ARTIFICAL INTELLIGENCE

Have you ever imagined a new industrial revolution fueled by Artificial Intelligence? 

There will be more time to spend with family and to pursue personal hobbies. A fully automated agricultural system that boosts output while lowering reliance on chemical fertilizers and pesticides.

Collateral damage and human casualties will be reduced by streamlining the military using effective Artificial Intelligence technology innovation. By bringing manufacturing onshore, transportation costs and carbon emissions will be reduced, making climate action more viable.


Machine Learning and Artificial Neural Networks.

Machine learning is a type of data analysis that automates the creation of analytical models. It's a field of artificial intelligence based on the notion that computers can learn from data, recognize patterns, and make choices with little or no human input.

Artificial neurons, which take the form of deep neural networks, are the design of many current Artificial Intelligence systems that use Machine Learning. Artificial Intelligence has progressed rapidly from simple data structures and symbolic algorithms to sophisticated artificial neurology based on data and hardware components.

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Artificial Neural Networks.

Artificial Neural Networks (ANNs) are based on biological neural clusters in the neocortex. Deep Neural Networks (DNNs) are ANNs with a large number of layers of neurons between the input and output - the network is deep. Deep learning entails training a DNN with a large amount of data until the network begins to be "molded" by the dataset's similarities. If we train a DNN with a large number of pictures of dogs, the DNN will be able to detect the common characteristics that define a dog.

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Recurrent Neural Networks.

Recurrent Neural Networks (RNN) are a type of Artificial Neural Network that, in deep learning, can take a sequence of inputs and save their state while processing the following sequence of inputs. Apple's Siri and Google's voice search both employ Recurrent Neural Networks (RNNs), which are the state-of-the-art method for sequential data. It is the first algorithm with an internal memory that remembers its input, making it ideal for machine learning issues involving sequential data. Traditional neural networks will analyze one input and then continue on to the next, regardless of the order in which they were received.


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Feed-Forward Neural Network.

A Feed-Forward Neural Network is a classification method that is biologically inspired. It is made up of a number of basic neuron-like processing units that are arranged in layers, with each layer's units linked to those in the preceding layer. Feed-forward Signals in ANNs can only go one way: from input to output. There are no feedback (loops), which means that the output of one layer has no effect on the output of another layer. Feed-forward ANNs are typically simple networks that connect inputs and outputs. They're utilized a lot in pattern recognition.

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Deep Learning.

Deep learning is a subset of machine learning techniques based on representation learning and artificial neural networks. Unsupervised, semi-supervised, and supervised learning are the three forms of learning. Another frequently stated virtue of deep learning models, in addition to scalability, is their capacity to conduct automated feature extraction from raw data, also known as feature learning.


The word ``supervised” here means our training data has a value (dependent variable) that represents what we’re training the Artificial Intelligence to predict. Machine Learning Practitioners have been playing with combining Supervised machine Learning (SL) components with Unsupervised machine Learning (UL) into a type of Machine Learning (ML) called Generative Adversarial Networks(GAN). GANs are semi-supervised models because they’re trained by arming an unsupervised model to learn ( and generate ) against a known supervised model (the adversary). GANs are powerful: they take bodies of known works and generate outputs that are similar enough to be useful. We can provide a corpus of classical music and ask the GAN to generate an endless supply of new music that somewhat “sounds like” the input.

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Fuzzy Logic

The phrase fuzzy refers to things that are unclear or ambiguous. In the actual world, we frequently come with situations when we are unsure if the statement is true or untrue. Fuzzy logic gives you a lot of flexibility when it comes to reasoning. Fuzzy Logic (FL) is a type of logic that is similar to human reasoning. FL's method is modelled after how people make decisions, which includes all options in between the digital values YES and NO. A computer can grasp a traditional logic block, which accepts exact input and generates a specific result as TRUE or FALSE, which is comparable to a human's YES or NO.



Unlike computers, human decision-making contains a spectrum of options between YES and NO, such as CERTAINLY YES, POSSIBLY YES, CANNOT SAY, POSSIBLY NO, CERTAINLY NO, according to Lotfi Zadeh, the originator of fuzzy logic. fuzzy logic is based on the degrees of input possibilities in order to produce a specific result.



fuzzy logic can control machines and consumer products. It may not provide accurate reasoning, but acceptable reasoning. Fuzzy logic helps to deal with the uncertainty in engineering.


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Metaheuristics

A metaheuristic is a higher-level procedure or heuristic used in computer science and mathematical optimization to find, generate, or select a heuristic that can provide a sufficiently good solution to an optimization problem, especially when there is incomplete or imperfect information or limited computation capacity.
Metaheuristics are search methods that help you find what you're looking for. The objective is to identify near–optimal solutions by effectively exploring the search space. Metaheuristic algorithms are made up of a variety of techniques ranging from simple local search operations to sophisticated learning processes. The optimization problem may be solved with the aid of a metaheuristic approach.

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