The system can thus give an alert to human attendants, which can ultimately help to avoid mishaps. They track unusual behaviour of people like standing motionless for a long time, stumbling, or napping on benches etc. The video surveillance system nowadays are powered by AI that makes it possible to detect crime before they happen. This is why the idea of training computers to do this job makes sense. Imagine a single person monitoring multiple video cameras! Certainly, a difficult job to do and boring as well. In the entire cycle of the services, ML is playing a major role. Jeff Schneider, the engineering lead at Uber ATC reveals in a an interview that they use ML to define price surge hours by predicting the rider demand. When sharing these services, how do they minimize the detours? The answer is machine learning. Online Transportation Networks: When booking a cab, the app estimates the price of the ride. Machine learning in such scenarios helps to estimate the regions where congestion can be found on the basis of daily experiences. While this helps in preventing the traffic and does congestion analysis, the underlying problem is that there are less number of cars that are equipped with GPS.
This data is then used to build a map of current traffic. While we do that, our current locations and velocities are being saved at a central server for managing traffic. Traffic Predictions: We all have been using GPS navigation services. Smartphones: Samsung Bixby on Samsung S8.Smart Speakers: Amazon Echo and Google Home.Virtual Assistants are integrated to a variety of platforms. Later, this set of data is utilized to render results that are tailored to your preferences. Machine learning is an important part of these personal assistants as they collect and refine the information on the basis of your previous involvement with them. You can even instruct assistants for certain tasks like “Set an alarm for 6 AM next morning”, “Remind me to visit Visa Office day after tomorrow”. For answering, your personal assistant looks out for the information, recalls your related queries, or send a command to other resources (like phone apps) to collect info. All you need to do is activate them and ask “What is my schedule for today?”, “What are the flights from Germany to London”, or similar questions. As the name suggests, they assist in finding information, when asked over voice. Siri, Alexa, Google Now are some of the popular examples of virtual personal assistants. Herein, we share few examples of machine learning that we use everyday and perhaps have no idea that they are driven by ML. One of the popular applications of AI is Machine Learning (ML), in which computers, software, and devices perform via cognition (very similar to human brain). Possibility is that you are using it in one way or the other and you don’t even know about it.
Artificial Intelligence (AI) is everywhere.
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