundamental to machine learning, and it’s important to repeat that they have nothing specifically to do with data about people. You could build a system that recognises imminent failure in gas turbines and not realise that your sample data has biased it against telemetry from Siemens sensors. Equally, machine learning is hugely powerful - it really can recognise things that computers could never recognise before, with a huge range of extremely valuable uses cases. But, just as we had to understand that databases are very useful but can be ‘wrong’, we also have to understand how this works, both to try to avoid screwing up and to make sure that people understand that the computer could still be wrong. Machine learning is much better at doing certain things than people, just as a dog is much better at finding drugs than people, but we wouldn’t convict someone on a dog’s evidence. And dogs are much more intelligent than any machine learning.

by Benedict Evans |  Read more:

Image: uncredited

Child Support vs. Deadbeat States

In most states in America, child support doesn’t actually go to children. Particularly when they are being raised in low-income families.

Confused? You’re not alone. Many people have no clue how the child support and public assistance systems operate.

The first thing to know: If you are a custodial parent (a majority of whom are mothers) and apply for public assistance (most commonly Temporary Assistance for Needy Families, or TANF), you are required by federal law to file a child support order.

“There is no choice for either parent,” says Jhumpa Bhattacharya, vice president for programs and strategy at the Insight Center for Community Economic Development. “For the custodial parent, you lose your much-needed benefits if you don’t comply. For the noncustodial parent, an order is set sometimes without your knowledge, and often not based on your actual economic situation, or an understanding of how you may be contributing in nonfinancial ways.” “What if you provide child care?” she asks. “Buy diapers or clothing regularly? Those things don’t count.”

In fact, despite the “deadbeat dad” stereotype often pinned on whole categories of nonwhite men by racist politicians, the Centers for Disease Control and Prevention report that black fathers actually spend more time feeding, dressing, playing with and reading to their children — whether they live under the same roof or apart — than fathers of other races.

But there’s more: When applying for public assistance, the custodial parent is required to give up the right to receive the child support payments. They go directly to the state, which, depending on its policies, either keeps it all or passes through a percentage of it. What happens next varies from state to state. Let’s say that the father actually sends the state his child support payment (the Office of Child Support Enforcement in the federal Department of Health and Human Services says only 66 percent of support due in the 2018 fiscal year was collected). In more than half of the states, all of that money essentially disappears, at least as far as poor families are concerned; it’s absorbed into the system, seen as “payback” for the welfare system that is supporting the child.

In other states, a state child support payment, usually around $50 and amounting to a small portion of what a parent paid in, is passed on to the child and his or her family. The rest, again, is absorbed by the state. Only two states — Colorado and Minnesota — pass the full amount of the support through to the custodial parent and child.

Another twist: In some states, that $50 is counted as income, and can push the custodial parent, usually a mother struggling to make ends meet, out of the range of eligibility for TANF entirely. (In other states, the “pass-through” money, as it’s known, is not counted as income.)

To look up your state, see here.

Now what happens if the noncustodial parent can’t pay?

A domino effect of penalties — again, varying from state to state — is set into motion. If the noncustodial parent, usually a father, is employed, his paycheck can be garnished. If he has a driver’s license, it can be taken away. Debt accrues. His credit score plummets. In many states, he is charged interest on the debt; in California, for example, that rate is 10 percent.

For many noncustodial parents, these penalties are economically cataclysmic. Many can’t get to work because of transportation barriers. Others have trouble securing housing because of low credit scores and end up homeless. Some work off the books in hopes of supporting themselves and their children directly, rather than seeing money go to the state. Keep in mind that many are already challenged by the stigma of having a criminal record or having been incarcerated.

The impacts are also emotional. Studies show that when fathers owe child support they have significantly less contact with their children, and when they do interact with them, they are less effective parents. Debt also leads to decreased mental and physical health and worsens family relationships.

“I have seen so many fathers cycle in and out of depression and anxiety as they battle systemic oppression and try to maintain relationships with their kids,” Charles Daniels, a therapist and the founder of a Boston-based nonprofit called Fathers’ UpLift, has written. His organization operated the country’s first mental health and substance abuse treatment facility specifically for absentee fathers and families.

Another cruel reality of the system: Even if the custodial parent manages to get off welfare, the noncustodial parent continues to get bills from the state. In fact, national data indicates that a majority of “payback” payments come from parents whose families no longer receive public assistance.

by Coutney E. Martin, NY Times |  Read more:
Image: Kameleon007/iStock, via Getty Images Plus

via:

'Suit Wedgie' Robs Anchorage High School Swimmer of a Victory and Sparks Controversy

A young lady from Anchorage, Alaska’s Dimond High School was disqualified at a high school swimming & diving dual meet between Dimond and Chugiak on Friday September 6th for a wardrobe violation while wearing a suit sized to fit snug for racing by the manufacturer and issued to her in accordance with uniform regulations by her team. It is the same suit being worn by each participant yet no other athletes in the program were disqualified. This comes after more than a year of tension regarding the fit of suits worn by athletes at youth swim meets in the state of Alaska. If the suit was issued by her team in accordance with national standards and she was wearing it as directed without prohibited modifications then why was she disqualified?

