For companies looking to predict user patterns or how investments will grow, the ability to mobilize artificial intelligence can save labor and protect investments. For consumers trying to understand the world around them, AI can reveal patterns of human behavior and help to restructure our choices.
One major challenge to understanding what impact AI and neural network architecture can have in our lives is the rareness of real-world applications. For a better understanding of how neural networks can help you and your business practice, here are six ways that you can save labor and get ahead of the competition:
Deep Learning Can Colorize Black And White Images
The painstaking labor of image colorization dates back to the beginning of photography. Before photographic chemical processes could translate the color spectrum to photo paper, artists would take careful measures to paint over photographs.
This handmade effort was time-consuming and often yielded middling results.
Deep learning and neural networks are already miles ahead of us in that regard. They can understand the context of images and begin to insert color where necessary. The many AI products on the market can fill in the blanks in the way a human mind would give access to a broad enough base of images.
By leveraging neural network architecture, AI software can go through millions of images to find the right tone to fit any image. This approach could be used to colorize still frames of black and white movies, surveillance footage or any number of images.
Adding Sound To Silent Video
Because neural networks can derive data from any number of resources with access to millions of sounds and videos, it can make predictive judgments. Neural network architecture can now synthesize audio to fill in the blank spots of a silent video.
By training a system with 1,000 examples of objects being struck by a drumstick, researchers recently showed the power of neural networks to create sound. Their deep learning model was able to associate certain motions in a video with certain sounds.
Later, trials were run with human subjects. Test subjects were asked to discern whether the sounds they were hearing in a video clip were real or synthesized. They found very often that humans couldn’t tell the difference.
Imagine what kind of applications this technology could have for your business.
Using a combination of image assessment technology and a deep understanding of contextual foreign language usage, machines are improving translation technology.
Neural network architecture can perform translations of text without preprocessing the sequence so that the algorithm can learn word relationships. The network then processes these relationships through its image mapping technology to create a contextual solution to a translation issue.
Convolutional neural networks can identify images that have letters and pull them out of the image. They will then be turned into text and translated. The image can then be recreated with the text translation added.
This is being called instant visual translation. If you work with distributors selling products all around the world, the ability to create an augmented reality app using IVT could boost both sales and communication.
Object Classification In Images
By getting access to a wide variety of images and learning the context of each one, neural network architecture can draw relationships between images. Images are analyzed by dividing objects up and placing each object in an image into a class of learned objects.
The larger your neural network architecture is, the more accurate your results can be.
You could have software crawl through a given network looking for instances of your products. You could figure out if a celebrity has been wearing a piece of jewelry that you created. You could even see if your cafe has been featured in any shots of a show made in your neighborhood.
More complex versions can draw a box around each instance of a given object to highlight the variations and eliminate clutter. The better your neural network architecture, the more accurate your results will be.
If you work in the banking or financial sectors, you know that handwriting is falling out of favor. You also probably know that you’re constantly using snail mail and faxes only for the fact that you need signatures.
By analyzing a single handwriting sample as the writer puts the specialized pen to the surface, neural networks can generate handwritten samples of letters. New examples of imitated handwriting can be generated on the fly.
This complicated machine learning exercise shows the flexibility of neural network architecture and the ability to change and adapt. This work could also be used by forensic teams to match samples and analyze data.
As the “infinite monkey theory” goes, with enough randomly generated text, you’re bound to end up with Shakespeare. Or even Harry Potter.
It turns out you don’t need monkey power. You need powerful neural network architecture. By performing text analysis, machines can learn to spell, punctuate and stylize the text of another writer. AI can learn the relationship between words and the intention of any writer if given enough examples.
There are infinite ways to imagine how this could help any business or individual. By using predictive relationships between past work and ideas, a team could generate ideas by feeding content into these machines. While they can’t necessarily predict the future of your ideas, they can certainly help prevent redundancies. This could be a great tool for a team or a writer who’s recently hit a roadblock in their work.
Writer’s block can be destroyed by advanced neural network architecture.