Rolandt

Never having learned to read music or play a musical instrument as a kid, I’m finding learning to play the harp quite incredible. The feedback loops between seeing marks on paper, speaking out the name of each note played (as recommended by several of the guides/tutorials I’ve seen), developing muscle memory and hearing audio feedback is just an amazing learning experience.

Progress is slow, and I’m struggling with metre and note length. I really should get a lesson or two with a teacher, not least so I can hear what my elementary practice tunes are supposed to sound like. (I have no idea what sort of models Google is building around all the Youtube videos of young children I seem to be watching (kids doing their practice pieces… You can probably imagine the level I’m at given I aspire to be that good!))

So… self-help… there’s loads of music related web apps out there, so I figured it might be useful to try to transcribe some of my practice tunes into a form that I can get some idea of what they should sound like.

The language I’ve opted for is abcjs (repo) which I discovered via the music21 package (see some music21 demos here) ; but it doesn’t need any of the Python machinery to run — it works directly in the browser.

Here’s an example of what it looks like:

X: 1
T: Blue Bells of Scotland
M: 4/4
L: 1/8
K: C
V:R
G2|c4B2A2|G4A2Bc|z8|z4z2G2|
V:L
z2|z8|z8|E2E2E2D2|C6z2|
V:R
|c4B2A2|G4A2Bc|z8|z8|
V:L
|z8|z8|E2E2F2D2|C6G2|
V:R
z8|c4G2Bc|B2G2A2B2|G4A2B2|
V:L
E2C2E2G2|z8|z8|z8|
V:R
c4B2A2|G4A2Bc|z8|z6|]
V:L
z8|z8|E2E2F2D2|C6|]

The M field gives the meter, the L the unit note length for the piece, and K is the key. V:R and V:L record right and left hand staves. Each separate line in the abcjs script corresponds to a separate line of music.

There are some handy notes (doh!) here — How to understand abc (the basics)
— and a some more complete docs here: The abc music standard 2.1 .

I’ve found that transcribing from sheet music to abcjs notation is also helping my music reading. The editor I use — https://abcjs.net/abcjs-editor.html — provides live rendering of the notes, so it’s easy to get visual feedback as I write in the notation about whether I’ve read to myself, and written, the correct one.

(The red highlight in the score follows the cursor position in the text editor.)

As well as live rendering of the score as you transcribe, you can also play back the tune using the embedded the music player. (I’m not sure if its possible to change the instrument type? It defaults to a sort-of piano…) The tempo is set by the Q parameter in beats per minute, so it’s easy enough to speed up and slow down the playback.

FWIW, I’ll start popping related tinkerings and doodlin’s here: psychemedia/harperin-on. abcjs will also support adding things like fingerings for each note, but I don’t want to break copyright too much when I do post transcribed scores, so I’ll be omitting that…

As far as learning goes, learning to write abcjs will also help me learn to read music better, I think, as well as reading it a bit more deeply.

It’s ages since I learned a new sort of thing (though I have also been trying to learn Polish pronunciation so I can sound out names appropriately in a history of Poland I’m reading at the moment). It’s fun, isn’t it?! And soooo time disappearing…

NPR Science: As Magnetic North Pole Zooms Toward Siberia, Scientists Update World Magnetic Model.

“North is on the move, and that’s a problem for your smartphone’s maps.

Earth’s geographic north pole is fixed. But the planet’s magnetic north pole — the north that your compass points toward — wanders in the direction of Siberia at a rate of more than 34 miles per year.

That movement may seem slow, but it has forced scientists to update their model of Earth’s magnetic field a year earlier than expected so that navigational services, including map-based phone apps, continue to work accurately.”

NPR: The North Magnetic Pole Is Shifting East, Fast. “NPR’s Ari Shapiro speaks with Nature reporter Alex Witze about a rapid shift in the Earth’s magnetic poles.”

I got a lot of questions about how to apply the Onion Architecture to particular situations. In this episode, I try to answer it with a specific example.

Transcript

Eric Normand: I answer some tough questions about the onion architecture. Hi, my name is Eric Normand and these are my thoughts on functional programming. This is season two and I hope to up the production quality that I had in the last season.

Let’s get right into it. A few episodes back I introduced something that’s called the onion architecture. I introduce it, meaning I explained it. Just real quick, the onion architecture is the layered architecture where you have your core domain implemented in a functional way in the middle.

It’s layers like an onion. Around that you have your business rules, also functional. Then finally, the last layer is this interaction layer. This is the layer where all the actions happen. This is the layer that talks to the database. It gets Web requests and makes other API calls.

This is an architectural pattern that you can use to have a functional-style architecture. After I posted that, I got a lot of variations on the same question, which is, “How do I make decisions in my domain model that then result in taking different actions in the outer layer?”

The idea is, if you weren’t going to architect this, let’s say you just had a very basic, straight forward implementation. It’s imperative. You would make an API call. Based on the results of that, you make some decision. Then you either call API A or you call API B. That’s the end. The logic of what you do is mixed in with the actions that you take.

How do you extract that out into something that you can call an Onion Architecture? Right. That is the real question that people have. I have to say, I got a couple of examples of what people wanted to turn into an Onion Architecture.

My first thought was, there’s not enough logic in it. There’s not enough business rules. There’s not enough domain in it to warrant coming up with different layers. Then I thought, “No, these are just simple examples that they’re giving, so let’s do it right.” Here’s an example that someone gave me. I should look up the name so I can reference them.

This is Andrew. Andrew, thanks for the question. In his example, you are implementing a web endpoint. This endpoint is for information about music albums. In the endpoint, you want to include images of the artists who worked on the album.

There’s some constraints. You need to have as many images as you can, up to five. Things like that. The problem is, there’s a lot of logic about…For instance, if there’s more than one artist on the album. It’s a compilation CD and you have a song from this artist and a song form that artist, you want to have one image from each artist. Not five of the first person.

The rules are based on…you read the thing in from the database, you make some decisions about where to get these images from, and then you go and find them. You do another database query to get the images. Then you put them together into a JSON and you send it back.

This is an example of something that, on the surface, if this were the real thing that I was implementing, I probably would put all of that logic right in the outer layer. Because it’s not that much.

The logic is something like, “Is it greater than five images?” [laughs] Then there’s not much more to it. I’m going to run with it. I’m going to turn it just as an exercise to explain it. I’m going to turn this into an onion architecture.

I’m trying to explain this in audio. That less than five, greater than five whatever it turns out to be, you could consider that a domain. Sorry, a business rule. The business rule is a valid artist or a valid album response has at least five images, something like that. That could be the expression in English of your rule.

Then in your business logic, you have a function called valid album response. It could be true, false or it could be something like true, then return or a list of problems. Now, this is convoluting it because the rules aren’t that complicated, but if you did have a lot of rules that you needed to apply, this thing could tell you all of the problems.

Now, these are business rules. Maybe those…what is the domain here? Is the domain album information and where to get images about different artists? I think that’s stretching it, but you could consider something like that, like album information, the repositories of images, of artist information, that kind of thing.

If you have an error in the business rule layer that says not enough images, then that kicks off at the outer layer a thing that says, “If I have not enough images, here’s how I rectify that.” Now, again I wouldn’t do it this way, because that’s such a very small bit of logic. There’s not much of a domain to build up there. There’s not much material there to work with, but you could imagine if the domain were more convoluted like a real-world domain would be, that you would have this sort of business rule layer that could decide, “Is this something I can respond with? If not, what are all the problems?”

