Learning through neural networks
Before we examine artificial intelligence, we must first understand how the human brain works. Neurons in the brain communicate with each other through electrical signals, forming a neural network. When we learn, new connections between neurons are formed, which allows for complex thought processes.
Artificial intelligence attempts to mimic human learning with artificial neural networks. Essentially, human thinking is transferred to a software, albeit simplified.
How a machine becomes intelligent
A developed AI can solve problems independently without human assistance. However, there is a long way to go before it can achieve this. Training data, such as images for automatic image recognition or words for speech recognition, serve as a basis. