Rolandt

Sony behauptet: Die neue RX0 ist „die kleinste und leichteste Premium-Kompaktkamera der Welt“. Die RX0 II ist eindeutig auf Vlogger ausgerichtet, mit einem breiten, aber nicht unbedingt GoPro-weiten 24mm-Objektiv, eingebauter Stabilisierung, einem überraschend guten Mikrofon [...]

Many online communities are filled with frustration.

People have problems and go to the community to find answers. They begin in a state of frustration and hope the community alleviates that frustration.

All members see are problems.

What if they saw successes instead?

What if instead of seeing an endless list of problems which need to be solved they saw an endless list of member achievements?

What if instead of soliciting more problems we solicited more success stories and how those successes were achieved?

What would happen if we changed the very tone and nature of the community from ‘come here when you need an answer’ to ‘come here when you want to be inspired’.

You have an amazing opportunity, in the present time more than ever, to create a destination people want to visit as opposed to one they would rather not visit at all.

One of my hobbies is to make sure to have a good backup strategy and to loose as little data as possible once the inevitable happens. Rsync and Luckybackup, a graphical frontend for rsync have been my friends for years and helped me to maintain identical copies of critical installations over the years. One weak spot in my backup strategy so far was that some devices are often out of the house and thus away from backup drives and emergency spare duplicates for weeks on end, so any loss of them would mean a significant loss of data. But I’ve got a solution for that, too, now!

When a remote network has a good uplink capacity (read: 10+ Mbit/s) it is entirely possible to use rsync over the network to synchronize an emergency spare copy of a notebook at home. Even if a gigabyte or more of data changes from one synchronization procedure to the next, double digit uplink datarates ensure the synchronization process finishes in a reasonable amount of time. The main problem with the approach is that not all remote local networks are suitable. Either they only have a meager uplink that is too slow when gigabytes of data have to be sychronized or the connection is metered and data transfers are too expensive. But this problem can be nicely circumvented by checking the Wifi SSID and only start an rsync session when a device is in a network that can handle the traffic.

So here’s a script I’ve come up that I run from an emergency spare device at home every 30 minutes via an entry in ‘/etc/crontab’. It connects via SSH to the roaming device via a tunnel the remote device has previously established to a jump host at home automatically. It’s a bit of a detour but NAT leaves one no other choice. The script then checks the SSID that is currently used on the other end. If it is in the list defined at the top of the script it starts the rsync session. As rsync only sends those parts of large files that have changed, it’s no problem to also synchronize files with a size of several gigabytes such as Thunderbird email archives in which only a few kilobytes have changed. Very efficient! In addition, the rsync ‘partial’ flag is handy to continue the transfer of large files instead of starting from scratch again in case connectivity is interrupted. The script is obviously not perfect, a lot of things could be put into variables at the top to make it easier to adapt. But I guess that’s not a big hurdle if you want to reuse it.

#!/bin/bash

# Configure the different Wifi SSIDs that are suitable for rsync here
wifi_ssids="eduroam|lalaland|A4711"

# Exit if rsync is already running
if pgrep -f "rsync --partial"; then

  echo 'rsync already running, exiting...';
  exit 1;
fi


cd /home/xyz/excluded-from-sync/auto-rsync

# -------------------------------------------

# The local file to which the Wifi info of the remote host is written to
remote_ssid_info="remote-wifi-info.txt"

rm $remote_ssid_info

# Get the current Wifi the remote side is connected to.

ssh -p 12345 xyz@my-domain.com "iwconfig" > $remote_ssid_info

date

# If one of the Wifi IDs given above are in the result file, do
# the rsync as there is enough bandwidth available.

cat $remote_ssid_info

if grep -E $wifi_ssids $remote_ssid_info
then
    echo; echo "Good Wifi SSID found, running rsync..."
    
    # Full rsync
    rsync --partial -h --progress --stats -r -tgo -p -l -D --delete-after --exclude 'excluded-from-sync' --exclude .local --exclude .mozilla --exclude .luckybackup-snaphots/ --exclude .cache --exclude .config --exclude .ssh --exclude .bash_history -e "ssh -p 12345" xyz@my-domain.com:/home/xyz/ /home/xyz/


else
    echo;
    echo "Not suitable or unknown Wifi, aborting"
fi

echo
date
echo
echo "--------------------------------------"
echo

One thing the end of the Mueller inquiry doesn’t change: a foriegn government manipulated the US election and the beneficiary isn’t unhappy at all to have gotten that aid.

Any time a new app launches in the same category as a first-party, pre-installed app, there's always a lot to prove. It's one thing to find new customers in a market limited to third-party options, where prospective users have to pay one way or another to access an app in that category. But when there's a free, built-in option, third-party apps not only have to prove that they're good apps, they also have to offer enough extra benefit above and beyond what the Apple-designed default provides. The bar for such apps is raised higher in many ways.

Facing that challenge today is a new app from Flexibits, Cardhop for iOS, which serves as the iPhone and iPad companion to the contacts app launched for Mac in late 2017. Powered by a convenient natural language input system, Cardhop includes a host of features that differentiate it from Apple's Contacts app and pose a strong threat to the iOS default.

Getting started with Cardhop is extremely easy because it taps into your existing contacts database as configured in the Settings app. All the contacts you see in Apple's Contacts will, upon granting permission, be automatically displayed in Cardhop as well.

At the center of Cardhop's appeal is a feature that's entirely unsurprising to find in a Flexibits app: a strong natural language input system. Flexibits' popular calendar app, Fantastical, has included natural language input as one of its core strengths for years now, and Cardhop proves that it's just as powerful when applied to contacts.

Near the bottom of Cardhop's interface on nearly every screen of the app, you'll find a search box. Typing in this search field using natural language enables quick entry of new contacts, adding new data points to existing contacts, or triggering an action that reaches out to one of your contacts.

Put simply, Cardhop's natural language system works extremely well. When you're adding a new contact, you can simply type out the person's name, email, phone number, and more in a continuous string, then hit 'Add Contact' when you're done. You don't need to specify what type of data you're entering (e.g. phone, address, email, etc.) because Cardhop does the work of recognizing that for you – as you type you'll see your input transferred automatically into the appropriate fields of a new contact that's being created by the app with each keystroke. It's very fun to watch. Just as Fantastical's event creation system easily bests that of Apple's Calendar, so is Cardhop's method of contact input clearly superior to Apple's Contacts.

As I mentioned, Cardhop's natural language system is not only for creating new contacts, but also adding to existing ones, and triggering actions. The former requires typing the targeted contact's name in the search field, but even here Flexibits has simplified the process by recognizing abbreviations rather than requiring full spellings, and offering multiple immediate suggestions as needed based on what you type. When you find the right contact, hitting 'Update Contact' will add the search field's data to their contact.

Actions are an interesting method of engaging with your contacts. Similar to how Apple's Contacts enables you to kick off a message to someone, or look up directions to their place, or initiate a call – all by hitting buttons saved in someone's contact – you can do the same in Cardhop, but without having to locate a person's contact card first. Instead, you can simply type in the search field what you want to do – whether call, email, FaceTime, tweet at, or any other supported option – along with the person's name, and trigger an action right from the search field. Actions are also available in two other places: in a special keyboard row that's present whenever you type, and in your main contact database you can access them with a swipe right on a contact's name.

Besides all the obvious supported actions, there's one action type I wanted to mention that's nice to have: copy. By typing 'Copy' then someone's name (or, more likely, just the start of their name), Cardhop will present a card featuring that contact's different contact details, all of which have a convenient button next to them that copies that specific data to your clipboard. It's a small thing, but it can be really useful on a regular basis.

Although I expect most users are in the habit of initiating communication not from their contacts app, but from the appropriate communication apps like Messages, FaceTime, or Mail, actions are still a nice option to have, and with a bit of forceful habit change they could make Cardhop a powerful one-stop-shop for your communication needs. Just as Drafts is "where text starts," Cardhop could be "where communication starts."

Cardhop's natural language input system, and the power it provides, is the clear highlight of the app. However, there are a lot of other niceties that make Cardhop a compelling alternative to Apple's Contacts.

