What is machine learning?

Machine learning explained

Arguably one of the most important aspects of computer science and technology, machine learning (ML) can be described as “a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so”.¹ Machine learning algorithms can receive historical data as an input to output new values or predictions. A machine learning model is generally defined as the output of an ML algorithm that has been run on data.

ML can also be described as “one way to use AI. It was defined in the 1950s by AI pioneer Arthur Samuel as ‘the field of study that gives computers the ability to learn without explicitly being programmed’”.² IBM adds that ML “focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy”.³

Margaret Rouse adds onto the definition of ML, noting that “in this context, the word machine is a synonym for computer program and the word learning describes how ML algorithms become more accurate as they receive additional data”.⁴ Rouse continues, stating that while machine learning as a concept isn’t modern, its modern applications are. The practical application of ML “in business was not financially feasible until the advent of the internet and recent advances in big data analytics and cloud computing [because] training an ML algorithm to find patterns in data requires a lot of compute resources and access to big data”.⁴

Why is machine learning important?

ML is an integral technology in today’s global economy and is often a significant competitive differentiator. ML models help enterprises propel innovation for developing new products and services and offer insight into emerging trends, such as customer behavior and business patterns. 

There are many real-world applications for ML and AI-powered devices in our daily lives. They include phishing and spam filters for our email applications and websites, banking algorithms that look for fraudulent transactions, and recommendation models and algorithms found on everything from audio and video streaming platforms to search engines. 

Machine learning algorithms vs. machine learning models

With the recent popularity and accessibility of ML, there is also an increase in the need for clarifying the difference between elements and types of ML, such as models and algorithms. 

Here’s a general explanation of the difference between the two:  

• ML algorithms are procedures data scientists run on datasets to recognize patterns and rules, learning from this data to create ML models that can then make predictions. 
• Machine learning models are the output of the algorithm and act similarly to a program that can be run to make new predictions on the initial data or on new data sets. A model usually can generate predictions with a certain level of confidence and precision that corresponds with the level of training that the algorithm received.  

Examples of machine learning algorithms

Different ML algorithms are usually applied depending on what their respective operators’ goals are. Other factors include how the algorithms are fed data and how the operators wants the algorithms to learn or be trained. 

ML algorithms include the following: 

• Reinforcement Learning – Leverages trial and error learning to reach their results according to pre-established actions, boundaries, and end values.

• Semi-supervised Learning – Leverages labeled and unlabeled data, and often learns to label the unlabeled data.  

• Supervised Learning – Receives datasets along with desired inputs and outputs. Data scientists actively tweak this algorithm, improving its accuracy until it reaches a desired level. 

• Unsupervised Learning – Unlike supervised learning algorithms, this algorithm receives no correction or interaction from operators and interprets and organizes data without pre-established actions, boundaries, and end values.

The difference between ML and AI

The terms “machine learning” and “artificial intelligence” are confused with one another or generally used as synonyms. “[Today] most AI initiatives have been narrow and most ML models were built to perform a single task, used supervised learning and required large, labeled data sets for training”.⁴

Let’s take a broader view of the differences between ML and AI. ML and deep learning (DL) are both subsets of AI. “AI is an umbrella term covering various interrelated, but distinct, subfields”.⁵ Here are several examples of the most common fields you’re likely to encounter within the broader field of AI:  

• ML: a subset of AI in which algorithms are trained on data sets to become machine learning models capable of performing specific tasks. 
• DL: A subset of ML, in which artificial neural networks (AANs) that mimic the human brain are used to perform more complex reasoning tasks without human intervention.  
• Natural Language Processing (NLP): A subset of computer science, AI, linguistics, and ML focused on creating software capable of interpreting human communication. 
• Robotics: A subset of AI, computer science, and electrical engineering focused on creating robots capable of learning and performing complex tasks in real-world environments”.⁵

Leveraging ML and AI

Healthcare

The healthcare industry has access to a staggering amount of data from patients, including records, lab work and other medical tests, and daily wellness data harvested from smartwatches and fitness applications. “One of the most prevalent ways humans use artificial intelligence and machine learning is to improve outcomes within the health care industry”.6 Machine learning models that scan x-rays for broken bones, tumors, and similar abnormalities, personal treatment plan generating programs, and resource allocation systems for hospitals are a few AI applications that can be found in different parts of the healthcare industry today.  

Businesses

AI and ML have a large image on businesses globally, and are leveraged for everything from automation to resource management tools to applications for gathering data and analytics. Business Wire’s NewVantage Partners Releases 2022 Data And AI Executive Survey noted several key findings as a result of data gathered from 94 Fortune 1000 or leading organizations:

“Investment in Data and AI initiatives continue to grow as efforts deliver measurable results –97.0% of participating organizations invested in Data initiatives and 91.0% invested in AI activities. This year, 92.1% of organizations report that they are realizing measurable business benefits, up from just 48.4% in 2017 and 70.3% in 2020.

Achieving data-driven leadership remains an elusive aspiration for most organizations – Less than half of respondents replied that they were competing on data and analytics – 47.4%; only 39.7% reported that they were managing data as an enterprise business asset; barely over a quarter – 26.5% -- report that they have created a data-driven organization; and just 19.3% indicate that they have established a data culture.

AI initiatives are accelerating, but implementation of AI into widespread production remains low – Organizations report a more than doubling of AI initiatives that have moved into widespread production – 26.0%, up from just 12.1% in 2021. Yet, this continues to represent a small proportion of companies that are using AI on a widespread basis. Overall, 95.8% of organizations have AI initiatives that are underway in pilot of limited production”.7

Supply Chains

AI and machine learning helps ensure speedy, consistent deliveries of global goods while minimizing disruption and slowdown. AI-enhanced digital supply chains help supply chain managers and analysts to monitor shipments, predict delays, and quickly resolve issues when they do occur.