Artificial intelligence (AI) is broadly defined as the automation of intelligent behavior and machine learning. AI enables machines to process tasks that require human-like intelligence to solve. The basic idea is to create an approximation of important functions of the human brain through machines. In doing so, machines are enabled to learn, make judgments, and solve problems through various methods such as machine learning, NLP (natural language processing), and deep learning.
In artificial intelligence, a fundamental distinction can be made between “strong” and “weak” AI. Weak AI are systems that are mostly rule-based and limited to a clearly defined task area, such as the recognition and processing of language. The majority of “weak” artificially intelligent systems, build on the principle of machine learning, which has become popular especially in application situations where large amounts of data are to be statistically evaluated and searched for patterns – e.g. spam filters, autonomous driving systems or medical diagnoses. So-called deep learning, which is expected in the future, is a subfield or further development of machine learning and could become the basis of “strong” AI.
“Strong” AI has intellectual skills that rely on Deep Learning algorithms on artificial neural networks whose structure and function are modeled on and at least equivalent to the human brain. Their insights should not be limited to one domain, but should be applicable to any number of other domains.
With a large information base and the structure of neural networks, Deep Learning systems can independently link learned content with new content. In addition, they are able to make analogies without being confronted with certain key terms in the context of a question. The more meaningful data such systems process, the more reliable they become. Thus, the “learning effect” ideally leads to continuous self-optimization. Deep Learning algorithms are based on artificial neural networks whose structure and mode of operation are modeled on the neurons of the human brain, which means that these Deep Learning systems can independently link learned content with new content in interaction with a large database.
Moreover, they are able to infer analogies without knowing specific key terms in the context of a problem. The more meaningful data such systems process, the more reliable they become. The “learning effect” thus leads in the best case to constant learning optimization.
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