Then your outer layer knows how to rectify those problems by asking the APIs or the databases of images, et cetera how to do that.

When you do architecture, you’re taking on…there’s a trade-off. You’re adding complexity at the beginning. It’s a known complexity. It’s a known cost. This is how we’re going to do our architecture. That’s how we’re going to structure our work, our code. This is the patterns that we’re going to use.

Then you get a benefit. Depending on how much code is going to fit into that architecture, you’re going to get a different benefit. With a very simple system, it’s probably not worth architecting just to be honest. If it could just be an imperative, “Fetch this, look at the response. If it’s good enough, send it. Otherwise, add more images.” That, to me, is not even that much code to write that. At some point, as your system grows, you’re going to think, “OK. I need some kind of organizing principle to make this less painful, less of a mess.” Once you’re at that point, that’s where architecture comes in.

This is a valuable way to architect it because, what happens is, let’s imagine your code was really long. It was 200 lines to make this response. You’re making Web requests. Then you’re doing all sorts of inline calculations. Then you’re making another request somewhere in the middle. You’re going to wish that you had it in pieces. Then where do you put those pieces? How do you cut those pieces up?

Those are the architectural questions. Just to recap, the onion architecture is saying you want three broad layers. The domain layer is where you encode the problem domain but without any of your own business’s policies, rules, and regulations that your business imposes. Your business might say, “The albums we deal with are only swing albums from the 1950s.” That might be a business rule.

That doesn’t mean that your domain has to know about that, especially since what if your business expands? Now you have all this code that you can’t use because it’s all assuming swing from the 1950s. It’s a silly example, but I hope the point is clear. That’s your domain code. It’s how do we see the problem without any influence about practical business things?

Then you have your business rules, which are things like, “Ah, we want to return five images in our response.” I’m trying to come up with business rules that make sense in this domain. Anyway, there’s business rules that are almost proprietary. “This is how we run the business.”

Then there’s this outer layer, which is simply for communicating that will query the business layer as you would query a third-party API. It queries the database. It takes that response. It asks the business-rule layer, “What should I do with this? Is this good enough? Should I return it?” There’s a little logic in the interaction layer, but it’s very much coordination.

It’s similar to the model-view controller architecture, which, if you look at it, the controller is where all of the coordination happens. The view is sending events to the controller. The controller is saying, “OK. When I get that event, that means I need to call this on the model. Take the results. Now it goes to this view because it’s going to change that.” It’s orchestrating everything.

All the real action, all the real change and important stuff, happens in the model or the view. The view is showing the user, and the model is maintaining the business rules and the consistency of the state. The controller is just saying, “OK. I need to do these three things and then send that off to the view. I choose this view.” Then boom. It’s good.

That outer layer is very much like that. It’s saying, “OK. I got this Web request. I know I need to call this business-rule function, which takes something that I get from the database. I got to get the database, query the database, ask the business-rule layer the question I have.” Then it will return the answer. Then that I can use. I make another decision. I branch.

I either do this or I do that. Then I send off the answer. That’s the idea. All right. My name is Eric Normand. Please subscribe. This is the first episode of Season Two. Season One was very impromptu. It was me walking around holding my camera or driving around. Sometimes there would be bad audio, bad video. I’m trying to fix that.

I constructed this little corner of my office that will be my little recording studio. These are whiteboards. This table is also a whiteboard. I’m looking forward to finding creative uses for that. You can contact me by email. I’m eric@lispcast.com. You could also find me on Twitter, @ericnormand with a D.

Just search for Eric Normand Clojure on LinkedIn if you’re into LinkedIn. You can follow me there, too. Don’t forget to subscribe. I will see you next time. Bye.

The post How to apply the Onion Architecture appeared first on LispCast.

Too much going on today! My mom is here! It’s nice! And, I went to help out a sick friend. Less nice but I’m glad I could be helpful. And I worked 8 hours at least. Busy busy busy busy. I’m so done!

Here, have a picture I took of a rainbow over Mission Street, next to a bus stop.

It has come to this. I’m hot-linking photos.

Trazodone take me away!

More and more, we’re seeing communities need to pick a side.

You can have a ferociously moderated community which attracts smart, generous, people and produces high-quality content. You invest heavily in moderation, support the smart folks, and apply tight controls in who can do what.

You can have a lightly moderated community which supports intense debates, attracts people who enjoy expressing contrary opinions, and facilitates a powerful sense of community between members.

If you’re like most people, you’re muddling somewhere in the middle between the two. You’ve set a few ground rules and hope people don’t violate them. You respond to issues as they arise without any guiding strategy for what you want the community to be.

The basic rules of “don’t be a jerk” don’t suffice anymore, so decide now what kind of community you want to build.

I have no idea…

Here’s a first attempt:

the act of creating machine readable representations using formal syntax.

Which is to say:

• act: something practical, possibly purposive, (so should that be intentional act?), which also makes it to be a skill and a craft?
• creating: so it’s about doing something new, that also admits of having to solve problems along the way, perhaps be inventive, and playful.
• machine readable: so coding produces something that a computer is capable of processing; does this implicitly unpacks further though, to take in notions of the machine actually processing the code  in order to bring about some sort of state transformation? So maybe replace with machine readable with machine interpretable and executable? But you don’t have to execute code? Eg if I encode a mathematical formula in LaTex, the machine will interpret that code to render the typographically laid out equation, but it hasn’t executed the code. So maybe machine interpretable and/or executable?
• representations: this is not so much about what the code looks like to us, but the way we use it to create models that represent something “meaningful” to us in a way that the machine can process it in a way that is also meaningful to us. Again, this admits of problem solving and the need to be creative, but also starts to bring in unstated ideas that the representation somehow needs to be coherent and stand in some sort of sensible relationship to the sort of thing the code things they are representing?
• using: so coding is about doing something with something…
• formal: …that something being formally defined and bound/constrained…
• syntax: …by a set of rules that determine how the representations are declared and the form in which those representations should be stated. Does adding and grammar help? Do programming languages add grammatical elements over and above syntactic rules? Is dot notation, for example,  a morphological feature or a syntactic one?

Note that there is nothing in there that distinguishes between text based languages and graphical languages (for example). Nor is the word language mentioned explicitly.

The preprint of Human-Computer Insurrection: Notes on an Anarchist HCI is now available:

The HCI community has worked to expand and improve our consideration of the societal implications of our work and our corresponding responsibilities. Despite this increased engagement, HCI continues to lack an explicitly articulated politic, which we argue re-inscribes and amplifies systemic oppression. In this paper, we set out an explicit political vision of an HCI grounded in emancipatory autonomy—an anarchist HCI, aimed at dismantling all oppressive systems by mandating suspicion of and a reckoning with imbalanced distributions of power. We outline some of the principles and accountability mechanisms that constitute an anarchist HCI. We offer a potential framework for radically reorienting the field towards creating prefigurative counterpower—systems and spaces that exemplify the world we wish to see, as we go about building the revolution in increment.