Above you see the modesty standards that guided the official’s decision on Friday night as well as several examples of popular brand name suits worn by other girls all over the state. Look at the cut of those suits. They’re not in compliance even before they get on the body of a swimmer. Some of these brands are currently being used as team suits. If lots of girls are wearing them, and they’re cut in a way that is “immodest”, why has only one swimmer been disqualified?

This young lady and her sisters are being targeted not for the way they wear their suits but for the way those suits fit their curvier, fuller figured bodies. The issue has come so far unraveled that parents in opposition of these girls and their swimwear have been heard saying that for the sake of their sons, the mother of these young ladies should cover up her daughters. Talk about thrusting modern women back into an era in which men were never held accountable for their behavior! Special legislation has been put forth regarding swimming costumes in this state as well and it is one official’s interpretation of national rules in which they come across as misguided by their spiritual beliefs regarding modesty and morality which have no place on the pool deck at a secular sporting event. While it will polarize Alaska Swimming to an unprecedented degree, it is crucial that this community rise up to protect these girls. They are being targeted not because they are wearing their suits to be scandalous, thus inspiring immorality among other young people, but rather because their ample hips, tiny waists, full chests, and dark complexions look different than their willowy, thin, and mostly pallid teammates. Some will argue this has nothing to do with race, but when the same officials targeting these girls have been heard saying that so-and-so white girl also shows too much skin but has never been disqualified for a similar violation the racial facet of this issue cannot be ignored. (...)

It gets much worse than last night’s injustice for the young lady whose victory was stolen from her when she discovered that a suit her high school team told her she could wear resulted in her disqualification. This same girl was the subject of one rogue team parent’s photography project last season in which they took graphic photos of her backside in her swimsuit without her knowledge or consent and circulated the images via email as evidence that her attire is immoral. She is a minor and that parent should be arrested for possession and distribution of child pornography. Her younger sister, one of the fastest athletes in the history of Alaska swimming, has told her family and friends she feels as though she’s being told by the community that her specific body is not appropriate for competitive swimming. It is the most heartbreaking thing to hear from a young person who is fit and healthy and who is just trying to ensure a brighter future for herself through this sport. We need to fight for these girls so that perverted adults can no longer single them out or judge them. Every organization associated with competitive youth swimming in the state of Alaska and the whole of the United States needs to protect them so they can get their focus back on swimming fast which is all they’ve ever wanted to do.

by Lauren Langford, Medium |  Read more:

Image: uncredited

[ed. I'm in Anchorage, AK this week (former hometown for 40 yrs.), and apparently not much has changed, particularly a strain of conservative meaness/wackiness that afflicts the body politic in general. See also: ‘Suit wedgie’ robs Anchorage high school swimmer of a victory and sparks a controversy (Anchorage Daily News). And in other news, She who won't be named is still a hot topic (TMP v. SLP, Craig Medred).]

I Have No Idea of What "Hard Work" Means

If there is one thing that unites all social and economic classes, it’s that we all love to talk about how hard we’ve worked. The 17-year-old who’s trying to get into college, the Silicon Valley wunderkind, your parents’ friend who just got a pool—you might hear from any of these people “I’ve worked so hard for everything I have” or “I’ve put blood, sweat, and tears (into this college application/useless app/aboveground pool).” The newest Supreme Court judge, of course, “worked his tail off” to get into an Ivy League college, all the way from the lowly position of “student at a school specifically designed to get kids into Ivy League colleges.” For many people, their long history of “hard work” is a point of pride that they hold very dear, especially when they are in a position of privilege and feel defensive about it. I don’t necessarily judge these people. I’m sure they do feel that they’ve worked very hard, and it might even be true.

But here’s the thing: I have literally no idea what the term “hard work” is supposed to mean.

There are certain people who I think we can all agree have worked hard. Coal miners? Sure. EMTs? Yup. Furniture movers? Definitely. Any job that requires intense physical labor is on the list of “for sure, you worked hard,” as is anything that involves great emotional and psychological resilience, such as social work. But after that it gets tricky. How about someone who founded a trading company or a real estate agency in the 1980s, riding the wave of the business-friendly Reagan years? They’ve worked for decades, undoubtedly with some late nights or unusual hours. But is that the same as getting black lung? What about someone who just worked a fairly normal 40 hour week, from age 18 to 65? Is that “hard work?” What about someone whose job is creative and enjoyable? Is that also “hard work?” How do we measure this, exactly? Can it be done by number of hours? Intensity of work? Sacrifices made? Cubits of human suffering?

Even in high school, I was always totally clueless as to who was “working hard” and who wasn’t. Part of the problem is that I myself do not work to an orthodox timetable. I have never been one of those enviable people who can set aside neat little one- or two-hour blocks each day for a project, slowly but surely putting everything together right on schedule. I only have two modes: lazy piece of crap, or obsessively focused on my work, the latter phase usually coming at entirely unhelpful times such as 3 a.m., or in the middle of a mediocre date. As a result, I have no clue how many hours I actually work, since my work patterns are so erratic. When I was in high school, the kids who dutifully went to the library to study for one (1) hour every day and go home were “hard workers” to me, but for all I know, the handful of manic work periods I typically had in a school year might have encompassed just as many, or even more “units” of work than them. (...)