Birthday tab. One of Cardhop's five main navigation tabs is a Birthdays screen, which lists upcoming birthdays from your contacts in a simple, well organized layout. Although many people have outsourced birthday management to Facebook, if you're like me and no longer use Facebook, Cardhop offers a valuable utility to help you better remember your loved ones' special days.

Group management. For some inexplicable reason, Apple has decided that the ability to create and manage contact groups is the type of pro task that only a Mac should be able to handle. Fortunately, Flexibits takes a different approach. In Cardhop you can create new contact groups, modify existing ones, and add or remove users from groups. You can also filter your main contacts view to only display certain groups, or even exclude some.

Business card. You can set up a custom business card for yourself in Cardhop, which will load on iPhone whenever you turn your phone into landscape orientation. This business card can exclude any data points you want from your personal contact, such as your address for example. The business card presents a QR code that can be scanned by the person you just met, or there's also an option to manually share via the share sheet.

Directory lookup. Cardhop integrates with your existing contacts database on iOS, but the app also supports tapping into external directories of contacts stored on services like G Suite or Exchange. From Cardhop's Settings ⇾ Directories you can add a directory to easily locate both personal and work contacts.

And much more. Browsing the Settings screen in Cardhop exposes a lot of other options that make the app ideal for power users who like additional control over their apps. You can set up Siri shortcuts for a host of different app functions, customize the keyboard actions and quick actions I mentioned earlier to your liking, set up notifications for upcoming birthdays, tweak the app's color theme, activate automatic clipboard detection for data input, and also set the app to instantly trigger the search field when you launch Cardhop.

The only negative takeaway I've had from my time with Cardhop is that the app's method of displaying contact photos varies slightly from Apple's app, such that while the same images from Contacts are visible inside Cardhop, they're cropped in such a way that, in some cases, you can't see the important details of a person. Many of my contact photos look fine in Cardhop, but a few unfortunately have the tops of their heads cropped out. Hopefully Flexibits can address this in future update.

---

Like many of Apple's built-in apps, Contacts on iOS isn't a bad app, but it does lack a lot in the area of power user features and appears largely development-stagnant. It's fine for the average user, but its limitations open ample opportunities for third parties like Flexibits to capitalize on. And capitalize they have.

Cardhop is a Contacts replacement that successfully addresses the limitations of Apple's app, anchored by a powerful natural language input system and a host of clever features that are absent from Contacts. It's the quintessential example of a great third-party alternative: while Apple's default app is fine for many, Cardhop brings the additional power and flexibility that other users crave.

Cardhop is available on the App Store as a universal app for iPhone and iPad (where it supports Split View and all iPad Pro sizes). Its normal price is $4.99, but for a limited time it's available for $3.99.

---

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Doc Searls, Doc Searls Weblog, Mar 27, 2019

The Cluetrain Manifesto was an influential document when it came out 20 years ago. It proclaimed such things as "markets are conversations". I was one of the signatories when it was first released. There's a lot of discussion about what Cluetrain means, but my attention focused on a remark about how it spread. Searls says, "we didn’t do it by mass media methods. We did it by hacker’s methods. We wrote something we thought was good and put it out for review. Lots of people agreed that it was good and word spread from there." That's not exactly true. It was promoted by the Wall Street Journal and became a book. If you or I write Cluetrain, it sits there dead and unnoticed. But if the writer has connections with mass media, it becomes a mass phenomenon. That's what happened with Cluetrain.

Web: [Direct Link] [This Post]

I don't think I need to extol the virtues of AirPods in 2019. Over two years after their debut, Apple's truly-wireless earbuds, in addition to providing users with a convenient, seamless way to stream music and podcasts to their ears, have become a cultural phenomenon that has spurred some of the greatest memes of Tech Twitter in recent history. Everybody loves AirPods – provided their unique size and design fit their ears – and, most of all, everybody likes to say how much they love their AirPods.

This article is no different, but there's a small twist in the usual narrative that prompted me to write this story after receiving my second-generation AirPods yesterday.

For the past couple of months, I had to live with malfunctioning AirPods. As I noted on the Connected podcast a few weeks ago, I started noticing the first problems with battery life: when my AirPods were new and well into their first year of usage, I could listen to an entire episode of Upgrade or Cortex (usually around 90 minutes) without hearing the AirPods' dreaded low-battery chime; earlier this year, one of the AirPods (the right one) couldn't even survive 30 minutes of podcast listening without warning me that its battery would be depleted soon. Then the problem got worse: a few weeks ago, my original AirPods (which I bought as soon as they came out in December 2016) began randomly disconnecting from the iPhone while working out and listening to podcasts or music; upon reconnecting them, the right AirPod would simply refuse to play audio or it would play at an extremely low volume. Eventually, I accepted the fact that my AirPods had been used too much over their two-year life and that, like others, I just had to wait for new ones. I had to work out while wearing large over-ear Sony headphones which, let me tell you, were not designed for high-intensity interval training. It was, to put it mildly, unpleasant.

There isn't much to say about the physical appearance of the second-generation AirPods, which I ordered as soon as Apple sneakily announced them via a press release last week. The AirPods themselves look the same as the first-generation model; the case, which I bought in the wireless charging flavor, features a tiny LED indicator to communicate charging and pairing status on the outside – effectively, the only visible difference from the original AirPods case.

What I did notice in my first 24 hours with the new AirPods is how much I was missing the pristine AirPods experience; at the same time, I've also begun to appreciate the newly added convenience granted by further cutting the wires that tie AirPods to the iPhone – both physically and conceptually.

The first thing I realized while listening to my workout mix is that my old AirPods haven't been playing audio at full volume for quite some time. These new AirPods are loud, but not because they're actually louder than the first-generation model – I believe it's because my old AirPods hadn't been operating at full capacity without me knowing for a while. For this reason, audio quality on the second-generation AirPods feels like a considerable upgrade, even though on paper it's not; once again, I'm impressed by the sound Apple was able to pack in such a tiny and inconspicuous design.

I don't consider AirPods a comparable alternative to my favorite over-ear headphones, but they continue to perform better than some sports-oriented earbuds I tried in the past, and using the second-generation model reminded me of this. Of course, some are already debating whether or not the new AirPods sound better than the old ones; my first reaction is that they seem to sound "better" because the old ones had slowly degraded in quality over time.¹ Which I guess is something that just naturally happens when you're dealing with such small lithium-ion batteries. I'm okay with the progressive degradation of my original AirPods given how much I've used them every single day, in cold and hot weather, while exercising or doing chores around the house, since December 2016.

Where the new AirPods objectively sound better than the old ones is Siri. On the original AirPods, invoking Siri (either with a gesture or by summoning 'Hey Siri' on a paired iPhone) usually involved a small delay and the assistant's voice was robotic and low-quality. Siri on the original AirPods sounded like it was coming through FM radio. It was not a good experience, and a far cry from the human-sounding voice of Siri on iOS 12 and the HomePod. The second-generation AirPods natively support 'Hey Siri', which means that activation is instant and Siri's voice is crystal clear, just like on the iPhone. For the first time since Apple launched AirPods in late 2016, it feels like Siri itself, not a poorly streamed version of it, is in your ears. Thanks to these improvements, Siri becomes a fundamental part of wearing AirPods, and I'm already witnessing a shift in my behavior when it comes to asking questions that require a response from the assistant.

Since yesterday, I've been using Siri to play songs from Apple Music, log glasses of water through my hydration shortcut, check my agenda in Reminders, and start workouts on the Apple Watch. All of these actions were supported before with the original AirPods, but on the new ones Siri is faster and feels more integrated with the product itself; it is more comfortable to use and engage in a conversation with – and the comfort of the user experience is something I noted about the Apple ecosystem before.

By adding 'Hey Siri' support to the new AirPods and enhancing the assistant's sound quality, the new AirPods feel more independent from the iPhone; because I no longer begrudge the inferior sound quality of Siri in my AirPods, I can use 'Hey Siri' more often knowing that the voice in my ears isn't any worse than the "real Siri" on the iPhone.² The improved voice quality of Siri creates the illusion that the entire Siri service lives inside the AirPods while, in reality, you're still streaming its voice from a nearby iPhone.³ Not to mention how, thanks to the improved beamforming microphones in the new AirPods, giving commands to Siri also seems more accurate than before, resulting in fewer iPhone pickups to fix what 'Hey Siri' got wrong.