Get it here

There are just so many Jupyter related settings and configs that I’m going to start making short posts about them, tagged JupyterTips (feed), to try to help me remember what they are and how to invoke them…

TIL (Today I Learned)…

…you can define which browser a newly launched Jupyter notebook server will open into. By default, this is the default browser. But you can override it with the --NotebookApp.browser argument. For example:

jupyter notebook --NotebookApp.browser=firefox

See more commandline settings at: Jupyter Notebook — Config file and command line options

Last November, my family visited Costa Rica for the first time.I wasn’t there long enough to gain a deep understanding of the country’s history and culture, but I left with an appreciation for how it prioritizes the environment as central to the country’s economy and well-being. 

Costa Ricans have a saying, Pura Vida, which they use as a hello, goodbye and thank you. It literally means “pure life,” but it refers to living a simple life in harmony with nature and community. This would explain why all of the people I encountered seemed genuinely happy and friendly to each other (and they love children…. my 4 year old son was like a VIP wherever we went). 

My lasting impression was that there was so much life everywhere. The plants we usually buy at Home Depot were flourishing off the side of the road, butterflies floated around on walks though the jungle, and monkeys swung through the trees while the sloths just hung out (as sloths do). 

Costa Rica could be considered an oasis when compared with the political turmoil and poverty surrounding it in nearby countries like Nicaragua, Guatemala and Venezuela. How did it make impressive gains in environmental protection? 

And more importantly, is there a way for other countries around the world that are currently being devastated by deforestation, desertification, and rampant biodiversity loss, to achieve similar results? 

Costa Rica is a small country, yet it continues to hold the No. 2 spot in the World Energy Council’s global “environmental sustainability” ranking (just being Switzerland). Compare this to it’s neighbour, Nicaragua which has the lowest sustainability ranking in the region (88), just below the United States (87). 

Here are some ways in which Costa Rica became the world’s leader in protecting the environment:

Clean energy: In the last 30 years, renewable sources such as wind, geothermal, solar and hydroelectric have been responsible for nearly 93 percent of Costa Rica’s energy (with 75.9 percent derived from hydroelectric power and 17.7 percent from other renewables like wind power). The country’s goal is to be the first carbon neutral country in the world by 2021. 

Protected ecosystems: Costa Rica is a small nation, yet it holds five percent of the world’s known biodiversity and 3.5 percent of all marine life. As a result, it has protected an impressive 28% of its land as national parks, reserves, and wildlife refuges. 

Reforestation:  Before the 1950s, over 75% of Costa Rica was covered in tropical rainforest. However following that time period, Costa Rica saw it’s forest as a cash cow and rampant and unchecked logging ensued. By 1983 only 26% of the country retained forest cover, and the deforestation rate had risen to 50,000 hectares per year. At this point, Costa Rica established a National Conservation Strategy for Sustainable Development to reverse this trend. And it worked. By 1989, the annual deforestation rate had dropped to 22,000 hectares per year. The figure dropped continued to drop and in 1998 the deforestation rate had dropped to zero. Today forest cover has increased to 52% (double 1983 levels) and the government has set the ambitious goal of further increasing this figure to 70% to achieve carbon neutrality by 2021.

Eco-tourism:  With 50% of Costa Rica’s GDP linked to adventure tourism and ecotourism — a commitment to the environment is good business. It has also allowed them to rely less on natural resources for their economic well-being. But, it is a delicate balance of protecting ecosystems while also allowing for some human impact. Almost all of Costa Rica’s tourist destinations involve a fee (locals get a discount), which helps maintain the infrastructure needed to support tourism and protect the environment. I also noticed many of the hotels I stayed at were part of a Certification for Sustainable Tourism program. It was designed by the Costa Rica Tourism Board to recognize and reward businesses for their sustainable practices. Based on the degree in which they comply, a large selection of attractions, hotels and restaurants have been officially classified as sustainable.

Sustainable farming: Agriculture accounts for about 6.5 percent of Costa Rica’s gross domestic product and 14 percent of its labour force; however farming practices that over-exploit the country’s mass-produced cash crops, such as coffee, bananas and pineapple, have threatened its biodiversity. Addressing this crisis has led to a shift to sustainable agriculture. In November of 2017, new public-private alliances were formed, including a Green Growth Program focused on converting 200 small and medium enterprises into green businesses that will export food products such as organics and superfoods, to international markets.

Costa Rica may be a small country, but it is showing the world what is possible when a nation is united in prioritizing and protecting its greatest resource: the environment.

Beginning in March, Surface Headphones will launch in eight new markets: Australia, Austria, Canada, France, Germany, Ireland, New Zealand and Switzerland.

I am very much looking forward to this experience. They look very comfy and the way you can adjust volume and ANC speak for themselves.

Potential downside: No support for AAC or AptX. Which does not bother most people.

More >

School children are traveling to The Hague in droves today, to demand climate action. The train is overly full, with youth and with energy. Not all fitted on to the train, so some were left on the platform to take the next one. Good to see the spirit of activism.

Stephanie Wilkinson @stephanieblack
The only way any of us write blog posts is by spending hours updating our static site generators first, and then fo… twitter.com/i/web/status/1…

Many folk will have seen stories or posts floating around the internet claiming that someone was talking to someone else about X one day and they suddenly started received adverts about it on their phone, the assumption being that the phone listened in on the conversation, picked out the keywords and sent an ad on that basis.

One likely alternative explanation is that the person experiencing this had just primed, or sensitised, themselves to that ad. We see and blank out thousands of ads every day, at least consciously, but that doesn’t mean we don’t see them. And by talking about a thing we are then primed (self-primed?) to consciously notice it if it does cross our attention path soon after (my cog psych knowledge is not that good; there are probably some really good experiments and mechanisms around to explain this… eg stuff).

Another possible contributory factor is that the models are getting better at prediction. You do a sporty thing at a particular location (your phone knows where you are) and talk about different deodorant products afterwards. You then spot a deodorant ad. Your phone has been listening to you. Or maybe your phone (or the services or networks it is connected to) spotted you were at a sporty location, there was no phone activity for an hour and a half, (not even jiggling around, as detected by the gyros, so you left your phone somewhere; or maybe the signal died when you put it in a locker) so maybe you were doing something sporty, so maybe: worth a shot at advertising a deodorant?

Now the phone may or may not be being used to listen to you in the audible sense  of hearing your spoken conversations (it’s certainly being used to “listen” to your actions in web tracking ways, for example), and the webcos et al. tend to protest that they don’t. But they don’t make life easy for themselves with the sorts of things they do announce they can do.

For example, in a recent blog post on the Google AI blog, Real-time Continuous Transcription with Live Transcribe, there’s this:

Today, we’re announcing Live Transcribe, a free Android service that makes real-world conversations more accessible by bringing the power of automatic captioning into everyday, conversational use. Powered by Google Cloud, Live Transcribe captions conversations in real-time…

Okay, so if you have a network connection, your phone could transcribe any audio it heard in real time. Google are celebrating that fact. There is no technology blocker if access to the microphone and internet connection are available, and the microphone is in range of the conversation.

Potential future improvements in mobile-based automatic speech transcription include on-device recognition, …

So they also want to be able to do it on the phone…

The world is full of such apparent contradictions. On the one hand, conspiracy theories about what the tech giants (increasingly, rather than “the state”, as in the case of China) are doing; on the other, announcements by the same companies about what they don’t (as a matter or policy), can (technically), and do want to (technically) do.

Which comes down to a question of trust that the policies they operate under are: a) sound; b) followed; c) not not followed.

