In fact, many enterprises have large volumes of binary data that are not used to their full potential because of the inherent complexity of ingesting and processing such data.
Here are a few examples of how one might work with binary data : Of course, there are many other use cases.
The advances seen in the Image Net competition showed the world what was possible, and also harkened the rise of convolutional neural networks as the method of choice in computer vision.
Convolutional neural networks have the ability to learn location invariant features automatically by leveraging a network architecture that learns image features, as opposed to having them hand-engineered (as in traditional engineering).
The 90km per hour flying drones reach their destination in minutes compare to hours to help saving lives.
Another case: Ada a personal mobile health guide, invented in Germany.Ada acts as a first diagnosis agent that helps people solve an issue.Of course Ada cannot replace a doctor, but the mobile app is used in countries where the next doctor might be hours away and such first diagnosis provides valuable information for next steps to be taken.The automotive industry has embraced computer vision (and deep learning) aggressively in the past five years with applications such as scene analysis, automated lane detection, and automated road sign reading to set speed limits.The media world is leveraging computer vision to recognize images on social media to identify brands so companies can better position their brands around relevant content.For instance, in China the giant is already using drones to deliver products to remote areas.Digital innovation helps companies to improve their businesses and they might even transform private households into gigantic storage rooms.The enterprise’s interest in machine vision techniques has ramped up sharply in the last few years due to the increased accuracy in competitions such as Image Net.Computer vision methods have been around for decades, but it takes a certain level of accuracy for some use cases to move beyond the lab into real-world production applications.We’re seeing applications of computer vision across the spectrum of the enterprise: In insurance, we see companies such as Orbital Insights analyzing satellite imagery to count cars and oil tank levels automatically to predict such things as mall sales and oil production, respectively.We are also seeing insurance companies leveraging computer vision to analyze the damage on assets under policy to better decide whom should be offered coverage.