What Is Machine Learning?

Machine learning is a subset of artificial intelligence that gives systems the ability to learn and optimize processes without having to be consistently programmed. Simply put, machine learning uses data, statistics and trial and error to “learn” a specific task without ever having to be specifically coded for the task. 

What Is Machine Learning?

Machine learning is a subfield of artificial intelligence in which systems “learn” through data, statistics and trial and error to optimize processes and innovate at quicker rates. Through machine learning, computers can apply human-like reasoning and decision-making to help solve some of the world’s toughest problems, ranging from cancer research to climate change.

Most computer programs rely on code to tell them what to execute or what information to retain. This is known as explicit knowledge, which contains anything that is easily written or recorded like textbooks, videos and manuals. With machine learning, computers gain tacit knowledge, or knowledge gained from personal experience and context. This type of knowledge is hard to transfer from one person to the next via written or verbal communication.

Facial recognition is a type of tacit knowledge. We recognize a person’s face, but it is hard for us to describe how or why we recognize it. We rely on our personal knowledge banks to connect the dots and immediately recognize a person based on their face. Another example is riding a bike: It’s much easier to show someone how to ride a bike than it is to explain it.

Computers no longer have to rely on billions of lines of code to carry out calculations. Machine learning gives computers the power of tacit knowledge to make connections, discover patterns and make predictions based on what they learned in the past. Machine learning’s use of tacit knowledge has made it a go-to technology for almost every industry from fintech to government.

How Does Machine Learning Work?

Machine learning compiles input data, which can be data gathered from training sessions or other sources, such as data set search engines, .gov websites and open data registries like that of Amazon Web Services. This data serves the same function that prior experiences do for humans, giving machine learning models historical information to work with when making future determinations.  

Algorithms then analyze this data, searching for patterns and trends that allow them to make accurate predictions. In this way, machine learning can glean insights from the past to anticipate future happenings. Typically, the larger the data set that a team can feed to machine learning software, the more accurate the predictions.    

The idea is that machine learning algorithms should be able to perform these tasks on their own, requiring minimal human intervention. This speeds up various processes as machine learning comes to automate many aspects of different industries.

Types of Machine Learning

There is a range of machine learning types that vary based on several factors like data size and diversity. Below are a few of the most common types of machine learning under which popular machine learning algorithms can be categorized.

Supervised Learning

Supervised learning involves mathematical models of data that contain both input and output information. Machine learning computer programs are constantly fed these models, so the programs can eventually predict outputs based on a new set of inputs.

Regression and classification are two of the more popular analyses under supervised learning. Regression analysis is used to discover and predict relationships between outcome variables and one or more independent variables. Commonly known as linear regression, this method provides training data to help systems with predicting and forecasting. Classification is used to train systems to identify an object and place it in a sub-category. For instance, email filters use machine learning to automate incoming email flows for primary, promotion and spam inboxes.

Unsupervised Learning

Unsupervised learning contains data only containing inputs and then adds structure to the data in the form of clustering or grouping. The method learns from previous test data that hasn’t been labeled or categorized and will then group the raw data based on commonalities. Cluster analysis uses unsupervised learning to sort through giant lakes of raw data and group certain data points together. Clustering is a popular tool for data mining, and it is used in everything from genetic research to creating virtual social media communities of like-minded individuals.

Semi-Supervised Learning

Semi-supervised learning falls in between unsupervised and supervised learning. With this technique, programs are fed a mixture of labeled and unlabeled data that not only speeds up the machine learning process, but helps machines identify objects and learn with increased accuracy.

Typically, programmers introduce a small amount of labeled data with a large percentage of unlabeled information, and the computer will have to use the groups of structured data to cluster the rest of the information. Labeling supervised data is seen as a massive undertaking because of high costs and hundreds of hours spent.

Machine Learning Examples and Applications

Financial Services

The financial services industry is championing machine learning for its unique ability to speed up processes with a high rate of accuracy. What has taken humans hours, days or even weeks to accomplish can now be executed in minutes. Over $11.5 trillion in card transactions were processed in 2023 by card brands like American Express. Ensuring these transactions are more secure, American Express has embraced machine learning to detect fraud and other digital threats. 

