Google made waves Monday when it made its new artificial intelligence system TensorFlow open source.
Google has used TensorFlow for the past year for a variety of applications. For example, Google Photos is scary good at search because it uses TensorFlow to recognise places based on popular landmarks or characteristics, like the Yosemite National Park mountain range.
Other Google products that use TensorFlow include Google search, Google’s voice recognition app, and Google Translate.
By making TensorFlow open source and letting any developer use it, Google can improve the system and see other ways it can be used beyond its current applications. That means it has the potential to make Google smarter at doing everything from delivering you better search results to recommending what YouTube videos to watch. It will also make third-party apps a lot more useful.
Google declined to comment for this story.
But what is TensorFlow? To fully understand it, you need to get artificial intelligence and deep learning on a basic level.
How does it work?
The easiest way to understand TensorFlow and Google’s approach to AI is with image recognition. In 2011, Google created DistBelief, which used machine learning to identify what’s in a photo by recognising certain patterns. For example, it will look for whiskers of a certain length to help determine if the image is of a cat.
The system was built using positive reinforcement — essentially, the machine was given an image and asked if the image was a cat. If the machine was correct, it was told so. But if the machine was wrong, the system was adjusted to recognise different patterns in an image so that it was more likely to get it right next time.
TensorFlow takes the concept a step further by using deep learning, or an artificial neural network composed of many layers.
Basically, TensorFlow sorts through layers of data, called nodes, to learn that the image it’s viewing is of a cat. The first layer will ask the system to look for something as basic as determining the general shape in the picture. The system then moves, or flows, to the next data set — like looking for paws in the photo.
Here’s a demo of TensorFlow in action:
The system moves from node to node to compile enough information to say that the image is, in fact, of a cat. That flow process is called a tensor, hence the name TensorFlow.
What’s its potential?
As Google writes on its blog: “TensorFlow is faster, smarter, and more flexible than our old system (DistBelief), so it can be adapted much more easily to new products and research.”
Stanford University researcher Andrej Karpathy recently built an AI system capable of looking at
2 million selfies and figuring out which ones are most likely to get a lot of love. Karpathy noted that the only tools available to developers for deep learning projects, such as Theano, are grown out of graduate students’ side projects.
“TensorFlow is the first serious implementation of a framework for Deep Learning, backed by both very experienced and very capable team at Google,” Karpathy wrote in an email to Tech Insider.
Jon Van Oast is a senior engineer for nonprofit WildMe, which compiles photos of different species of animals for research purposes.
The project began by collecting photos of Whale Sharks. Each Whale Shark has a unique configuration of spots, which allows you to identify one from another. WildMe uses a machine learning system called AdaBoost that allows researchers to upload a photo of a Whale Shark and see if there is a match for it already in the system.
If the system finds a match, the researcher now has more information about that whale’s life, such as where it was previously located. WildMe has extended from that original purpose to sort through other species of wildlife as well.
Van Oast told Tech Insider that he thinks TensorFlow could help sort through more images at a faster rate, providing researchers with more information.
“It’s a way to look at a big set of data where humans can’t discern what’s important in it,” he said.
Delip Rao, who consults with startups on machine learning and former engineer for Twitter, told Tech Insider that it allows for better natural language processing. However, he declined to share specific examples of how he would use TensorFlow.
“Its natural language processing will have a deep understanding of the text, so if you’re building an app — like Facebook’s virtual assistant (M) — it would be able to understand the content of the message (sent to the app),” he said.
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