Here are some more possible contradictions…

We trust the Amazon store as a place to shop, right? Like a supermarket or department selling branded goods. But hold on a minute… For a start, it’s increasingly a market, and just like a free market or a car boot sale, buyer beware. At scale. Why? Well, Amazon Warned Apple of Counterfeit Products in 2016 and is now Warning Investors that Counterfeit Products are a Problem; there are plenty of other stories about counterfeit products on Amazon out there.

Something else to note about Amazon is that they are like a supermarket in a certain respect: they sell products from a wide range of own brand items, although you may not realise it. One way of trying to track down what brands they own is to look at the WIPO Trademark database.

(I built a trademarks by company explorer once, using OpenCorporates data and OpenRefine, but I suspect it’s rotted by now. Maybe worth revisiting, along with something that mines companies in a corporate grouping and grabs trademarks associated with all of them?)

So how about another Google story — Advancing research on fake audio detection:

When you listen to Google Maps driving directions in your car, get answers from your Google Home, or hear a spoken translation in Google Translate, you’re using Google’s speech synthesis, or text-to-speech (TTS) technology. …

Over the last few years, there’s been an explosion of new research using neural networks to simulate a human voice. These models, including many developed at Google, can generate increasingly realistic, human-like speech.

While the progress is exciting, we’re keenly aware of the risks this technology can pose if used with the intent to cause harm. Malicious actors may synthesize speech to try to fool voice authentication systems, or they may create forged audio recordings to defame public figures.

…

We’re taking action. When we launched the Google News Initiative last March, we committed to releasing datasets that would help advance state-of-the-art research on fake audio detection.  Today, we’re delivering on that promise…

On the one hand, the Goog is trying to create ever authentic voices. On the other, so are the bad guys. (Google, by implication, is not a bad guy).

By releasing some of their data, they hope to encourage third parties to create systems to distinguish real voices from machine generated ones.

The way research works, of course, is that the folk (at Google…) who create machine generated voices will presumably try to improve their creations to avoid detection by the new improved machine generated voice detectors on the grounds of “improving customer experience”…

In their defense:

As we published in our AI Principles last year, we take seriously our responsibility both to engage with the external research community, and to apply strong safety practices to avoid unintended results that create risks of harm.

So I wonder, will Google add something inaudible to its machine generated voices that flag out a voice as machine generated, even one sent over a low pass filtered phone connection, in a spirit of responsibility? This would make it trivially easy to detect a Google generated voice and prevent lazy bad guys from using it to fool other machines.

Finally, and this may seem like more Google bashing, but hey, this is just a sample pulled from today’s feeds (I’m on catch up), how do these big cos develop the trust that supports a belief that they can be trusted to formulate and adhere to sound policies that are not just for corporate benefit but also, at the very least, do no harm to the rest of society, if not actually benefitting it? By being good (corporate) citizens in wider society? Google pays more in EU fines than it does in taxes. Erm…?

Interessant: Das wird zum Telefonieren taugen:

Insgesamt acht Mikrofone – mit jeweils zwei Mikrofonen für Sprache sowie zwei Mikrofonen für das ANC pro Ohrmuschel – ermöglichen ein exzellentes Hör- und Anruferlebnis und bieten eine optimierte Sprachqualität für Telefonkonferenzen. Über die smarte und automatische Pause- und Wiedergabefunktion werden Songs oder Videos unterbrochen, sobald die Kopfhörer abgenommen werden. Verbinden lassen sich die Surface Headphones mit bis zu acht Devices, um leicht zwischen verschiedenen Geräten zu wechseln. Das funktioniert kabellos via Bluetooth auf allen gängigen Plattformen – die Kopfhörer sind kompatibel mit Windows 10, iOS, MacOS und Android Geräten. Über einen 3,5mm Audio-Anschluss lassen sich die Surface Headphones zudem via Kabelverbindung nutzen.

Das Dokument sagt acht Devices, Ralf spricht von fünf. So oder so genügend.

Mit einer Akkulaufzeit von bis zu 15 Stunden bieten sie außerdem genug Ausdauer für unterwegs. Sollten die Kopfhörer dennoch einmal leer sein, erlaubt eine Schnellladefunktion, den Akku innerhalb von fünf Minuten auf einen Stand zu bringen, mit dem sich wieder annähernd eine Stunde Musik hören lässt. In weniger als zwei Stunden ist der Akku über den USB-C-Anschluss vollständig aufgeladen.

USB-C ist gut, 15 Stunden eher knapp im Vergleich der Konkurrenten.

With the talk about the scourge of bottled water this week, let’s not forget that people like Leo Broderick and Mary Boyd have been calling for its elimination for years.

Obscura 2.0 was one of our favorite iOS app updates of 2018, which garnered it a MacStories Selects Best App Update Runner-Up award. Since it was released eight months ago, developer Ben McCarthy has continued to refine the app and add new features such as iPad support, new editing controls, localizations, and a Photos extension. With the latest update out today, Obscura has added a histogram visualizer, a redesigned image viewer, iPad keyboard shortcuts, and more.

The histogram is available from Obscura’s control wheel alongside its other tools. Tapping on the histogram icon on the control wheel provides options to turn it on and off. Once enabled, a histogram animates from the bottom of the screen showing you the range of pixels exposed by the camera, which can help you tell if the shot you’re taking is over or underexposed.

The histogram animates in real time as you move your iPhone or iPad’s camera around and adjust things like exposure, which makes dialing in a good shot easier. My one concern with this feature is that the histogram uses dark gray bars on the black chrome surrounding the app’s controls, which may be difficult to read in certain lighting conditions like on a sunny day, though I haven’t been able to test it in sunlight thanks to Chicago’s perpetually gloomy winter weather. Still, I’m glad the histogram feature has been added because it’s a nice visual confirmation of the exposure of a photo.

The other significant update to Obscura is to the image viewer, which is a substantial improvement to the existing viewer. The information is better organized into card-like clusters of related information, uses color to convey temperature and tint information visually, includes filter data for images shot with Obscura, and on an iPhone, seems to use less vertical space than before.

The interaction on the iPhone has changed from a single scrollable view to a scrollable view that pushes the image up the screen but not offscreen. That serves as a point of reference reminding you of the image you are viewing but means that only the portion of the view under the image is scrollable and there is less room overall for the metadata. The new image viewer has also migrated all image actions – marking an image as a favorite, sharing, copying, duplicating, hiding, and deleting – to buttons directly beneath the image, which is a substantial improvement aesthetically as well as functionally.

On the iPad, Obscura adds keyboard shortcuts for navigating between the app’s library and camera views, taking a photo, navigating among images in the library view, and performing most of the actions on individual images like applying a filter and sharing them. I don’t shoot photos on my iPad very often, but it’s my favorite device for viewing and editing photos, so the addition of keyboard controls for functionality like navigating my library, adding filters, and sharing them is a welcome addition.

The image viewer works differently on the iPad. Instead of a scrollable view under the image, the metadata sits in a panel to the right of a photo and can be hidden by tapping the tag icon below it. The design makes good use of the iPad’s extra space, but the image viewer has a few bugs that need to be addressed to make it something I’d use regularly.

