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This week Stephen Wolfram, founder and chief executive of Wolfram Research, announced a new component of the Wolfram Language for programming called ImageIdentify. Wolfram also introduced a new website, dubbed The Wolfram Language Image Identification Project, that demonstrates the language’s new capabilities.
The new site lets you upload images and get inferences and definitions in response. You can provide feedback, which should help it become more accurate. You can hit buttons like “Great!,” “Could be better,” “Missed the point,” and “What the heck?!” After you choose one, the service offers a few more guesses, and a text box where you can type in a tag. Then you can type in your email address, so it can tell you “when ImageIdentify learns more about your kind of image.”
The service uses a trendy type of artificial intelligence called deep learning. It draws on artificial neural networks, which train on a large quantity of information, like pictures, and then make inferences when you give it new information, like a new picture. Big web companies like Facebook, Google, and Microsoft use deep learning for various purposes, and increasingly smaller companies have been exposing deep learning tools for pretty much anyone to try out.
To get a rough sense of the power of the new Wolfram technology, I decided to put it up against other existing image-recognition systems you can test out on the Internet today, from CamFind, Clarifai, MetaMind, Orbeus, and IBM-owned AlchemyAPI. I chose images from Flickr that seemed to clearly fall into the 1,000 categories used for the 2014 ImageNet visual recognition competition. It was unscientific — just for the sake of curiosity.
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What I found is that Wolfram’s new system doesn’t seem to be all that bad. It wasn’t overly conservative or vague, and it didn’t make many obvious mistakes — although it wasn’t as consistently accurate as MetaMind, for one. With time, Wolfram’s technology should improve — especially as people point out its flaws.
Here are 10 of the tests I ran to reach my conclusion.
Wolfram ImageIdentify: tea
CamFind: white ceramic mug
Clarifai: coffee cup nobody tea mug cafe hot ceramic coffee cup cutout
MetaMind: Coffee mug
Wolfram ImageIdentify: magic mushroom
CamFind: white mushroom
Clarifai: mushroom fungi fungus toadstool nature grass fall moss forest autumn
Wolfram ImageIdentify: spatula
CamFind: black kitchen turner
Clarifai: steel wood knife handle iron fork equipment nobody tool chrome
Wolfram ImageIdentify: scoreboard
CamFind: baseball scoreboard
Clarifai: scoreboard soccer stadium football game competition goal group north america match
5. German shepherd
Wolfram ImageIdentify: German shepherd
CamFind: black and brown German shepherd
Clarifai: dog canine cute puppy mammal loyalty grass sheepdog fur German hepherd
MetaMind: German Shepherd, German Shepherd Dog, German Police Dog, Alsatian
Wolfram ImageIdentify: tufted puffin
CamFind: toucan bird
Clarifai: bird one north america nobody animal people adult nature two outdoors
7. Indian cobra
Wolfram ImageIdentify: black-necked cobra
CamFind: brown and beige cobra snake
Clarifai: snake nobody reptile cobra wildlife daytime sand rattlesnake north america desert
MetaMind: Indian cobra, Naja Naja
Wolfram ImageIdentify: strawberry
CamFind: red strawberry ruit
Clarifai: fruit sweet food strawberry ripe juicy berry healthy isolated delicious
Wolfram ImageIdentify: cooking pan
CamFind: gray steel frying pan
Clarifai: ball nobody pan cutout kitchenware north america tableware competition bowl glass
Orbeus: frying pan
AlchemyAPI: (No tags)
10. Shoe store
Wolfram ImageIdentify: store
CamFind: black crocs
Clarifai: colour street people color car mall road fair architecture hotel
MetaMind: Shoe Shop, Shoe Store
Orbeus: shoe shop
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