Speaking of freedom from the iPhone, the addition of wireless charging to the AirPods case also plays into this feeling of owning two small wearable computers that no longer live by the iPhone's rules. In the two years since Apple added wireless charging to the iPhone line, my girlfriend and I have scattered a variety of wireless charging mats around our apartment. Each of us has one on the nightstand; we have a Samsung charger in the hallway; we have one in the kitchen; and, I recently added this one on top of the "foot" of the LG UltraFine 4K display at my desk. The more we can remove Lightning wires dangling off our furniture, the better – especially with two playful dogs who just love to chew on them when we're not looking. But with the old AirPods, we still had to keep at least one Lightning cable around to top off the case, and as a result we often ended up plugging our iPhones in too out of laziness. We had to keep that Lightning cable around anyway, so why not?

With the new AirPods and their wireless charging case, we can finally say goodbye to that lone Lightning cable plugged into a USB charging dock and take advantage of all the wireless chargers we've disseminated throughout our house. It may be a trivial improvement, but it's nice not to have to keep "the iPhone cable" around anymore, and the AirPods lifestyle is, in my opinion, all about small niceties that are seemingly unnecessary, yet obvious once you get used to them. There's nothing to set up, no additional steps to consider: you just place the AirPods' case on any Qi charger, the LED indicator lights up, and it starts charging.⁴ It's a small increment of freedom that feels natural and user-friendly. The next logical step, of course, would be a wireless charger that lets you place any Apple device on top of it to charge it; to paraphrase what Robert Smith once said, it is the hope of all that might have been that fills me with the hope to wish impossible things.

Lastly, as I noted in my Instagram stories yesterday⁵, I ought to mention how the new H1 chip in the second-generation AirPods vastly improves connection times with faster speeds when switching AirPods between different Apple devices. Where the old AirPods used to take well over 5 seconds (and usually around 10, especially on the Mac) to switch as the active headphones between the iPhone, iPad Pro, or Mac mini, the second-generation AirPods can switch between devices/platforms in 2-3 seconds thanks to their improved wireless architecture. It's downright remarkable how fast the new connection is. Whatever Apple has done with their new H1 chip in the AirPods, I hope to see adoption of it in more audio products from the company in the near future – starting, perhaps, with proper over-ear headphones.

---

When you write about technology long enough – and I should know this, given that this very website turns 10 next month – it's easy to let an undercurrent of cynicism color your every opinion about tech products you're writing about. I get it – more than ever, corporations like Apple should be held accountable for their impact on our society and economy, and it's our job as reporters, writers, and podcasters to critique the minutiae of their decisions. I say this as the person who writes an annual essay about a mobile operating system that dissects APIs and developer frameworks. It's easy to be swept up in criticism at all costs.

I want to remind myself, and by extension MacStories readers, that it's okay to appreciate a product for what it is and be enthusiastic about it when it's delivered to consumers in a way that transcends spec sheets and console war-style debates. The new AirPods are such a product.

Just like the Apple Watch, despite the fact that they've been out for years now, AirPods still feel like a marvel of technological advancement that, more often than not, bring the word "magic" to mind. They're tiny wearable computers that can play music in your ears without wires, that have an inbuilt smart assistant, and stay with you during workouts or dance rehearsals, and that can be recharged by dropping them in a small plastic case which, somehow, now charges when it's placed on top of a mat. Even though the cynic might note that, well, actually, none of this is a technical mystery, and even though we'll probably be arguing with each other about what Apple should add to AirPods a year from now, today I just want to think of the fact that I can go for a walk wearing a wrist computer and two in-ear computers that go on the Internet, keep me company with music, and keep me in touch with other people in my life.

The new AirPods are a substantial improvement over the first generation in every regard. But for once, I just want to consider how they still feel like little bits of everyday magic.

---

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Tonight’s dish was Mueller chicken.

I didn’t plan it that way. I had put in the time and done my usual sous vide preparation, which has never failed before. Tonight’s version promised to be up to par. It smelled great. Even the first small taster bite was perfect.

As Katerina and I settled into the second and third bites, however, the story changed. The meat turned tough across sections of the breast. It happens, I’ve heard, but usually, it’s the quality of the chicken that defines how well it turns out when using sous vide. But this was the premium organic stuff. It wasn’t supposed to be this way.

So, I named it Mueller chicken. Something we really were really looking forward to but were ultimately so dissapointed by.

Like Gruber, I am not especially enthusiastic about yesterday’s announcements (largely because the most interesting are, as usual, US-centric). I’m more interested in News than anything else, but that’s just the core service, not the magazines–and I am expecting to be disappointed in terms of local sources.

Apple Pay in Portugal by end of this year would be nice, but that’s as far as my expectations go.

Brilliant. Simply brilliant. Although I’m skeptical public shaming will actually fix the problem, Apple fully deserves this kind of (very) public feedback regarding the MacBook keyboard, and then some.

Google recently announced a new advisory board on responsible AI, and it’s a nice demonstration of precisely how fucked up things are in AI ethics right now.

The biggest and most obvious WTF is the presence of the President of the Heritage Foundation, an American far-right thinktank whose politics are eye-wateringly terrifying. From xenophobic “build the wall” rhetoric to climate change denial, they have it all, including anti-abortion stances and positions in favour of discriminating against gay people.

Their primary horror from my perspective is their virulent transphobia. Seeking to treat trans rights as a wedge issue to split LGBT rights overall, they’ve funded the British TERF movement and actively campaigned against trans-inclusive legislation - campaigns that their President, in particular, publicly acknowledges and supports.

Quite why Google thinks they add to a conversation about responsibility is unclear, although Joanna Bryson, a professor also appointed to the panel who thinks that slavery is a good rights model for robot ethics, accidentally made the subtext text when she noted that they wanted to be able to reach the American far-right. Bryson happens to endorse this; indeed, she’d work with war criminals if it got something done.

But whatever the reason, the Heritage Foundation’s presence - and the attitudes expressed by the self-proclaimed “ethicists” appointed from out of academia - are damning to both Google and the academics appointed. If you are on this panel, you are communicating that you consider transphobia, homophobia and racism acceptable attitudes in a colleague if the status bump is good enough. There is nothing ethical or responsible in that: instead, it marks the absolute absence of responsibility: it marks you saying that you’d trade your queer or immigrant students’ comfort, safety and certainty for an unpaid google role. Every day the Heritage Foundation’s president remains on the panel, and people like Bryson, Luciano Floridi and other academics do not quit people are watching and taking note. And in the long term, this is worse for Google’s ethical reputation, and the reputations of appointees, than no advisory panel at all.

Joanna Stern for the WSJ:

Nop, I havn’t fogottn how to wit. No did my dito go on vacation.

You s, to sha th pain of using an Appl laptop kyboad that’s faild aft fou months, I could only think of on ida: tak all th bokn ltts out of my column. Thn I alizd that would mak th whol thing unadabl. So to...

You can switch on the broken E's and R's. Genius!

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watchOS 5.2 ist da und bringt erwartungsgemäß die EKG App der Apple Watch 4 auch nach Deutschland. Die Benachrichtigung bei einem unregelmäßigen Rhythmus gibt es für alle Apple Watch außer der Originalserie.

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Mozilla and a cohort of independent researchers have detailed the key traits that make for an effective ad archive API — and more transparent elections

 

On March 28 — after urging from dozens of civil society organizations — Facebook is set to launch its advertising archive API. This tool is intended to provide researchers, journalists, and users with transparency into political ads and audience targeting on Facebook.

Google also pledged to launch a similar tool ahead of the May 2019 EU Parliamentary elections (but postponed their initial March launch date.) As disinformation continues to spread across online platforms with the potential to interfere with democratic elections, it’s critical that these tools are accessible and effective.

So today, Mozilla and a cohort of 10 independent researchers are publishing five guidelines that these APIs must meet in order to truly support election influence monitoring and independent research.