Additionally, machine learning is used by lending and credit card companies to manage and predict risk. These computer programs take into account a loan seeker’s past credit history, along with thousands of other data points like cell phone and rent payments, to deem the risk of the lending company. By taking other data points into account, lenders can offer loans to a much wider array of individuals who couldn’t get loans with traditional methods.

Trading firms are using machine learning to amass a huge lake of data and determine the optimal price points to execute trades. These complex high-frequency trading algorithms take thousands, if not millions, of financial data points into account to buy and sell shares at the right moment.

Healthcare

The healthcare industry uses machine learning to manage medical information, discover new treatments and even detect and predict disease. With machine learning computer systems, medical professionals can easily view patient medical records without having to dig through files or have chains of communication with other areas of the hospital. Updated medical systems can now pull up pertinent health information on each patient in the blink of an eye.

AI and machine learning can automate maintaining health records, following up with patients and authorizing insurance — tasks that unnecessarily take up vital healthcare resources for 68 percent of physicians, according to the American Medical Association.  

Machine learning-enabled AI tools are working alongside drug developers to generate drug treatments at faster rates. Essentially, these machine learning tools are fed millions of data points and configure them to help researchers view which compounds are successful and which aren’t. Instead of spending millions of human hours on each trial, machine learning technologies can produce successful drug compounds in weeks or months.

Radiology and pathology departments all over the world use machine learning to analyze CT and X-ray scans and find diseases. After being fed thousands of images of diseases through a mixture of supervised, unsupervised or semi-supervised models, advanced machine learning systems can diagnose diseases at higher rates than humans. Machine learning has also been used to predict deadly viruses, like Ebola and Malaria, and is used by the CDC to track instances of the flu virus every year.

Social Media

Machine learning is employed by social media companies to create a sense of community and weed out bad actors and malicious information. Machine learning fosters the former by looking at pages, tweets, topics and other features that an individual likes and suggesting other topics or community pages based on those likes. It’s essentially using your preferences as a way to power a social media recommendation engine.

The spread of misinformation in politics has prompted social media companies to use machine learning to quickly identify harmful patterns of false information, flag malicious bots, view reported content and delete when necessary.

Retail and E-commerce

The retail industry relies on machine learning for its ability to optimize sales and gather data on individualized shopping preferences. Machine learning offers retailers and online stores the ability to make purchase suggestions based on a user’s clicks, likes and past purchases. Once customers feel like retailers understand their needs, they are more likely to stay with that company and purchase more items.

Visual search is becoming a huge part of the shopping experience, too. Instead of typing in queries, customers can now upload an image to show the computer exactly what they’re looking for. Machine learning will analyze the image and produce search results based on its findings.

Machine learning has also been an asset in predicting customer trends and behaviors. These machines look holistically at individual purchases to determine what types of items are selling and what items will be selling in the future. For example, maybe a new food has been deemed a “super food.” A grocery store’s systems might identify increased purchases of that product and send customers coupons or targeted advertisements for all variations of that item. Additionally, a system could look at individual purchases to send you future coupons.

What Is Deep Learning?

Deep learning is a subfield within machine learning, and it’s gaining traction for its ability to extract features from data. Deep learning uses artificial neural networks (ANNs) to extract higher-level features from raw data. ANNs, though much different from human brains, were inspired by the way humans biologically process information. The learning a computer does is considered “deep” because the networks use layering to learn from and interpret raw information.

For example, deep learning is an important asset for image processing in everything from e-commerce to medical imagery. Google is equipping its programs with deep learning to discover patterns in images to display the correct image for whatever you search. If you search for a winter jacket, Google’s machine and deep learning will team up to discover patterns in images — sizes, colors, shapes, relevant brand titles — that display relevant jackets.

Deep learning is also making headwinds in radiology, pathology and any medical sector that relies heavily on imagery. The technology relies on its tacit knowledge — from studying millions of other scans — to immediately recognize disease or injury, saving doctors and hospitals both time and money.

History of Machine Learning

The brief timeline below tracks the development of machine learning from its beginnings in the 1950s to its maturation during the twenty-first century.

1950

Alan Turing jumpstarts the debate around whether computers possess artificial intelligence in what is known today as the Turing Test. The test consists of three terminals — a computer-operated one and two human-operated ones. The goal is for the computer to trick a human interviewer into thinking it is also human by mimicking human responses to questions.

1956

Arthur Samuel publicly reveals a computer that can determine the optimal moves to make in a checker match.

