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Interested in exploring artificial intelligence? Here are the Top 35 AI terms that you need to know:
Machine learning (ML) is a part of artificial intelligence that aims to let machines mimic the way in humans learn things. It enables computers to process and analyze data, identify patterns, learn from the data, and make decisions. It even helps discover insights without being programmed to find those specific insights.
ML algorithms employ statistical algorithms for analyzing data, making predictions and classifications, finding insights from the data and making use of those insights to help in making future decisions. Machine learning systems become more accurate as they are used.
Deep learning mimics the working of the human brain to process data and create patterns that can be used for decision making. Deep learning is also known as deep neural learning and uses a deep neural network.
Deep learning neural networks can learn without supervision from data that is unstructured and unlabeled.
Natural Language Processing
Natural Language Processing (NLP) is a field focused on enabling machines to process and understand natural language (human language). It employs computational linguistics along with machine learning, statistical, as well as deep learning models so that machines can analyze natural language and figure out the actual meaning of text or voice data.
Natural Language Understanding
Natural Language Understanding is dedicated to converting human language into machine-readable formats. It interprets the meaning from the communication sent by the user, and classifies it into the right intents. NLU rearranges unstructured data, allowing machines understand and analyze it.
It even makes it possible for machines to identify context and draw insights from natural language data.
Natural Language Generation
Natural Language Generation (NLG) is used to convert structured data into readable text. If you use data in the right format, you could generate thousands of pages of data-driven narratives in minutes by making use of natural language generation.
It works in six stages: content determination, data interpretation, document planning, sentence aggregation, grammaticalization, and language implementation.
Semantic analysis makes use of machine learning and natural language processing to figure out the actual context of natural language. It is used in search engines and in Engati’s chatbots. It enables systems to derive vital information from unstructured data and even detect and identify emotions and sarcasm. Semantic analysis can performed by using text classification and text extraction.
In machine learning, supervised learning involves learning a function that maps an input to an output based on example input-output pairs. It uses labeled training data made up of a set of training examples to infer a function.
Unsupervised learning involves using artificial intelligence to identify patterns in datasets made up of datapoints that aren’t classified or labeled. Unsupervised machine learning algorithms can classify, label, and/or group the data points without any external guidance or influence in carrying that task out.
Algorithms are finite sequences of well-defined, computer-implementable instructions that usually explain how to solve a class of particular problems or to perform a computation. The steps are stated precisely, the results of every step are uniquely defined and are dependent only on the input and the result of the preceding steps.
Bias refers to assumptions that a model makes to simplify the process of learning to perform the task assigned to it. The majority of supervised machine learning models perform better when they have low levels of bias because those assumptions can negatively affect the results.
Chatbots are designed to interact with people via text or voice in a manner that mimics human conversation. Intelligent chatbots (like those built on Engati) use NLP and semantic analysis to understand the true meaning of the user’s queries and deliver the most appropriate response.
Cognitive computing involves using computer models to simulate human thought processes in complex situations which may have rather vague answers. It is all about understanding and mimicking human reasoning and behavior. As you expose these systems to more data, they grow in accuracy.
Artificial Narrow Intelligence
Artificial narrow intelligence is essentially the AI that is present today. It is also known as narrow AI or weak AI. Narrow AI is rather effective at performing singular tasks like facial recognition, speech recognition, etc., but it operates under a narrow set of constraints and limitations.
Artificial General Intelligence
Artificial general intelligence would be intelligence that has the ability to understand the world and perform a range of tasks as well as a human can.
Artificial Super Intelligence
Artificial super intelligence would essentially be AI that has capabilities greater than that of a human. We haven’t even achieved artificial general intelligence yet, so artificial super intelligence is a very long way off.
Artificial Neural Network (ANN)
Artificial neural networks make use of sets of algorithms which are loosely modeled after biological neural networks. They make use of a reduced set of concepts from biological neural networks.
Recurrent Neural Network
These are neural networks in which the output from the previous step gets fed in input into the current step. Recurrent neural networks also have a hidden state that remembers some information about a sequence.
