Is the Artificial Intelligence Classified as Machine Learning Becoming More Personable?

Please welcome Samantha Higgins, who defines herself as a professional writer with a passion for research, observation, and innovation. She resides in Portland, Oregon with her husband and her two twin boys. When she’s not writing about artificial intelligence and other technology subjects, Samantha loves kayaking and reading creative non-fiction. In this her first contribution to 21st Century Tech Blog, she talks about the progress being made by those who create the neural networks that make computers learn about the patterns in human existence. That’s what machine learning is all about.

Machine learning is a technology that gives us language translation applications, word prediction when composing emails and texts, and suggestions on the order presentation within social media feeds. It is a technology used by many industries from healthcare where it can aid in medical diagnosis and interpretation of radiology images, as well as in the operation of autonomous vehicles.

Machine learning is a subcategory of artificial intelligence (AI), software tools that learn without explicitly relying on programming. Many companies deploying AI today are primarily using machine learning to help reduce labor costs and increase productivity.

How it differs from other AI is in its statistical focus. It studies large amounts of data and filters it through models focused on finding patterns. It processes mountains of data quickly and efficiently and is particularly good at analyzing data feeds from devices connected to the Internet in what is called the Internet of Things (IoT).

Can Machine Learning Become Personable?

It can and does because of its ability to fit with so many applications. The recommendation algorithm that Facebook (now Meta) uses for its news feeds is an excellent example of applying machine learning to fit personal preferences. Meta recognizes when you stop reading a particular group of posts and news items. Its recommendation algorithm starts showing a wider range of content in the feed. So when you change your normal practice of reading a particular group of posts and switch to another it is already learning to feed you content from this new stream. But there is more to machine learning than recommendation algorithms.

Object Detection and Image Analysis

Some companies and businesses use machine learning technology to collect varying information from analyzing different images. The technology helps businesses identify people and gather information about their character traits.

Facial recognition, seen as controversial, is a form of machine learning. This is a technology that can identify individuals and grant them access to a site. But it is also technology being used to pick individuals out of a crowd for immigration and law enforcement purposes.

Medical Imaging and Diagnostics

One of machine learning’s greatest successes is in analyzing medical images. Machine learning is proving to be better and faster at detecting cancer and other diseases than radiologists and oncologists. It can examine vital patient information collected from laboratory specimens to determine diagnosis and treatment at speeds and in volumes not possible by diagnosticians. It can also review image databases and provide medical researchers with material for studies looking at causes and potential cures for conditions and diseases.

Chatbots and Helplines

Today when you contact many companies through chat and online help, it’s not a human you are interacting with, but rather a machine language algorithm. Chatbots rely on machine learning to determine the language of communication, and the appropriate responses to a customer enquiry based on analyzing the language used in the chat, and building a knowledge base of a customer based on his or her responses, and on previous conversations and communication.

Fraud Detection

Machine learning technology is great at analyzing patterns. Banks and credit card companies find this capability very useful as customers buy and spend. Machine learning gets to know not only what you buy, but where you buy from. Changes to the normal pattern are noted and sometimes lead to a query from the bank or card company. This form of machine learning is great at catching not just transaction fraud, but also multiple attempts at log-ins to accounts that don’t fit your normal patterns.

Online Marketing

Machine learning can leverage data and information to make decisions. It consistently learns through audio, speech, and language processing to adjust to becoming autonomous in its predictive abilities. The more data it sees, the more reliable is the feedback it provides. This is particularly useful for online marketers who use machine learning to analyze phrases, content, and keywords used by prospects to promote products and services to them. Marketers use the knowledge gained to personalize content for any customer increasing the likelihood of successful interactions and increased sales.

The Bottom Line

When a parent suggests you try an unfamiliar food, read a new book, or any other normal transaction, it is likely that you will follow those suggestions in your formative years. That’s because your parents likely know you better and know what you like and dislike. In that sense, machine learning is exactly like that parent.

Think of businesses trying to get to know you better. They don’t have the benefit of years of parenting to figure out what are your preferences. But machine learning gives them a shortcut to get there. The technology’s ability to gather information about you as a customer is why it can be a “pseudo” parent making suggestions on what you may want to purchase or try. Talk about being personable. It becomes hard to resist.