The scientific study of statistical models and algorithms used by computer systems to perform specific tasks without human involvement and explicit instructions, machine learning (ML) uses inference and patterns to be able to work on its own.
Different from traditional computation, ML allows the computer to perform a more advanced analysis of data without a human supervising. Human operators provide the computer with initial data, instructing their analytical software to detect specific information or provide answers to vaguely formulated questions.
Unsupervising Machine Learning: An Exciting Time
Unsupervised ML involves giving a computer the freedom to gather information as needed, including from real-time processes. From there, the computer will perpetually analyze the data to identify new patterns, detect new opportunities, and report comprehensive information to human operators.
Thus enabled systems can be assigned tasks for businesses like data collection or customer service, particularly within the realm of artificial intelligence (AI).
The Difference Between AI and ML
Artificial intelligence refers to a wide range of IT platforms (like cloud and others) capable of performing advanced, complicated tasks. From navigating unmanned cars and powering systems like Uber to everyone’s favorite digital assistant as either Siri or Cortana, AI is all around us every day.
Machine Learning lies within the realm of artificial intelligence, allowing computer systems to think for themselves by using information gathered from their environments. ML functions as a branch of AI that requires logic and instructions beyond what’s given by data scientists.
IP Geolocation and Machine Learning
AI is powered by data. The ways it is stored and used constantly shifts and grows. More data is accumulated in databases and as they grow, the manner in which their contents are used changes. IP addresses once only served to tell servers where a connection request was coming from. Companies could use IP addresses to identify where their clients came from.
Currently, IP addresses aren’t considered as reliable a source as they used to be. With IP masking tools and VPN, advanced IP geolocation tools like IP Geolocation API lets servers make really strong educated guesses as to the true location of their users.
How does a more reliable location data enhance the entries of modern databases? The right ML platform can detect patterns based on the originating requester’s location. Such data could determine the best whereabouts for a billboard or a roadsign for businesses wishing to expand in areas where geolocation data indicated interest from the general public.
Businesses are also turning to AI platforms to help them meet customer needs. The location data from such platforms can help automated customer service bots and chatbots help customers with their needs based on their location and expressed in their native language and currency.
Changing the Way the World Does Business
Whether your goal is to seek insights into consumer behavior, handle basic-level customer service, or play games for entertainment, IP geolocation and machine learning will continue to change and improve how we go about our day-to-day routines. The opportunity to add location context to the data collected is greatly beneficial in numerous ways. From AI customer service representatives to other self-help services and resources, such servers backed by powerful IP geolocation utilities will continue to bring about an exciting evolution in many areas of our lives.