Author: Rose de Fremery
Date published: October 17, 2024
According to a PwC survey, 73% of US firms have adopted artificial intelligence (AI) into their business. Organizations in nearly every sector are actively exploring how AI can help reduce costs, automate business processes, innovate the customer experience and boost revenue.
Machine learning and deep learning are key components in these advancements, helping organizations digitally transform. Although these two technologies are related, they are distinct concepts. Accordingly, any organization seeking to leverage AI can benefit from understanding the differences between deep learning vs. machine learning, how they work and the use cases for which they are best suited.
Machine learning (ML) is a branch of AI that uses algorithms and data to make predictions and decisions based on trends that can be found in historical data. Over time, when trained on sufficient high-quality data, an ML model can steadily become more proficient at identifying patterns, surfacing key insights and potentially solving complex problems. There are several types of machine learning, including supervised machine learning, unsupervised machine learning and reinforcement learning.
In supervised machine learning, an ML model is fed structured or labeled data. A data scientist oversees the ML model's outputs, confirms their accuracy and corrects mistakes when necessary. Once the ML model can consistently identify key patterns based on that initial data set, it is fed unstructured data to see whether it can correctly identify similar patterns without guidance. Businesses can use this type of machine learning for purposes such as image recognition, supply chain optimization, fraud detection and sales forecasting.
Unsupervised machine learning involves feeding an ML model unstructured data from the beginning of a project. Upon ingesting this data, the algorithm begins identifying patterns without human help. This form of machine learning is often used for clustering and association problems. Businesses can tap it to identify patterns in customer data to personalize the customer experience, group inventories according to manufacturing and sales metrics, and even process data sets for use in other unsupervised machine learning applications.
Reinforcement learning takes a page from humans' trial-and-error approach to learning. This type of algorithm uses a reward and punishment model of sorts, so the ML model can learn from the positive or negative feedback it receives after producing each output. In the cases when the model produces an incorrect response, the data scientist feeds it the correct answer. This machine-learning style is especially useful for mastering complex tasks associated with large data sets and edge computing. Some organizations use reinforcement learning for use cases such as financial predictions and cloud optimization.
Deep learning (DL) is an advanced form of machine learning that is intended to simulate the human brain's ability to engage in complex decision-making. Rather than turning to humans for guidance, it relies on multi-layered neural networks known as deep neural networks (DNN) to learn on its own and make autonomous decisions. These deep neural networks aid the deep learning model in continuously refining and optimizing its outputs for improved accuracy. As a result, they can help deep learning models determine whether their own predictions are correct.
Deep learning relies on vast data sets. Upon receiving data, a deep learning algorithm runs it through the input layer and hidden layers before generating an output. This sophisticated form of machine learning is prominently featured in many of the AI technologies in use today. It can help enable digital assistants, speech recognition text-to-speech services and more.
Although machine learning and deep learning are similar in that they are both algorithms that use data sets to learn and draw conclusions, there are also key differences to keep in mind with respect to deep learning vs. machine learning.
For starters, machine learning applications can work with smaller data sets than deep learning; thousands of data points as opposed to millions of data points in the case of deep learning. Machine learning can also be trained in a shorter time period than deep learning—hours or days. Deep learning training can take several days, weeks or more.
Machine learning must be explicitly programmed to carry out its objective. Although it can continuously learn and improve, there is a limit to how far it can carry out these processes without human intervention. Deep learning is designed to be as autonomous as possible, requiring little human assistance. Compared to its machine-learning predecessor, it is far more adaptable. As a result, it can gradually build extensive knowledge in a fashion somewhat similar to the human brain.
Organizations are increasingly looking for ways to use AI to gain a competitive advantage. Machine learning and deep learning are two subsets of AI that are most commonly used today, each with its advantages and ideal use cases. Machine learning excels at pattern recognition and is often used for image recognition, fraud prevention and supply chain optimization. Machine learning, since it does have some human touch, is fairly well understood as to how it works, so the results can be somewhat predictable. Deep learning, on the other hand, mimics the learning capabilities of the human brain and is especially well suited to more advanced use cases such as language translation and autonomous vehicles. On the other hand, deep learning has the disadvantage that since its process for generating the results are often opaque, they are harder to understand by humans and can sometimes have odd or unexplained outcomes.
There are many ways to get started with machine learning and deep learning. Organizations can use these new technologies to improve safety, carry out repetitive work and provide near real-time information and insights to continuously optimize business processes. In doing so, they can free up employees to focus on projects and initiatives of even greater strategic business value. By leveraging the power of AI, organizations can ultimately become more agile and adaptable, developing crucial resilience and fully capitalizing on new opportunities.
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The author of this content is a paid contributor for Verizon.