AI APPLICATIONS
4 | Edge Computing . Enables IoT devices to manage data on-premise . This tactic , computing near the source of the data , is useful any time that companies want to amp up security and minimise delay . Imagine that you ’ re talking to your Alexa . Using the traditional cloud , there ’ s a noticeable lag as she processes your voice commands , sends data to the cloud , and receives your answer back . But if Amazon starts to use AI chips to bring cloud capabilities to the device itself , we ’ ll see lower latency , better predictive maintenance , and fewer centralised security hacks .
• Mercedes : Ensures real-time authentication between vehicles and servers to prevent its cars from cybersecurity attacks .
5 | Data Lakes . AI helps make sense of collected data . Essentially centralised storage areas for all of your structured and unstructured data , data lakes hold information from IoT devices , websites , mobile apps , social media , and corporate accounts — you name it . Once you have all the data in one place , you can analyse or integrate different data sources with the help of AI and ML to generate usable insights . Some even incorporate real-time data to help make decisions .
• Amazon ’ s AWS : Provides a data lake solution that allows companies like Netflix , Zillow , NASDAQ , and Yelp to tag , search , transform , analyse , and share subsets of data .
6 | Data Democratisation . Anyone can use AI insights . Self-service analytics tools have made it possible for the average non-technical user to access digital info without having to involve IT . In an MIT Sloan Management Review study , 77 % of individuals
86 August 2021