https://bugs.documentfoundation.org/show_bug.cgi?id=152269
Bug ID: 152269 Summary: What is AI Product: Document Liberation Project Version: unspecified Hardware: All OS: All Status: UNCONFIRMED Severity: normal Priority: medium Component: libcdr Assignee: libreoffice-bugs@lists.freedesktop.org Reporter: koushik12...@gmail.com The mimicking of human thought processes by machines, particularly computer systems, is known as artificial intelligence. Expert systems, natural language processing, speech recognition, and computer vision are examples of AI applications. How Does AI Work? In general, AI systems operate by consuming huge volumes of labeled training data, evaluating the information for patterns and structures, and then using these patterns to forecast future states. By examining millions of examples, a chatbot that provided case studies of text chats can figure out how to make lifelike dialogues with people, while an image recognition program can learn to recognize and describe items in photographs. Learning, Reasoning, and Self-correction are the three cognitive functions that AI programming focuses on: Learning Process: This component of AI programming is concerned with gathering data and developing rules for converting the data into usable information. The rules, known as algorithms, teach computing equipment how to execute a certain task in a step-by-step manner. Reasoning Process: This part of AI programming emphasizes selecting the best algorithm to achieve a given result. Self-correction Process: This element of AI programming is intended to constantly fine-tune algorithms to produce the most reliable data feasible. Why AI is a Need of the Hour? AI is essential because this will provide organizations with previously unknown insights into their business and, in some situations, can do tasks better than humans. AI systems generally accomplish operations quickly and with minimal errors, especially when it comes to repeated, detail-oriented activities like evaluating vast quantities of legal papers to verify key fields are filled in correctly. This has contributed to an increase in efficiency and opened up totally new business options for certain larger firms. Before the current wave of AI, it would have been difficult to conceive of employing computer software to connect riders to cabs, but Uber has grown to become one of the world's largest firms by doing precisely that. It employs powerful machine learning algorithms to estimate when people are likely to require trips in specific places, allowing drivers to be on the road ahead of time. Today's biggest and most successful businesses have utilized AI to better their operations and acquire a competitive advantage. Popular Algorithms Used in Artificial Intelligence Classification Algorithms Naïve Bayes Decision Tree Random Forest Support Vector Machines K Nearest Neighbors Regression Algorithms Linear Regression Lasso Regression Logistic Regression Multivariate Regression Multiple Regression Clustering Algorithms K-Means Clustering Fuzzy C-means Expectation-Maximization (EM) Hierarchical Clustering Classification Algorithm Supervised learning includes classification algorithms. These algorithms are used to categorize the subjected variable and then forecast the class for a given input. Classification algorithms, for example, can be used to determine whether an email is a spam or not. Artificial intelligence identifies a new category of observations in classification algorithms based on previous data, which we may also refer to as training data. The application learns from the previously provided dataset. Let's look at some of the most popular classification algorithms. Naïve Bayes Unlike other classification algorithms, the Naive Bayes algorithm is based on the Bayes theorem and uses a probabilistic approach. For each class, the algorithm has a set of prior probabilities. When data is supplied into the process, the probabilities are updated to generate what is known as posterior probability. This is useful when you need to forecast whether or not an input corresponds to a specific set of classes. Decision Tree The decision tree algorithm seems more like a flowchart-like process, with nodes representing tests on input attributes and branches representing test results. It is a very simple type of probabilistic tree that allows you to make decisions about a process. This tool is based on a tree-like concept and its probable outcomes. Random Forest Random forest functions similarly to a grove of trees. The source data set is partitioned and fed into various decision trees. The average of all decision tree outputs is taken into account. When compared to the Decision tree technique, Random Forests provide a more accurate classifier. Random forests are employed in a variety of industries, including healthcare, manufacturing, banking, and retail. One of the practical uses of random forest is determining whether or not an email is a spam. Support Vector Machines SVM is a data classification algorithm that uses a hyperplane to ensure that the gap between the support vectors and the support vectors is as little as possible. It is a supervised learning technique that can be applied to classification or regression issues. Face detection, image classification, handwriting detection, text and hypertext categorization, and other applications of SVM are examples. K-Nearest Neighbor To forecast the class of a new sample data point, the KNN algorithm employs a large number of data points that have been classified. It is referred to as a "lazy learning algorithm" since it is relatively short in comparison to other algorithms. KNN has applications in finance and medicine, such as bank customer profiles and credit ratings, among