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A selection of 11 links for you. Use them wisely. I’m @charlesarthur on Twitter. Observations and links welcome.
as I’ve argued before, excessive focus on things like vaccine denialists teaches the wrong habits. It’s a desire to take a degenerate case, the rare situation where one side is obviously right and the other bizarrely wrong, and make it into the flagship example for modeling all human disagreement. Imagine a theory of jurisprudence designed only to smack down sovereign citizens, or a government pro-innovation policy based entirely on warning inventors against perpetual motion machines.
And in this wider context, part of me wonders if the focus on transmission is part of the problem. Everyone from statisticians to Brexiteers knows that they are right. The only remaining problem is how to convince others. Go on Facebook and you will find a million people with a million different opinions, each confident in her own judgment, each zealously devoted to informing everyone else.
Imagine a classroom where everyone believes they’re the teacher and everyone else is students. They all fight each other for space at the blackboard, give lectures that nobody listens to, assign homework that nobody does. When everyone gets abysmal test scores, one of the teachers has an idea: I need a more engaging curriculum. Sure. That’ll help.
This is a stunning essay; and it’s a great guide to how to really persuade people who hold very different views from you, politically. (Of course, as he says, this could work both ways.)
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Apple’s trajectory on the GPU side very closely follows their trajectory on the CPU side. In the case of Apple’s CPUs, they first used more-or-less stock ARM CPU cores, started tweaking the layout with the A-series SoCs, began developing their own CPU core with Swift (A6), and then dropped the hammer with Cyclone (A7). On the GPU side the path is much the same; after tweaking Imagination’s designs, Apple is now to the Swift portion of the program, developing their own GPU.
What this could amount to for Apple and their products could be immense, or it could be little more than a footnote in the history of Apple’s SoC designs. Will Apple develop a conventional GPU design? Will they try for something more radical? Will they build bigger discrete GPUs for their Mac products? On all of this, only time will tell.
However, and these are words I may end up eating in 2018/2019, I would be very surprised if an Apple-developed GPU has the same market-shattering impact that their Cyclone CPU did. In the GPU space some designs are stronger than others, but there is A) no “common” GPU design like there was with ARM Cortex CPUs, and B) there isn’t an immediate and obvious problem with current GPUs that needs to be solved. What spurred the development of Cyclone and other Apple high-performance CPUs was that no one was making what Apple really wanted: an Intel Core-like CPU design for SoCs. Apple needed something bigger and more powerful than anyone else could offer, and they wanted to go in a direction that ARM was not by pursuing deep out-of-order execution and a wide issue width.
On the GPU side, however, GPUs are far more scalable. If Apple needs a more powerful GPU, Imagination’s IP can scale from a single cluster up to 16, and the forthcoming Furian can go even higher. And to be clear, unlike CPUs, adding more cores/clusters does help across the board, which is why NVIDIA is able to put the Pascal architecture in everything from a 250-watt card to an SoC. So whatever is driving Apple’s decision, it’s not just about raw performance.
What is still left on the table is efficiency – both area and power – and cost. Apple may be going this route because they believe they can develop a more efficient GPU internally than they can following Imagination’s GPU architectures, which would be interesting to see as, to date, Imagination’s Rogue designs have done very well inside of Apple’s SoCs.
There isn’t an immediate and obvious problem with current GPUs? Except that they’re not powerful enough for the next set of problems such as augmented reality and virtual reality.
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Among the departees now confirmed to be working at Apple from LinkedIn postings, notable high-level staff members are the ex-chief operating officer of Imagination Technologies John Metcalfe, Senior Design Manager Dave Roberts, Vice President of Hardware Engineering Johnathan Redshaw, and 17-year veteran of the company and Senior Software Engineering Manager Benjamin Bowman.
Metcalfe is now a senior director at Apple. Roberts is an engineering manager at Apple’s iOS GPU software group, and Bowman is a GPU architect for the company. Redshaw is listed as a director at Apple, with no specific branch of the company declared.
Imagination Technologies has licensed high-performance GPU designs, known as PowerVR graphics series, for use in Apple’s A-series system on a chip (SoC) dating back to the original iPhone in 2007. The hires may herald an internal project to develop an Apple-designed GPU for use in future iOS projects, rather than rely on third parties for the technology.
Apple issued a statement in March admitting it had “some discussions” with Imagination involving an Apple buyout, but that it did not “plan to make an offer for the company at this time.” Apple owns a 10% stake in the company.
Now you can see why Imagination might be a bit grumpy about the idea that Apple has developed this GPU tech without any reference to Imagination’s intellectual property.
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With the 2016 MacBook Pro line, Apple ditched the beloved MagSafe connector, which disengaged with the slightest amount of pressure. This saved many Mac laptops from a disastrous plummet when someone accidentally snagged the power cable. However, a recent patent filing (number 20170093104) for a “magnetic adapter” hints that it could return.
The patent is for adapters that may have a MagSafe connector receptacle and a Universal Serial Bus Type-C connector insert. This may allow MagSafe chargers to be used to charge devices having Universal Serial Bus Type-C connector receptacles. This also may provide the breakaway characteristic of a MagSafe connector system for a device that does not include a MagSafe connector receptacle. Other adapters may have other types of magnetic connector receptacles and connector inserts.