1957

Frank Rosenblatt creates the first neural network for computers, known as the perceptron. This invention enables computers to reproduce human ways of thinking, forming original ideas on their own. 

1962

Samuel builds on previous versions of his checkers program, leading to an advanced system made for the IBM 7094 computer. In 1962, the computer defeats checkers master Robert Nealy in a match. 

1979

Descending from a line of robots designed for lunar missions, the Stanford cart emerges in an autonomous format in 1979. The machine relies on 3D vision and pauses after each meter of movement to process its surroundings. Without any human help, this robot successfully navigates a chair-filled room to cover 20 meters in five hours.

1981

Gerald Dejong explores the concept of explanation-based learning (EBL). This approach involves providing a computer with training data, which it analyzes to develop a rule for filtering out unnecessary information. The idea is that this data is to a computer what prior experience is to a human being.

1985

Researcher Terry Sejnowksi creates an artificial neural network of 300 neurons and 18,000 synapses. Called NetTalk, the program babbles like a baby when receiving a list of English words, but can more clearly pronounce thousands of words with long-term training. 

1990s

The 1990s marks a shift in the realm of machine learning. Scientists focus less on knowledge and more on data, building computers that can glean insights from larger data sets.

1997

Scientists at IBM develop a computer called Deep Blue that excels at making chess calculations. The program defeats world chess champion Garry Kasparov over a six-match showdown.

2006

The term “deep learning” is coined by Geoffrey Hinton, a long-time computer scientist and researcher in the field of AI. He applies the term to the algorithms that enable computers to recognize specific objects when analyzing text and images.

2010

Microsoft releases a motion-sensing device called Kinect for the Xbox 360. The device contains cameras and sensors that allow it to recognize faces, voices and movements. As a result, Kinect removes the need for physical controllers since players become the controllers.

2011

IBM’s Watson competes on Jeopardy! against two of the show’s most decorated champions. That same year, Google develops Google Brain, which earns a reputation for the categorization capabilities of its deep neural networks.

2012

Computer scientists at Google’s X lab design an artificial brain featuring a neural network of 16,000 computer processors. The network applies a machine learning algorithm to scan YouTube videos on its own, picking out the ones that contain content related to cats.

2014

Facebook unveils its new face recognition tool DeepFace. Composed of a deep network of millions of data points, DeepFace leverages 3D face modeling to recognize faces in images in a way very similar to that of humans.

2015

Amazon develops a machine learning platform while Microsoft releases its Distributed Machine Learning Toolkit. In response to the proliferation of machine learning and AI, over 3,000 AI and robotics researchers — including names like Stephen Hawking and Elon Musk — sign an open letter warning of AI-powered warfare.

2017

Google’s AI algorithm AlphaGo specializes in the complex Chinese board game Go. The algorithm achieves a close victory against the game’s top player Ke Jie in 2017. This win comes a year after AlphaGo defeated grandmaster Lee Sedol, taking four out of the five games.

2018

OpenAI releases GPT-1 and Google releases BERT, two language models based on transformer networks. These models signal the rise of language models, paving the way for large language models to take on increased importance in machine learning research.  

2019

Microsoft introduces the Turing Natural Language Generation model, which contains 17 billion parameters. Google also releases a family of convolutional neural networks called EfficientNets, which perform just as well as larger models while remaining compact. 

2020

OpenAI releases GPT-3, which contains 175 billion parameters. Combining natural language processing and machine learning, GPT-3 displays far more advanced abilities than GPT-2 in understanding human language and generating human-like text. 

2021

OpenAI launches DALL-E, a multimodal tool that can produce images based on text prompts. However, it doesn’t become popular until the 2022 release of DALL-E 2.  

2022

Google DeepMind reveals AlphaTensor, a system meant to speed up the process of building optimal algorithms that can handle complex tasks. In addition, OpenAI releases ChatGPT, a chatbot that offers a more conversational form of generative AI for users. 

2023

OpenAI releases GPT-4 and Anthropic releases Claude AI, both of which can process and produce different data like text, images and audio. These tools lead efforts to make multimodal AI more accessible and commonplace. 

2024

Google releases a family of multimodal models called Gemini, along with a chatbot by the same name, which was formerly known as Bard. These models come in various sizes and with different capabilities, and are being incorporated into several Google products, including Gmail, Docs and its search engine.    

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