With how fast technology has been growing, it’s no wonder that various industries are growing increasingly reliant on it to improve their products and services. One of the recent innovations gaining traction is video annotation. Video annotation involves labeling elements in video clips to train artificial intelligence (AI) models. Here are some applications of this technology.
You may have heard of self-driving cars, but have you ever wondered how they achieved this feat? Through video annotation, machines learn which obstacles to avoid, which turns to take, and how to interpret road signals. Developers feed it video recordings and labeling items like streetlights, pedestrians, road signs, etc., and the machine learns how to interpret these various elements. It’s a very tedious process, but necessary to guarantee the driver’s safety and their passengers but people outside.
Nowadays, video games look more realistic than ever, and we have AI to thank for that. Motion sensors are attached to various points on human models to serve as data points for AI. Whenever they move, the sensors convert their movement into code, which the programmer can use to develop three-dimensional models of characters. They also use video annotations for AI-generated scenery and code the behavior of non-playable characters (NPCs), decreasing the developers’ workload and allowing them to focus on the game’s more important aspects, like the main characters, gameplay, and story. This technology also creates a more immersive world as NPCs can populate towns, perform tasks throughout the day, and even interact with each other.
Scientists have been working together to apply machine learning to assess x-rays and MRI scans to detect cancer and other diseases. Machines are given thousands of images and videos of scans that depict tumors and anomalies, and with this information, they can accurately and quickly point out the issues. AI can also process and diagnose more scans in a day than a radiologist or a doctor, which is vital when patients have diseases where immediate care is necessary.
Thanks to AI, a new type of agriculture was born, known as precision agriculture. Combining traditional agriculture with machine learning increases the amount of produce harvested, profit, and overall sustainability. With video annotation, machines that monitor the crops can learn precisely when crops are ready to be harvested. It can also determine how healthy a plant is, whether it needs water, better soil, a different pesticide, or other adjustments required to improve quality and yield. It also serves as an early warning system to farmers if the crop shows signs of pest infestations or diseases like fusarium wilt.
There are other ways developers train AI for their intended purpose besides using annotated videos. More advanced AI systems are taught language processing, voice detection and are even placed in robotic bodies so they can carry out real-life tasks instead of just virtually processing and relaying information. I’m sure we are all eager to see what else AI has to offer in the future.