So, all the jobs it’s possible to have—unless maybe you’re an heiress with a jewelry line—require huge amounts of the best years of your life, and they all involve some aspects that can be classified as “hard work.” (To clarify, I’m not saying that a vlogger has it anywhere near as tough as a factory worker—just that both could make reasonable justifications for saying that they work hard). Why, then, are people so insistent on using “I’ve worked hard” as a statement that they are somehow special? Why is it so often used as a shield from criticism, whether it’s by millionaires who argue against tax hikes, or divorced dads making videos in their cars explaining why they yell at strangers for looking Mexican?

I think the issue is that when people say they’ve “worked hard,” they’re implicitly suggesting superiority. I’m deserving of reward, not like those people who are lazy (“those” people being immigrants, poor people, liberal arts majors, whoever it is you seek to contrast yourself against). In a society where competition for the top spots (or even the doing-comfortably-okay spots) has become increasingly ruthless, simply having a job is not enough to prove you should be treated with respect, be compensated well, and enjoy a good retirement. You have to prove you are exceptional, and if you talk a big game about how hard you work, you can justify either your current position (if you’re privileged) or why you, personally, deserve a better position (if you’re not). This focus on defining yourself by the nebulous idea of how “hard” you’ve worked is a distraction from the more important issue, which is whether the situation you’re in is just.

by Aisling McCrea, Current Affairs |  Read more:

Image: uncredited

Food Rescue: Combating Food Waste and Climate Change

HELSINKI, Finland — “Happy hour” at the S-market store in the working-class neighborhood of Vallila happens far from the liquor aisles and isn’t exactly convivial. Nobody is here for drinks or a good time. They’re looking for a steep discount on a slab of pork.

Or a chicken, or a salmon fillet, or any of a few hundred items that are hours from their midnight expiration date. Food that is nearly unsellable goes on sale at every one of S-market’s 900 stores in Finland, with prices that are already reduced by 30 percent slashed to 60 percent off at exactly 9 p.m. It’s part of a two-year campaign to reduce food waste that company executives in this famously bibulous country decided to call “happy hour” in the hopes of drawing in regulars, like any decent bar.

“I’ve gotten quite hooked on this,” said Kasimir Karkkainen, 27, who works in a hardware store, as he browsed the meat section in the Vallila S-market. It was 9:15 and he had grabbed a container of pork mini-ribs and two pounds of shrink-wrapped pork tenderloin.

Total cost after the price drop: the equivalent of $4.63.

About one-third of the food produced and packaged for human consumption is lost or wasted, according to the Food and Agriculture Organization of the United Nations. That equals 1.3 billion tons a year, worth nearly $680 billion. The figures represent more than just a disastrous misallocation of need and want, given that 10 percent of people in the world are chronically undernourished. All that excess food, scientists say, contributes to climate change.

From 8 to 10 percent of greenhouse gas emissions are related to food lost during harvest and production or wasted by consumers, a recent report by the Intergovernmental Panel on Climate Change found. Landfills of rotting food emit methane, a gas that is roughly 25 times more harmful than carbon dioxide. And to harvest and transport all that wasted food requires billions of acres of arable land, trillions of gallons of water and vast amounts of fossil fuels.

For consumers, cutting back on food waste is one of the few personal habits that can help the planet. But for some reason, a lot of people who fret about their carbon footprint aren’t sweating the vegetables and rump steak they toss into the garbage.

“There’s been a lot of focus on energy,” said Paul Behrens, a professor in energy and environmental change at the University of Leiden in the Netherlands. “But climate change is as much a land issue and a food issue as anything else.”

Reducing waste is a challenge because selling as much food as possible is a tried, tested and ingrained part of all-you-can-eat cultures. Persuading merchants to promote and profit from “food rescue,” as it is known, is not so obvious.

“Consumers are paying for the food, and who wants to reduce that?” said Toine Timmermans, director of the United Against Food Waste Foundation, a nonprofit in the Netherlands composed of companies and research institutes. “Who profits from reducing food waste?”

A growing number of supermarkets, restaurants and start-ups — many based in Europe — are trying to answer that question. The United States is another matter.

“Food waste might be a uniquely American challenge because many people in this country equate quantity with a bargain,” said Meredith Niles an assistant professor in food systems and policy at the University of Vermont. “Look at the number of restaurants that advertise their supersized portions.” (...)

Some of the most promising food waste efforts are apps that connect food sellers to food buyers. Think Tinder, except one party in this hookup is a person and the other is an aging loaf of bread.

Among the most popular is Too Good to Go, a company based in Copenhagen, with 13 million users and contracts with 25,000 restaurants and bakeries in 11 countries. Consumers pay about one-third of the sticker price for items, most of which goes to the retailer, with a small percentage paid to the app.

by David Segal, NY Times |  Read more:

Image: Juho Kuva for The New York Times
[ed. See also: There’s a $218 billion design problem sitting in your fridge right now (Fast Company).]

The Trick to Life Is to Keep Moving

For many people, roommates and romances are the most important relationships of their late teens and early 20s. For me it was Cora Brooks, a poet and activist 51 years my senior. She taught me how to make bread without measuring the flour or water or yeast, to not fear improvising. Through Cora I learned slowness and grace.

