Drones have quickly become a technology almost everyone is familiar with. Whether it's professional photographers capturing wild landscapes or amateurs racing mini quadcopters around their living room: drones are here to stay. But not all drones are the same. If your mission is to use the power of a drone to capture your most unmissable moments, then the Airlango Mystic Drone is your answer. The Airlango Mystic Drone sets itself apart from its competition by incorporating a range of technologies to make it easy for anyone to be a professional drone photography pilot.

EyeSense, an iPad app developed in Egypt, has the ability to "learn" objects in its environment, having been trained by its users. A visually impaired person can point their device in the direction of where they think something might be–say, a coffee cup–and a voice will say that the app recognizes that object. "The key strength of the app is that it also recognizes basic facial expressions, like winks or smiles. This enhances human interaction," says Joanna Marczak, a spokesperson for its developer, ID Labs. To train the app, you place things in front of the device's camera at several angles, telling it about the items.

Digital video consumption has surged, igniting new monetization opportunities for these modern distribution outlets and content makers, a point I explored in a previous essay. The challenge I didn't touch on, however, is how to optimize these monetization events in the most efficient manner. Machine learning is at the root of the answer. Machine learning is when computers learn and analyze new data without being programmed. It's when software can recognize patterns and draw conclusions.

Google is one company that can be seen to push forward in the world of artificial intelligence continually. So, the fact that announced a new machine learning API that is capable of recognizing objects in videos and making them instantly searchable is no real surprise is it? This new Video Intelligence API allows developers to build applications that have the ability to extract objects from a video. Up until now, this has only been done through still images rather than videos, but moving forward this new API will allow so much more. Developers will be able to construct creative applications that let the user search for information relating to what it is they're watching.

Fei-Fei Li, chief scientist of artificial intelligence and machine learning at Google Cloud, came on stage at Google's Next Cloud conference today to talk about the current and next-generation applications of AI that Google's working on. These technologies will make a difference in self-driving cars and healthcare, sure, but also Snapchat's filters and Google Photos' search capabilities. But the big highlight came when she announced a new way to allow software to parse video. This new "Video Intelligence API" was demoed onstage, and it offered the kind of "whoa" moment you expect from a Google keynote. By playing a short commercial, the API was able to identify the dachshund in the video, when it appeared in the video, and then understand that the whole thing was a commercial.

Facebook's artificial intelligence (AI) team has built a visual search system that can recognize content that appears in photos and return relevant search results. Called Lumos, Facebook originally created the platform so that its visually impaired users could understand the content of photos. But Facebook recognized that everyone could benefit from this type of visual search system. Facebook's image search system can detect and segment objects, scenes, animals, places, and clothes that appear in images or videos – and understand them. For instance, let's say you search for "black shirt photo."

Advances in machine learning and deep learning systems are bring us much closer to developing true artificial intelligence (AI) than ever before. One major limitation to these systems, though, is the effort required to teach them, with most requiring thousands or even hundreds of thousands of examples before they can "learn" something new. Self-driving car systems absorb miles of traffic data to learn basic driving lessons, and this scary image generator had to be fed 200,000 images for it to recognize a normal face. However, a new development from the team at Google DeepMind may be the start of leveling out that steep learning curve for AI systems. To speed up the learning process, Google DeepMind researcher Oriol Vinyals added a memory component to a deep-learning system.

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Advances in machine learning and deep learning systems are bring us much closer to developing true artificial intelligence (AI) than ever before. One major limitation to these systems, though, is the effort required to teach them, with most requiring thousands or even hundreds of thousands of examples before they can "learn" something new. Self-driving car systems absorb miles of traffic data to learn basic driving lessons, and this scary image generator had to be fed 200,000 images for it to recognize a normal face. However, a new development from the team at Google DeepMind may be the start of leveling out that steep learning curve for AI systems. To speed up the learning process, Google DeepMind researcher Oriol Vinyals added a memory component to a deep-learning system.

Can artificial intelligence help visually impaired people recognize objects around them and improve their quality of life? EyeSense, an iPad app developed in Egypt, has the ability to "learn" objects in its environment, having been trained by its users. A visually impaired person can point their device in the direction of where they think something might be--say, a coffee cup--and a voice will say that the app recognizes that object. "The key strength of the app is that it also recognizes basic facial expressions, like winks or smiles. This enhances human interaction," says Joanna Marczak, a spokesperson for its developer, ID Labs.