Artificial Intelligence and Its Development in Machine Learning
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Ever heard of the term Artificial Intelligence? Artificial Intelligence or Artificial Intelligence is not a new term and a new technology for researchers. Even the history of Artificial Intelligence itself is much older than we imagine, History proves Artificial Intelligence has been the scope of research by experts even before the internet developed and touched all lines of our lives as it is today. Although the development has only been felt massively in the last few years. Without realizing it, various applications today have utilized Artificial Intelligence technology. For example, opening a cellphone with Face ID features, advanced navigation on Google Maps, self-driving cars and much more. Artificial Intelligence (AI) refers to the simulation of human intelligence in machines programmed to think like humans and imitate their actions. When machines become intelligent, they can understand requests, connect data points, and draw conclusions. They can also reason, observe and plan. The benefits of AI technology certainly improve the quality of human life, in other words, it can facilitate people’s daily lives.
Artificial Intelligence and Its Development in Machine Learning For those of you who want to learn Artificial Intelligence, you need to know that Artificial Intelligence material is a broad field of study that includes many theories, methods, and technologies, as well as main sub-fields, namely Machine Learning, Neural Networks, Deep Learning, Computer Vision and Natural Language Processing. . The concepts of Machine Learning and Deep Learning are often considered the same, even though they are two different things. Then, what are the differences between Machine Learning and Deep Learning?
What is Machine Learning? Understanding Machine Learning is an application branch of Artificial Intelligence (AI) that focuses on developing a system that is able to learn on its own without having to be repeatedly programmed by humans. But before removing a data result from object behavior, machine learning requires initial data as learning material. Fundamentally, how machine learning works is to learn like humans, by using examples and then only being able to answer a related question. Machine Learning learning process uses data called train dataset. What are datasets? The dataset is a machine learning algorithm, this data acts as input. In the Machine Learning method, an algorithm based on data is needed to produce a desired pattern or conclusion, Machine Learning is a machine that studies algorithms and statistics, to perform certain tasks without instructions. The following are the types of algorithms in Machine Learning.
Supervised Machine Learning Algorithms Supervised Machine Learning is a machine learning algorithm in which it helps apply existing information to existing data by giving certain labels. This model can also be interpreted as an approach to a data that has been trained. This type of algorithm can provide output targets that are carried out by comparing the experience of execution of problems in the past. This model is given instructions at the beginning to learn something and how to learn it. For example, in the supervised learning model the existing algorithm is used to predict the possibility of natural disasters, such as earthquakes and tsunamis.
Unsupervised Machine Learning Algorithms In contrast to Supervised Machine Learning algorithms, unsupervised learning algorithms do not have targets or variables to set. There is no need for a special label to predict a data. The process is carried out only by inputting data correctly, then for the output business, the machine will determine its own path. The algorithm in this model is often applied to data that does not have information that can be applied directly. In addition, this algorithm is also able to find hidden patterns in the data itself. An example of an Unsupervised Machine Learning model is creating market segmentation to conduct campaigns effectively based on clustering.
Semi-Supervised Machine Learning Algorithms The Semi-Supervised Machine Learning Algorithm combines both supervised and unsupervised algorithms where the input samples used to perform data learning are labeled and some are unlabeled. This method can be used with other methods such as classification, regression, and prediction. An example of using semi-supervised learning is to identify a person’s face on a webcam or smartphone camera.
Reinforcement Machine Learning Algorithms In the Reinforcement Machine Learning algorithm, machines are trained to make specific decisions based on business needs that aim to maximize their performance. This algorithm learns a policy of how to take action based on observations of the existing environment, then each action produces consequences for that environment, and the environment provides feedback to guide the algorithm. This model makes a software or machine train itself continuously based on the environment it influences. In addition, this model is also applied to be able to solve problems in a business. This example of implementing Machine Learning Reinforcement is usually used for robotics, game creation, and navigation.
Machine Learning Example Not only on artificial intelligence, Machine Learning can also be implemented in making applications. Examples of Machine Learning applications that you can find in everyday life such as Text Analysis, Image Processing, Finance, Search and Recommendation Engines and Speech Understanding. As for the application of Machine Learning in technology, namely Siri on Apple, Film Recommendation on Netflix, automatic routes on Google Maps, Tesla’s advanced car that can drive itself, and many more.
Definition of Data Training and Data Testing Machine learning techniques can be applied if data is available. Without data, Machine Learning algorithms will not work. The data held are generally divided into 2, namely training data and testing data. The training data is used to train the algorithm, while the testing data is used to determine the performance of the previously trained algorithm when it finds new data that has never been seen before.
What is Deep Learning? Understanding Deep Learning is a method of implementing machine learning with the aim of imitating the workings of the human brain using an artificial neural network. Deep learning uses a number of algorithms as ‘neurons’ to work together to determine and digest certain characteristics in a data set. Deep learning programs are typically programmed with more complex capabilities for studying, digesting, and classifying data. Deep Learning has several types of algorithms, as follows.
Convolutional Neural Networks (CNNs) CNNs, also known as ConvNets, consist of several layers and are mainly used for image processing and object detection. CNNs are widely used to identify satellite images, process medical images, predict time series, and detect anomalies.
Long Short Term Memory Networks (LSTMs) LSTM is a type of Recurrent Neural Networks (RNN) that can learn and memorize long-term dependencies. LSTM stores information over time. LSTM has a chain-like structure in which four interacting layers communicate in a unique way. In addition to time series prediction, LSTMs are commonly used for speech recognition, music composition, and pharmaceutical development.
Recurrent Neural Networks (RNNs) The RNN has connections that form a directional cycle, which allows the output of the LSTM to be fed as input to the current phase. RNNs are commonly used for image text generation, time series analysis, natural-language processing, handwriting recognition, and machine translation.
Generative Adversarial Networks (GANs) GAN is a generative deep learning algorithm that creates new data instances that resemble training data. GAN has two components, namely a generator that learns to generate false data, and a discriminator that learns from the false information. The use of GANs has increased over a period of time. GANs help produce realistic images and cartoon characters, create photos of human faces, and create 3D objects.
Radial Basis Function Networks (RBFNs) RBFN is a special type of feedforward neural network that uses a radial basis function as an activation function. RBFN has an input layer, a hidden layer, and an output layer and is mostly used for classification, regression, and time series prediction.
The application of deep learning technology is quite a lot and we can find such as Google Photos, face unlock on smartphones, automatic ticketing systems, and so on. Deep learning itself is also the main component that makes artificial intelligence more similar to humans.
That’s an explanation of Artificial Intelligence and its development in Machine Learning.