The metadata panel doesn’t always appear when I open an image. Most often this seems to be the case with pictures taken with other camera apps and screenshots, though not always. Nor does the filter thumbnail beneath an image or the metadata panel consistently update as I swipe horizontally between images. I noticed this issue on the iPhone too, though not nearly as frequently. The RAW, JPG, and Live Photo badges found on thumbnails in the library on the iPhone are missing from the iPad too. The cumulative effect of these bugs makes for a rougher experience on the iPad than I’d like, which is too bad because it detracts from what is otherwise a much-improved design.

If you use Obscura on the iPhone, this is a solid update. The histogram improves the experience of taking photos, and the new image viewer makes quickly scanning the data about images easier. I don’t use Obscura often on my iPad, and the bugs in this update won’t change that, but I’ll revisit the iPad version when the bugs are cleared up because I like the new design and keyboard shortcuts.

Obscura 2 is available on the App Store for $4.99. Additional filters are available as In-App Purchases.

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En vrac numérique

• Does Facebook Really Know How Many Fake Accounts It Has?. Facebook est très flou sur ces sujets. Facebook supprime entre 600 million et 800 millions de faux comptes par trimestre (presque 9 millions par jour !) et reconnait qu’il y a plein de comptes dupliqués (213 millions sur Q4 2017) sans donner de chiffres récemment. Sur 12 mois Facebook a supprimé 2,8 milliards de faux comptes. Le souci : les annonceurs payent pour des faux clics générés par des comptes dont on ne sait pas s’ils sont authentique ou pas ;
• Facebook veut intégrer ses trois messageries (Messenger, WhatsApp, Instagram). D’un coté, c’est bon signe pour les utilisateurs qui trouveront de l’interopérabilité. D’un autre, c’est imposer un monopole de la messagerie dont il sera plus difficile de se passer : la loi de Metcalfe démontrant que la valeur d’un réseau augmente avec le carré du nombre de noeuds ;
• Une fausse information a 70% de plus d’être retweetée qu’une vraie, et forcément c’est toxique pour nos communautés. Combiné avec les bulles de filtre, c’est une recette pour un désastre ! Bref, avant de proposer une info, validez qu’elle est vraie…
• Quand Google, Facebook et Microsoft financent les climatosceptiques : Google, Facebook, and Microsoft Sponsored a Conference That Promoted Climate Change Denial… ils se font moucher par la député US Alexandria Ocasio-Cortez ;
• Réinventer le numérique – signez la tribune ! ;
• In 5G Race With China, U.S. Pushes Allies to Fight Huawei, ou comment les USA, très inquiets de voir le chinois Huawei prendre le lead sur la 5G, essaye de le discréditer (après l’avoir espionné et tenté d’installer des backdoors). Par ailleurs, Huawei n’est pas non plus blanc comme neige, mais mérite-t-il un tel tir de barrage judiciaire ?
• L’irrésistible ascension du lecteur vidéo « VLC », une révolution française (et Libre, faut-il le rappeler). Un peu l’antithèse des GAFAM, quoi ;
• YouTube ne recommandera plus de vidéos complotistes, et c’est bien. Mais pourquoi avoir attendu si longtemps ? Ca tout juste un an qu’on a les preuves mesurables du problème, par exemple voir cet article du Guardian de début février 2018, au point que Youtube est appelé le grand radicaliseur depuis 11 mois ;
• I Tried to Block Amazon From My Life. It Was Impossible. Ca n’est pas tant amazon.com qui est si difficile à ne plus utiliser, mais les millions d’adresses IP d’Amazon Web Services (service de Cloud Amazon), donc tous les services (Slack, Signal, Netflix, etc.). Dans sa quête visant à se passer des GAFAM, la journaliste s’est passée de Facebook. Prochaine étape, à venir : Google !
• Comme chaque année, les JDLL approchent ! Cette années le thème est “Ecologeek : pour une terre communautaire”. Appel à conférence est encore ouvert jusqu’au 14/02. Il est assorti d’une campagne de financement des Journées du Logiciel Libre de Lyon ;
• Fabuleux : un historien à Davos met les pieds dans le plat à propos des taxes et des impôts. “1500 jets privés ont atterri à Davos pour écouter Sir Attenborough nous expliquer qu’on détruit la planète. On entend les gens parler de participation, de justice et d’équité mais quasiment personne ne parle du vrai problème, celui de l’évasion fiscale. Les plus riches ne payent pas leur part. J’ai l’impression d’être dans une conférence de pompiers où il est interdit de parler de l’eau”. ;
• Holi : la startup française qui avait levé 800 000 € sur Kickstarter pour un réveil s’est volatilisée ;
• Journée de la protection des données : il est peut-être temps de changer de navigateur. “Vos choix d’aujourd’hui sont l’internet de demain” ;
• Le torchon brule entre Huawei et les USA :
  • Les États-Unis accusent Huawei de vol et plusieurs employés de fraude ;
  • Suite à l’inculpation de Huawei, la Chine réagit aux accusations des États-Unis ;
  • Les français ne sont pas en reste : L’amendement « anti-Huawei », une porte pour les backdoors du renseignement français ;
  • Dans Libération, Amaelle Guitton fait le point : Aux États-Unis, Huawei entre en zone rouge ;
• Super article sur les ex de la NSA devenus mercenaires pour les Émirats Arabes Unis : Project Raven — Inside the UAE’s secret hacking team of American mercenaries. Cocasse de voir que Lori Stroud, centre de l’article, a quitté la NSA parce qu’elle avait fait embaucher Snowden et que donc sa réputation était ruinée !
• Karma, outil de piratage à distance des iPhones. Ne nécessitant que le numéro de téléphone de l’appareil visé, il a fonctionné entre 2016 et 2017 (avant d’être bloqué par les correctifs d’Apple) ; cet outil permettait la collecte des « photos, emails, textos, position GPS, mots de passe stockés dans le téléphone », ces derniers étant utilisés pour accéder à d’autres services en se faisant passer pour l’utilisateur visé ;
• Google fait l’objet de mesures d’urgente de la part de l’Autorité de la Concurrence en bloquant la publicité de la société Amadeus. “Les pratiques de Google ont placé Amadeus dans une situation critique. Elle a subi une perte massive et très brutale de son chiffre d’affaires (-90 % entre 2017 et 2018) et elle présente des résultats négatifs depuis le début des pratiques, au point qu’elle risque d’être conduite à sortir prochainement du marché. ” ;
• Faire respecter le RGPD, principale mission de Marie-Laure Denis, nouvelle présidente de la CNIL ;
• Lettre de Mozilla à la Commission Européenne à propos de l’absence de transparence de Facebook sur les publicités.
• Apple règle 500 millions d’euros d’arriérés d’impôts au fisc français suite à 10 ans de désaccords avec Bercy ;
• Apple stocke des données utilisateurs sur des serveurs russes (comme en Chine) ;
• 29 apps Android ‘Beauty Camera’ détournaient des photos de leurs utilisateurs (Source). Pourquoi aura-t-il fallu attendre que les trois premières aient atteint le million de téléchargement chacune ?
• J’étais l’invité de Xerfi Canal pour une rapide interview vidéo : Numérique gratuit : la grande arnaque sur les datas ;
• New York Insurers Can Evaluate Your Social Media Use—If They Can Prove Why It’s Needed. Dans l’état de New York, les assureurs peuvent maintenant utiliser ce que vous publiez sur les réseaux sociaux (et via des sources “”non-traditionnelles” à conditions de prouver pourquoi ils en ont besoin. Recommendations du Wall Street Journal : ne pas publier de photos de vous en train de fumer, publiez la preuve que vous faites un sport non dangereux, faites-vous pister par votre GPS quand vous allez à la gym mais laissez votre téléphone à la maison quand vous allez dans un bar. Achetez de la nourriture saine en ligne. Toutes ces traces pourraient bien limiter la hausse des primes d’assurance… Je suis étonné qu’on ne vous ai pas proposé de payer vos burgers et pizzas en liquide…
• Le Podcast IRL proposé par Mozilla vient de sortir une émission sur The Surveillance Economy, avec l’excellente Soshana Zuboff (à qui on doit le concept de “Surveillance Capitalism”) ;
• The Age of Surveillance Capitalism by Shoshana Zuboff review – we are the pawns ;
• Quels traqueurs seront bloqués à terme par Firefox ;
• On vient de découvrir qu’un gadget Google pour la maison disposait d’un micro caché. Google confirme. La feuille des spécifications est catégorique : le micro n’y est pas listé. À quel moment ils ont cru que ça allait passer ?
• Google recommande aux possesseurs de caméras Nest de changer leur mot de passe… vite, mais sans donner d’explications ;
• “Développeurs, designers, testez tous vos sites dans Firefox”, explique Jeffrey Zeldman, “Même si vous aimez Chrome, adorez Gmail et passez votre vie dans GDocs et Google Analytics, il ne fait pas bon laisser une seule société contrôler l’Internet, encore moins quand il s’agit d’un géant du pistage des utilisateurs” ;
• Le New York Times taille un short à Zuckerberg pour les 15 ans de Facebook. À rapprocher de la vidéo du Guardian sortie il y a quelques semaines.

