What Is Machine Learning? Understanding ML
Machine learning (ML) algorithms are behind some of the most significant innovations in recent years. Learn more about machine learning, how it works, and why it’s critical to the future of work.
Machine learning (ML) algorithms are behind some of the most significant innovations in recent years. Learn more about machine learning, how it works, and why it’s critical to the future of work.
In this guide to understanding machine learning, we discuss:
Since the rise of mechanization in the early 1800s, humanity has continued to develop smarter machines to improve our quality of life. From the steam engine through to the self-driving car, each era of the modern age has been defined by its technological advancements—and machine learning (ML) is no different. What sets machine learning apart is that its impact isn’t limited to any single aspect of everyday life. In fact, machine learning algorithms have been behind the majority of technological innovations from the last five years.
Whether you know it or not, you likely already encounter machine learning on a daily basis. Here are four areas where machine learning has already been responsible for significant change outside the realm of science fiction:
As early as 1959, Arthur Samuel, an early artificial intelligence (AI) pioneer, defined machine learning as “the field of study that gives computers the ability to learn without being explicitly programmed.” Now, almost 70 years later, that definition has expanded to include a wide variety of different algorithms and models. Read on to learn what machine learning is, how it differs from other forms of artificial intelligence, and why it matters to your business.
Machine learning is a subfield of artificial intelligence focused on developing computers that learn similarly to humans. Through the use of algorithms that seek to imitate human intelligence, machine learning enables AI to improve outcomes through iteration. This ability to solve problems and generate predictions without explicit programming makes machine learning highly adaptable.
While machine learning enables computers to learn with a degree of independence, the computers still require human input. Data scientists feed training data into a machine learning algorithm to create a machine learning model. By adding live data to that model (once the training is complete), users are then able to generate new predictions. Finally, the outcomes of those predictions serve as further training data, increasing the model’s accuracy in what is referred to as the “flywheel effect” of accelerating progress.
Machine learning (ML) is a subfield of artificial intelligence (AI) focused on developing computers that learn similarly to humans.
Artificial intelligence refers to any technology that enables machines to simulate human intelligence. AI and machine learning are closely related with similar goals. However, AI encompasses many methods beyond ML on its own, including search algorithms, rule-based systems, and genetic algorithms. Not only that but AI also has a wider cultural context, and there is significant ongoing discussion and debate regarding theoretical future AI developments and directions.
If AI refers to the broader concept, then ML is one specific application of AI. Every AI methodology shares the same broad objective: enabling a machine to complete a complex task effectively. As a subfield of AI, machine learning achieves this goal by analyzing large volumes of data. However, the focus of an ML model is narrower, since each model is typically dedicated to one specific task.
Deep learning is a type of machine learning that uses neural networks to more closely mimic the structure of the human brain. Deep learning requires much more data and computing power than machine learning, since it reduces the need for human intervention. By using multiple layers of neural network processing, deep learning models can analyze and learn from massive unstructured datasets. Just as artificial intelligence is an overarching term, machine learning is an umbrella category that deep learning sits under.
The key to understanding deep learning lies with neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs). Neural networks consist of thousands (or even millions) of simple processing nodes that are connected in a layered structure. As a result, they can model complex nonlinear relationships between input and output data, and classify data more efficiently. This is particularly useful in computer vision—the process by which machines decode visual imagery like humans do.
Deep learning is a type of machine learning that uses neural networks to more closely mimic the structure of the human brain
Machine learning models can take a wide range of forms (more on that later) but the guiding principles remain relatively consistent. According to UC Berkeley, ML algorithms typically consist of three components:
For example, to train an image recognition system, a data scientist could provide the algorithm with a labeled set of dog and cat pictures. The algorithm would take that input data and begin to distinguish the differences between cats and dogs. These different parameters could include the size and profile of each animal, the differing types of fur, and placement of facial features.
The algorithm would then assign each of these parameters a weight, depending on its perceived usefulness and relevance. If the algorithm correctly identified a cat, the weights wouldn’t be adjusted, but if it was incorrect, the parameters used to reach that conclusion would be given lesser weighting. That way, the model gradually reduces the likelihood of making further mistakes.
ML models are most often used to either classify data or predict outcomes.
