Before addressing AI vs Machine Learning vs Deep Learning, let’s understand “what is AI?” “what is Machine Learning? and “what is Deep Learning?”.
Artificial Intelligence, Machine Learning, and Deep Learning are trendy buzzwords that everyone appears to use nowadays.
AI, ML and DL are popular terms in the IT industry and are most often interchangeably used, especially when companies are trying to market their products. These terms, however, are not synonymous; there are significant distinctions.
AI vs. Machine Learning vs. Deep Learning
In simple words, AI or artificial intelligence relates to systems or machines that imitate human intelligence to accomplish tasks and can iteratively improve themselves based on the information they collect.
Furthermore, AI is much more profound about the process and the capability for superpowered thinking and data analysis than any precise format or function. However, AI brings up images of high-functioning, human-like robots taking over the world but can’t replace humans. It is to enhance human capabilities and contributions significantly. That makes it a precious business asset.
Types of Artificial Intelligence
These are systems that only react. Hence, these systems don’t form memories, and they don’t use any past happenings for making new decisions.
These systems reference the past, and information gets added over some time. The referenced information is temporary.
Theory of Mind
These include systems that can understand human emotions and how they transform decision-making. Therefore, they get trained to adjust their behaviour accordingly.
These systems are composed and created to be aware of themselves. Hence, they realize their internal states, predict other people’s feelings and act appropriately.
Applications of Artificial Intelligence
Machine Translation, e.g., Google Translate
Self-Driving Vehicles, e.g., Google’s Waymo
AI Robots, e.g., Sophia and Aibo
Speech Recognition applications, e.g., Apple’s Siri or OK Google
Machine learning is a subset of artificial intelligence that allows systems to learn and improve from experience without being explicitly programmed automatically. In ML, there are different algorithms (e.g., neural networks) that help to solve problems.
Therefore, it is the study of making machines more human-like in their behaviour and decisions by providing them with the ability to learn and develop their programs. Hence, this is possible with the lesser human intervention, i.e., no explicit programming.
How Does Machine Learning Work?
Machine learning accesses enormous amounts of data (both structured and unstructured) and determines from it to predict the future. Furthermore, it learns from the data by utilizing multiple algorithms and techniques.
Machine learning uses two main techniques:
Supervised learning allows one to collect data or produce a data output from a prior ML deployment. Therefore, it is interesting because it operates in much the same way humans learn.
In supervised tasks, one presents the computer with a collection of labelled data points called a training set (e.g., a set of readouts from a system of train terminals and markers where they had setbacks in the last three months).
Unsupervised machine learning
Unsupervised machine learning helps one to find all kinds of unknown patterns in data. Moreover, the algorithm learns some inherent structure to the data with only unlabelled examples. Two everyday unsupervised learning tasks are clustering and dimensionality reduction.
In clustering, one attempts to arrange data points into meaningful clusters such that elements within a given set are similar but dissimilar to those from other collections. Hence, clustering is beneficial for tasks such as market segmentation.
Dimension reduction models lessen the number of variables in a dataset by grouping similar or correlated attributes for better understanding (and more effective model training).
Machine Learning Applications
Sales forecasting for various products
Fraud analysis in banking
Stock price prediction
The deep learning subset of machine learning uses neural networks to examine different factors with a structure like the human neural system.
Therefore, these neural networks try to simulate the behaviour of the human brain, allowing it to learn from large amounts of data. It is a neural network with three or more layers, and it can still make approximate predictions with a single layer. Additional hidden layers can help optimize and refine for accuracy.
How Does Deep Learning Work?
Neural networks are layers of nodes like the human brain is made up of neurons. Nodes inside individual layers are attached to adjacent layers. Accordingly, the network is said to be deeper based on the number of layers it has.
Therefore, a single neuron in the human brain experiences thousands of signals from other neurons. In an artificial neural network, signals migrate between nodes and assign corresponding weights. Any heavier weighted node will exert more effect on the next layer of the node. Hence, the final layer collects the weighted inputs to produce an output.