Climat et vélotaf

• 50°C en Australie, -50°C aux États-Unis, un aperçu du changement climatique ? ;

L’intensité des catastrophes qui nous menaceront demain dépend encore de notre comportement aujourd’hui. Entre de très rudes vagues de chaleur et une « Terre étuve » devenue « inhabitable » dans de nombreuses régions, autrement dit entre un réchauffement de 2°C et un emballement de 5°C, la différence tient autant à nos capacités à changer (d’alimentation, de banque, de consommation, etc.) qu’à faire changer radicalement les orientations politiques de nos dirigeants. Pour l’instant, ni la France ni aucun pays de l’Union européenne ne tient ses engagements climatiques.

• Le CES de Las Vegas ou l’urgence de la sobriété numérique ;
• Corporate America Is Getting Ready to Monetize Climate Change, où l’on constate que Coca Cola s’inquiète que les sécheresses futures pourrait l’empêcher de se fournir en eau pour faire le Coca, et Google redoute que le changement climatique limite l’intérêt des internautes pour la pub, mais se réjouit en se disant que Google Earth pourrait être plus utilisé pour constater les résultats du changement climatique ;
• Puma lance aussi des baskets connectées (avec laçage depuis l’iPhone/Apple Watch). Donc là on remplace des lacets par un mécanisme avec un moteur, une batterie, du bluetooth, un smartphone et une application. Pour seulement 330$ !
• Le sas vélo, c’est cette bande qui se trouve souvent avant un passage piéton au pied d’un feu tricolore. Peu de gens le réalisent, mais il est interdit aux voitures, motos et scooters de s’y arrêter. Depuis peu, il y a un système de verbalisation automatique pour les gens qui ne le respectent pas, et les PV commencent à tomber : 388 PV par jour à Paris à 35€ chacun ;
• En Belgique, collégiens et lycéens font la « grève » pour le climat ;
• À propos de la cohabitation entre scooters, vélos, piétons, voitures et camionnettes : Mais tu vas la bouger, ta merde, un texte fort sympathique et intelligent sur l’empathie dans la circulation.

Boris Mann @bmann
@Stv @rtanglao this tweet & thread speak to this twitter.com/stevenwhatevr/…

I've talked quite a lot about the impact of machine learning and computer vision in general on everything from e-commerce recommendation to social to all kinds of cool industrial applications, but it's also interesting just to look at the effect that machine learning is having on actual cameras. 

For both Apple and Google, most of the advances in smartphone cameras now happen in software. The marketing term for this is ‘computational photography’, which really just means that as well as trying to make a better lens and sensor, which are subject to the rules of physics and the size of the phone, we use software (now, mostly, machine learning or ‘AI’) to try to get a better picture out of the raw data coming from the hardware. Hence, Apple launched ‘portrait mode’ on a phone with a dual-lens system but uses software to assemble that data into a single refocused image, and it now offers a version of this on a single-lens phone (as did Google when it copied this feature). In the same way, Google’s new Pixel phone has a ‘night sight’ capability that is all about software, not radically different hardware. The technical quality of the picture you see gets better because of new software as much as because of new hardware. 

Most of how this is done will be invisible to the user. HDR went from a garish novelty to a setting in the camera that sometimes worked to, now, something automatic that you never need to know about. I expect the separate ‘portrait mode’ or ‘night sight’ options will disappear, just like the ‘HDR’ button did. 

This will probably also go several levels further in, as the camera goes better at working out what you’re actually taking a picture of. When you take a photo on a ski slope it will come out perfectly exposed and colour-balanced because the camera knows this is snow and adjusts correctly. Today, portrait mode is doing face detection as well as depth mapping to work out what to focus on; in the future, it will know which of the faces in the frame is your child and set the focus on them. 

So, we are clearly well on the way to the point at which any photograph a normal consumer takes will be technically perfect. However, there’s a second step here - not just “what is this picture and how should we focus it?” but “why did you take the picture?”

One of the desire paths of the smartphone camera is that since we have it with us all the time and we can take unlimited pictures for free, and have them instantly, we don’t just take more pictures of our children and dogs but also pictures of things that we’d never have taken pictures of before. We take pictures of posters and books and things we might want to buy - we take pictures of recipes, catalogues, conference schedules, train timetables (Americans, ask a foreigner) and fliers. The smartphone image sensor has become a notebook. (Something similar has happened with smartphone screenshots, another desire path that no-one thought would become a normal consumer behavior.) 

Machine learning means that the computer will be able to unlock a lot of this. If there's a date in this picture, what might that mean? Does this look like a recipe? Is there a book in this photo and can we match it to an Amazon listing? Can we match the handbag to Net a Porter? And so you can imagine a suggestion from your phone: “do you want to add the date in this photo to your diary?” in much the same way that today email programs extract flights or meetings or contact details from emails. 

This is an interesting product design challenge. Some of this can be passive, as with automatically detecting flights in email - you wait until you know you have something. Machine learning means we now have this with face recognition and object classification: every image on your phone is indexed by default, and you can ask for ‘all pictures of my son at the beach’ or ‘every picture of a dog’. But you can do many more analyses than this, and we take lots of photos, and there will be something you could analyse in all of them. You can perhaps index or translate all of the text in all the photos you take (presuming that isn’t resource-prohibitive), but should you do a product search on every object in every picture on the phone? At some point, you probably need some sort of ‘tell me about this’ mode, where you explicitly ask the computer to do ‘magic’.

Asking a computer to ‘tell me about this picture’ poses other problems, though. We do not have HAL 9000, nor any path to it, and we cannot recognise any arbitrary object, but we can make a guess, of varying quality, in quite a lot of categories. So how should the user know what would work, and how does the system know what kind of guess to make? Should this all happen in one app with a general promise, or many apps with specific promises? Should you have a poster mode, a ‘solve this equation’ mode, a date mode, a books mode and a product search mode? Or should you just have mode for ‘wave the phone’s camera at things and something good will probably happen’?