Machine learning models are typically grouped based on how each algorithm is designed to learn. The four most common types of machine learning are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. However, these categories are broad, and many machine learning models will incorporate aspects of each. For example, deep-learning models can fall under any of these four categories.
Choosing the right type of machine learning for a task depends largely on the specific goal and the dataset that a data scientist is working with. In fact, algorithms will often be adapted based on the specific challenges a data science team (or their users) are facing. The best way to assess what machine learning model will work best for your needs is to understand how each algorithm works.
Supervised machine learning, also referred to as supervised learning, works by using labeled training data. Data scientists assign labeled data one or more tags to give the algorithm useful context, such as distinct categories or number values. For example, a set of emails may be labeled as “spam” and “not spam,” providing the ML algorithm with a structured guide to learn from.
By analyzing the relationship between the input (data) and the output (labels), the algorithm learns to map one onto the other. Once this training is complete, with the weights properly adjusted, the model can then predict the output for new data. Due to its relative simplicity, this is the most common form of machine learning today. Examples of supervised learning techniques include:
Unsupervised machine learning, also referred to as unsupervised learning, exclusively works with unlabeled datasets. Unsupervised ML algorithms analyze datasets for trends, clustering data points into different sets in the process. Working with reduced human intervention, these algorithms often find patterns in data that would otherwise remain hidden.
While unsupervised learning is primarily used for data-clustering tasks, there are many areas where it can prove useful. Common uses for unsupervised learning algorithms include:
Semi-supervised machine learning, or semi-supervised learning, bridges the gap between supervised and unsupervised learning methods—as its name suggests. Semi-supervised learning models use both labeled and unlabeled data during the training process. By feeding small amounts of labeled data into an algorithm, it can apply those learnings to the full unlabeled data set. Since labeling data can be a tedious, costly process, semi-supervised learning is often an efficient solution.
As semi-supervised learning is a happy medium between the two previously mentioned methods, its applications remain similar. Here are three situations where semi-supervised learning can prove valuable:
Reinforcement machine learning, also called reinforcement learning, works using trial and error. Unlike other methods, a reinforcement learning algorithm is programmed with a specific goal and a clear set of rules to follow. Additionally, the data scientist further includes a points-based goal—positive results add points, while negative results subtract them. This feedback loop serves to improve results over time.
Reinforcement machine learning algorithms are most useful for sequential decision-based scenarios, such as games, robotics, or project management. By finding the most efficient route to the maximum reward, reinforcement learning can be a powerful decision-making tool.
According to Workday research, 80% of decision-makers believe that AI is necessary to keep their business competitive.
In the digital world of work, data has become a company’s most valuable asset. Machine learning represents an opportunity for companies to leverage historical data to better strategize for the future. As augmented workforces become the norm, companies that continue to rely on manual processes and fail to fully utilize their data will fall behind.
According to Workday research, 80% of decision-makers believe that AI is necessary to keep their business competitive. Despite this, 76% say their knowledge of AI and ML applications needs improvement. To succeed, business leaders must understand where machine learning can bring the most value to their business.z
Below are a few examples of how Workday customers are already using our embedded machine learning:
Workday research shows that 98% of CEOs believe there would be some immediate business benefit from implementing AI and ML.
Workday C-suite global AI research shows that 98% of CEOs believe there would be some immediate business benefit from implementing AI and ML. However, only 1% would classify their adoption as mature. Accordingly, the potential competitive advantage for businesses that integrate AI across their organization is huge.
One of the major barriers business leaders face is trust. At Workday, we believe in responsible AI that is transparent, explainable, private, and secure. That means providing our customers with AI technologies that have been designed with responsible AI at the core. We document our alignment to existing and developing regulations and best-practice frameworks, in addition to providing customers with fact sheets describing how our solutions are developed, assessed, and maintained.
Furthermore, Workday AI is built into the core of our platform so that we can rapidly deliver new AI capabilities that offer meaningful business value. Our AI is trained on the industry’s largest and cleanest set of finance and HR data, so it’s always grounded in reality. With over 65 million users on the same version of Workday, only our customers have the trusted data necessary to move forever forward with machine learning.
To learn more about how Workday can support you in your AI and ML journey, explore our technology page.
*HiredScore is a Workday company.
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