Cora taught me that there are worse things than dying — that getting older is a process of losing your children to distance and coping with incontinence and memory loss, yes, but also of becoming more unapologetically yourself. She got angry at the government, at the Vermont Yankee Nuclear Power Station, at her body’s failings, at her family. Her secret to recovering from multiple strokes? Turn on the radio and teach herself to dance, step by wobbly step. “The trick is to keep moving,” she told me.

I met Cora through the Schlesinger Library on the History of Women in America in Cambridge, Mass. The Schlesinger houses over 100,000 volumes of books and periodicals, photos and films, and the collected papers of various prominent American women. Julia Child’s papers are there, alongside Helen Keller’s and June Jordan’s. In 2011, when I was a sophomore in college, I received a research grant to study the work of 13 female poets who had their work archived in the Schlesinger. I started alphabetically: Brooks, Cora. I never made it to the others.

Twice a week I signed in at the front desk, deposited my backpack in a locker (only pencils could be brought upstairs) and entered a quiet and cold reading room. A few minutes later, a librarian would emerge from an elevator pushing a cart of gray boxes with folders inside: the contents of Cora’s life in 43 ordered boxes. I read through diaries and to-do lists and newspaper clippings from the 1960s along with paragraphs about her two children.

I learned that in 1981 Cora staged a protest of the reinstatement of registration for the draft at a post office in Chelsea, Vt. The postmistress called the chief of police, who tried to handcuff her. But she was tiny and slid the handcuffs off her wrists. “These don’t work on me,” she said, handing them back. (...)

One afternoon as I read through her writing in the Schlesinger, I realized that Cora was still alive. This is rare in an archive. Most people donate their papers after death. I found her address from 2009, scrawled on an envelope, and asked a librarian if I could send her a letter. The librarian shrugged. If the address was there, I could write to her.

I pulled out a pencil and a clean sheet of paper, and right there in the reading room I wrote her a note, an invitation to visit Harvard and lead a poetry workshop. She wrote back three days later, on a hand-painted postcard with flowers on the back: “I’m much too old to come to Harvard,” she wrote, “but why don’t you come to Vermont?”

I ran from my mailbox all the way to my roommate’s bedroom door, where I knocked, breathless. “Cora’s handwriting is exactly like it is in the archives!”

I borrowed my family’s Volvo and drove to Vermont. It was my first trip on the highway alone and it was snowing. When I turned into her driveway, I found a turquoise house with an empty clothesline strung in the backyard. No cell service. A bell tinkled as I pushed through the back door to Cora’s porch, propped open by an old book. A striped cat wound his way around my ankles. Cora enveloped me in a hug.

We spent four days together. We watercolor-painted postcards to send to friends. Her cat had different names: Zebra Tattoo, Charles, Sir Stripey. Cora smoked a cigarette each night after dinner, perched under the whirring fan of her stove. We sat and talked throughout the afternoons, and then took trips together to the Hunger Mountain Co-op, where we found that we both loved candied ginger and vegetable stews. That first trip was followed by several more.

At Harvard, my life was measured in minutes: I hurried from class to rowing practice and back. I was good at being in a rush. Cora taught me to slow down.

We talked about death, often. She said she would welcome hers.

“I’m in the afterlife already,” she told me one day, her hands covered in paint. “Each day is a bonus.” (...)

Months later, while cycling through New Zealand, I was interviewed by the BBC. Cora heard the broadcast while sitting in her living room that I remembered so well: the thick yellow carpet, old lunar calendars tacked to the walls and watercolor paintbrushes lined up in empty pasta sauce jars on the shelf. A handwritten sign that said “no mouth to mouth, no jump start, no tubes" was taped to the front door, alongside her children’s phone numbers.

Cora wanted to have a choice in exiting the world. Debilitated by successive strokes, and frustrated by her inability to care for herself, she decided in the spring of 2018 to stop eating that fall. Dying turned out to be a slow process, spread out over a month. When I called, she said she was surprised that her body wanted to keep living.

“Cora lived a thoughtful, intentional life, and she died  a thoughtful, intentional death,” her obituary read. “In April, she announced to friends and family that she was going to cease eating and drinking on Sept. 24, near the equinox. She held true to her word, eating only one basil leaf, one lemon drop, and one lime Popsicle after her self-appointed date.” When she died, Cora was 77 years old. We had known each other for seven years.

by Devi Lockwood, NY Times | Read more:
Image: Bénédicte Muller
[ed. I want that choice too. It's not the fear of death so much as the fear of a bad death.]

[ed. Traveling... back soon.]
Image: markk

via:

via:

The Real Donald Trump Is a Character on TV

Try to understand Donald Trump as a person with psychology and strategy and motivation, and you will inevitably spiral into confusion and covfefe. The key is to remember that Donald Trump is not a person. He’s a TV character.

I mean, O.K., there is an actual person named Donald John Trump, with a human body and a childhood and formative experiences that theoretically a biographer or therapist might usefully delve into someday. (We can only speculate about the latter; Mr. Trump has boasted on Twitter of never having seen a psychiatrist, preferring the therapeutic effects of “hit[ting] ‘sleazebags’ back.”)

But that Donald Trump is of limited significance to America and the world. The “Donald Trump” who got elected president, who has strutted and fretted across the small screen since the 1980s, is a decades-long media performance. To understand him, you need to approach him less like a psychologist and more like a TV critic. (...)