This last is the approach Google is taking with ‘Lens’, which is integrated into the Android camera app next to ‘Portrait’ - point it at things and magic happens. Mostly.

Yes, this is ‘Khrushchev Remembers’
Yes, this is a Taccia lamp
No, this is not a bath

These three screenshots actually show quite a lot of moving parts:

1. In the first, text is recognized (and I can copy it), and then the book itself is recognized (by the text or the image?) and Lens delivers a product match. Success.

2. In the second, the app isn’t managing to recognise the object, so the photo is being passed on to Google Image search and a match is found on a bunch of web pages, but Google doesn’t know what this actually is. This works, from the consumer’s perspective, but there’s no knowledge graph.

3. Third, what should be a highly recognizable product (an Alvar Aalto vase) is taken from an angle that probably doesn’t match an image on a website, but Google’s object detection thinks it’s a free-standing bath. If I manually give the image to Google Image Search, it suggests ‘club chair’. (Technically, the phone might be able to work out how big this object is and do something with that, but that’s probably for next year.)

These illustrate questions of both discoverability and expectation. What can it do, what should I not expect it to do, and how should you react when you don’t have a good result? This is in fact another manifestation of the challenge seen in voice assistants - they can do enough different things that you don’t want to give the user a list of all of them, but not nearly enough different things that you can expect it to handle anything you throw at it. So how do you build the communication and discovery of what your ‘AI’ system can do?

In the second example here we are failing down to Google image search, and voice assistants sometimes fall back to reading the top result from a Google web search. Here, this tactic worked. In the third example Google is confident (going direct to product search rather than image search), but wrong - how do I react to that? Would no suggestion at all have been better? I would have lost respect for the product if it hadn’t found the book, but I understand that matching the vase was a lot harder and give it a pass, and I can see why the vase might look like a bath to a ‘dumb’ computer. Conversely, I suspect that one of the problems with Siri was that Apple’s marketing gave the impression that you really could ask this thing anything: consumer expectations did not match the product’s capabilities.

In a sense, these questions are also brand questions. We know that Shazaam only does recorded music. Amazon’s app got a better match for the Krushchev book, linking not a modern reprint as Google did but a second-hand copy of the exact edition with the same cover. But, it failed totally on the lamp and the vase, even though they’re both for sale on Amazon. Do I have different expectations of Amazon? How intelligent do I expect the AI to be?

The alternative approach, as for Shazam, is to go vertical. Suppose there was an app that you could wave at a recipe in a book, that would generate a shopping list or maybe give you nutrition information. You could make that really reliable and you would have no ‘AI discoverability’ problem at all, but the app itself would have a discovery problem (even if it was from Google) - how would people find out about it? Either way, this approach isn’t workable for Google (or Amazon) - if they can recognise 50 categories now and 200 in two years, they can’t have 200 apps or 200 modes in the camera app any more than they can have 200 modes on the search page. You need either to have a general purpose front end or make the whole thing passive or invisible (face recognition, HDR, putting flight details into your calendar).

Language translation is another of these possible modes - and Google Translate does have its own app, for now. Google Translate is a visual Babelfish, and of course the Babelfish was a wearable. The long-term context for many of these questions is not an sensor in your pocket but a sensor that you wear. In, say, five years time, you might be able to buy, as a consumer product, a pair of ‘glasses’ that combine both a transparent, colour 3D display and a cluster of image sensors. Those image sensors map the space around you, so you can make the wall a display or play Minecraft on the table, but they also recognise things around you. At this point we won’t be taking photos-as-notes at all. You won’t take a picture of the conference schedule - you’ll just look at it, and then later that day say ‘hey Google, what’s the next session?’ Or, ‘I met someone at an event last week and their name badge said they worked for a Hollywood studio - who were they?’ So what suggestions will we get, and what will be remembered for you? How do you know what the glasses can do (and what someone else’s glasses might be doing)? And how do the brands associated with this map against intelligence and discovery on one hand and privacy and trust on the other? 

mkalus shared this story from xkcd.com.

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mkalus shared this story .

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mkalus shared this story from Home – News – The Conversation.

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Sex as we know it is about to change.

We are already living through a new sexual revolution, thanks to technologies that have transformed the way we relate to each other in our intimate relationships. But we believe that a second wave of sexual technologies is now starting to appear, and that these are transforming how some people view their very sexual identity.

People we refer to as “digisexuals” are turning to advanced technologies, such as robots, virtual reality (VR) environments and feedback devices known as teledildonics, to take the place of human partners.

Defining digisexuality

In our research, we use the term digisexuality in two senses. The first, broader sense is to describe the use of advanced technologies in sex and relationships. People are already familiar with what we call first-wave sexual technologies, which are the many things that we use to connect us with our current or prospective partners. We text each other, we use Snapchat and Skype, and we go on social apps like Tinder and Bumble to meet new people.

These technologies have been adopted so widely, so quickly, that it is easy to miss what a profound effect they have had on our intimate lives.

It is fascinating to study how people use technology in their relationships. Not surprisingly, in our research we can already see people displaying different attachment styles in their use of technology. As with their human relationships, people relate to their technology in ways that may be secure, anxious, avoidant or some (often disorganized) combination of the three.

There is a second, narrower sense, in which we use the term digisexuals for people whose sexual identity is shaped by what we call second-wave sexual technologies.

These technologies are defined by their ability to offer sexual experiences that are intense, immersive and do not depend on a human partner. Sex robots are the second-wave technology people are most familiar with. They don’t exist yet, not really, but they have been widely discussed in the media and often appear in movies and on television. Some companies have previewed sex robot prototypes, but these are nothing close to what most people would consider a proper sexbot. They are also incredibly creepy.

Refining sexbots

There are several companies, such as the Real Doll company, working on developing realistic sexbots. But there are a few technical hurdles they have yet to overcome. Truly interactive artificial intelligence is developing slowly, for instance, and it is proving difficult to teach a robot to walk. More interestingly, some inventors have begun experimenting with innovative, non-anthropomorphic designs for sexbots.

Meanwhile, VR is progressing rapidly. And in the sex industry, VR is already being used in ways that go beyond the passive viewing of pornography. Immersive virtual worlds and multi-player environments, often coupled with haptic feedback devices, are already being created that offer people intense sexual experiences that the real world possibly never could.

Investigative journalist Emily Witt has written about her experience with some of these technologies in her 2016 book, Future Sex: A New Kind of Free Love.

There is compelling evidence that second-wave technologies have an effect on our brains that is qualitatively different from what came before.

MIT professor Sherry Turkle and others have done studies on the intensity of the bond people tend to form with what she calls “relational artifacts” such as robots. Turkle defines relational artifacts as “non-living objects that are, or at least appear to be, sufficiently responsive that people naturally conceive themselves to be in a mutual relationship with them.” Immersive VR experiences also offer a level of intensity that is qualitatively different from other sorts of media.

Immersive experiences

In a lecture at the Virtual Futures Forum in 2016, VR researcher Sylvia Xueni Pan explained the immersive nature of VR technology. It creates what she describes as a placement and plausibility illusion within the human brain.

