Artificial Intelligence Solutions
If the definition of intelligence is a speed in understanding and intuition, and an intellectual and cognitive activity that the mind performs, and it is not a condition that intelligence is associated with academic or methodological achievement as is known to some.
Then it may go beyond it to other aspects such as social, linguistic, and mathematical intelligence, so a type or more types of intelligence distinguish each person. Artificial intelligence is the ability of a machine to simulate the human mind and its way of working, such as its ability to think, discover and benefit from previous experiences.
Artificial intelligence also defined as a group of systems that aim to make digital machines, computers, and modern technologies able to achieve certain goals in a way that is similar to humans or exceeds the ability of humans in most cases. In other words, they are systems that simulate human intelligence to perform tasks and that have the ability to improve themselves by using the information they collect.
When researchers talk about intelligence, they are referring to a specific set of skills that include brain abilities, learning, and planning to solve problems. The interesting thing is that people who are good at one of these skills are good at the rest. These skills seem to reflect a broad mental ability that dubbed general intelligence. They said, “Know the intelligence for us … we make an artificial intelligence for you.”
To solve global challenges, everyone must work to connect artificial intelligence innovators with problem owners. The near future will witness a significant impact in our lives by artificial intelligence. With recent advances in artificial intelligence, machines will gain the ability to learn, improve, and make calculated decisions in ways that will enable them to perform tasks that previously believed to depend on human experience, creativity and ingenuity.
Today, artificial intelligence offers valuable solutions, creative solutions, and even innovative solutions, for most jobs, businesses and fields. Scientists expect that artificial intelligence will soon solve the United Nations’ Sustainable Development Goals as AI holds great promise by leveraging the unprecedented amounts of data that are now being generated about emotional behavior, human health, trade, communications, and migration.
Learning strategies for artificial intelligence
Machine learning is a form of artificial intelligence, and artificial intelligence is not always machine learning. Most machine learning algorithms rely on the intervention of data scientists to derive features and patterns of data before these algorithms consume it. Algorithms learn by observing a large number of cases and focusing on manually preset patterns and features.
In simple terms, most machine learning algorithms rely on two basic steps in their learning: observation and simulation (prediction) – and this is in a group of algorithms that rely on supervised learning, or learning by observing previous events with known results. First, it monitors the input data and tries to devise distinct patterns and characteristics of this data, and then it simulates the behavior of jobs based on the connections and relationships that formed by monitoring the process of converting the input data into specific outputs.
The primary function of machine science is to predict results based on data given to them. The more diverse the data provided to her, the easier it is for her to find patterns and predict outcomes. Where there are two methods of data collection, manually and automatically, the manual method is the most accurate and most secure to obtain correct and accurate data, but it takes a longer time to collect. While the automatic method is faster, but the correctness and accuracy of the data not guaranteed. Among the most important basic sciences for machine learning are mathematics, including calculus, linear algebra, statistics, probability, graph theory, and programming skills.
The importance of machine learning highlighted in helping to choose the optimal decision from among a set of available alternatives and ensuring more accurate results and decisions in the fastest time. Especially when a large amount of data is available, since computerized data processing considered less costly than employing human hands and that the computer has succeeded in analyzing data at various levels, whether simple or complex, especially using machine learning.
Deep learning is one of the forms of machine learning, which in turn is a branch of artificial intelligence. Machine learning depends on algorithms that can be fed with structured data and then analyze it to reach conclusions. As for deep learning, it characterized by the presence of different levels of algorithms that form artificial neural networks that have the ability to understand unorganized data and patterns. As complex as languages, speech and images.
What defines deep learning algorithms is that they can learn tasks and automate them without explicit programming. By explicit programming, we mean writing specific commands and conditional tools to test data in order to reach a specific result or extract data features manually by data scientists.
Deep learning algorithms can automatically extract the most important features and repetitive patterns of data by looking at many of the input data and then analyzing it to find direct or indirect links and relationships between the input data and the desired output. This is in contrast to previous machine learning algorithms that required a deep understanding of data and a great effort that manually extrapolate its features and patterns by data scientists.