As TV evolved from the homogeneous three-network mass medium of the mid-20th century to the polarized zillion-channel era of cable-news fisticuffs and reality shocker-tainment, he evolved with it. In the 1980s, he built a media profile as an insouciant, high-living apex predator. In 1990, he described his yacht and gilded buildings to Playboy as “Props for the show … The show is ‘Trump’ and it is sold-out performances everywhere.”

He syndicated that show to Oprah, Letterman, NBC, WrestleMania and Fox News. Everything he achieved, he achieved by using TV as a magnifying glass, to make himself appear bigger than he was.

He was able to do this because he thought like a TV camera. He knew what TV wanted, what stimulated its nerve endings. In his campaign rallies, he would tell The Washington Post, he knew just what to say “to keep the red light on”: that is, the light on a TV camera that showed that it was running, that you mattered. Bomb the [redacted] out of them! I’d like to punch him in the face! The red light radiated its approval. Cable news aired the rallies start to finish. For all practical purposes, he and the camera shared the same brain. (...)

If you want to understand what President Trump will do in any situation, then, it’s more helpful to ask: What would TV do? What does TV want?

It wants conflict. It wants excitement. If there is something that can blow up, it should blow up. It wants a fight. It wants more. It is always eating and never full.

Some presidential figure-outers, trying to understand the celebrity president through a template that they were already familiar with, have compared him with Ronald Reagan: a “master showman” cannily playing a “role.”

The comparison is understandable, but it’s wrong. Presidents Reagan and Trump were both entertainers who applied their acts to politics. But there’s a crucial difference between what “playing a character” means in the movies and what it means on reality TV.

Ronald Reagan was an actor. Actors need to believe deeply in the authenticity and interiority of people besides themselves — so deeply that they can subordinate their personalities to “people” who are merely lines on a script. Acting, Reagan told his biographer Lou Cannon, had taught him “to understand the feelings and motivations of others.”

Being a reality star, on the other hand, as Donald Trump was on “The Apprentice,” is also a kind of performance, but one that’s antithetical to movie acting. Playing a character on reality TV means being yourself, but bigger and louder. (...)

Reality TV has often gotten a raw deal from critics. (Full disclosure: I still watch “Survivor.”) Its audiences, often dismissed as dupes, are just as capable of watching with a critical eye as the fans of prestige cable dramas. But when you apply its mind-set — the law of the TV jungle — to public life, things get ugly.

In reality TV — at least competition reality shows like “The Apprentice” — you do not attempt to understand other people, except as obstacles or objects. To try to imagine what it is like to be a person other than yourself (what, in ordinary, off-camera life, we call “empathy”) is a liability. It’s a distraction that you have to tune out in order to project your fullest you.

Reality TV instead encourages “getting real.” On MTV’s progressive, diverse “Real World,” the phrase implied that people in the show were more authentic than characters on scripted TV — or even than real people in your own life, who were socially conditioned to “be polite.” But “getting real” would also resonate with a rising conservative notion: that political correctness kept people from saying what was really on their minds.

Being real is not the same thing as being honest. To be real is to be the most entertaining, provocative form of yourself. It is to say what you want, without caring whether your words are kind or responsible — or true — but only whether you want to say them. It is to foreground the parts of your personality (aggression, cockiness, prejudice) that will focus the red light on you, and unleash them like weapons. (...)

Mr. Trump has been playing himself instinctually as a character since the 1980s; it’s allowed him to maintain a profile even through bankruptcies and humiliations. But it’s also why, on the rare occasions he’s had to publicly attempt a role contrary to his nature — calling for healing from a script after a mass shooting, for instance — he sounds as stagey and inauthentic as an unrehearsed amateur doing a sitcom cameo.

The institution of the office is not changing Donald Trump, because he is already in the sway of another institution. He is governed not by the truisms of past politics but by the imperative of reality TV: Never de-escalate and never turn the volume down.

by James Poniewozik, NY Times | Read more:
Image: via

Private Equity and Surprise Medical Billing

Surprise medical billing has become a critical issue facing Americans across the country because of purposeful corporate practices designed to increase profits. As hospitals have outsourced emergency rooms and other specialty care to reduce costs, private investors have bought up specialty physician practices, rolled them into powerful national corporations, and taken over hospital emergency services. The result: large out-of-network surprise bills. The hidden actors: Leading private equity firms looking for ‘outsized’ returns.

Surprise medical billing made headlines in 2019 as patients with health insurance found themselves liable for hundreds or even thousands of dollars in unforeseen medical bills. When patients with urgent medical problems go to an emergency room (ER) or are treated by specialty doctors at a hospital that is in their insurance network, they expect that the services they receive will be ‘in-network’ and covered by their insurance. But often a doctor not in their insurance network is under contract with the hospital and actually provides the care. When this happens, patients are stuck with unexpected and sometimes unreasonably high medical bills charged by these ‘out-of-network’ doctors. This typically occurs when the hospital has outsourced the ER or other specialized services to a professional staffing firm or a specialty doctors’ practice. This problem has exploded in recent years because hospitals are increasingly outsourcing these services to cut costs. And more and more patients are faced with surprise medical bills — adding substantially to the already impossible medical debt that working people face.

