A model is a representation of a certain area or object in simplified form. They can be based on an already existing real object or they can be chosen or constructed completely freely and abstractly.
A model is based on the freedom of the creator and his or her desire for representation, but also follows clear basic rules. Three factors are defined that apply to a model:
Factor 1 refers to the Illustration representing the model. This is related to a natural or artificial original. This means that a model reproduces something that already exists.
Factor 2 describes the Nature of a model. It generally refers only to the relevant facts of an original and thus not to all existing attributes. Which facts must be considered relevant is determined by the creator of the model.
Factor 3 denotes the Sense of a model. In general, it cannot be clearly assigned, but rather serves a substitution function. They serve as a simplified illustration and thereby refer to essential W-questions, for whom, when or also for what something is to serve or be used.
How does modelling work?
Model building describes the process in which a model is created. In this process, relevant data is collected, which is later incorporated into the model, and the exact structure is determined. Depending on the type of model, it may well be that a longer observation period must precede this in order to be able to achieve suitable results. These observation periods are primarily necessary whenever a model also relies on statistical values and analyses.
The idea of modelling aims at the Creation of a clear model that can be used to find solutions to problems more easily..
Types of models
The term "model" is used in many areas and fields and often describes very different things. Typical areas of application are science, mathematics and computer science.
In science, a model is increasingly used to explain known topics. Facts or known objects serve as a basis and are shown in a model for simplified explanation.
In mathematics, a model is often defined by the use of formulae. It represents the mathematical logic of various topics that are to be illustrated. How far this spectrum is extended, i.e. how many mathematical topics are included in the model, depends on the creator himself. All models serve a clear purpose in mathematics and that is to prove a certain mathematical logic.
A model in computer science is increasingly used for a representation of a section of reality in order to determine new approaches to solutions for existing problems. Various sources of information are used and processed for this purpose. These model structures are also known as domain models. They are used to create new software or architectures that can be used in computer science or other scientific fields.
Machine vision is a technology that, with the help of Artificial Intelligence (AI)detects objects and takes pictures. Vision is technology and at the same time a prerequisite for industrial automation. It is a sub-area of Computer Vision.
An algorithm is used to classify each individual photo and the objects displayed are given their own keywords. Good object recognition is very helpful for automated processing. This way, optical signs, patterns and objects are recognised.
Using computer vision models, objects can now be classified and localised. Large amounts of data are searched to create motion analyses, image descriptions and image reconstructions.
Using these technologies and methods, the Product quality significantly improved and production significantly accelerated become. Machine vision thus contributes significantly to improving the work and process quality of industrial plants. Further applications can be found in security technology (biometrics, camera monitoring) and in material testing such as quality assurance of automation and traffic technology.
What technology does machine vision need?
In the context of machine vision, in addition to automated software based on artificial intelligence, excellent and novel technical solutions are always sought. Important components of optical image capture are:
Frame grabbers (circuit unit)
Control elements (for data transmission or communication)
Camera types exist for a wide variety of applications. These range from simple two-dimensional images to taking thermal images, thermal anomalies and X-rays to detect microscopic defects and metal fatigue.
What systems and solutions exist on the market?
There are different systems for machine vision. So can extracts extensive information from images and videos with Microsoft Azure be used. With optical character recognition (OCR), printed or handwritten texts are recognised and extracted from images. With image analysis, you can benefit from an extensive ontology that directly uses more than 10,000 concepts and objects to add value to virtual resources.
Other excellent solutions for image recognition are available from companies such as Matrox and Power Arena.
Machine translation Refers to the automatic translation of texts by a computer program.. The two best-known machine translation tools are currently Google Translate and DeepL.
How does machine translation work?
Ideally, good translation software not only provides accurate and natural-sounding translations, but is also easy and intuitive to use. That is why most programmes are designed similarly and have the following features:
The user enters the text he wants to have translated in an input field. The text is translated in the background and the result is displayed in an output field. Different providers use different methods to translate the texts.
Neural machine translation
Both the Google translator and DeepL rely on this machine translation technique. A neural network, i.e. a kind of artificial intelligence, analyses various bilingual texts and tries to recognise and learn the connections between the two languages. These translators can translate not only single words, but longer texts with several sentences. The tools based on neural machine translation are also often more precise than the competition and can form authentic-sounding sentences. Their biggest disadvantage is that the developers of the tools can hardly trace how the learned results came about.