The learning process in deep learning algorithms takes place in two ways: Supervised learning, in which the machine is learning based on a set of predefined data with its correct result, such as forecasting store prices after seeing a large number of similar store prices. Unsupervised learning where the machine is learning using a set of the data, but without recognizing the correct result in advance, such as the grouping of data points into certain groups based on patterns detected automatically by the algorithm.
Deep learning algorithms can also predict a specific outcome after looking at a large number of similar patterns, and then the algorithm can automatically discover the most important features that characterize these patterns and then use these features and patterns for prediction.
Therefore, the greater the number of patterns that observed during the model training process, the more the likelihood of improvement in forecast results and their accuracy increased. Where deep learning has succeeded in many areas such as price prediction, forecasting degrees, predicting the rate of achievement of goals, and other areas.
Deep learning has also succeeded in many areas, including communications, banking, medical treatments, genetic fingerprints, and cybersecurity as well, whether in the field of image processing or audio processing.
Artificial intelligence languages
The LISP language is one of the oldest high-level programming languages as the scientist John McCarthy introduced its specifications in 1958 and it enables you to achieve what they told you that it is impossible as it characterized by rapid prototyping and the creation of dynamic objects with great flexibility. It is one of the most recommended languages for programming Artificial Intelligence because of its effectiveness in solving problems and an accurate understanding of what the programmer writes, which makes it different from other artificial intelligence languages at the present time, this language is used in most machine learning projects and inductive logic problems.
Python is one of the most important programming languages that used in the development and teaching of artificial intelligence. Through the Python language, machines that will run with artificial intelligence can now be programmed and taught, so any machine can programme to do the work that it wants to do and at the same time the machine learns by itself and develops itself and this is the future Artificial intelligence.
Python also owns several libraries specializing in artificial intelligence, such as the Numpy and Scipy library for scientific computing and advanced computing, and the Pybrain library, which is one of the most popular libraries used in machine learning.
Prolog is a high-level language and it is one of the most important languages of artificial intelligence and expert systems, and the secret of this language lies in the attempt of its developer to use explicit regional phrases to give orders to the computer and carry it out. It considered an interactive language between humans and computers as a natural language. Prolog also plays an important role in several fields, specifically artificial intelligence, and this comes because it deals with logical sentences in the form of relationships that clarify rules and facts alike.
Among the most prominent characteristics that characterize Prolog language and its uniqueness from other programming languages is the standardization, retraction, and self-recall feature, where the expressions on the command lines in this language made similar to each other in terms of structure and composition. The program can also execute the previous task in the event that one of the tasks fails it also has the feature of self-recall has become one of the most important programming languages in the search.
Among the features of Prolog is the ease of creating databases, the great ease in performing matching patterns by relying on the method of self-recall, the ability to build lists with flexibility and relying on logical methods to achieve the desired goal of the queries.
C ++ is a high-level language, and it is one of the distinct languages that are used in artificial intelligence applications. The most important characteristic of this language and making it highly efficient in use in artificial intelligence applications is the speed it enjoys, as it considered one of the fastest programming languages at all. C ++ has also proven the best language of the Brahma language, especially for developing games that depend on artificial intelligence.
Java is one of the most popular programming languages at all, it is an object-oriented language. It is one of the distinct languages that is used in artificial intelligence applications. Its large community that can help you with any problem that you may face, in addition to the ability of this language to expand distinguishes the Java language.
Artificial Intelligence Application Platforms
Google Cloud AI
Google Cloud AI Platform provides machine learning, deep learning, NLP applications, speech applications, and vision applications for cloud applications. It provides APIs for speech-to-text and text-to-speech using neural network models. Also, the speech-to-text API designed to convert speech to text supports 120 different languages. In addition to its speech recognition capabilities, it also features the capabilities of converting texts into audio files.