Hospital outsourcing of emergency, radiology, anesthesiology, and other departments has provided an opening for physician practices to operate these services as independent organizations. Initially, hospitals outsourced these services to small, local doctors’ groups. But over the past decade, private equity firms have become major players — buying out doctors’ practices and rolling them up into large corporate physician staffing firms that provide services to outsourced emergency rooms, anesthesiology and radiology departments, and other specialty units. By 2013, physician staffing firms owned by Blackstone Group and Kohlberg, Kravis Roberts & Co. (KKR) – among the largest PE firms in the country – cornered 30 percent of this market. Since then, private equity ownership of these services has continued to grow. Private equity firms also own two of the three largest emergency ambulance and air transport services – another major source of surprise medical billing.

Private equity ownership matters because the business model of private equity firms is to use a lot of debt in a leveraged buyout of companies they acquire and then extract as much cash as possible out of them in order to pay down the debt and reward their investors with ‘outsized returns’ that exceed stock market gains. They can be thought of as for-profit corporations on steroids. Buying up specialty practices is financially attractive because there is a large and growing demand for outsourced doctors, and out-of-network doctors can command a substantial premium for their services. Emergency rooms and certain medical services provided in hospitals are not really part of a competitive ‘marketplace’ because patients in emergency medical situations rarely have a choice: they need immediate medical care and cannot ‘shop around’ for an in-network trauma doctor or radiologist. Thus, surprise bills are difficult to avoid if patients face a medical emergency and must go to the ER or if they are hospitalized and require access to specialty medical services.

How Widespread is Surprise Billing and Why Has It Grown?

Surprise medical billing is exacerbating the already serious problem of medical debt in this country, which is a leading cause of bankruptcy for American families. And surprise billing is growing rapidly. Forty percent of Americans surveyed by the Kaufman Family Foundation in April, 2019, reported receiving an unexpected medical bill; and 20 percent of those surveyed said it was due to out-of-network charges – or surprise billing. A study by health researchers at Stanford University, for example, examined fees charged to patients with private insurance who were treated by the emergency department of a hospital. They reviewed 13.6 million trips to the ER that occurred over the period 2010 to 2016. About a third (32.3 percent) of these trips in 2010 resulted in a surprise medical bill. But by 2016, that figure had increased to 42.8 percent. That is, more than 4 in 10 trips to the ER ended with patients getting a surprise medical bill. For in-patient stays, surprise billing rose from 26 percent to 42 percent, and the average costs per patient also jumped from $804 to $2,040. At this rate of increase, the estimated percent of hospital visits resulting in a surprise bill would be 48 percent in 2019 – or almost one half. The study also found that in 2016, 86% of ER visits and nearly 82% of hospital admissions incurred surprise ambulance service bills.

Similarly, another 2019 study found that patients who are admitted to a hospital from the ER are much more likely to receive an out-of-network charge — as many as 26% of admissions from the emergency room were found to include a surprise bill. The study also found that 38 percent of Americans are ‘very worried’ and another 29 percent are ‘somewhat worried’ about being able to afford surprise medical bills. People particularly vulnerable to these charges are those with coverage from large employers that are self-insured. And vulnerability also varied by region, with Texas, New York, Florida, New Jersey, and Kansas having higher rates of surprise billing; and Minnesota, South Dakota, Nebraska, Maine, and Mississippi having lower rates.

While large surprise medical bills are typically associated with doctors in the ER or in specialties such as radiology, anesthesiology, or critical care units such as neo-natal, burn, or trauma centers, other out-of-network physicians may also issue surprise bills. For example, those who assist a patient’s doctor in a procedure or hospitalists who check on patients during hospital stays can also charge separately for their services. The Stanford study found that the likelihood that a patient admitted to an in-network hospital would face a surprise medical bill because at least one out-of-network doctor cared for them increased from 26.3 percent 2010 to 42.0 percent in 2016. A particularly egregious instance occurred when an assistant surgeon sent a bill for $117,000 to a patient who had surgery for herniated discs in his neck. The patient’s own in-network surgeon sent a bill for $133,000, but accepted a fee of $6,200 negotiated with the insurance company. The out-of-network assistant surgeon is seeking full payment of his charges. This is a particularly egregious example, but surprise bills for a few thousand dollars are not uncommon.

The problem of surprise billing has grown substantially in recent years because hospitals have been under financial pressure to reduce overall costs and have turned to outsourcing expensive and critical services to third-party providers as a cost-reduction strategy. Outsourcing is not new, as hospitals began outsourcing non-medical ancillary services such as facilities management and food services in the 1980s, in response to a round of structural changes in government financing. By the 1990s, hospitals were experimenting with the use of independent ‘hospitalists’ to care for patients between rounds by the local admitting doctors who had a hospital affiliation. Hospitalists’ numbers increased over the next two decades as hospital staffing firms grew and provided a range of temporary or short-term professionals to fill shortages in nursing, technical, or clinical positions.

