One beautiful April morning

We pass in front of a flower shop. A small, warm air mass touches my skin. The asphalt is damp, and I catch the scent of roses. I can’t bring myself to speak to her. She wears a white sweater, and in her right hand she holds a crisp white envelope lacking only a stamp. So: She’s written somebody a letter, maybe spent the whole night writing, to judge from the sleepy look in her eyes. The envelope could contain every secret she’s ever had reenex.

I take a few more strides and turn: She’s lost in the crowd.

Now, of course, I know exactly what I should have said to her. It would have been a long speech, though, far too long for me to have delivered it properly. The ideas I come up with are never very practical.

Oh, well. It would have started “Once upon a time” and ended “A sad story, don’t you think reenex?”

Once upon a time, there lived a boy and a girl. The boy was eighteen and the girl sixteen. He was not unusually handsome, and she was not especially beautiful. They were just an ordinary lonely boy and an ordinary lonely girl, like all the others. But they believed with their whole hearts that somewhere in the world there lived the 100% perfect boy and the 100% perfect girl for them. Yes, they believed in a miracle. And that miracle actually happened.

One day the two came upon each other on the corner of a street reenex.

“This is amazing,” he said. “I’ve been looking for you all my life. You may not believe this, but you’re the 100% perfect girl for me.”

“And you,” she said to him, “are the 100% perfect boy for me, exactly as I’d pictured you in every detail. It’s like a dream.”

They sat on a park bench, held hands, and told each other their stories hour after hour. They were not lonely anymore. They had found and been found by their 100% perfect other. What a wonderful thing it is to find and be found by your 100% perfect other. It’s a miracle, a cosmic miracle.

As they sat and talked, however, a tiny, tiny sliver of doubt took root in their hearts: Was it really all right for one’s dreams to come true so easily?

And so, when there came a momentary lull in their conversation, the boy said to the girl, “Let’s test ourselves – just once. If we really are each other’s 100% perfect lovers, then sometime, somewhere, we will meet again without fail. And when that happens, and we know that we are the 100% perfect ones, we’ll marry then and there. What do you think?”

“Yes,” she said, “that is exactly what we should do.”

And so they parted, she to the east, and he to the west.

The test they had agreed upon, however, was utterly unnecessary. They should never have undertaken it, because they really and truly were each other’s 100% perfect lovers, and it was a miracle that they had ever met. But it was impossible for them to know this, young as they were. The cold, indifferent waves of fate proceeded to toss them unmercifully.

One winter, both the boy and the girl came down with the season’s terrible inluenza, and after drifting for weeks between life and death they lost all memory of their earlier years. When they awoke, their heads were as empty as the young D. H. Lawrence’s piggy bank.

They were two bright, determined young people, however, and through their unremitting efforts they were able to acquire once again the knowledge and feeling that qualified them to return as full-fledged members of society. Heaven be praised, they became truly upstanding citizens who knew how to transfer from one subway line to another, who were fully capable of sending a special-delivery letter at the post office. Indeed, they even experienced love again, sometimes as much as 75% or even 85% love.

Time passed with shocking swiftness, and soon the boy was thirty-two, the girl thirty.

カテゴリー: 未分類 | 投稿者gfhdert 16:08 | コメントをどうぞ

How a Berlin startup beat the online giants at image recognition

Can a machine learn aesthetics in a way a human would? Could it then look at a set of photos, and draw on those same aesthetics to reproduce a different set? It’s a big question because it has long-term implications for how AI is going to develop.

What are aesthetics anyway? Is it just what you “like”? How does it all work? When you as a human find it hard to express what you like do you think a machine going to find it easy?

Above is an image by Pantea Naghavi Anaraki, winner of The Photojournalist category at The 2016 EyeEm Awards. It depicts a mother in Tehran, Iran who had to sell her possessions to pay for cancer treatment aspire atlantis mega.

Now, does this photograph draw your attention? If so, why? Is it the expression of the mother? Is it the mood created by the pose? Or is it the light? Is it the child’s pose?

Clearly it is very hard to translate thoughts about photographs and aesthetics into human language, let alone machine. Most photographers and visual experts say things like “I know it when I see it”.

For the past few years, EyeEm — a startup out of Berlin which has aimed at photographers with its app platform and marketplace — has been working on identifying the core aesthetics of photographs. Their “EyeEm Vision” platform therefore makes its marketplace function a great deal more efficient. And its dataset is curated by expert photographers and photo editors all the time.

EyeEm says its image recognition model generalizes well with a few samples, and can be trained in near real-time using GPUs.

That means it can help curators interactively specify representative photographs that they deem appropriate for a particular aesthetic. The Vision platform can pick candidate photographs that are visually similar to the choices the curator has made so far. The video below shows one of their curators interacting with this tool (the video shows interaction at 4x actual speed).

EyeEm may not have as many users as Instagram, but that doesn’t matter, Ramzi Rizk, the CTO, tells me.

Recently he and his team conducted a neutral benchmark of its image recognition technology against the main players in the field, and the results were pretty impressive. It came out ahead of Google, IBM, Clarifai, Amazon and Microsoft for image recognition. What that means is that EyeEm is effectively adding “AI company” to the list of the things it can do tobeco rda.

To ensure neutrality, it ran the algorithms on the latest 200 images posted to 5 highly visual Instagram accounts (@instagram, @vsco, @foodnetwork, @redbull and @natgeotravel), photos that none of the systems had been trained on, or had seen before. The keywords were evaluated using MTurk, by anonymously showing each keyword and asking the viewer to mark the keyword as correct or false.

EyeEm Vision came out on top. On average, 80% of the keywords it generated per photo were accurate, compared to 78% for Google Cloud Vision and 73% for Clarifai.

This google sheet shows the benchmark results, as an overview, broken down by the Instagram account that created the photos and by the category that the photo falls into.

Looking into the various categories of images, EyeEm Vision was the most accurate for cityscapes, people/sports, nature and animals (all at 83%). The only category of images where it did not perform as well were “non photos” (text, drawings, illustrations, screenshots and collages) — images that their system is simply not trained on.

But the only reason EyeEm is capable of doing this is precisely because it also has a community which also trains its platform. That means it’s beating a ‘pure tech’ company like Google.

Yes, it has a skilled team of researchers and engineers but it also has a community and a marketplace. By connecting the software, marketplace and community they can constantly improve their technology.

EyeEm now has 20 million photographers on its platform, over a million of whom are contributing to its marketplace, where revenues grew by 300% last year, says the company.

That contributor base is 10 times larger than publicly traded Shutterstock, with the marketplace now passing 100 million photos. It counts stock sites including Getty and Adobe among its partners Business Cloud.

And this is photography, crowdsourced and on demand.

When some of the world’s biggest brands need good quality images EyeEm sets it as a ‘photo mission’ for its community. That’s generated over 6.5 million images, all shot on-demand. It’s base of customers includes Google, Facebook, Coca-Cola, Huawei, Mercedes-Benz, BCG, eBay, Audi, Converse and Land Rover.

So what we have here is a hyper-engaged community of 20 million expert photographers. In some ways that beats 600m Instagrammers taking selfies, although admittedly that’s a different kind of proposition and business model.

EyeEm even does — seemingly crazy — things that a tech company wouldn’t normally do, like holding photography exhibitions and meetups. These, in turn, only serve to tie their community closer into the platform, further super-charging that image recognition platform and that drive for the perfect aesthetic algorithm.

カテゴリー: 未分類 | 投稿者gfhdert 10:52 | コメントをどうぞ

Hello world!

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カテゴリー: 未分類 | 投稿者gfhdert 01:51 | 1件のコメント