{"id":68444,"date":"2021-06-14T10:59:18","modified":"2024-04-17T16:34:30","slug":"what-is-ai","status":"publish","type":"post","link":"https:\/\/www.tele-online.com\/en\/news\/what-is-ai\/","title":{"rendered":"What is AI?"},"content":{"rendered":" \n

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AI – Artificial Intelligence (AI) – is the attempt to simulate human learning and thinking with a computer system so that it can learn and solve problems on its own. This sounds like a big task, because the question of what intelligence is in the first place is not so easy to answer. Our brain is extremely complex \u2013 just intelligent. It can learn, reason and become smarter through knowledge – in other words, it encompasses a whole bundle of abilities that science must first decipher bit by bit in order to be able to reproduce them artificially. AI thus encompasses learning (acquiring information and rules for using the information), reasoning (using the rules to draw approximate or definitive conclusions), and self-correction.<\/p>\n

 <\/p>\n

AI or not AI?<\/h3>\n

What distinguishes an AI from a simple program? A simple program is based on rules that are always processed in the same way, e.g. if X occurs, then do Y. With artificial intelligence, each individual step is no longer predetermined, but it functions with an algorithm that can set its own steps. This is illustrated by the example of image recognition: images, videos or machine data are so-called unstructured data. Since the advent of IoT and the mass availability of (cloud) storage, they already account for a good 80 percent of all data. An AI can learn from a number of existing images what a particular person looks like and then apply that rule to new images and recognize that person. It can do the same with billions of other faces in a fraction of a second by finding patterns and inferring actions. AI can thus also react to a certain extent to previously unknown situations and learn from experience. They are thus able to draw information from data that a human could never grasp, for example because they are too numerous or the underlying patterns are too complex. But, even though AI today are already capable of learning, they are not yet intelligent in the true sense. They can process data tremendously well, recognize patterns and make better and better decisions. However, they do not yet possess a real mind and can therefore only provide answers to the specific questions for which they have been programmed, such as Siri, Apple’s assistance system. But how does an AI learn now?<\/p>\n

 <\/p>\n

Learning and recognizing like a brain<\/h3>\n

As mentioned earlier, the basic idea of AI is to teach machines the functions of the human brain – learning, judging and problem solving. In doing so, an AI system learns from a number of examples, and can generalize them after the learning phase. Machine learning is used, for example, to automatically filter spam emails, predict customer churn or price trends, or make purchase recommendations.<\/p>\n

Currently, there are different ways in which this machine learning can proceed. In Supervised learning, for example, a data set with already known outputs is used for training. For this, data sets are labeled in such a way that patterns can be recognized and used to label new data sets. This can be used to compute problems such as regression, classification, recommendations, and missing data in statistical calculations (imputation).<\/p>\n

In Unsupervised Learning, algorithms can themselves detect patterns in data and build a statistical model without labeling the data first. Unsupervised learning algorithms typically require a large amount of data to perform clustering and thus make an appropriate prediction about an unknown or an unseen data set.<\/p>\n

A third option is learning by reward and punishment (Reinforced Learning), where the algorithm independently detects whether the learning component benefits the entire system (reward) or not (punishment).<\/p>\n

 <\/p>\n

Where AI technology comes into play<\/h3>\n

Machine Vision<\/h3>\n

Machine vision lets computers see. That is, it captures and analyzes visual information using cameras, analog-to-digital conversion and digital signal processing. As a result, the process can be used for a range of applications, such as signature identification, inspection processes in manufacturing or medical image analysis.<\/p>\n

 <\/p>\n

Natural Language Processing (NLP)<\/h3>\n

NLP attempts to capture human speech and process it using rules and algorithms in a computer-based manner. NLP must recognize, analyze, and “get the meaning” of both spoken and written language. This also requires the comprehension of complete textual contexts and facts. The particular challenge here is the complexity of human language and its ambiguity. One of the best-known examples of NLP is spam detection, which scans the subject line and text of an e-mail and decides whether it is junk.<\/p>\n

 <\/p>\n

Robotics<\/h3>\n

Robots are mostly used where activities are difficult or dangerous for humans to perform. They can also perform tasks with extreme precision, even if they have to do them a thousand times. For example, robots work in assembly lines for automotive production or in aerospace, where heavy objects are moved or inhospitable conditions prevail. More recently, machine learning has been used to build robots that can interact with humans in social environments, for example as assistants.<\/p>\n

 <\/p>\n

AI-powered apps<\/h3>\n

Most of us now carry AI in our pockets, because a large number of apps are also AI-supported. They are often programmed in such a way that they can recognize problems relatively independently and offer the user suitable solutions. Google Health, for example, has developed an app that can determine whether you have skin cancer or not based on three photos of a skin change.<\/p>\n

In industry and facility management, pattern recognition with neural networks is used for predictive maintenance. For example, an AI app can recognize certain patterns that occur when a motor or pump stops running smoothly and, based on this data, suggest appropriate maintenance intervals to avoid downtime in advance.<\/p>\n

The app “BirdNet” from Chemnitz University of Technology and Cornell University (USA) can currently identify around 3,000 bird species based on their song using an AI algorithm. The possibilities for AI are therefore almost endless and the technology has long since arrived in everyday life.<\/p>\n

Are you already using AI for your products and solutions? Maybe you already have an idea and are looking for open companies that want to share their experiences around IoT and AI with you? Contact us, we look forward to your inquiry!<\/p>\n

 <\/p>\n

More about AI<\/h3>\n

https:\/\/www.computerweekly.com\/de\/definition\/Kuenstliche-Intelligenz-KI<\/a><\/p>\n

https:\/\/www.alexanderthamm.com\/de\/blog\/ki_artificial-intelligence-ai-kuenstliche-neuronale-netze-machine-learning-deep-learning\/<\/a><\/p>\n

https:\/\/www.wfb-bremen.de\/de\/page\/stories\/digitalisierung-industrie40\/was-ist-kuenstliche-intelligenz-definition-ki<\/a><\/p>\n

https:\/\/news.sap.com\/germany\/2018\/03\/was-ist-kuenstliche-intelligenz\/<\/a><\/p>\n

https:\/\/www.computerwoche.de\/a\/ansaetze-und-vorgehensweisen-beim-maschinellen-lernen,3331036<\/a><\/p>\n","id":"","linie_oben_einblenden":false,"linie_unten_einblenden":false,"container":"normal","hintergrund":"weis","box":false,"abstand_oben":"","abstand_unten":""}],"seo":[{"googleAuthor":"","googlePublisher":"","schemaName":"","schemaDescription":"","schemaImage":false}],"type":"automatisch","footer_link_block":false,"news_max_hohe":"70vh","footer":{"footer_headline_powerd_by":"Powered By TELE","footer_text_powered_by":"","footer_logos":null,"footer_mylayout_logos":"normal","footer_text_oben":"

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