others. KNN has several advantages, including its ease of implementation and comprehension, as well as its simplicity and intuitiveness. Regression Algorithms Regression algorithms are a common type of supervised machine learning method. Based on the input data points supplied into the learning system, regression algorithms can predict the output values. Regression algorithms' principal applications include predicting stock market prices, predicting the weather, and so on. The regression algorithms also aid in predicting the output values based on the data input attributes. There are several types of regression, including linear regression, polynomial regression, and so on. The most commonly used algorithms in this section include Linear Regression It is utilized to assess genuine qualities by taking into account consistent variables. It is the most basic of all regression techniques, however, it can only be used in circumstances where there is a linear relationship or a linearly separable problem. The method predicts new values by drawing a straight line between data points known as the best-fit line or regression line. One common application of linear regression is in medical practice, where practitioners recognize the association between sugar intake and high blood sugar levels. Lasso Regression The Lasso regression technique finds the subset of predictors that minimizes prediction error for a given response variable. This is accomplished by constraining data points and allowing some of them to shrink to zero value. The lasso regression method is used to identify a group of predictors that aid in minimizing prediction error. Lasso constrains the model parameters, causing the regression coefficients to shrink to zero. Logistic Regression Logistic regression is mostly employed in binary classification. You can use this strategy to assess a group of variables and predict a category outcome. Its main uses include forecasting customer lifetime value, housing values, and so on. Banking is one of the many real-world applications of logistic regression. A credit card company can determine whether or not the transaction amount and credit score will result in a fraudulent transaction. Multivariate Regression When there are multiple predictor variables, this approach must be utilized. This algorithm is widely used in retail sector product recommendation engines, where customers' chosen products are determined by a variety of characteristics such as brand, quality, price, review, and so on. The multivariate regression method aids in the discovery of relationships between numerous variables. Also, discover the relationship between independent and dependent variables. Multiple Regression Algorithm Several Regression Algorithm employs a hybrid of linear and non-linear regression techniques, with multiple independent variables as inputs. The most common uses are social science study, insurance claim authenticity, behavioral analysis, and so on. Clustering Algorithms Clustering is the method of categorizing and organizing data items into groups that are similar among group members. This is an example of unsupervised learning. The basic goal is to group comparable products. For instance, it can group all fraudulent transactions based on specific attributes of the transaction. K-Means Clustering It is the most basic unsupervised learning algorithm. The method groups comparable data points into clusters. Clustering is accomplished by first determining the centroid of the collection of data points and then computing the distance of each data point from the centroid of the cluster. The examined data point is subsequently given to the closest cluster based on its distance. The letter 'K' in K-means indicates the number of clusters into which the data points are divided. Fuzzy C-Means The FCM algorithm is based on probability. Each data point is thought to have a chance of belonging to another cluster. Because data points do not have absolute membership in a single cluster, the technique is referred to be fuzzy. Fuzzy C- Means is a clustering approach in which the data set is divided into N clusters, with each data point belonging to one of the groups in some way. Expectation-Maximization It is based on the Gaussian distribution, which we learned about in statistics. To answer the challenge, data is represented as a Gaussian distribution model. Following the assignment of a probability, a point sample is calculated using expectation and maximization formulae. The Expectation-Maximization (EM) approach is employed when it is necessary to discover the local maximum likelihood parameters of a statistical model. It is also utilized for solving equations that cannot be solved directly. Hierarchical Clustering After learning the data points and generating similarity observations, these algorithms arrange clusters in hierarchical order. It can be of two kinds. For a top-down method, use divisive clustering. Bottom-up clustering via agglomerative clustering Strong AI vs Weak AI AI is classified as either weak or strong. Weak AI, also known as narrow AI, is an artificial intelligence system that is created and taught to do a specific task. Weak AI is used by industrial robots and virtual personal assistants such as Apple's Siri. Strong AI, often known as artificial general intelligence (AGI), refers to programming that can mimic the cognitive capacities of the human brain. When faced with an unexpected issue, a powerful AI system can employ fuzzy logic to apply information from one domain to another and find a solution on its own. A strong AI program should, in theory, be able to pass both the Turing Test and the Chinese room test. Types of Artificial Intelligence AI is divided into four categories, beginning