Says Ashley Boyd, Mozilla’s VP of Advocacy: “Researchers play a critical role in tracking and reporting disinformation, and then sharing this information with the public and public officials. These API guidelines — developed with technical and policy experts — represent baseline requirements which would empower researchers to better understand and document how disinformation spreads, how it influences elections, and how it impacts society.”

Boyd continues: “Our goal is to ensure lawmakers and the public can critically assess Facebook and Google’s transparency efforts. And, can hold the tech companies accountable if they fall short.”

The 10 experts are based at Oxford University, the University of Amsterdam, Vrije Universiteit Brussel, Stiftung Neue Verantwortung, and other institutions.

These guidelines are being shared publicly with European Commissioners Mariya Gabriel, Julian King, Andrus Ansip, and Vera Jourova, who are responsible for assessing how platforms are upholding their commitments under the EU Code of Practice on Disinformation. The guidelines are also being shared with Facebook and Google.

The guidelines + our letter to policymakers and the tech companies:

 

To: Google, Facebook, Twitter

Cc: Vice President of the European Commission Andrus Ansip, European Commissioners Mariya Gabriel, Vera Jourova, Julian King

We, the undersigned, are independent researchers investigating the wide variety of issues that are crucial to understanding the impact of disinformation on our societies. This includes research into:

• inauthentic accounts and behaviour on social media,
• political and issue-based advertising,
• micro-targeting practices and ad placements by political parties and other entities,
• the effectiveness of self-regulation measures to counter disinformation
.

To do this work effectively, there must be fully functional, open APIs that enable advanced research and the development of tools to analyse political ads targeted to EU residents. This requires access to the full scope of data relevant to political advertising, and that access must be provided in a format that allows for rich analysis. Tools provided often lack the necessary data or, due to limited functionality, do not allow for analysis.

A functional, open API should have the following:

[1] Comprehensive political advertising content. The APIs should include paid political ads and issue-based ads, without limiting access on the basis of pre-selected topics or keywords. “Political” ads might include, but are not limited to:

• direct electioneering content
• candidates or holders of political office
• matters of legislation or decisions of a court
• functions of government

Non-paid, public content that is generated by users who are known political content purveyors should also be available.

[2] The content of the advertisement and information about targeting criteria, including:

• The text, image, and/or video content and information about where the ad appeared (newsfeed, sidebar, etc.).
• The targeting criteria used by advertisers to design their ad campaign, as well as information about the audience that the ad actually reached.
• The number of impressions that an ad received within specific geographic and demographic criteria (e.g. within a political district, in a certain age range), broken down by paid vs. organic reach.
• The amount of engagements that an ad received, including user actions beyond viewing an ad.
• Information about how much an advertiser paid to place the ad.
• Information about microtargeting, including whether the ad was a/b tested and the different versions of the ad; if the ad used a lookalike audience; the features (race, gender, geography, etc.) used to create that audience; if the ad was directed at platform-defined user segments or interests, and the segments or interests used; or if the ad was targeted based on a user list the advertiser already possessed.

[3] Functionality to empower, not limit, research and analysis, including:

• Unique identifiers associated with each advertisement and advertiser to allow for trend analysis over time and across platforms.
• All images, videos, and other content in a machine-readable format accessible via a programmatic interface.
• The ability to download a week’s worth of data in less than 12 hours and a day’s worth of data in less than two hours.
• Bulk downloading functionality of all relevant content. It should be feasible to download all historical data within one week.
• Search functionality by the text of the content itself, by the content author or by date range.

[4] Up-to-date and historical data access, including:

• Availability of advertisements within 24 hours of publication.
• Availability of advertisements going back 10 years
• APIs should be promptly fixed when they are broken
• APIs should be designed so that they either support or at least do not impede long-term studies

[5] Public access. The API itself and any data collected from the API should be accessible to and shareable with the general public.

In the spirit of upholding the EU Code of Practice on Disinformation, we expect you to empower the research community by implementing open, functional APIs of the quality outlined in this letter — just as we expect elected officials and public authorities to fully support the release of such data in a privacy-compliant fashion to enable independent research and inform public debate. Your action on this is essential to ensuring the integrity of the upcoming European Parliamentary elections — as well as elections happening all around the globe — is upheld.

Yours Sincerely,

Mozilla Foundation

Co-written by

1. Jef Ausloos (University of Amsterdam, NL)
2. Jason Chuang (Mozilla)
3. Chloe Colliver (Institute for Strategic Dialogue, UK)
4. Laura Edelson (NYU Tandon School of Engineering, US)
5. Sasha Havlicek (Institute for Strategic Dialogue, UK)
6. Natali Helberger (University of Amsterdam, NL)
7. Stefan Heumann (Stiftung Neue Verantwortung, GER)
8. Sam Jeffers (Who Targets Me, UK)
9. Rasmus Nielsen (University of Oxford, Reuters Institute for the Study of Journalism, UK)
10. Alex Sängerlaub (Stiftung Neue Verantwortung, GER)
11. Michael Veale (University College London and University of Birmingham, UK)
12. Mathias Vermeulen (Vrije Universiteit Brussel, BE + Mozilla Foundation)

Co-signed by

13. Amelia Acker (University of Texas at Austin, US)
14. Alexandre Alaphilippe (EUDisinfoLab, BE)
15. Jonathan Albright (Columbia University, US)
16. Isabelle Augenstein (University of Copenhagen, DK)
17. Dominik Batorski (University of Warsaw, PL)
18. Anja Bechmann (Datalab, University of Aarhus, DK)
19. Reuben Binns (University of Oxford, UK)
20. Tobias Blanke (King’s College London, UK)
21. Kalina Bontcheva (University of Sheffield, UK)
22. Ruben Bouwmeester (Deutsche Welle, DE)
23. Elda Brogi (European University Institute, IT)
24. Axel Bruns (Queensland University of Technology, AUS)
25. Paul-Olivier Dehaye (Personaldata.Io, BE)
26. Leon Derczynski (IT University of Copenhagen, DK)
27. José van Dijck (Utrecht University, NL)
28. Marius Dragomir (Central European University, HU)
29. Charles Ess (University of Oslo, NO)
30. Aline Franzke (University Duisburg-Essen, DE)
31. Erika Franklin Fowler (Wesleyan University, US)
32. Michael Franz (Bowdoin College, US)
33. Frederic Guerrero-Solé (Universitat Pompeu Fabra of Barcelona, ES)
34. Jakub Grue Simonsen (University of Copenhagen, DK)
35. Luca Hammer (University of Paderborn, GER)
36. Jaron Harambam (University of Amsterdam, NL)
37. Anne Helmond (University of Amsterdam, NL)
38. Phil Howard (Oxford University, UK)
39. Vladan Joler (Share Lab, US)
40. Steve Jones (University of Illinois at Chicago, US)
41. Brian Keegan (University of Colorado, Boulder, US)
42. Aleksi Knuutila (Open Knowledge, FI)
43. Aleksandra Korolova (University of Southern California, US)
44. Alex Krasodomski (Demos, UK)
45. Philip Kreißel (Ich bin here, DE)
46. Aleksandra Kuczerawy (University of Leuven, CITIP, BE)
47. Paddy Leersen (University of Amsterdam, NL)
48. Christina Lioma (University of Copenhagen, DK)
49. Sílvia Majó-Vázquez (University of Oxford, UK)
50. James Meese (University of Technology Sydney, AUS)
51. Divina Meigs (Sorbonne Nouvelle University, FR)
52. Marianela Milanes (Asociación por los Derechos Civiles, ARG)
53. Carl Miller (Demos, UK)
54. Lisa-Maria Neudert (Oxford University, UK)
55. Aviv Ovadya (The Thoughtful Technology Project, US)
56. Symeon Papadopoulos (Centre for Research and Technology Hellas, GR)
57. Cameron Piercy (University of Kansas, US)
58. Thomas Poell (University of Amsterdam, NL)
59. Oreste Pollicino (Università Bocconi, IT)
60. Travis Ridout (Washington State University, US)
61. Luca Rossi (IT University of Copenhagen, DK)
62. Yotam Shmargad (University of Arizona, US)
63. Javier Ruiz Soler (European University Institute, IT)
64. Damian Tambini (London School of Economics, UK)
65. Emily Taylor (Chatham House, UK)
66. Rebekah K. Tromble (Leiden University, NL)
67. Claes De Vreese (University of Amsterdam, NL)
68. Abby Wood (University of Southern California Gould School of Law, US)
69. Brahim Zarouali (University of Amsterdam, NL)
70. Amy X. Zhang (MIT CSAIL, US)
71. Arkaitz Zubiaga (Queen Mary University of London, UK)

If you are a researcher working on these issues and want to co-sign this letter please contact us at mathias@mozillafoundation.org, and we will add names on a rolling basis to this letter.

