For all those who are also on their way to discovering AI for themselves and do not have to deal with it every day, we clarify the most important technical terms in this article. Are you wondering what data mining actually means and what Python is all about? You'll find it here, simple and straightforward.
Artificial intelligence (AI) is a mega trend and is increasingly conquering a wide variety of areas as a relevant technology of the future. Rapidly advancing technical developments and the enormous increase in available data volumes due to the digital transformation enable new systems and tools to contribute to optimisation. With the help of programmed algorithms, data-supported strategies are being developed that redefine content. There are also many new possibilities in marketing, which is why we as an agency are increasingly dealing with the topic in order to accompany marketeers and clients here in the best possible way. In our glossary for Artificial Intelligence, you will find all relevant vocabulary explained briefly.
A standardised process that a machine can follow step by step to learn how to complete a task.
Artificial intelligence uses computers and machines to mimic human intelligence. Data sets and computer science are combined to simulate the human mind. This involves transferring the learning and communication skills, as well as the problem-solving and decision-making abilities of humans, to machines so that an AI can solve problems and act autonomously.
A machine is said to be autonomous if it can perform its tasks without human intervention.
Enormous amounts of data that are too huge or too complex to be used by conventional data processing applications. Big Data technologies can be used to store, analyse and process such data sets.
A chatbot is a programme that mimics a human conversation and is designed to communicate in the same way with humans through text or voice commands.
A field within artificial intelligence primarily concerned with the development and analysis of machine learning algorithms.
The systematic analysis of large data sets using complex computational methods to discover new patterns that could improve the model. This is done by drawing on insights from computer science, mathematics and statistics to identify new cross-connections, patterns or trends.
A collection of related data, usually with a consistent order and tags. They are used so that AI can learn from them.
A discipline of artificial intelligence and a specific method of processing information that mimics human learning behaviour. AI does not learn from algorithms programmed for a specific task, but works with neural networks that mimic the human brain and combine different algorithms. This makes it possible to process even unstructured data, so that already learned skills can be enriched and linked with new content. Without training by a human, the system itself finds distinguishing features in the data. Deep learning is a subcategory of machine learning, but requires much more data.
A generic term for different types of learning algorithms, including machine learning and deep learning.
This subfield of AI works with algorithms that are adapted by the system itself after it has received feedback from a human. Initially, structured and categorised data is input into a system to train it how to classify such new data so that programmed activities can subsequently be performed.
A broad term that refers to the product of AI training that results from running a machine learning algorithm on training data.
A widely used programming language in AI research with a very wide range of machine and deep learning frameworks.
Our AI glossary does not claim to be complete, as it is allowed to grow: we will continue to update it and add new terms.
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