with task-specific intelligent systems that are widely used today and progressing to sentiment systems that do not yet exist. The following are the categories: Type 1: Reactive Machines - These AI systems have no memory and are only used for specialized tasks. Deep Blue, the IBM chess software that defeated Garry Kasparov in the 1990s, is one example. Deep Blue can identify pieces on the chessboard and make predictions, but it cannot use past experiences to influence future ones since it lacks memory. Type 2: Limited Memory – Because these AI systems have memories, they can use prior experiences to make better decisions in the future. This is how some of the decision-making functions in self-driving automobiles are created. Type 3: Theory of Mind - The theory of mind is a psychological concept. When applied to AI, this indicates that the machine has the social intelligence to comprehend emotions. This sort of AI will be able to predict human behavior and infer human intents, which is an essential talent for AI systems to become integral members of human teams. Type 4: Self-Awareness - AI systems in this category have a feeling of self, which provides them with awareness. Machines with self-awareness are aware of their current state. This form of artificial intelligence does not yet emerge. Applications of AI In today's culture, artificial intelligence is applied in a variety of ways. It is becoming increasingly significant in today's society because of its ability to tackle complex problems in a range of fields such as healthcare, entertainment, banking, and education. As a result of artificial intelligence, our daily lives are getting more comfortable and efficient. Healthcare To save human lives, many corporations and healthcare institutions are moving to artificial intelligence (AI). There are countless examples of how artificial intelligence has aided patients all across the world. Let's look at some of the applications of AI in healthcare areas. - Administration - Assisted Diagnosis - Robotic Surgery - Health Monitoring E-Commerce AI is providing a competitive advantage to the e-commerce industry, and it is growing increasingly popular in the market. AI can help shoppers identify related products in their selected size, color, or brand. Let's look at some AI applications in e-commerce. Personalized Shopping AI-Powered Assistance Fraud Detection and Prevention Robotics The field of robotics was progressing even before AI has become a reality. Currently, artificial intelligence is supporting robotics in producing more efficient robots. AI-enabled robots have found applications in a wide range of verticals and sectors, especially in manufacturing and packaging. AI, or artificial intelligence, offers computer vision to robots, allowing them to navigate, sense, and react correctly. Manufacturing Transport Surgery Space Exploration Finance In finance, artificial intelligence is transforming the way we interact with money. AI is supporting the financial industry in streamlining and optimizing procedures ranging from credit determinations to quantitative trading and financial risk management. Artificial intelligence in finance provides features such as risk assessment, fraud detection and management, financial advisory services, and automated trading. Personal Finance Consumer Finance Corporate Finance Facial Recognition Facial Recognition is a sort of technology that maps and saves a person's facial characteristics as a face print. To authenticate identification, the software uses deep learning techniques to match a current image to a stored facial print. Image processing and machine learning are the foundations of this technology. The next time you log in without typing a password, your phone will unlock itself using only your imagination. This is because when you take a selfie and register it for facial recognition, your phone learns a face recognition algorithm. Marketing Applications of artificial intelligence (AI) are frequently employed in online retail marketing. Artificial intelligence (AI) marketing generates automated decisions based on information gathering, analysis, and additional observations of audience or economic patterns that may affect marketing efforts. In marketing initiatives when speed is essential, AI is routinely used. Social Media Social networking companies utilize artificial intelligence to sift through vast volumes of data to identify patterns, hashtags, and trends. The understanding of user behavior is aided by this research. Let's examine several well-known social media platforms' or applications' use of artificial intelligence: Instagram: When choosing which posts to display in your Explore tab on Instagram, AI takes into consideration your preferences and the accounts you follow. Facebook(meta): DeepText is a method that is used in conjunction with artificial intelligence. Using this technology, Facebook can analyze conversations more accurately. It can be used to translate posts across languages automatically. Twitter: Twitter employs AI for the elimination of hateful content, propaganda, and fraud identification. Depending on the kinds of tweets users interact with, Twitter also utilizes AI to make suggestions to users. Conclusion In conclusion, artificial intelligence plays a big role in people's lives. The development of artificial intelligence at the beginning of the twenty-first century greatly increased the use of technology in a range of disciplines. Sharpen your abilities with in-demand AI technologies with Softlogic Systems' AI Training in Chennai and IBM Certification. URL : https://www.softlogicsys.in/artificial-intelligence-training-in-chennai/ -- You are receiving this mail because: You are the assignee for the bug.