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This work is part of Mozilla’s larger effort to combat online disinformation ahead of the upcoming EU elections, and other elections around the world in 2019. We’re closely following the commitments these companies made in the EU Code of Practice on Disinformation, in order to influence the development and assessment of the most effective tools possible.

Earlier this year, Mozilla and dozens of our allies sent a public letter to Facebook, demanding the company make good on their promises to provide more transparency around political advertising. Mozilla also conducted an EU-wide survey on the state of misinformation, and found that nearly 84 percent of people polled suspected (or knew for certain) that they had seen misinformation while using the internet that very week.

The post Facebook and Google: This is What an Effective Ad Archive API Looks Like appeared first on The Mozilla Blog.

I’m in San Francisco meeting people all week.

There’s no agenda for any of these meetings.  

I do this about 2 to 3 times a year. I book a flight, stay in a hotel, and meet as many interesting people in our community as possible. It’s not cheap, but the results are invaluable.

Every single meeting yields something valuable; a future blog post, a resource idea, a feature idea, a connection to make with another member. At the very least, they confirm or refute ideas we’ve held about community building.

The idea from my last book, the training courses we create, the events we’ve hosted, and many potential clients, have all come from in-person meetings with no agenda.

I’m surprised organizations investing hundreds of thousands of dollars per year in a community don’t invest just $2k more to have their staff meet their members on their own turf.

Try it, you might be surprised a) just how many members are happy to meet and b) the incredible information they give you. 3 to 5 meetings a day really adds up.

Gestern hatten wir es noch von den unzuverlässigen MacBook-Tastaturen, heute kommt eine Meldung von Microsoft zu einer sehr guten Alternative:

Surface Book 2 bekommt eine Auffrischung: Die Variante mit 13,5-Zoll-Display, 256 GB Speicher und 8 GB Arbeitsspeicher wird mit Intels i5 Quad-Core Prozessoren der 8. Generation ausgestattet. Das neue Modell wird am dem 28. März 2019 auf dem deutschen Markt verfügbar sein. Privatkunden bekommen die neue Variante zum unveränderten Preis von 1.749 Euro (UVP, inkl. MwSt.) im Microsoft Store oder bei Surface Fachhändlern. Geschäftskunden erhalten es ebenfalls weiterhin für 1.749 Euro (UVP, inkl. MwSt.) über einen autorisierten Reseller. Surface Book 2 verbindet hohe Leistungsfähigkeit mit High-End-Komponenten in einem designstarken Gehäuse. Es kommt mit einem USB-C Port, bis zu 17 Stunden Akkulaufzeit und einem hochauflösenden PixelSense-Display für eine Touch-Bedienung mittels Finger oder Stift. Das Device ist auch mit 15-Zoll-Bildschirm mit Intel Core i7 in verschiedenen Ausstattungsvarianten erhältlich.

Das ist das Gerät, bei dem man das Display abnehmen kann, um es als Tablet zu verwenden. Mit dem Core i5 der siebten Generation übrigens 400 Euro billiger. Das wäre mein Favorit.

Two weeks ago the MozReview developers got together to do some focussed hacking. It was a great week, we got a lot of stuff done, and we clarified our priorities for the coming months. We deployed several new features and improvements during the week, and we made good progress on several other goals. For this week, we actually opted to not go to a Mozilla office and instead rented an AirBNB in Montreal—our own little hacker house!

Sometimes you really do need to get away. Escape, part search engine and part research project from students at the MIT Senseable City Laboratory in Singapore, shows you the cheapest flights out of any given city. Just put in a location, and you get color-coded connections to everywhere around the world.

Tags: flights, prices

The shrouded exoskeleton of Province House, lit from within.

This 1986 image shows changes to this part of Robson Street have been subtle, and minimal over 33 years, but that’s about to change. The trees have grown bigger, so fortunately these are winter shots, although despite that the Blue Horizon Hotel on the right in the distance now disappears behind the tree trunk. The most obvious change to come has already started in the middle distance. This ‘current’ image is already out of date – The Empire Landmark Hotel (once a Sheraton) is steadily being demolished to be replaced by two condo and non-market rental towers, with new retail and office space in the podium. There are more towers underway further down the hill as well.

On the right of the picture the First City Trust Building was completed in 1969, designed by Frank Roy with Thompson, Berwick Pratt and Partners. Now known by its address, 777 Hornby, in the 1990s it received a makeover that included redesigning the podium glazing. Before it was built the Richmond Apartments stood on the corner. Mayfair House, on the left, completed in 1980, still looks the same.

Image source: City of Vancouver Archives CVA 800-2672

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Over the past few decades, we have witnessed a quiet revolution in the understanding of probability and, with it, the production of knowledge. One could call it the Bayesian revolution, after the 18th century statistician Thomas Bayes, who proposed that probability represents not an objective assessment of an event’s frequency but a subjective measurement of belief: an informed prediction about whether an event will occur.

While relatively simple mathematically, Bayesian statistics is computationally intensive, which, until recently, made it prohibitively difficult to operationalize. But with the development of modern computational capacity, Bayesian probability is ascendant, offering a way to turn the large volumes of data now being captured into predictions and “insights.” It provides the underlying logic for Google’s search engine, Five Thirty-Eight’s mode of political coverage, and algorithmic trading in financial markets. Most everything that we think of as artificial intelligence is also a product of applying statistics and probability to a growing field of problems — everything from predictive policing to online dating to surveillance assessment to targeted ads to voice detection and interpretation. Any time you see a moving needle assessing an election night outcome, use a spam filter, or receive a recommendation based on what others “like you” have done, you are experiencing the products of Bayesian probability in action.

Bayesian thinking has been hailed by a handful of technologists and scientists as a panacea for our broken times. Countless articles (including this one in Aeon and this peer-reviewed article in PLOS ONE about replication problems in psychology) have suggested that Bayesian statistics could, as though by magic, cure the replication crises that have plagued various scientific disciplines, addressing the corrupting influences of incentives. The Bayesian approach has even inspired a self-help movement of sorts devoted to promoting “rational thinking,” published under the moniker Less Wrong. Bayesian thinking has become idealized as a norm to aspire to and ultimately to be controlled by.

Whereas previous ways of conceiving statistics imagined creating a hypothesis and designing an experiment to evaluate it, Bayesian statistics starts with a belief about the world and updates that belief as new data becomes available. This makes it more suited for an era where computers can work on a constant stream of real-time data. As Michael Lynch, the founder of the data analytics company Autonomy, explained in a article in Wired in 2000, “Bayes gave us a key to a secret garden … With the new, superpowerful computers, we can explore that country.” But that country is turning out to be a place where seeking the most profitable course of action supplants efforts to evaluate other kinds of truth.

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To appreciate how revolutionary Bayesian probability is, it is helpful to understand the kind of statistics it is supplanting: frequentism, the dominant mode of statistical analysis and scientific inference in the 20th century. This approach defines probability as the long-run frequency of a system, with statisticians designing experiments to gather evidence to prove or disprove a proposed claim about what that long-run frequency is. (Though it should be noted there are many debates internal to frequentism about exactly what is proved or disproved in an experiment.) This initial claim does not change during the analysis but is instead determined as disproven or not. For example, a statistician might claim that a flipped coin will land heads 50 percent of the time and hold this claim static while they gather evidence (i.e. flip the coin). Their prediction addresses not a particular coin flip but rather what is expected to happen over a series of flips, based on some theory.

