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These computer science terms are often used interchangeably, but what differences make each a unique technology?
Technology is becoming more embedded in our daily lives by the minute. To keep up with the pace of consumer expectations, companies are relying more heavily on machine learning algorithms to make things easier. You can see its application in social media (through object recognition in photos) or in talking directly to devices (such as Alexa or Siri).
While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. This blog post clarifies some of the ambiguity.
The easiest way to think about AI, machine learning, deep learning and neural networks is to think of them as a series of AI systems from largest to smallest, each encompassing the next.
AI is the overarching system. Machine learning is a subset of AI. Deep learning is a subfield of machine learning, and neural networks make up the backbone of deep learning algorithms. It’s the number of node layers, or depth, of neural networks that distinguishes a single neural network from a deep learning algorithm, which must have more than three.
Artificial intelligence or AI, the broadest term of the three, is used to classify machines that mimic human intelligence and human cognitive functions like problem-solving and learning. AI uses predictions and automation to optimize and solve complex tasks that humans have historically done, such as facial and speech recognition, decision-making and translation.
The three main categories of AI are:
ANI is considered “weak” AI, whereas the other two types are classified as “strong” AI. We define weak AI by its ability to complete a specific task, like winning a chess game or identifying a particular individual in a series of photos. Natural language processing and computer vision, which let companies automate tasks and underpin chatbots and virtual assistants such as Siri and Alexa, are examples of ANI. Computer vision is a factor in the development of self-driving cars.
Stronger forms of AI, like AGI and ASI, incorporate human behaviors more prominently, such as the ability to interpret tone and emotion. Strong AI is defined by its ability compared to humans. AGI would perform on par with another human, while ASI—also known as superintelligence—would surpass a human’s intelligence and ability. Neither form of Strong AI exists yet, but research in this field is ongoing.
An increasing number of businesses, about 35% globally, are using AI, and another 42% are exploring the technology. The development of generative AI, which uses powerful foundation models that train on large amounts of unlabeled data, can be adapted to new use cases and bring flexibility and scalability that is likely to accelerate the adoption of AI significantly. In early tests, IBM has seen generative AI bring time to value up to 70% faster than traditional AI.
Whether you use AI applications based on ML or foundation models, AI can give your business a competitive advantage. Integrating customized AI models into your workflows and systems, and automating functions such as customer service, supply chain management and cybersecurity, can help a business meet customers’ expectations, both today and as they increase in the future.
The key is identifying the right data sets from the start to help ensure that you use quality data to achieve the most substantial competitive advantage. You’ll also need to create a hybrid, AI-ready architecture that can successfully use data wherever it lives—on mainframes, data centers, in private and public clouds and at the edge.
Your AI must be trustworthy because anything less means risking damage to a company’s reputation and bringing regulatory fines. Misleading models and those containing bias or that hallucinate (link resides outside ibm.com) can come at a high cost to customers’ privacy, data rights and trust. Your AI must be explainable, fair and transparent.
Machine learning is a subset of AI that allows for optimization. When set up correctly, it helps you make predictions that minimize the errors that arise from merely guessing. For example, companies like Amazon use machine learning to recommend products to a specific customer based on what they’ve looked at and bought before.
Classic or “nondeep” machine learning depends on human intervention to allow a computer system to identify patterns, learn, perform specific tasks and provide accurate results. Human experts determine the hierarchy of features to understand the differences between data inputs, usually requiring more structured data to learn.
For example, let’s say I showed you a series of images of different types of fast food: “pizza,” “burger” and “taco.” A human expert working on those images would determine the characteristics distinguishing each picture as a specific fast food type. The bread in each food type might be a distinguishing feature. Alternatively, they might use labels, such as “pizza,” “burger” or “taco” to streamline the learning process through supervised learning.
While the subset of AI called deep machine learning can leverage labeled data sets to inform its algorithm in supervised learning, it doesn’t necessarily require a labeled data set. It can ingest unstructured data in its raw form (for example, text, images), and it can automatically determine the set of features that distinguish “pizza,” “burger” and “taco” from one another. As we generate more big data, data scientists use more machine learning. For a deeper dive into the differences between these approaches, check out Supervised versus unsupervised learning: What’s the difference?
A third category of machine learning is reinforcement learning, where a computer learns by interacting with its surroundings and getting feedback (rewards or penalties) for its actions. And online learning is a type of ML where a data scientist updates the ML model as new data becomes available.
To learn more about machine learning, check out the following video:
As our article on deep learning explains, deep learning is a subset of machine learning. The primary difference between machine learning and deep learning is how each algorithm learns and how much data each type of algorithm uses.
Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required. It also enables the use of large data sets, earning the title of scalable machine learning. That capability is exciting as we explore the use of unstructured data further, particularly since over 80% of an organization’s data is estimated to be unstructured (link resides outside ibm.com).
Observing patterns in the data allows a deep-learning model to cluster inputs appropriately. Taking the same example from earlier, we might group pictures of pizzas, burgers and tacos into their respective categories based on the similarities or differences identified in the images. A deep-learning model requires more data points to improve accuracy, whereas a machine-learning model relies on less data given its underlying data structure. Enterprises generally use deep learning for more complex tasks, like virtual assistants or fraud detection.
Neural networks, also called artificial neural networks or simulated neural networks, are a subset of machine learning and are the backbone of deep learning algorithms. They are called “neural” because they mimic how neurons in the brain signal one another.
Neural networks are made up of node layers—an input layer, one or more hidden layers and an output layer. Each node is an artificial neuron that connects to the next, and each has a weight and threshold value. When one node’s output is above the threshold value, that node is activated and sends its data to the network’s next layer. If it’s below the threshold, no data passes along.
Training data teach neural networks and help improve their accuracy over time. Once the learning algorithms are fined-tuned, they become powerful computer science and AI tools because they allow us to quickly classify and cluster data. Using neural networks, speech and image recognition tasks can happen in minutes instead of the hours they take when done manually. Google’s search algorithm is a well-known example of a neural network.
As mentioned in the explanation of neural networks above, but worth noting more explicitly, the “deep” in deep learning refers to the depth of layers in a neural network. A neural network of more than three layers, including the inputs and the output, can be considered a deep-learning algorithm. That can be represented by the following diagram:
Most deep neural networks are feed-forward, meaning they only flow in one direction from input to output. However, you can also train your model through backpropagation, meaning moving in the opposite direction, from output to input. Backpropagation allows us to calculate and attribute the error that is associated with each neuron, allowing us to adjust and fit the algorithm appropriately.
While all these areas of AI can help streamline areas of your business and improve your customer experience, achieving AI goals can be challenging because you’ll first need to ensure that you have the right systems to construct learning algorithms to manage your data. Data management is more than merely building the models that you use for your business. You need a place to store your data and mechanisms for cleaning it and controlling for bias before you can start building anything.
At IBM we are combining the power of machine learning and artificial intelligence in our new studio for foundation models, generative AI and machine learning, IBM® watsonx.ai™.
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