Overfitting is a situation in which the model models the training data a bit too well. It makes the model relevant only to the dataset on which it was trained and completely irrelevant to any other dataset. Overfitting has a negative impact on the performance of the model on new data.
A parameter is a variable inside the model that assists it in making predictions. The value of the parameter is estimated by using data.
Hyperparameters are the variables that determine the network structure as well as how the network is trained. They affect the way your model learns and are generally et manually outside the model.
Predicitive analytics makes use of data mining along with machine learning to forecast what will happen within a specific timeframe on the basis of historical data and trends.
This involves analyzing a piece of text to identify opinions and judgements. It helps you understand whether the text in question is positive, negative, or even neutral. It is also known as opinion mining or emotion AI.
The turing test is considered to be a test of whether a system is artificially intelligent. It involved three terminals hidden from each other. One is operated by a human questioner, one is operated by a human respondent and the other is operated by a computer program.
If the questioner can’t figure out which terminal is handled by the computer and which is handled by a human, the system is said to have passed the Turing test.
This is the study of abstract machines and automata, along with the computational problems that can be solved by making use of them. Automata theory is a theory in theoretical computer science as well as discrete mathematics
Backpropagation uses gradient descent for supervised learning of artificial neural networks. It calculates the gradient of the error function with respect to the neural network's weights.
Backpropagation was one of the initial techniques to demonstrate that artificial neural networks have the ability to learn good internal representations. The objective of backpropagation is to optimize the weights and make it possible for the neural network to learn how to correctly map arbitrary inputs to outputs.
These are probabilistic graphical models that employ Bayesian inference to carry out probability computations. Bayesian networks are probabilistic because they are created by using probability distributions.
The bias-variance tradeoff is an inversely proportional relationship between bias and variance. When the bias increases, the variance falls, and as the variance increases, the amount of bias decreases.
A Boltzmann machine is a type of RNN in which the nodes make binary decisions with some level of bias. It’s an unsupervised deep learning model where all the nodes are connected to each other.
Boolean satisfiability problem
The boolean satisfiability problem, also known as propositional satisfiability problem and even B-SAT, involves figuring out whether there is an interpretation that satisfies a specified Boolean formula. It checks whether the variables of a given Boolean formula can be consistently replaced by the values TRUE or FALSE in a manner that would cause the formula to evaluate to TRUE.
Computational complexity theory
Computational complexity theory is a branch of theoretical computer science that deals with classifying and comparing the difficulty involved to solve computational problems regarding finite combinatorial objects.
It aims to figure out the level of resources that would be required to solve a specific problem and even tries to understand why some are undecidable or intractable.
In machine learning, predictive modeling, and data mining, concept drift refers to the gradual change in the relationships between input data and output data in the underlying problem. It happens when the statistical properties of the variable which the model is trying to predict change over time. There is a change in the context that the model is not aware about.
Concept drift takes place when the patterns that predictive models learned are not valid any more.
Convolutional neural network
A convolutional neural network (CNET or CNN) is a deep learning algorithm that processes images, assigns importance to objects in the image by making use of learnable weights and biases and has the ability to differentiate images from each other.
It is a neural network that uses a convolution layer and pooling layer. The convolution layer convolves into a smaller area for the purpose of extracting features and the pooling layer chooses the data with the greatest value within an area.
Data augmentation is essentially a way for you to synthesize new data from existing data. It uses techniques to add slightly edited versions of existing data or even create synthetic data by making use of existing data, thus increasing the actual amount of data available.
Data augmentation is performed to enhance the downstream performance of your model.
Dimensionality reduction involves reducing the number of input variables in the training data for machine learning models.
Data with a smaller amount of input variables can be handled by machine learning models that have a simpler structure and fewer degrees of freedom (parameters). These simpler models tend to generalize in a better manner.
Echo state network
An echo state network (ESN) is a type of recurrent neural network that has a sparsely hidden layer. This layer usually tends to have less than 10% connectivity. The connectivity and weights of the hidden layer’s neurons are fixed and are assigned at random. Echo state networks are part of the reservoir computing framework.
ESNs provide an architecture and a supervised learning principle for recurrent neural networks.