In states that legalized medical marijuana, US hospitals failed to see a predicted influx of pot smokers, but in an unexpected twist, they treated far fewer opioid users, a new study shows.
Hospitalization rates for opioid painkiller dependence and abuse dropped on average 23% in states after marijuana was permitted for medicinal purposes, the analysis found. Hospitalization rates for opioid overdoses dropped 13% on average.
At the same time, fears that legalization of medical marijuana would lead to an uptick in cannabis-related hospitalizations proved unfounded, according to the report in Drug and Alcohol Dependence.
“Instead, medical marijuana laws may have reduced hospitalizations related to opioid pain relievers,” said study author Yuyan Shi, a public health professor at the University of California, San Diego.
“This study and a few others provided some evidence regarding the potential positive benefits of legalizing marijuana to reduce opioid use and abuse, but they are still preliminary,” she said in an email…
…Last week, U.S. Attorney General Jeff Sessions, the nation’s top cop, reiterated his concerns about marijuana and heroin, an illegal opioid.
“I am astonished to hear people suggest that we can solve our heroin crisis by legalizing marijuana,” he told law enforcement officers in Virginia, “so people can trade one life-wrecking dependency for another.”
Jeff Sessions, showing what belief disconnected from the scientific method looks like.
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This exploratory network analysis is meant to:
• Find the most active suspected automated and “semi-automated” accounts across “pro-Trump” political hashtags the last week of the election; and
• Get a sense of how these political “bot-like” accounts engage, as well as visualize how they might be connected to certain topics.
This data represents the bulk of tweets for the less active “Pro-Trump” hashtags such as #wakeupamerica and#projectveritas, and a fair amount of tweets for the more popular hashtags such as #Trump and #draintheswamp. Sample size was not a limiting factor here, because the worst offenders tweet so often that they are likely be found in almost any sample during the last week of the election. No account in this analysis had less than 192,000 tweets at the time of data collection (12 Dec 2016), and the highest volume account had close to 2.5 million tweets…
…The themes suggest that the accounts seem to be somewhat focused content-wise on attacking Hillary, promoting Trump, pushing for military involvement in Syria, and getting out the vote. Last but not least, here are the top co-occuring “secondary” hashtags found in the Group 1 and Group 2 data.
One downside to focusing on hashtags is that while these “pro-Trump” (and “anti-Hillary”) hashtags were part of the election-related conversation, Twitter is a small part of the overall news sphere. At the same time, these accounts are sustaining a level of activity and influence that is well beyond the capability of any single person.
Fascinating though this is, it does feel a little like fighting the last war. The election isn’t going to be reversed.
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As he tried to log off at 7:13 a.m. on New Year’s Day last year, Josh Streeter, then an Uber driver in the Tampa, Fla., area, received a message on the company’s driver app with the headline “Make it to $330.” The text then explained: “You’re $10 away from making $330 in net earnings. Are you sure you want to go offline?” Below were two prompts: “Go offline” and “Keep driving.” The latter was already highlighted.
“I’ve got screen shots with dozens of these messages,” said Mr. Streeter, who began driving full time for Lyft and then Uber in 2014 but quit last year to invest in real estate.
Mr. Streeter was not alone. For months, when drivers tried to log out, the app would frequently tell them they were only a certain amount away from making a seemingly arbitrary sum for the day, or from matching their earnings from that point one week earlier.
The messages were intended to exploit another relatively widespread behavioral tic — people’s preoccupation with goals — to nudge them into driving longer.
Over the past 20 years, behavioral economists have found evidence for a phenomenon known as income targeting, in which workers who can decide how long to work each day, like cabdrivers, do so with a goal in mind — say, $100 — much the way marathon runners try to get their time below four hours or three hours.
While there is debate among economists as to how widespread the practice is and how strictly cabdrivers follow such targets, top officials at Uber and Lyft have certainly concluded that many of their drivers set income goals. “Others are motivated by an income target for sure,” said Brian Hsu, the Lyft vice president in charge of supply. “You hear stories about people who want to buy that next thing.” He added, “We’ve started to allow drivers to set up those goals as well in the app.”
Great investigation into the gamification of the gig economy. Don’t miss the interactive graphics either.
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Google says its YouTube ad problem is ‘very very very small’ but it’s getting better at fixing it anyway • Recode
Q: But it’s enough of a problem that we’re talking about it now.
PS: It should always be smaller. It’s our responsibility to make it smaller. Let’s not take away from that. But remember, we’ve had that problem, at scale, for a long time. The whole industry [has], even traditional. The problem comes from the fact that somebody is aggressively putting it onto the front page.
Q: Do you think someone is actively campaigning against Google and YouTube?
PS: That’s not how I would say it. There’s a lot of spotlight on the problem at the moment. And advertisers just don’t like something like this to be dragged out into the public. And they’re unhappy with that, and I can fully understand that they’re unhappy with that.