As a result of its real-time positioning, 3D stereo display and its total field of view, the user’s brain comes to believe that the user is really present. As she says: “If situations and events that happen in VR actually correlates to your actions and relates personally to you, then you react towards these events as if they were real.”

As these technologies develop, they will enable sexual experiences that many people will find just as satisfying as those with human partners, or in some cases more so.

We believe that in the coming decades, as these technologies become more sophisticated and more widespread, there will be an increasing number of people who will choose to find sex and partnership entirely from artificial agents or in virtual environments.

And as they do, we will also see the emergence of this new sexual identity we call digisexuality.

Sexuality and stigma

A digisexual is someone who sees immersive technologies such as sex robots and virtual reality pornography as integral to their sexual experience, and who feels no need to search for physical intimacy with human partners.

Marginal sexual identities almost invariably face stigma, and it is already apparent that digisexuals will be no exception. The idea of digisexuality as an identity has already received strong negative reactions from many commentators in the media and online.

We should learn from the mistakes of the past. Society has stigmatized gays and lesbians, bisexuals, pansexuals, asexuals, consensually non-mongamous people and practitioners of bondange/discipline-dominance/submission-sadomasochism (BDSM).

Then, as time goes on, we have gradually learned to be more accepting of all these diverse sexual identities. We should bring that same openness to digisexuals. As immersive sexual technologies become more widespread, we should approach them, and their users, with an open mind.

We don’t know where technology is going, and there are definitely concerns that need to be discussed — such as the ways in which our interactions with technology could shape our attitudes towards consent with our human partners.

Our research addresses one specific piece of the puzzle: the question of how technology impacts sexual-identity formation, and how people with technologically based sexual identities may face stigma and prejudice. Yes, there are dangers. But whips and paddles can hurt too.

The authors do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

Email’s beginning was perfectly unremarkable: “QWERTYUIOP.” A keyboard burp. Something your cat might type. A nothing message sent by Ray Tomlinson in 1971 to test the system. But email has stayed, and has largely stayed decentralized, and from that — its ubiquity and lack of central authority — email has become one of the most boringly powerful publishing platforms around. I fear we’re entering an era of newsletter fatigue, but a month ago I started a new one.

Kanopy If you have a library card, you most likely have access to Kanopy, which is full of excellent movies, including a selection from the Criterion Collection, and the Bicycle Thief. I’m recommending it because I am still surprised at how few people know about it. The kids section is also full of good movies, sans garbage like My Little Pony Equestria Girls: Movie Night and Barbie and the Magic of Pegasus.

Recomendo, the mailing list from Kevin Kelly, Mark Frauenfelder & Claudia Dawson always has something to discover. KK wrote Cool Tools previously, which was a source of so many good links, and there is also a book of Recomendos.

DF Tube This is a chrome plugin that eliminates all the cruft around a YouTube video. For the past few months I’ve been trying to force YouTube to show me palatable content on my sidebar, but no matter how much crap I reject as “Not Interested” more crap appears. Since the fountain of crap is inexhaustible, I searched for a YouTube cleanup plugin. DF Tube–Distraction Free Tube– is so great! Everything bad — recommendations on the sidebar, crap on the homepage, inane commentary–vanishes!

Scouts. Girls can now formally eligible to form boy scout troops. Not only was the copy editor asleep at their desk, the author did not note the inherent sexism of this odd new eligibility. Why not call both Girl Scouts and Boy Scouts “Scouts”? And are there boys clamoring to form girl scout troops? That would show some progress. As it has often been said, equality will have a chance not when girls can be more like boys, but boys can be more like girls.

Water. The state of water in the world in less than 500 words. This will be a big issue in the future if the population continues to grow as projected, and this is a good summary of the regions the problems will arise, and who is getting in front of the issue.

Lorena Bobbit. Of course the real story was not what was emphasized on late night TV–the penis–the real story was about years of marital rape, domestic violence and male entitlement. Did you know John Wayne Bobbit became a porn star after his penis reattachment surgery and was convicted and served time for tying a woman up and repeatedly raping her? All those jokes at Lorena Gallo’s expense are hard to countenance–but an upcoming documentary may set the story straight.

 

Swedish audio streaming company Spotify has acquired podcast production company Gimlet Media and do-it-yourself podcasting platform Anchor to increase its podcast offerings.

Spotify paid a reported $230 million to buy Gimlet Media, according to Recode. Information is not available regarding how much the music streaming service spent to acquire Anchor.

“With these acquisitions, Spotify is positioned to become both the premier producer of podcasts and the leading platform for podcast creators. Gimlet will bring to Spotify its best-in-class podcast studio with dedicated IP development, production and advertising capabilities. Anchor will bring its platform of tools for podcast creators and its established and rapidly growing creator base,” reads Spotify’s press release. 

It looks like Spotify bought these platforms to build out its offering for podcast producers. If the service can offer the means to build, record, and share podcasts right on its platform, that could be a big step forward for the company.

Spotify to acquire Gimlet Media Inc. and Anchor. Read the press release here to learn more about how we’re accelerating growth in podcasting: https://t.co/Lif57Bkrz9

— Spotify News (@SpotifyNews) February 6, 2019

Spotify doesn’t seem like it’s finished spending money on acquisitions yet, either. In the company’s earnings report, Spotify shared its plans to spend $400 to $500 million on new properties this year. As a result, unless it dropped $270 million on Anchor, Spotify likely has a few more companies to buy.

In a separate blog post, company CEO Daniel Ek said, “just as we’ve done with music, our work in podcasting will focus intensively on the curation and customization that users have come to expect from Spotify. We will offer better discovery, data, and monetization to creators.”

Spotify already claims to be the world’s second largest podcasting platform. Therefore, if the service can get more people to listen to a higher number of podcasts, maybe it can even become the world’s largest.

The company expects the deals to finish by the end of the first quarter in 2019, but it hasn’t said if this means the Anchor app or anything at Gimlet Media will change.

You can still download Anchor on iOS or Android.

Source: Spotify, Recode

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Various models of the Google Pixelbook are currently on sale on Amazon Canada.

The i5/8 GB RAM/256GB version of the Chrome OS touchscreen laptop is available for $1,449.99 CAD on Amazon Canada, $149 down from its regular price.

Meanwhile, the i7/16 GB RAM/512GB variant is on sale for $1,589.99 ($509 off).

The Pixelbook can be ordered from Amazon here.

For more on the Pixelbook, read MobileSyrup‘s review here.

Source: Amazon Canada

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The post Function over form: Mophie’s new power bank goes all-in on USB-C appeared first on MobileSyrup.

Kathleen Morris, The Edublogger, Feb 06, 2019

If your answer to the question is "yes" then you may want to visit this post on Edublogger, read the overview, and if you're inspired, fill out the form. The occasion is the service's Student Blogging Challenge held every March and October - read more here.

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Jason Cole, e-Literate, Feb 06, 2019

The article barely mentions Google, so I imagine it's mentioned just for completeness. But there are two major strands of interest. First is the stand of no-shows at the large English technology conference: "the absence of the major LMS vendors besides Instructure Canvas... D2L, Moodle, the UK Moodle partners, and Blackboard had no discernible presence." Second, "When Microsoft makes their push, the learning system won’t look like an LMS, but it will look like Teams... Microsoft is rapidly integrating service platforms for email, calendar, business logic, business intelligence, AI, device management, and cloud services into the Teams platform." Which totally makes sense to me.

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