Recent outsourcing, however, has expanded to critical care areas – emergency rooms, radiology, anesthesiology, surgical care, and specialized units for burn, trauma, or neo-natal care. Now hospitals contract with specialty physician practices or professional physician staffing firms to provide these services – even if the patient receives treatment at a hospital or at an outpatient center that is in the patients’ insurance network. According to one study, surprise billing is concentrated in those hospitals that have outsourced their emergency rooms. A recent report found that almost 65 percent of U.S. hospitals now have emergency rooms that are staffed by outside companies. (...)

Private Equity’s Business Model: Its Role in Outsourcing and Consolidating Specialty Services

Private equity firms have played a critical role in consolidating physicians’ practices into large national staffing firms with substantial bargaining power vis-à-vis hospitals and insurance companies. They have also bought up other emergency providers, such as ambulance and medical transport services. They grow by buying up many small specialty practices and ‘rolling them up’ into umbrella organizations that serve healthcare systems across the United States. Mergers of large physician staffing firms to create national powerhouses have also occurred. As these companies grow in scale and scope and become the major providers of outsourced services, they have gained greater market power in their negotiations with both hospitals and insurance companies: hospitals with whom they contract to provide services and insurance companies who are responsible for paying the doctors’ bills.

Hospitals have consolidated in order to gain market share and negotiate higher insurance payments for procedures. Healthcare costs have been driven up further by the dynamics associated with payments for out-of-network services. As physicians’ practices merge or are bought out and rolled up by private equity firms, their ability to raise prices that patients or their insurance companies pay for these doctors’ services increases. The larger the share of the market these physician staffing firms control, the greater their ability to charge high out-of-network fees. The likelihood of surprise medical bills goes up, and this is especially true when Insurance companies find few doctors with these specialties in a given region with whom they can negotiate reasonable charges for their services.

by Eileen Appelbaum and Rosemary Batt, Institute for New Economic Thinking | Read more:
Image: uncredited

There Has Been Just One Buyer Of Stocks Since The Financial Crisis

Over the weekend we showed a chart which demonstrated that the bulk of the 21st century has been characterized by equity retail fund outflows offset by a tsunami of bond inflows, i.e. a reverse "great rotation." The chart also illustrated that periods of "big bond inflows often preceded big policy changes", hinting that some major event was coming; meanwhile big bond outflows (e.g. 2008/13/18) tended to coincide with the most bearish returns across asset classes, which may explain why in a time of record bond inflows, i.e., right now, stocks are trading near all time highs.... even if it did - as we said on Sunday - pose a question: "just who is buying stocks here?"

Now, in his latest Flow Show weekly report, BofA CIO Michael Hartnett confirms that the flows continued for one more week, as another $11.4 billion flowed into bonds, while $8.4 billion was redeemed from stocks (a clear sign investors are not worried about bond bubble for now, with chunky inflows to both IG ($7.9bn) & govt bond ($3.5bn) funds).

More importantly, when looking at the bigger picture and finding $213 billion in redemptions from equity funds stands in stark contrast to $337bn inflows to bond funds; Hartnett answered our pressing question: who is buying stocks here?

His answer: "the sole buyer of US stocks remain corporate buybacks, not institutions" as shown in the chart below. (...)

This is notable not only because it means that without the buyback bid (made possible by record cheap debt, which is used to fund corporate stock repurchases) stocks would be far, far lower, but because it is a carbon copy of what we observed almost exactly two years ago, suggesting that between the summers of 2017 and 2019 absolutely nothing has changed.

Meanwhile, as Credit Suisse notes, one of the major features of the US equity market since the low in 2009 is that the US corporate sector has bought over 20% of market cap, while institutions have sold 7% of market cap.

Why this rush by companies to buyback their own stock, and in the process artificially boost their Eearning per Share? There is a very simple reason: as Reuters explained some time ago, "Stock buybacks enrich the bosses even when business sags." And since bond investors are rushing over themselves to fund these buyback plans with "yielding" paper at a time when central banks have eliminated virtually all yield and risk, who is to fault them.

by Tyler Durden, ZeroHedge |  Read more:

Image: BofA Merrill Lynch Global Investment Strategy, Bloomberg, Fed Reserve Bank

Keiichi Ichikawa, Yokohama, Japan
via:

Cat Watching a Horror Movie

The American Medical System Is One Giant Workaround

The nurses were hiding drugs above a ceiling tile in the hospital — not because they were secreting away narcotics, but because the hospital pharmacy was slow, and they didn’t want patients to have to wait. I first heard about it from Karen Feinstein, the president and chief executive of the Jewish Healthcare Foundation, who reported it at a board meeting several years ago. I wasn’t surprised: Hiding common medications is a workaround, an example of circumventing onerous rules to make sure patients get even basic care.

Workarounds are legion in the American health care system, to the extent that ECRI (formerly the Emergency Care Research Institute) listed them fourth among its list of top 10 patient safety concerns for health care organizations in 2018. Workarounds, the group writes, are an adaptive response — or perhaps one should say maladaptive response — to “a real or perceived barrier or system flaw.”

Staff use workarounds because they save valuable time. According to Anita Tucker, a business professor at Boston University, system breakdowns, or what she calls “operational failures,” and the workarounds they stimulate, can “consume up to 10 percent of a nurse’s day.” Most hospital nurses are stretched to their limits during their 12-hour shifts. No nurse has 90 minutes to lose to a slow pharmacy or an inefficient hospital bureaucracy.

