30 March 2017

Select All: “Why Facebook is Trying to Turn Itself into Snapchat”

At the top left is Facebook’s new camera functionality. Top right takes you to Messenger, which is a separate app. Below the camera is Direct, a messaging product separate from Messenger that exists within Facebook Prime. Next to Direct is Stories, which are different from the Stories in Instagram or Messenger, and which now get the app’s prime real estate. Below Stories is the standard Facebook status prompt that you know and love. And below that is the News Feed from whence everyone gets their Fake News. That’s no less than five separate Facebook products throwing themselves in your, well, face as soon as you open the app. And on top of determining the proper posting context, users also have to figure out privacy options and which people their audience, composed of multiple friend groups and contexts, should be comprised of.

This is as clear a visual indication as any that Facebook hasn’t settled on a strategy to get it through the next internet epoch. For nearly a decade, Facebook has built an unbelievable advertising empire thanks to the News Feed — the product that most people would describe if you asked them what Facebook is. But the News Feed, for all of its reach, is clearly flawed, filled with aggressive advertising, brands, and performative posts from friends you might not have spoken to in months or years. Worse (for Facebook), there are early indications that the News Feed is in trouble: For a while now, Facebook has been concerned internally that people are sharing less, a result of the “context collapse” that results from combining friends from school, college, and multiple jobs under one online presence.

Brian Feldman

Very little about these new features makes sense to me. Let’s take Direct for example, a new way to send (disappearing) messages to friends; why wasn’t this launched as a Messenger feature, along with regular and encrypted messages? What is the point of having separate apps if their features overlap to the point of becoming indistinguishable? In a way, Facebook’s presence in social media has become so massive that it’s now competing against it’s own apps: Direct against Messenger and WhatsApp; Camera Effects against Instagram; and Stories… well… let’s just say I still can’t understand the appeal of this format and having it forced upon me from all directions does nothing to improve my impressions.

28 March 2017

Twitter app for Windows 10 experiments with tabs

Speaking of (good) Windows apps, Twitter is another example of Windows app done well: fast and responsive, with a clean design and a full feature set. And… something more! A couple of weeks back I noticed it added new features, namely tabs and a tiny extra menu. It’s probably a tentative step to more power-user features in the Windows app, but for now it’s still very much an experiment available to some random users (apparently, me included). Every time you create a new tab with the + button, it starts with ‘Home’, your default timeline, but that’s about it: you can’t save or restore the current tab configuration, you can’t navigate quicker to obscure sections of Twitter – like your likes or lists – that’s still manual work. Also, you can’t create more than four tabs at this time. But it’s nice that you can reference information from other tabs when you’re replying to someone, for example, or that you don’t have to lose your place in the timeline if you want to check notifications: just open a new tab and check notifications there.

Twitter app for Windows 10 adds tabs

27 March 2017

Searching for a podcast client on Windows 10? Try Grover Podcast

After introducing the touch-first Metro design language, Windows has long struggled with the low number of apps built specifically for the platform. With the introduction of Windows 10, things have started improving somewhat. At least the major social networks are well covered, from Twitter to Facebook, Messenger and Instagram – and let’s not forget that Instagram hasn’t launched a proper iPad client yet! But what about niche use cases like listening to podcasts? Sometime last year I started searching for a podcast client to use on my tablet. Fortunately, I didn't have to search for long until I found the best solution yet: Grover Podcast.

The app has all the basic features you would expect in a podcast player: subscribing to podcasts (manually from RSS feeds or by searching the large selection of the iTunes store), streaming episodes, options to automatically download new episodes and removed played files, notifications for new episodes, and importing/exporting subscription lists as OPML files. The design is pretty great as well, respecting the Windows 10 guidelines, making the app similar in look and feel to the system music player Groove Music – the name Grover actually originates from this music player. And, like it, Grover is integrated with system audio playback, meaning you can use the standard keyboard shortcuts and the lock-screen widget to play/resume. The only major feature missing from the regular app is syncing subscriptions and played status between devices – though I understand it’s available in the Pro version, which also includes a Windows mobile app. I can live without syncing for the time being, since I do most of my listening on the iPhone anyway.

26 March 2017

Peter Watts – Collateral

in Bucharest, Romania
Upgraded – Neil Clarke

După ce o misiune de recunoaștere de rutină ce se soldează cu o tragedie, caporalul Nandita Becker se așteaptă la represalii în interiorul forțelor armate. În schimb, spre surprinderea ei, superiorii ei se arată extrem de indulgenți, căci situația e destul de complicată: o înregistrare a momentelor în care armele autonome ale lui Becker măcelăresc șase studenți nevinovați a ajuns cumva în mâinile presei și opinia publică trebuie potolită pentru a păstra suportul pentru misiunile de pacificare. Așa că Becker este politicos și ferm instruită să participe la un interviu cu Amal Sabrie, o ziaristă faimoasă și critic fervent al acțiunilor militare, pentru a încerca să spele imaginea proastă și să atragă simpatia către propria dramă, un soldat ale cărui arme iau singure decizii de viață și de moarte în intervale atât de scurte că mintea umană nu poate ține pasul.

Amal Sabrie stood at her approach. “You look—” she began.

like shit. Becker hadn’t slept in three days. It shouldn’t have shown; cyborgs don’t get tired.

“I mean”, Sabrie continued smoothly, “I thought the augments would be more conspicuous.”

Great wings, spreading from her shoulders and laying down the wrath of God. Corporal Nandita Becker, Angel of Death.

“They usually are. They come off.”

21 March 2017

The New York Times: “The Great A.I. Awakening”

AI Awakening cover

It is important to note, however, that the fact that neural networks are probabilistic in nature means that they’re not suitable for all tasks. It’s no great tragedy if they mislabel 1 percent of cats as dogs, or send you to the wrong movie on occasion, but in something like a self-driving car we all want greater assurances. This isn’t the only caveat. Supervised learning is a trial-and-error process based on labeled data. The machines might be doing the learning, but there remains a strong human element in the initial categorization of the inputs. If your data had a picture of a man and a woman in suits that someone had labeled “woman with her boss”, that relationship would be encoded into all future pattern recognition. Labeled data is thus fallible the way that human labelers are fallible. If a machine was asked to identify creditworthy candidates for loans, it might use data like felony convictions, but if felony convictions were unfair in the first place — if they were based on, say, discriminatory drug laws — then the loan recommendations would perforce also be fallible.

A neural network, however, was a black box. It divined patterns, but the patterns it identified didn’t always make intuitive sense to a human observer. The same network that hit on our concept of cat also became enthusiastic about a pattern that looked like some sort of furniture-animal compound, like a cross between an ottoman and a goat.

Gideon Lewis-Kraus

Great story – though a bit too long – about Google’s efforts to integrate machine learning into translation. Lately I have used Google Translate more than usual and the quality of the results does show, but improvements are apparent mostly on longer sentences. When translating single words, on the contrary, I regularly find situations where Translate wouldn’t recognize words that, when queried through Google Search, had clear definitions. Evidently Translate could still use some work if Google Search still does a better job at identifying words.