The terms artificial intelligence (AI), machine learning (ML), deep learning (DL), and neural networks (CNN) are often used interchangeably, but what are the differences between them? How do you tell them apart?
AI technology is becoming more embedded in our everyday lives, and in order to keep up with consumer expectations, companies are increasingly relying on AI algorithms to make things easier.
These technologies are often associated with artificial intelligence, machine learning, deep learning, and neural networks, and while they all play a role, the terms are often used interchangeably, leading to some confusion among the general public about their nuances. In this article today, we will discuss the differences between them in detail.
How do ARTIFICIAL intelligence, machine learning, neural networks and deep learning relate?
Perhaps the easiest way to think about artificial intelligence, machine learning, neural networks and deep learning is to think of them as Matryoshka dolls. Each is essentially a component of the preceding item.
In other words, machine learning is a subfield of artificial intelligence. Deep learning is a sub-field of machine learning, and neural networks constitute the backbone of deep learning algorithms. In fact, what distinguishes a single neural network from a deep learning algorithm is the number or depth of nodes in the neural network, and a deep learning algorithm must have more than three layers.
What is a neural network?
Neural networks — more specifically, artificial neural networks (ANN) — mimic the human brain through a set of algorithms. At the basic level, a neural network consists of four main parts: inputs, weights, biases or thresholds, and outputs. Similar to linear regression, the algebraic formula is as follows:
If the output of any single node is above the specified threshold, that node is activated and the data is sent to the next layer of the network. Otherwise, no data is passed to the next layer of the network. Now imagine that this process is repeated multiple times for a single decision, because neural networks tend to have multiple “hidden” layers as part of deep learning algorithms. Each hidden layer has its own activation function that may pass information from one layer to the next. Once all outputs of the hidden layer are generated, they are used as inputs to compute the final output of the neural network. Most real-world examples of applications are non-linear and much more complex.
The main difference between regression and neural networks is the effect of change on individual weights. In regression, the developer can change the weight without affecting other inputs in the function. However, this is not the case with neural networks. Since the output of one layer is transmitted to the next layer of the network, a single change can have a cascading effect on other neurons in the network.
How is deep learning different from neural networks?
Although this is implied in the interpretation of neural networks, it is worth noting more explicitly. The “depth” in deep learning refers to the depth of the middle layer of a neural network. A neural network consisting of more than three layers (including inputs and outputs) can be considered a deep learning algorithm. This is usually represented as follows:
Most deep neural networks are feedforward, meaning they flow in only one direction from input to output. However, developers can also train models by backpropagation; That is, it moves in the opposite direction from the output to the input. Back propagation allows us to calculate and attribute the errors associated with each neuron, allowing us to adjust and fit the algorithm appropriately.
How is deep learning different from machine learning?
Deep learning is just a subset of machine learning. The main differences are in how each algorithm learns and the amount of data each algorithm uses. Deep learning automates much of the feature extraction process, eliminating some of the human intervention required. It also supports the use of large data sets, a feature that will be particularly interesting as we begin to explore the use of unstructured data more, especially since it is estimated that 80-90% of an organization’s data is unstructured.
Classical or “non-deep” machine learning relies more on human intervention to learn. Experts identify hierarchies of features to understand differences between data inputs, often requiring more structured data to learn. For example, suppose I wanted to show you a series of pictures of different types of fast food, “grabby,” “hamburger,” or “barbecue.” Human experts on these images will determine the features that distinguish each image as a particular type of fast food. For example, bread for each food type might be a prominent feature in each image. Or, you can simplify the learning process by supervised learning by just using labels, such as “grab pie,” “Burger,” or “grill.”
“Deep” machine learning can leverage labeled datasets (also known as supervised learning) to inform its algorithms, but it doesn’t necessarily need labeled datasets. It can ingests unstructured data in raw form (text, images, for example), and can automatically identify a set of features that distinguish “grabby,” “burger,” and “barbecue.”
By observing patterns in the data, a deep learning model can properly cluster the inputs. Using the same example from the previous example, we can group the pictures of grab-cakes, burgers, and barbecues into their own categories based on similarities or differences identified in the images. Having said that, deep learning models require more data points to improve their accuracy, whereas machine learning models rely on less data given the underlying data structure. Deep learning is primarily used for more complex use cases, such as virtual assistants or fraud detection.
What is artificial intelligence?
Finally, artificial intelligence (AI) is the broadest term used to classify machines that mimic human intelligence. It is used to predict, automate, and optimize tasks that have been performed throughout human history, such as speech and facial recognition, decision making, and more.
AI falls into three main categories:
- Narrow Artificial Intelligence (ANI)
- General Artificial Intelligence (AGI)
- Super Artificial Intelligence (ASI)
ANI is considered “weak” AI, while the other two types are classified as “strong” AI. Weak AI is defined as its ability to accomplish very specific tasks, such as winning a chess game or identifying specific individuals in a series of photographs. As we move into more powerful forms of AI, such as AGI and ASI, more combinations of human behaviors become more prominent, such as the ability to interpret tone and emotion. Chatbots and virtual assistants like Siri only scratch the surface of the problem, but they’re still examples of ANI.
Strong AI is defined as its ability compared to humans. General artificial Intelligence (AGI) will perform on a par with another person, while Super Artificial intelligence (ASI) — also known as super intelligence — will surpass human intelligence and capabilities. Neither form of strong AI exists yet, but research in the field continues. Since this field of artificial intelligence is still evolving rapidly, the best example I can offer is Dolores, the character from HBO’s Westworld.
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