Deep learning systems require robust hardware because they have a large volume of processed data and involve different complex mathematical calculations. Nonetheless, with such advanced hardware, deep learning training computations can take weeks.
Deep learning systems expect large amounts of data to return accurate results; information provided is as large data sets. Artificial neural networks can classify data with answers from binary true or false questions involving highly complex mathematical calculations when preparing the data.
For instance, a facial recognition program operates by learning to detect and identify edges and lines of faces, more vital parts of the faces, and, ultimately, the overall representations. Further, the program trains itself, and the possibility of correct answers increases. Therefore, in this case, the facial recognition program will accurately identify faces with time.
Deep Learning Applications
Cancer tumor detection
Caption Bot for captioning an image
AI vs. Machine Learning vs. Deep Learning
Let’s compare AI, deep learning, and machine learning based on some parameters.
Artificial intelligence is a technology that lets a machine simulate human behaviour.
Machine learning is a subset of AI which permits a machine to learn from past data without programming explicitly automatically.
Deep Learning is the subset of machine learning or known as a unique kind of machine learning. It operates technically in the same way as machine learning does but with distinct capabilities and approaches.
AI operates best when large amounts of rich, big data are available.
Although machine learning depends on a vast amount of data, it can work with a smaller amount of data.
Deep Learning algorithms depend on a large amount of data, so one needs to feed a large amount of data for reliable performance.
Ordinarily, 40% of companies said it takes more than a month to deploy an ML model into production, 28% do so in eight to 30 days, while only 14% could do so in seven days or less.
On the other hand, machine learning algorithm takes a shorter time to train the model than deep learning; however, it takes a long-time duration to test the model.
However, deep learning takes a long execution time to train the model but less time to test the model.
In AI, the users can resolve the problem by implementing logical algorithms, utilizing polynomial and differential equations, and administering modelling paradigms. There can be numerous solutions to a single issue, which is possible by different heuristics.
On the contrary, to solve a given problem, the traditional ML model breaks the problem into sub-parts, and after working on each part, produces the final result.
The problem-solving approach of a deep learning model is different from the other two, as it takes input for a given problem and produces the result. Hence it follows the end-to-end approach.
Based on capabilities, AI has three types: Weak AI, General AI, and Strong AI.
Machine learning also has mainly three types: supervised learning, Unsupervised Learning, and Reinforcement Learning.
Deep Learning is also of four types: Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Generative Adversarial Network (GAN), Deep Belief Network (DBN).
Type of data
AI entirely deals with Structured, semi-structured, and unstructured data.
Machine learning models often require data in a structured form.
Deep Learning models can work with both structured and unstructured data as they rely on the layers of the Artificial neural network.
AI is operating to create an intelligent system that can execute various complex tasks.
On the other hand, machine learning is creating machines that can operate only those specific tasks in which they get trained.
Deep learning works with artificial neural networks (ANN) designed to imitate how humans think and learn.
How do artificial intelligence, machine learning, neural networks, and deep learning relate?
Possibly the most obvious way to think about artificial intelligence, machine learning, neural networks, and deep learning is to deem them like Russian nesting dolls. All are essentially a component of the initial term.
However, machine learning is a subset of artificial intelligence. Deep learning is a subset of machine learning, and neural networks are the backbone of deep learning algorithms. Therefore, the number of node layers, or depth, of neural networks defines a single neural network from a deep learning algorithm, requiring more than three.
Artificial intelligence has many excellent applications that are transforming the world of technology. Therefore, it is presenting a cognitive ability to a machine. Examining AI vs. Machine Learning, early AI systems practiced pattern matching and expert systems.
Accordingly, the idea behind machine learning is that the machine can acquire without human intervention. Hence, the device needs to discover a way to learn how to solve a task.
Deep learning is the finding in the field of artificial intelligence. Deep learning delivers impressive results when there is adequate data to train, especially for image recognition and text translation. The principal reason is the feature extraction done automatically in the different layers of the network.
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