Text-to-speech: Listening instead of reading
Text-to-speech (TTS) – this term is virtually self-explanatory: It is the conversion of text into spoken. The programs used are being continually further developed words. Although there are still no applications today in which the machine origin of the spoken word is not discernible, the technological progress is unstoppable. What are the advantages of text-to-speech systems? Vision impaired people can benefit from those systems, besides they can also be used by companies as a means of expanding their outreach.
Read moreApplication areas of Natural Language Processing (NLP)
Natural language processing, also known as NLP, describes the machine processing of natural language. NLP is a sub-field of artificial intelligence (AI). Humans are more and more frequently coming into contact with AI in their daily lives – whether with Alexa at home, with OK Google on their smartphone or when making a call to customer support. Today, humans are speaking more often with machines. And the areas of application of NLP are steadily on the rise.
Read moreRealistic training data for machine learning
Data are the foundation for training algorithms. The more realistic the data, the better the results. This is because artificial intelligence is based on precise and reliable information for training its algorithms. This is obvious but it is often overlooked. The training data are realistic when they reflect the data that the AI system gathers in real operation. Unrealistic data sets prevent machine learning and lead to expensive false interpretations.
Read moreAI solutions for eCommerce
Artificial intelligence in e-commerce is more than a hype. Developments that are still in their infancy at present will probably be the norm in a short space of time. The successes are measurable and speak for themselves. But how can one benefit from AI in e-commerce? Simply by being up to date and keeping pace with the trends in AI solutions.
Read moreTraining data for AI: There is more to it than quantity
Artificial intelligence is being used in an increasing number of areas of application. Machines require large amounts of data to perform similarly to human beings. Quantity is what counts. That makes sense, in particular when addressing challenging problems and complex issues. However, the quality of the data is also significant – especially for training data that is used in machine learning. With this information the algorithms can develop themselves and machines learn how to learn.
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