“Can machines think?” Alan Turing pondered on this question, and in the 1950s, dramatically changed the way we look at machines. 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 often vendors promote how their products and services access AI.
What exactly is Artificial Intelligence? AI is the ability to incorporate human intelligence into machines through a set of rules (algorithm). 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. One can consider AI to be the study of training computers to mimic a human brain and its thinking capabilities. AI focuses on 3 skills which are learning, reasoning, and self-correction to obtain maximum efficiency.
Machine Learning is an off-shoot of Artificial Intelligence itself. Machine Learning (ML) is the application that provides the computer to learn automatically through experiences it has had and improve according to the situation being explicitly programmed, i.e, being flexible. ML is mainly used for developing programs so that it can reach the dataset to use it for itself. The entire process is a self-evaluation as it makes observations on data to spot possible patterns that are being formed and create better future decisions according to the dataset being provided to them. The quality of the data matters immensely, without a proper data bank, the machine cannot learn accurate solutions. The major aim of ML is to allow the systems to learn on their own via their experience without any kind of human intervention.
Deep Learning is also a subset of AI and machine learning. Deep Learning (DL) makes use of Neural Networks (A neural network or simulated neural network (SNN), which is an interconnected group of natural or artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation.) to mimic human brain-like behavior. DL algorithms create an information processing pattern mechanism to discover patterns. It is similar to what our human brain does and ranks the information accordingly. DL works on larger sets of data, compared to ML and the prediction mechanism is an unsupervised process as in DL, the computer self-administrates.
Differences between AI, ML, and DL
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. Deep learning (DL) is an algorithm of ML that uses several layers of neural networks to analyze data and provide output accordingly.
AI uses complex math. If one has a clear idea about the logic(math) then only it is involved behind the scenes. In machine learning, one can visualize complex functionalities like K-Mean, Support Vector Machines,(different kinds of algorithms), etc.
In deep learning, if one knows the math involved in it but doesn’t have a clue about the features, one can break the complex functionalities into linear/lower dimension features by putting in more layers.
The aim of an AI is to basically boost chances of success so it does not focus much on accuracy. In machine learning, the aim is to increase accuracy but there is not much focus on the success rate of the same. Deep Learning mainly focuses on accuracy, and out of the 3 delivers the best results. Deep learning needs to be trained with a large amount of data.
There are 3 types Of AI like 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.
Artificial Intelligence is made up of internal cogs like Machine Learning and Deep Learning. AI has become increasingly popular and has several real-world applications like Google’s AI-Powered Predictions, Ridesharing Apps Like Uber and Lyft, Commercial Flights Use an AI Autopilot, etc. There are good Examples of ML applications that include Virtual Personal Assistants: Siri, Alexa, Google, etc., Email Spam, and Malware Filtering. Real-world examples of DL applications include Sentiment based news aggregation, Image analysis, and caption generation. When one speaks about AI, ML and DL, the three go hand in hand: AI, just like DL, is a part of ML. Different sectors use different kinds of algorithms to fulfill their need. We can see many examples of AI making sectors more effective and cost-friendly.
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