Google made a site that shows how millions of people draw the same object

Back in November, Google released artificial intelligence experiment that asks you to draw a random object and see if the neural network can identify your doodle. Quick, Draw! was eventually turned into a tool that transformed drawings into clip art based on the best results it got, helping people add a visual icon to their work without requiring any particular artistic talent. Alongside Google I/O this week, Google has now released the data it received from Quick, Draw! to show you how 15 million people drew the same set of objects. It’s a fascinating look at how humans interpret a random item, from monkeys to parachutes to phones.

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Google’s 18-Month Quest To Redesign Its Terrible Emoji

 

“It’s a communication issue,” says Rachel Been, Creative Director on Google’s Material Design. “If I sent my friend the dancing woman on iOS, and I’m on an Android device and I see a blob, there’s a miscommunication.” And now, thanks be to Google, that miscommunication is being fixed. “We’re doing a full redesign of the emoji set,” says Gus Fonts, Product Manager, Android. “We took a look at many things, but mostly the thing that’s most striking is, perhaps, that yes, the candy dots or blobs, are now substituted with a set of squishy circles–for a lot of good reasons.”

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Systems Smart Enough to Know When They’re Not Smart Enough

 

So the more Google and other answer machines become the authorities of record, the more their imperfect understanding of the world becomes accepted as fact. Designers of all data-driven systems have a responsibility to ask hard questions about proper thresholds of data confidence—and how to communicate ambiguous or tainted information. How can we make systems that are not only smart enough to know when they’re not smart enough… but smart enough to say so and signal that human judgment has to come into play?

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15 fun things to type into Google

Barrel Roll

Here’s something to amuse yourself for a while. I knew about some of these already but others were new to me. 

Google’s easter eggs – funny little images, programs or widgets – are legendary, but many of them lie dormant, just waiting for users to type the magic words into the search box. Are they clever? Some are. Are they useful? Most aren’t. But they’re all a welcome distraction from working. They all work in Chrome on desktop, most work on mobile too, and some of them also work in other browsers. Enjoy

Read the full list here. I think ‘Star Wars text’ may be my favourite.

What graphic designers think about the Google logo

Google’s seen a lot of changes recently, and the latest came yesterday, when the tech company surprised everyone with their new logo. In one of the biggest changes since 1999, Google’s new logo uses a simpler sans-serif typeface. The new logo had to work well in constrained spaces and maintain consistency across many products, the company explained in a blog post. Many iterations of the new logo ended up on the cutting room floor. So how did Google do? We picked the brains of some graphic and type designers to see what they thought.

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Right to be forgotten: Swiss cheese internet, or database of ruin?

Imagine, 25 years ago, someone telling you: we really need to redress this massive social ignorance that, when you meet someone for the first time, you don’t know everything about them. What we ought to do is assemble a giant database. On everyone. Brilliant idea. But there are a couple of provisos, they add. This database will be sourced from whatever scraps of information are lying around about you – whether carefully crafted, or pulled from the streets. The product of your life’s work; or just some odd thing you once said or did, long ago, somewhere that the database decides to rank highly and eternally. The database will contain the most intimate, embarrassing, destructive things. But they will be mere flecks in a torrent of utility. And because of that: you have no rights or say over the database. Your entry – and that of everyone else who can’t afford a reputation manager – is subject to the whims of the untouchable logic of the machine, scraping the sticky, pocked underbelly of the web. Some would call that idea visionary. Others would call it nuts. But it’s what we’ve got. It’s called a search engine. Or, for most of us, in the monoculture of our digital universe: Google.

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Yes, androids do dream of electric sheep

What do machines dream of? New images released by Google give us one potential answer: hypnotic landscapes of buildings, fountains and bridges merging into one. The pictures, which veer from beautiful to terrifying, were created by the company’s image recognition neural network, which has been “taught” to identify features such as buildings, animals and objects in photographs. They were created by feeding a picture into the network, asking it to recognise a feature of it, and modify the picture to emphasise the feature it recognises. That modified picture is then fed back into the network, which is again tasked to recognise features and emphasise them, and so on. Eventually, the feedback loop modifies the picture beyond all recognition.

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Yes, androids do dream of electric sheep

What do machines dream of? New images released by Google give us one potential answer: hypnotic landscapes of buildings, fountains and bridges merging into one. The pictures, which veer from beautiful to terrifying, were created by the company’s image recognition neural network, which has been “taught” to identify features such as buildings, animals and objects in photographs. They were created by feeding a picture into the network, asking it to recognise a feature of it, and modify the picture to emphasise the feature it recognises. That modified picture is then fed back into the network, which is again tasked to recognise features and emphasise them, and so on. Eventually, the feedback loop modifies the picture beyond all recognition.

Read the full story here

Yes, androids do dream of electric sheep

What do machines dream of? New images released by Google give us one potential answer: hypnotic landscapes of buildings, fountains and bridges merging into one. The pictures, which veer from beautiful to terrifying, were created by the company’s image recognition neural network, which has been “taught” to identify features such as buildings, animals and objects in photographs. They were created by feeding a picture into the network, asking it to recognise a feature of it, and modify the picture to emphasise the feature it recognises. That modified picture is then fed back into the network, which is again tasked to recognise features and emphasise them, and so on. Eventually, the feedback loop modifies the picture beyond all recognition.

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How to get a job at Google: meet the man who hires and fires

In his office, Laszlo Bock, head of people operations, handles the claims from outsiders asking: “Please let me be Googley.” Each year, around 2 million apply for a job here and 5,000 are hired. Bock puts the average applicant’s odds at about 400/1. On a wall he keeps a small display of some of the worst (Bock prefers “silliest”) submissions that have come in. People try to grease him, impress him, plead with him, threaten him. He was offered, once, a discount on a motorhome in return for an offer. And somebody mailed in a shoe; with this foot-in-the-door joke the hope, presumably, that an acceptance letter would be sent by return post.

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