Microsoft Azure AI
The Microsoft Azure platform is a popular choice for developing artificial intelligence among software developers who offer the main AI capabilities, especially in the field of speech processing such as speech recognition, machine learning, vision processing, object recognition, and language capabilities, such as: machine translation and knowledge mining.
The IBM Watson AI platform enables integration and training on a flexible information architecture for developers to accelerate the development and deployment of AI application models.
It provides tools for developers, such as ready-made packages and detailed documentation, and developers can integrate Watson Assistant to build conversational AI-powered interfaces.
Artificial Intelligence Applications
The start-up projects related to the Internet of Things have become dependent on the use of artificial intelligence techniques in a very large way, and it is increasing. Where language processing, which is used in voice, aids, especially with the proliferation of smart speakers, those devices that can use and process audio data to perform various tasks according to what the user enters.
Because of artificial intelligence and the Internet of Things, for example, sensors connected to smart devices in the future will be able to collect various data and act on their own based on the data captured by these sensors, which will severely affect the way we deal with these smart devices. It will also allow the data extracted from sensors in smart devices that all connected to the Internet of things to assist service providers, especially electricity companies, in making better strategies for energy distribution and use.
Therefore, the integration of artificial intelligence with the Internet of Things will produce the superpowers of innovation in the future
Artificial intelligence models have become a prominent role in the decision-making process, as they simulate human mental capabilities and patterns of work, such as the ability to deduce, react, learn and gain experiences.
Where artificial intelligence intended to simulate and bypass the human mind through the capabilities of collecting and analyzing data, and making intelligent, accurate and high-level decisions.
Indeed, some applications have begun to appear that help the decision-maker to reach a decision based on data, analyzes and predictions with the ability to reach decisions with a high degree of reliability. Artificial intelligence has contributed to making decisions that contribute to economic and expansionary gains by relying on independent algorithms more than talented managers, today, management science with algorithms is among the skills that companies are keen to provide to ensure sustainability and accuracy in decision-making.
By extract data, AI applications are already using predictive analytics to make better decisions. These analyzes allow companies to predict events by looking at a data set and trying to accurately guess what will happen at a certain time in the future.
Data science is the science of using algorithms, methods, and systems to extract knowledge, statistics, and insights from structured and unstructured data. It uses analytics and machine learning to help users make predictions, improve optimization, and improve processes and decision-making.
The data science life cycle begins with collecting data from relevant sources, refining it, putting it into a format that machines can understand, and then using statistical techniques and other algorithms to find patterns and trends. Models then programmed and built to predict and forecast; finally, the results interpreted. Evident advances in artificial intelligence and machine learning have raised the standards for data science tools in various commercial and industrial fields.
Natural language processing is a sub-science of artificial intelligence that is a branch of informatics, and it overlaps greatly with the sciences of linguistics that provide the required language description for a computer. This science is the basis of the software industry that can analyze, simulate and understand natural languages.
Natural languages have different levels of analysis. As for written texts, their analysis passes through several stages that differ according to the method of analysis. However, one of the most common methods of analysis follows the following three stages: morphological analysis, syntactic analysis, and semantic analysis.
In addition, natural language processing has various fields such as automatic text reading, speech recognition, automatic text generation or speech, machine translation, understanding and answering questions, information generation, information extraction, text editing, translation techniques and automatic summarization.
Artificial intelligence has good results in image processing, as many scientists have used artificial intelligence to create a high-definition version of a low-resolution image. The technology for creating a large image size from a low-resolution image known as a super-single-image technique. This technique studied for decades, but has limited results.
However, smart systems took a new approach to give images a realistic texture when convert them from small to large using machine learning techniques, where artificial intelligence is applied and an adaptive algorithm for sampling the images takes advantage of the experience learned in order to improve the results. As the learning process here is very similar to the human learning process, as the algorithm gives the task of uploading a preview of millions of low-resolution images to convert them into high-resolution images, and then displaying the original images. The algorithm notices the difference, and then learns from its error. ”Once an algorithm trained, it will no longer need the original images.