Have you ever considered how much data exists in our world? Data growth has been immense since the creation of the Internet and has only accelerated in the last two decades. Today the Internet hosts an estimated 2 billion websites for 4.2 billion active users.
In one day, you can expect 5.5 billion Google searches, 223 million emails, and 5.9 billion video views. The rate at which we create data far outpaces the rate which humans can absorb and interpret that data. That is where artificial intelligence comes in. Artificial Intelligence gives us the opportunity to mimic human intelligence to collect and analyze data quickly and efficiently.
Many businesses are exploring how they can implement machine learning, which is a subset of artificial intelligence focused on teaching machines to analyze and learn from data autonomously.
Machine learning is based on algorithms that can learn from data without relying on rules-based programming. While machine learning may sound like a foreign concept, it is actually something that you interact with every single day. Machine learning is the process Netflix uses to recommend content on their platform, Google uses to control its self-driving cars, Duolingo uses to power its language learning app, and Instagram uses to determine which posts you’ll see on your newsfeed.
Machine learning is all about learning from data. The “learning” is accomplished through algorithms which can be categorized as associations, classifications, supervised learning, unsupervised learning, and reinforcement learning.
In associations, the algorithm finds associations between two actions and can assign a probability based on the frequency of that action.
Classifications are machine learning systems that deploy models that can make predictions based on previously analyzed datasets.
Supervised learning uses models to provide the correct output data through rules and input data. The rule is determined by a supervisor or the human who builds the model.
Unsupervised learning removes the human element and allows the model to make associations independent of any rules.
Reinforcement learning focuses on sequential actions required to achieve a goal. Once the reward and possible actions are defined, the model will run through scenarios to achieve the desired outcome.
As stated earlier, machine learning is a subset of artificial intelligence. Often the two buzzwords are used interchangeably, but they have some distinct differences. The simplest way to understand the difference between machine learning vs artificial intelligence is by identifying the end goal.
While artificial intelligence is the general concept of computers mimicking human intelligence, machine learning specifically focuses on building machines that can analyze data and learn without human interference. Artificial intelligence leads to intelligence. It is used for decision-making to increase the likelihood of success by finding the optimal solution. Machine learning is more focused on knowledge. It is used to increase accuracy by learning from data and developing new insights autonomously.
Business leaders are using machine learning to utilize the huge amounts of data that they've collected to develop actionable predictions that can be used to invest resources and grow their company. AI-driven software is already helping companies increase efficiency, improve customer relationships, and boost sales. Here is a fraction of how businesses are using machine learning today.
Following preventive and corrective maintenance procedures is an inherent part of many manufacturing businesses. With machine learning, companies can increase the efficiency of these processes. For example, IBM’s Watson system is able to detect shipping container damage through visual patterns to signal when it's time to replace them. One industry that can benefit greatly from machine learning is the construction industry. Machine learning in construction can capture job site data, expand drone capabilities, and streamline project workflows.
Data Entry Automation
Many businesses are still relying on manual data entry. With machine learning, businesses can implement data entry automation processes that will eliminate human error, save enormous amounts of time, and keep up with competitors. No more spreadsheets!
The convenience and cost-effectiveness of e-commerce websites has made it the go-to shopping destination for many consumers. Machine learning can help online retailers upsell products by making recommendations based on consumer purchase history, behavior, and unseen patterns. This example of unsupervised learning enables retailers to earn more revenue with little to no effort.
Image recognition has a number of powerful applications that create a great deal of value from a business perspective. Also known as computer vision, image recognition can interpret graphics and images to identify patterns for greater insights. Image recognition can be seen in facial recognition features, security and surveillance, object recognition, gesture recognition and many more. Image recognition can be paired with drones to improve land surveying and worker safety.
The AI market will grow to a $5.05 billion dollar industry by 2020. Because of the potential of artificial intelligence, more business leaders are using AI to power their business operations and be smarter with their data. AI & ML applications can be used for a multitude of applications such as face detection, brand detection, handwritten text recognition and more. However, developing custom ML algorithms using AI frameworks can expand its capabilities to process specific data that can be used for healthcare, manufacturing, financial among other industries.
Did we miss any other machine learning trends? What is your experience with AI & machine learning technology? Let us know in Community.
Also published at https://www.invonto.com/insights/machine-learning-trends-in-2020/
Create your free account to unlock your custom reading experience.