The upside of this approach to probability is its apparent objectivity: Probability merely represents the expected frequency of a physical system — or something that can be imagined as analogous to one. What one believes about the next coin flip does not matter; the long-run frequency of heads will be the basis for evaluating whether the hypothesis about its probability is accurate (i.e. whether the flips we observe correspond with how we believe the system operates). In this way, scientific analysis proceeds by setting a hypothesis at the beginning, and, only after all of the data is gathered, evaluating whether that hypothesis objectively corresponds with the data.

While this approach seems sensible, it is also limiting. It prevents statisticians from directly assigning a probability to a single event, which has no long-run frequency. Actual events do not have a probability: They happen or they do not. It rains tomorrow or it doesn’t; a car crashes or it doesn’t; a scientific hypothesis is true or not. A coin lands on head or tails; it does not half-land on heads. So for a frequentist, a claim like “there is a 70 percent chance of rain tomorrow” requires imagining a series of similar days — what statisticians refer to as a “reference class” — that could function like a series of coin flips. Moreover, this means under a traditional frequentist interpretation, a hypothesis cannot be given a probability since it is not a frequency (e.g. either a coin is fair, or it isn’t).

Frequentism thus required extensive human involvement in defining problems and designing experiments. Despite frequentism’s often-claimed objectivity, the selection of the reference class is subjective. For a coin flip, for instance, the reference class could be every flip with a given coin, or it could be only those made under specific atmospheric conditions or those performed by a specific flipper. Different reference classes can have substantial effects on the long-run frequency of an event and hence the accuracy of hypotheses about its probability. In the 1970s, two Bayesian statisticians, D.V. Lindley and L.D. Phillips demonstrated how under a frequentist interpretation of probability, variations in what a researcher thinks about an experiment can substantially change the results even with the same data.

A Bayesian approach, however, provides a means to do away with the reference class. It further proposes that one can dispense with the established process of formulating a hypothesis, testing it, and then evaluating it. Instead researchers make a generalized model of some problem and then update the model as new evidence is gathered. As opposed to the supposedly “objective” understanding of probability as a stable long-run frequency, Bayesian interpretations understand probability to be an ever-updating subjective measure of what researchers believe about whether an event will occur or not. This allows probability to be established for single events, and any event happening or not can be its own hypothesis.

Researchers might initially assume a coin will land heads 50 percent of the time, but if results defy that prediction, they will adjust their expectations accordingly to correspond with the conditions on the ground — like an election night prediction needle — without having to invent a whole new theory. In fact, Bayesians can treat every possible bias for the coin as a possible hypothesis and calculate the probability that each is true. When frequentists say a coin flip should be 50/50, they are saying something constant about a whole series of flips. If that proves false, they come up with a new hypothesis to test. But a Bayesian would say, I believe it is 50/50 now, and I will adjust my beliefs based on the results as they come in. For example after a few flips 51/49 might appear the most probable bias for the coin.

To understand how this works with a more practical example, imagine a drug test for cyclists that can detect doping 98 percent of the time, with a 3 percent false-positive rate. We may initially assume that if a rider tests positive, there is only a 3 percent chance that they are not doping. But Bayes’s theorem can be used to calculate the probability a cyclist is doping based on how pervasive we believe doping is in the field. Bayesian inference starts with a gut feeling (5 percent of bikers are doping) and, as more evidence is obtained (the results of actual drug tests), the probability becomes increasingly objective. If we believe that only 5 percent of the field is doping, it turns out that a positive test corresponds with just over a 60 percent chance that a cyclist is doping. This may seem counterintuitive, but it is simply because, based on our assumption about how prevalent doping is generally, there are so many more non-doping cyclists who could produce a false positive than doping cyclists who could produce a true positive. As we conduct more tests, we increase the accuracy of our guess about how much of the field is doping, as the output of subsequent rounds of calculation reflect the data more and more and our initial assumptions less and less.

Though people start from different assumptions, beliefs (such as how common doping is) should converge with additional evidence, becoming less subjective and more objective. In this way, Bayesian thinking provides a guide for how to think — or more specifically, how to turn our subjective beliefs into increasingly objective statements about the world. Using Bayes’s theorem to update our beliefs based on evidence, we arrive ever closer to the same conclusions that others with the same evidence arrive at, slowly moving away from our starting assumptions.

Bayesian statistics adopt a behavioral as opposed to epistemological understanding of statistics, building on the work of early frequentist statisticians like Jerzey Neyman and Egon Pearson. Prior to this behavioral shift — and in the way many introductory statistics classes are still taught — the point of evaluating data was to determine whether one can say some hypothesis is true, e.g. whether a daily low dose of aspirin really reduces the risk of a heart attack, yes or no. As Leonard Savage, an early American Bayesian, noted, “the problems of statistics were almost always thought of as problems of deciding what to say rather than what to do.”

But for many Bayesians, the question is not whether it is permissible to say a claim is true; the point is whether it is profitable to act as if it were true. With a Bayesian approach, it is possible to weigh the certitude of one’s predictions against the costs of being wrong. It allows computation to capture subjective belief and translate it directly into value, into an economic calculation. For example, if one knows the costs of shutting down a production line to recalibrate it as well as the cost of selling defective products, you can calculate how many products to inspect in order to minimize needless inspections and lost production time while keeping the rate of defective products within an acceptable range. In this way, one worries less about knowing the truth — in this case assuring the quality of every product — and more about how to behave as if one knows it.

By understanding statistics as measuring how profitable something will be rather than how accurate a statement about it is, the Bayesian revolution provides a methodology for data analysis that can be applied directly to maximize the return on investment (whether for ads, mortgages, or political campaigns) that can be squeezed from any data at hand. We can see this same underlying logic at work in a whole host of other technologies, from online advertising to determining who will repay credit card debt. The Bayesian revolution ultimately ties knowledge to its ability to be worth something. Statistics — and with it machine learning — turns out to be, in a sense, a subdiscipline of economics.

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The Bayesian revolution taught the world how to use any available data as evidence to produce predictions instead of theories. It has likewise convinced a generation of engineers and data scientists that computers can calculate belief just as well as humans can establish and test their own beliefs. In her book about Bayesian statistics, for instance, Sharon McGrayne quotes Peter Norvig, Google’s research director, who told her that “there must have been dozens of times when a project started with naïve Bayes” — one of the simplest and most straightforward implementations of the Bayes classifier, which probabilistically classifies things into categories — “just because it was easy to do and we expected to replace it with something more sophisticated later, but in the end the vast amount of data meant that a more complex technique was not needed.” 

Bayesianism in machine learning allows computers to find patterns in large data sets by creating a nearly infinite set of hypotheses that can be given probabilities directly, and can be constantly updated as new data is processed. In this way, each possible category for a given thing can be given a probability, selecting the one with the highest probability as the one to use (at least until the probabilities update when new data is gathered). Thus no complex theory is needed to set up a hypothesis or an experiment; instead a whole field of hypotheses can be automatically generated and evaluated. With cheap computing and massive data stores, there is little cost to evaluating these myriad hypotheses; the most probable can be extracted from a data set and acted upon immediately to produce profit, by selling ads, moving money to new investments, or introducing two users to each other, and so on.

It’s this approach to data that has led to such claims as Chris Anderson’s 2008 declaration in Wired that we are approaching the end of theory: “Petabytes allow us to say: ‘Correlation is enough.’ We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns.” The subjective theory of statistics has allowed some to dream of the complete evacuation of human involvement from science, rather than asserting the importance of human understanding to that process. Taken to its extreme, the outcome of this thinking is that humans should think and evaluate their own beliefs more like computers rather than the other way around.

Bayesian thinking and its valorization of a science of doing rather than knowing has allowed a whole host of human activities to be predicted rather than theorized. Read in its most dystopian light, this revolution has allowed algorithms to treat subjective knowledge as though it were objective, calculable, and ultimately predictable. This belief in the ability to predict probabilistically has allowed data scientists to try to control who sees what news, which friends people make, what dates they go on, the credit they are given, the jobs their applications are considered for. In the cases of predictive policing, antiterrorism operations, and prison sentencing, algorithms overseen by probability dictate how and to whom state violence is meted out — often reproducing racial, ethnic, and gender biases, as numerous studies (including this recent work by Pro Publica on predicting recidivism rates and Safiya Noble’s book about racial bias in search algorithm results) have shown.