I saw the common sense that can underlie workarounds when my hospital floor instituted bar code scanning for medication administration. Using a hand-held scanner to register bar codes on medications and patients’ hospital bracelets sounds smart. But then some medications routinely came without bar codes, or had the wrong bar codes, and we nurses weren’t given an easy way to report those errors. Patients’ wrist bands could be difficult to scan and the process disturbed them, especially if they were asleep. The lists of medications on the computer screen were also surprisingly hard to read, which slowed everything down.

But the biggest problem was that the scanning software did not work with our electronic medical records — so all drugs had to be checked off in both systems. This is a huge problem when dealing with patients like those receiving bone-marrow transplants, who might get 20 drugs every morning — some of which are delivered through IVs and come with nonstandard doses. What was already a lengthy process suddenly took twice as long.

Some nurses responded to the arrival of the bar code system with workarounds, including refusing to use the scanner, or taping copies of patient bar codes to their med carts. I tried to adhere to the rules, but if I was especially busy or couldn’t get a medication to scan, I would chuck the whole process.

However, because bar code scanning has been shown to reduce errors in medication administration, the hospital officials wanted it to be done consistently. They produced a public list of all the nurses on the floor. Each nurse was labeled green, yellow or red, depending on the percentage of medications he or she administered using bar codes. Family members, doctors — anyone could see how a nurse was graded.

Over time the list worked, but the sting of it also endured. We were being punished for taking time for patients, even if it meant bending the rules. No one among the managerial class seemed to understand that nurses care a lot about patient safety. The unheard concern was that a green light for bar code scanning meant a patient could fall into the red zone for something else.

Workarounds in health care always involve trade-offs like this, and often they are trade-offs of values. Increasingly, the entire health care system is built on workarounds — many of which we don’t always recognize as such.

Consider the use of medical scribes, who complete doctors’ electronic paperwork in real time during patient visits. The American College of Medical Scribe Specialists reported that 20,000 scribes were working in 2014, and expects that number to climb to 100,000 in 2020.

I have heard doctors say they need a scribe to keep up with electronic medical records, the mounting demand of which is driving a burnout epidemic among physicians. Scribes allow doctors to talk with and examine patients without having a computer come between them, but at base they are a workaround for the well-known design flaws of electronic medical records.

As a nurse, when I first learned about scribes, I was outraged. On the job, nurses hear repeatedly how health care companies can’t afford to have more nurses or aides to work with patients on hospital floors — and yet, money is available to pay people to manage medical records. Doctors who use scribes tend to see their productivity and work satisfaction increase, but the trade-off is still there: Scribes demonstrate the extent to which paperwork has become more important than patients in American health care.

The Affordable Care Act, which I support because it has made health care available to millions of previously uninsured Americans, is also an enormous workaround. The act expanded Medicaid, protected patients with pre-existing conditions and offered subsidies to make private insurance more affordable. Obamacare, though, was never intended to make sure that all Americans had affordable care; it works around our failure to provide health care to all our citizens. In its own way, the Affordable Care Act is as jury-rigged as using ceiling tiles to stash medications.

by Theresa Brown, NY Times | Read more:
Image: Runstudio/The Image Bank, via Getty Images

[ed. This is one where you really should read the comments section.]

Google’s New Feature Will Help You Find Something To Watch

Google Search can now help you find your next binge. The company this morning announced a new feature which will make personalized recommendations of what to watch, including both TV shows and movies, and point you to services where the content is available.

The feature is an expansion of Google’s existing efforts in pointing web searchers to informative content about TV shows and films.

Already, a Google search for a TV show or movie title will include a “Knowledge Panel” box a the the top of the search results where you can read the overview, see the ratings and reviews, check out the cast, and as of spring 2017 find services where the show or movie can be streamed or purchased.

The new recommendations feature will instead appear to searchers who don’t have a particular title in mind, but are rather typing in queries like “what to watch” or “good shows to watch,” for example. From here, you can tap a Start button in the “Top picks for you” carousel to rate your favorite TV shows and movies in order to help Google better understand your tastes.

You can also select which subscriptions you have access to, in order to customize your recommendations further. This includes subscriptions services like Netflix, Hulu, HBO GO and HBO NOW, Prime Video, Showtime, and Showtime Anytime, CBS All Access, and Starz.

You can also indicate if you have a cable TV or satellite subscription. And it will list shows and movies available for rent, purchase or free streaming from online marketplaces like iTunes, Prime Video, Google Play Movies & TV, and Vudu, plus network apps like ABC, Freeform, Lifetime, CBS, Comedy Central, A&E, and History.

To get started, you’ll use a Tinder-like swiping mechanism to rate titles. Right swipes indicate a “like” and left swipes indicate a “dislike.” You can also “skip” titles you don’t know or have an opinion on.

After giving Google some starter data about your interests, future searches for things to watch will offer recommendations tailored to you.

The company notes that you can even get specific with your requests, by asking for things like “horror movies from the 80’s” or “adventure documentaries about climbing.” (This will help, too, when you can’t remember a movie’s title but do know what it’s about.)

Google’s search results will return a list of suggestions and when you pick one you want to watch, the service will — as before — let you know where it’s available.

by Sarah Perez, TechCrunch |  Read more:

Image: uncredited