“Can machines think?” Alan Turing pondered this question, and in the 1950s dramatically changed the way we look at machines. Then, in 1956 John McCarthy coined the term artificial intelligence (AI) which described machines that perform tasks that usually require human intelligence. In the past few years, AI has become increasingly popular and has so many use cases in our world. Before long, every company will leverage AI in some capacity.
What exactly is artificial intelligence? AI is the ability to incorporate human intelligence into machines through a set of rules (algorithms). AI is made up of two words: “artificial” meaning something created by humans and “intelligence” meaning the ability to understand or think according to the situation or problem and to come up with a solution.
AI trains computers to mimic a human brain and its thinking capabilities. To do so, AI focuses on three skills: learning, reasoning, and self-correction to obtain maximum efficiency.
AI real-world uses cases are vast, and are evolving every day. This ranges from robotics to healthcare, and from banking to universities.
Machine learning (ML) is an offshoot of artificial intelligence. ML is the application that teaches the computer to learn automatically through experiences it has had—much like a human. It then allows the computer to improve according to the situation being explicitly programmed. Essentially, ML uses data and algorithms to mimic the way humans learn, and it gradually improves and gains accuracy.
Algorithms are trained to make classifications or predictions, and to uncover key insights in data. These insights can then drive decision for applications and business goals.
The quality of the training data matters immensely, since without a proper data bank the machine cannot learn accurately. The major aim of ML is to allow the systems to learn on their own via their experience.
Deep learning is also a subset of AI and machine learning. Deep learning makes use of neural networks (interconnected groups of natural or artificial neurons that uses a mathematical or computational model for information processing) to mimic the behavior of the human brain.
The goal is for it to "learn" from large amounts of data, to make predictions with high levels of accuracy. DL drives many AI applications that improve automation, performing analytical tasks without human intervention. This can range from things like caption generation to fraud detection.
DL algorithms create an information-processing pattern mechanism to discover patterns. It is similar to what our human brain does as it ranks the information accordingly. DL works on larger sets of data than ML, and the prediction mechanism is an unsupervised process as in DL the computer self-administrates.
Deep learning and machine learning are commonly confused, so it pays to look at the distinction between the two.
DL is a subset of ML. The main difference hinges on the way that each algorithm learns and how much data it needs.
DL requires a lot less manual human intervention since it automates a great deal of feature extraction. Meanwhile, ML is much more dependent on human intervention to learn. Human experts determine the hierarchy of features to understand the differences between data inputs.
This means that ML algorithms leverage structured, labeled data to make predictions. Specific features are defined from the input data, and that if unstructured data is used it generally goes through some pre-processing to organize it into a structured format.
Meanwhile, DL can leverage labeled datasets (through supervised learning) to inform its algorithm, but this isn't required. DL can also take unstructured data in its raw form and automatically determine the set of features which distinguish items from one another.
AI is a computer algorithm that exhibits intelligence via decision-making. ML is an algorithm of AI that assists systems to learn from different types of datasets. DL is an algorithm of ML that uses several layers of neural networks to analyze data and provide output accordingly.
AI uses complex math. In ML, one can visualize complex functionalities like K-Mean, Support Vector Machines—different kinds of algorithms—etc. In DL, if you know the math involved but don’t have a clue about the features, you can break the complex functionalities into linear/lower dimension features by putting in more layers.
AI does not focus as much on accuracy but focuses heavily on success and output. In ML, the aim is to increase accuracy but there is not much focus on the success rate. DL mainly focuses on accuracy, and out of the three delivers the best results. DL needs to be trained with a large amount of data.
There are three types of AI: Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI). Three types of ML are: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. DL can be visualized as neural networks with a large number of layers lying in one of the four fundamental network architectures: Unsupervised Pre-trained Networks, Convolutional Neural Networks, Recurrent Neural Networks, and Recursive Neural Networks.
When we're looking at AI, ML and DL, the three go hand in hand: ML—just like DL—is an offshoot of AI. Different sectors use different kinds of algorithms to fulfill their need, and the use cases of each are growing and evolving by the day. We can see many examples of AI making sectors more effective and cost-friendly.
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