We may look back at this revolution in statistical methodology as being equally important as the Taylorist and Fordist revolutions in production at the turn of the 20th century. While those earlier revolutions created the conditions for the “automatic” production of material goods, the Bayesian revolution is cementing the conditions for the automatic production of knowledge. Those 20th century revolutions greatly increased the power and scope of the capitalist class as they desubjectified labor, making workers more like machines. Likewise, the Bayesian automation of knowledge production has now concentrated wealth and information, threatening to circumscribe both the world we see and the way the world sees us.

While it is perhaps tempting to be nostalgic for frequentism’s commitment to knowing over doing, it is increasingly unlikely we could ever go back. Today frequentism appears broken and untenable. (One has only to look at the number of articles — including this recent statement from the  American Statistical Association and this editorial in Nature — declaring the dangers of p-values and significance testing, two of the workhorses of frequentism.) The task ahead is to make modes of knowing that are more just, that do not simply serve to reproduce social injustices or concentrate wealth and knowledge in the hands of the few. Without an understanding of the stakes and implications of the Bayesian revolution, this would not only be improbable but impossible.

What a difference four years make, and some apologies need to be made by several people to the Duke of Data and Director of Simon Fraser University’s City Program Andy Yan. It was the Fall of 2015 when Andy first crunched the numbers and in his very straight forward way told the CBC that his review of 172 property sales on Vancouver’s west side found 66 percent of owners had “non-anglicized Chinese names” suggesting they were recent arrivals. At that time 18 percent of these residences were purchased without a mortgage which is a pretty jaw dropping feat for many local residents. That was potentially a significant group of people entering the local housing market, and could represent the commodification of housing as a holding, not a place to live in.

Despite the fact Andy was basing his analysis on real data,  real estate pundits were quick to fingerpoint about “intolerance, racism, singling out certain groups of people …to blame” and even the past Mayor of Vancouver fell into the rhetoric stating “This can’t be about race, it can’t be about dividing people. It needs to get to the core issue about addressing affordability and making sure it’s fair.”

Well we all know where that logic took us in terms of housing affordability and it revealed an interesting cultural trait of Canadians about not wanting to offend, even when data suggested there was a trend that should be examined.

A new report from CMHC (Canada Mortgage and Housing Corporation) suggests that 11 percent of Metro condos are owned by people who don’t live in Canada. Reviewing the data Andy Yan found that in Richmond 25 percent of recently constructed condominium units were owned by a non-resident owner.

In total of $965 billion dollars worth of housing in greater Vancouver $75 billion had at least one  non-resident owner. Looking at Vancouver, “non-residents of Canada are involved in the ownership of at least $34-billion worth of residential real estate, about 10 percent of the $341-billion total in the city.”

As Dan Fumano writes in the Vancouver Sun it is now “impossible to know what might have been different, if voters, bureaucrats and elected officials alike had access three years ago to this clearer picture. We don’t know what policy decisions or priorities might have looked like at the municipal, provincial or federal level.”

It was the reporter Douglas Todd who wrote an opinion piece in the Vancouver Sun in early 2016 that encapsulated the debate. Three years ago Todd quoted Vancouver immigration lawyer Samuel Hyman who stated that Andy Yan “should be thanked by politicians, many of whom have for decades displayed “cowardice” by failing to document how trans-national migration and international money transfers shape housing prices. Given that most countries closely monitor the nationalities of who is buying property, the claims of “racism” from the mouths of some Metro Vancouverites are unprecedented compared to elsewhere in the world.”

What a shame it has taken three years for us to all realize that Andy was right. What could have been done differently had this data been taken seriously at the time?

 

Images:SFU&Shawmedia

 

Ookla’s recent study of airports with the fastest internet across the U.S. and Canada has revealed interesting results.

Regarding Canadian airports, Calgary’s ‘YYC’ comes in third place with a mean download speed of 67.23Mbps and mean upload of 87.99Mbps. The other three Canadian airports featured in the study, which Ookla says only includes the largest airports across North America, are Toronto’s Pearson International, Montreal’s Pierre Elliott Trudeau International and Vancouver International Airport.

In what shouldn’t come as a surprise to anyone who frequently passes through Pearson, the airport comes in second last when it comes to internet speeds, with a mean download speed of 7.14Mbps and a mean upload speed of 8.99Mbps. Montreal’s Pierre Elliott Trudeau, on the other hand, sits in dead last with a mean download speed of 6.41Mbps and a mean upload speed of 6.81Mbps.

In terms of other Canadian airports included in the list, Vancouver’s YVR sits in the seventh position with 50.11Mbps download and 72.19Mbps upload.

The top airport in North America regarding internet download speeds is Seattle’s Seatac Airport with a 103Mbps download speed. Ookla says the airport stole this title from Denver, which held the top slot for the last two years. Denver’s ‘DEN’ now sits in second place with a 78.68 download speed and a 98.75 upload speed.

Ookla performed these speed tests from January through to April 2018 on each airport’s free Wi-Fi. These speeds were then compared with those from March through May 2017.

Source: Ookla 

The post Calgary’s YYC has fastest download speed of all Canadian airports: Ookla appeared first on MobileSyrup.

Facebook has announced it is banning any “praise, support and representation of white nationalism and separatism” on its platform.

A blogpost on its website said the new ban will go into effect next week, adding that the concept and groups associated to the concept “have no place on our services.”

“Our policies have long prohibited hateful treatment of people based on characteristics such as race, ethnicity or religion — and that has always included white supremacy. We didn’t originally apply the same rationale to expressions of white nationalism and separatism because we were thinking about broader concepts of nationalism and separatism — things like American pride and Basque separatism, which are an important part of people’s identity,” Facebook wrote.

It was noted that moving forward, “white people will still be able to demonstrate pride in their ethnic heritage” but will not “tolerate praise or support for white nationalism and separatism.”

Facebook Canada noted that people will be able to notify the company when they see content associated to white nationalism or separatism as well.

Source: Facebook

The post Facebook bans content that supports white nationalism and separatism on its platform appeared first on MobileSyrup.

Earlier this week, Apple finally unveiled its TV+ video streaming platform, which will offer various original content from big-name talent, including Steven Spielberg and Oprah Winfrey.

While Apple didn’t reveal specific pricing or availability for the service beyond a fall 2019 launch window, the company did detail some of the series it has been working on.

Most notably, one of the Apple TV+ original dramas, See, was filmed in various parts of Vancouver.

The 10-episode series, which stars Jason Momoa (Game of Thrones, Aquaman) as the warrior Baba Ross, takes place in a future when humans have lost all sight and need to find other means to survive. At the same time, they will have to deal with a set of twins who are miraculously born with the ability to see.

Having survived for years without vision, these people live rather primitively in the wild, making Vancouver’s mountainous environment a suitable filming location. Specifically, filming took place between September 2018 and February 2019 in such areas as Comox and Strathcona Provincial Park.

Vancouver is a popular filming location in the entertainment industry due to lucrative tax breaks given out by the government. Some of the most notable recent movies and TV shows shot in and around the city include all of the CW’s DC Comics ‘Arrowverse’ shows (Arrow, The Flash, DC’s Legends of Tomorrow and Supergirl), both of 20th Century Fox’s Deadpool films (starring Vancouver’s own Ryan Reynolds) and Paramount Pictures’ Star Trek Beyond.

Momoa himself is no stranger to filming in Canada either, with the Hawaiian actor’s Discovery Channel historical period drama Frontier shooting in Newfoundland and Labrador, Nova Scotia and Ontario, among other locations.

During Apple’s keynote earlier this week, Momoa and co-star Alfre Woodard (Marvel’s Luke Cage) — who plays Ross’ advisor Paris — hinted at what to expect from the show.

“Try to think about the world [of See] this way: heard, touched, smelled, sensed. Imagine every human experience available to you — love, joy, discovery, despair, and home — imagine it was all experienced this way … without seeing,” Momoa teased.

