Behind the hype
A short history of AI
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?
The history of AI
Intelligence is a tricky thing
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.
Data, storage, algorithms – The three pillars of AI
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.
"Not at all. I like to work with people"
Computer HAL 9000 (2001: A Space Odyssey)
AI – The theories
Ten Theories on Artificial Intelligence
How do we approach a topic whose effects range from the payment process at the supermarket checkout to the depths of medicine? That alter work and private life alike? With radical simplification – and an eye on what is feasible. That‘s the idea behind our ten theories on Artificial Intelligence.
They highlight the benefits, prejudices and practices about AI: acquiring data, developing applications, training models. However, it’s also about discovering new things, involving employees, inspiring customers. It is the point where all these elements meet that you will find all those who successfully plan, implement and use AI applications.
Learning and enabling others to learn
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.
AI – The technologies
How AI applications can produce what they do, regardless of whether they use patterns, language, images or words.
Do you have any questions about AI?
Are you wondering what possibilities AI can open up in your company? Would you like to learn more about its applications and the technology? We cannot give you a pack full of stock responses, but we can share with you our specialist knowledge, our curiosity about your company and our passion for technology.
We would be delighted to talk to you.Contact
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The AI Process Model: Building AI-based Systems
Developing data-driven AI applications requires a different project structure and expertise than traditional IT solutions. Our "Building AI-based System" fulfill these differences. Coming soon ...Read more