Direct machine translation
Other translators work with direct machine translation, such as Pons or Leo.org. Here are, based on a dictionary, translates individual words from the source language into the target language and stored in the system. When queried, the words are loaded from the system and reproduced in the target language. Direct machine translation was also used to programme the first electronic translators, such as the English-Russian translator of the US military. For single word queries, this technique is advantageous, because if the translations have been neatly stored, the most applicable words are always output. For longer texts, however, this method is not dynamic enough and the translations can quickly appear artificial.
Other machine translation methods
In addition to the methods mentioned above, there are also five other common translation techniques:
The Transfer Method
The Interlingua Method
The example-based machine translation
The statistical machine translation
Machine translation with human help
Who is currently the best provider for machine translation?
Finally, the question remains as to which translation service is currently the best on the market. It is difficult to measure the linguistic quality of the respective tools, but many established IT magazines agree:
DeepL is a German translation service by the company DeepL GmbH, which is based in Cologne. The translator went online in August 2017 and quickly gained attention for its outstanding and precise translations. With its eloquent and always accurate formulations, DeepL has technically overtaken its biggest competitor to date, Google Translate. The translator also offers the functions of uploading and translating various file types directly via drag-and-drop and using synonyms for various words and phrases.
What are the advantages and disadvantages of Machine Translation?
A Markov Chain is a special stochastic process. The aim is to give probabilities for future events when applying this chain. This Markov chain is defined in such a way that even with knowledge of only a limited past history, a forecast of a future development is just as good as with knowledge of the entire past history of a process.
Markov chains can be distinguished according to different orders. Thus, a first-order Markov chain is defined in such a way that the future state of a process is conditioned only by the current state and is not influenced by past states. The mathematical formulation may require only the notion of a discrete distribution and a conditional probability in the case of a finite set of states and the concepts of a filtration and a conditional expectation in the continuous-time case.
Markov chains can be excellently used to model random state changes in a system, if there is a reason to assume that the state changes only influence each other over limited periods of time or that they are even memoryless.
There are discrete finite Markov chains and discrete infinite Markov chains.
What are Markov chains used for?
Markov chains are simple and descriptive models to represent real-world processes mathematically accurately. For example, given known and assumed constant probabilities, it is possible that the probable state of a system can be predicted for any future. Markov chains are the basis for stochastic processes, which on the one hand can be based on memoryless randomness, and on the other hand can be possible with state transitions to given probabilities in each case. The Markov chains can model general stochastic Petri nets and rankings based on subjective recommendations.
What are the areas of application?
Markov chains are used in spam filters, for example, which are far more effective than Bayesian filters. Queuing processes and probability distributions of objects in moving systems can also be calculated very easily. In addition, it is possible to create an objective ranking for the entire system based on many subjective recommendations.
Google's PageRank is also based on Markov chains. Classic applications are queues and exchange rates. The behaviour of a dam can also be modelled with the help of a Markov chain. It is also possible to model a speed control system for motor vehicles. The analytical evaluation of mobility algorithms, such as random walk, is also possible.
Population dynamics can also be modelled to predict population growth of humans or animals. Brownian molecular motion can also be modelled. Statistical programming and the simulation of equilibrium distributions with the software "Statistik Software R" are particularly relevant.
Machine Learning is a field of study that deals with algorithms, statistical models and computer systems. The goal of machine learning is to give computers the ability to learn some tasks explicitly programmed for them. Because machine learning uses so many statistical methods, it is often referred to as statistical learning or applied statistics. Unlike statistics, it uses more computer science (programming) and typically has a slightly different goal. Nevertheless, statistics and machine learning share many common methods.
Machine Learning is a discipline that deals with the study and production of algorithms, statistical methods and computer systems that are used to perform certain tasks without explicit instructions. With machine learning, we are able to develop computer programs that act autonomously and intelligently, predict the future and even automate certain tasks for us.
Machine learning and the increased availability of data are the main drivers of the development of AI systems. Almost all of the major breakthroughs in the field of artificial intelligence and the leading AI systems available today have been developed using machine learning methods. This trend will continue as the data that is the main fuel of machine learning becomes more widely available.