The idea of AI predates the first computers. The initial euphoria soon gave way to a longer period of disillusionment. But AI technologies are now poised to play an important role in more and more areas of life. Which minds shaped this progress? What technologies are working in the background? What are the factors that will shape its development?
Opinions differ vastly when it comes to defining artificial intelligence. However, in day-to-day business operations, the perfect definition of AI is not what’s important. It’s about the right application. We consider AI systems to be systems that can automatically or independently take decisions and respond to input such as images, the written word or even spoken language.
AI technologies are feverishly toiling in the background, often without the user being aware of them: from route planning and automatic translations, to floods of messages on social media.
Nearly all of the data that were ever created in human history have been produced within the last few years – whether by human, machine, sensor or website input. At the same time, it is becoming increasingly economical to store these data. The storage costs for data are falling tremendously, and the areas of computational power and algorithms continue to see advances. Special graphics processing units (GPUs) and methods such as deep learning reduce the time and effort required for developing new applications.
"Have you ever suffered from the fact that, despite your enormous intelligence, you depend on people to carry out your tasks?"
"Not at all. I like to work with people"
Computer HAL 9000 (2001: A Space Odyssey)
It is not possible to have intelligence without learning or modelling patterns. And it’s no different in the case of artificial intelligence. Machine learning (ML) is the ability to automatically learn a model by using data. There are the following types of ML: Experts determine the correct decision for the process in supervised learning by providing a set of training data each time. In unsupervised learning the system analyses the data based on their similarities without needing experts to input training data. Reinforcement learning is when experts consolidate processes, which learn by direct feedback and not through the input of training examples.
Searches for patterns or data points that are important in the context of the application and are generated by disturbances or other influences. Frequently, these patterns are only produced by data sequences over time or as a function of environmental variables.
Image recognition uses algorithms to attempt to identify objects in images and assign these to a category. It enables systems to observe their surroundings.
Text-based dialogue systems that consist of text input and output. They are based on analyses and production of natural language.
An umbrella term for different application areas of ML that deal with the interpretation of recurring patterns. Examples are image recognition and speech-to-text.
Text generation creates audio signals to allow the system to transmit information back to the user via speech. ML methods are used here, making it possible to use stylistic devices, emphasis and suchlike.
Systems recognise the connections and meaning of the spoken word. Language is not unambiguous. It is characterised by stylistic devices that are easily misinterpreted. This means communication with machines is becoming more and more natural.
Applications convert the spoken word to written text. To be able to do so, the system needs to suppress background noise and recognise words despite a variety of pronunciation possibilities.
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Artificial intelligence is not a replacement for human discussion
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