“In this world, we have our evil queens, brave heroes, and thrilling adventure, but beyond the adventure, See will ask questions you may have already started asking yourselves…how much of my experience of the world is visual? Without sight, will it change who I am?” added Woodard.

The post Jason Momoa’s Apple TV+ drama series ‘See’ filmed in Vancouver appeared first on MobileSyrup.

Following the publication of a critical column by The Wall Street Journal’s Joanna Stern where the story was written without the letters ‘E’ and ‘R’ being usable on the keyboard, Apple has released a statement regarding the reliability issues customers are experiencing with the MacBook Pro.

“We are aware that a small number of users are having issues with their third-generation butterfly keyboard and for that we are sorry,” an Apple spokesperson told Stern in a statement. “The vast majority of Mac notebook customers are having a positive experience with the new keyboard.”

Apple’s ‘Butterfly’ mechanism keyboard has been under fire for the last few years. Following criticism from The Outline’s Casey Johnston, Apple quietly added a rubber membrane under the keyboard in the 2018 MacBook.

Although Apple said it added the membrane to make the keyboard quieter, most believe it was added to keep dust and other particles from getting stuck under the keys. Further, Apple launched an extended repairs program that didn’t include the 2018 version of the MacBook Pro keyboard.

It eventually turned out that the addition of the membrane didn’t solve the keyboard debris issue, with people who own 2018 MacBooks still reporting experiencing keyboard reliability issues

I use a 2017 MacBook Pro and have occasionally had keyboard reliability problems despite cleaning my entire laptop probably once every three days. I’ve been able to solve the issue myself so far by spraying compressed air at the keyboard as well as sliding a thin piece of paper under the key to remove unwanted debris.

Though it’s nice that Apple is finally acknowledging the issue, it also confirms many people’s assumptions about the Pro’s keyboard.

Apple will likely need to move swiftly with a Butterfly keyboard mechanism redesign to distance itself from the problem.

Source: The Wall Street Journal

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Google’s long rumoured Pixel 3a and 3a XL might be getting closer to release.

Germany-based publication WinFuture reportedly spoke to European online retailers that claim that the Pixel 3a and 3a XL will be the official name for the midrange handsets and that they’ll feature 64GB of internal storage in Europe. It’s possible that in other regions the devices will feature a 32GB configuration.

WinFuture says that the two devices will come in the standard ‘Black’ and ‘White’ options but will also feature an ‘Iris’ colour variant. Iris seems to represent one of two different colours, ‘blue iris’ which seems like a deep royal blue with a tint of purple or ‘purple iris,’ which resembles the iris flower. 

Additionally, the European retailer also indicates that the smaller model will cost 450€  (roughly $678.83 CAD), with the larger variant be a little more expensive.

Previous rumours suggested that the Pixel 3a will feature a 5.6-inch OLED display with a 2,220 x 1,080 pixel resolution, 4GB of RAM, a Snapdragon 670 chipset, a 3,000mAh battery, a 8-megapixel selfie shooter and a 12-megapixel rear camera. In contrast, the larger variant will sport a 6-inch OLED display.

It’s still unclear exactly when Google will launch the two devices. However, another rumour from Android Police indicated a spring 2019 release.

Source: WinFuture 

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To promote the upcoming release of the eighth and final season of Game of Thrones, HBO has tasked fans with finding six hidden Iron Thrones around the world.

As it turns out, one of these thrones was in Canada all along — Tumber Ridge’s Babcock Creek in British Columbia, to be exact.

We stand on guard for thee. Join the Quest #ForTheThrone.#ThroneofIce https://t.co/Mv6O3ruMxP pic.twitter.com/t37COwGlir

— Game of Thrones (@GameOfThrones) March 25, 2019

The throne was discovered by Kevin Sharman and his wife Brigit while they were on a cross-country skiing trip in Tumbler Ridge.

Sharman, who’s a part of the Tumbler Ridge UNESCO Geopark Society that works to attract tourists to Tumbler Ridge, was approached by a reporter who asked him about geographical features in the area in relation to the HBO contest.

Sharman  told CBC that he doesn’t actually watch Game of Thrones but knew quite quickly where the throne would be located after the reporter shared clues shared by HBO.

 

View this post on Instagram

 

A true queen goes anywhere #ForTheThrone, even beyond the Wall. Birgit has staked her claim​. #ThroneofIce forthethrone.com/quest​

A post shared by HBO Canada (@hbocanada) on Mar 26, 2019 at 3:50pm PDT

Fans have found thrones in and Beberibe, Brazil, Puzzlewood, England; Castillo de Atienza, Spain and Björklinden, Sweden. Clues to the location of the sixth and final throne are expected to be revealed later this week.

In the meantime, Sharman said he plans to finally watch the show after the whole experience, which he’ll be able to do once the eighth season premieres on HBO Canada and Bell’s Crave service on Sunday, April 14th.

Via: CBC

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A British review of Huawei’s 5G networking equipment concluded that it poses “significant” security issues, noting that “underlying defects” could make the software easy for attackers to exploit.

The Huawei Cyber Security Evaluation Centre Oversight Board conducted the evaluation. It released the report on March 28th.

The report doesn’t call for an outright ban, but says that the defects are concerning and create “significantly increased risk to U.K. operators.”

The board was established in 2010 and is used to monitor Huawei’s security risks.

The results of the review give more weight to the U.S.’s warnings. The country’s government has argued that its allies should not work with the Shenzhen-based telecommunications giant.

In a statement, Huawei said it was taking the report “very seriously.”

“The issues identified in the [oversight board] report provide vital input for the ongoing transformation of our software engineering capabilities,” Huawei said.

The U.K. government disapproved Huawei for not following through on addressing security concerns that were raised in previous assessments.

In Canada, the federal government is still undertaking a review of 5G and of whether it’s safe to allow Huawei to provide equipment to telecom operators in order to deploy the next mobile network iteration.

During a roundtable, Huawei chairperson Eric Xu said the company’s equipment follows standards set out by the 3rd Generation Partnership Project (3GPP). He added that Huawei is currently researching ways to make its equipment interoperable with equipment built by other vendors.

Interoperability means equipment from different vendors can be used together in order to deploy spectrum. Using equipment from different vendors helps protect an operator from cybersecurity attacks since the entire network isn’t vulnerable to a single exploit.

Source: CNBC

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A couple of new leaked videos could showcase an upcoming Pixelbook successor, a Chromebook codenamed ‘Atlas.’

Last year rumours indicated that Google has plans to unveil two Chromebooks, ‘Nocturne’ and ‘Atlas.’ However, what we got was only Nocturne, or what’s formally known as the Pixel Slate. Now, About Chromebooks and tipster Brandon Lall (@brandonlall36) have scoured through the Chromium Bug Tracker and discovered videos of the unreleased Atlas Chromebook.

The laptop seems to sport a 16:9 aspect ratio with rather thick upper and lower bezels. Currently, on the lower bezel, Atlas features the placeholder ‘ProductName,’ which is odd considering Google typically just uses its G logo on the back of the display. Additionally, the Chromebook includes a clamshell-style and doesn’t look to be a convertible.

According to the video, Google has placed the speaker grills on both sides of the keys.  The keys are also a brown-like colour which contrasts against Atlas’ black body.

9to5Google believes that the laptop likely has a 4K resolution display, but states that this estimate is based on comparing the display with the screen on the 4K Lenovo Yoga Chromebook.

With such differences from previous Pixelbooks, About Chromebooks and 9to5Google are both unsure if this is actually going to be the successor to Google’s previous laptops and believes that another manufacturer might me making this Chromebook.

A previous report indicated that Google has asked a number of its employees from the Google Create team, the division responsible for the Pixelbook and Pixel Slate, to transfer to other teams within Google.

It’s possible that ‘Atlas’ was a Google project, but with the Google Create team disappearing, perhaps another manufacturer has taken the codename for their own product.

It’s also possible Google might have scrapped Atlas altogether.

Source: About Chromebook, Twitter (@brandonlall36)

Via: 9to5Google 

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