They’re unhappy with two things. Let’s be honest:
Number one, that the mistake even happens. That’s what we have to get better at. Again, as before, we cannot promise a perfect system. [But] whenever it happens, it’s bad, and it shouldn’t happen. The second piece is, apart from the mistake happening, that there’s so much focus being put on it publicly. They obviously don’t appreciate that.
Q: What’s changed between now and a year ago? Is there more hate speech on YouTube, or are more people talking about it?
PS: The first thing that changed is that more public attention has been put on what is, percentage-wise, a pretty small problem. Again, not to minimize it. The second thing that has changed is that the problem has become a bit more multifaceted. It’s relatively easy to [block] clear “hate” — clear, specific words, that are very clearly triggering something. A lot of things historically have been very black or white. And things are becoming more gray-ish. A lot more shades of gray.
Take the N word. If you would just block [videos] when people refer to the black community with N word, you would take out a pretty significant percentage of all rap videos. You would probably take out a lot of pro-black activist groups. But obviously you want to take it out when somebody says “we hate all N words.”
The problem is now, the machines have to start understanding context in a much different way.
“It’s difficult” isn’t quite enough when brands are seeing their reputations being hurt; and this is far from a novel problem.
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Tesla regularly communicates detailed information about crashes involving its cars with the media whenever a driver points a finger at its automation software following an accident.
“Autopilot has been shown to save lives and reduce accident rates, and we believe it is important that the public have a factual understanding of our technology,” said a company spokesperson in an email.
The Guardian could not find a single case in which Tesla had sought the permission of a customer who had been involved in an accident before sharing detailed information from the customer’s car with the press when its self-driving software was called into question. Tesla declined to provide any such examples and disputed the description of its automation software, called Autopilot, as “self-driving”.
Data that shows up in the press often comes from the onboard computers of the cars themselves and can tell the public – and law enforcement officials – whether a customer’s hands were on the wheel, when a door was opened, which of its self-driving processes were active at the moment and whether or not they had malfunctioned.
In only one case – the May death of Canton, Ohio, Tesla driver Joshua Brown – has the company publicly admitted that its software made a mistake. In that case, the Autopilot software did not “see” the white side of a tractor-trailer as it moved in front of the car against the white sky. The driver was reportedly watching one of the Harry Potter movies at that moment and did not see the vehicle, either.
Tesla takes issue with the characterization of Autopilot’s performance in the crash as a failure and told the Guardian that it only distributes detailed information from the site of auto accidents to the press when it believes someone quoted in the media is being unfair.
..unfair to Tesla, that is. This is a terrifically clever piece of journalism: it’s not based on an event, or an announcement. It’s based on observation which reveals something deeper about how we’re being manipulated by these companies.
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There have been rumours, but Panopticon can confirm that the appeal to the Supreme Court in Google v Vidal-Hall on the disapplication of section 13(2) of the Data Protection Act 1998 has been withdrawn following an agreement being reached between the parties. This is obviously a disappointment to those wanting to see what the Supremes would make of the Court of Appeal’s very important judgment permitting damages claims for distress without the need to show pecuniary loss (and indeed to those interested in the use of the Charter of Fundamental Rights to disapply primary legislation). What it does mean is that the Court of Appeal decision stands (as discussed here).
So this needs unpacking, as they say. “Google v. Vidal-Hall” was a test case brought by an Apple user whose Safari browser cookies had been hacked by Google to track her for advertising. (Yes, it was back in 2010.) The High Court upheld it, and in effect introduced a new tort (legalese for “harm”) of invading one’s data privacy. The Appeal Court upheld it.
Now it seems Google gave up in its appeal to the Supreme Court, which means that this precedent stands. Did Google pay damages to the plaintiffs? If so, how much?
The other point made by Knight is that this should open companies up to lawsuits for torts around data privacy. Anyone in the UK who has ever been phoned by a company which got your details by shady means now has a precedent to lean on in the small claims court.
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the online giant’s director of product management Jonathan Bellack confirmed the overhaul to the trade publication. “We are collecting the price each exchange would pay, including AdX, and then putting it in a unified auction where the highest price wins,” Bellack said.
Previously, the DoubleClick AdExchange would wait for other exchanges to submit their bids before making its own, a dynamic that left it always in a position to outbid its rivals. By having the “last look”, Google’s exchange could simply bid $5.01 when the highest bid for a particular user from another exchange was $5. However, under the new auction news it would not be able to use that advantage to secure the bid.
The move could be the start of a change in the way Google works with adtech partners. Exchanges and publishers have listed Google’s ‘last look’ as one of their chief concerns, with Trinity Mirror’s head of programmatic Amir Malik treating header bidding as a way for his business to resist Google dominating ad inventory.
If you’re thinking “header bidding?” (I was) then this piece by Lara O’Reilly explains it. (O’Reilly has now left Business Insider; very interested to see where she next surfaces.)
Also, the idea that Google could put its thumb on the scales for this sort of bidding implies it has been driving out rivals for years. Why is it stopping now? Apparently because Facebook is offering header bidding, and exchanges like that, so fewer would bid for slots Google has to offer.
Seems like another sign that adtech is approaching some sort of minor implosion.
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Errata, corrigenda and ai no corrida: none notified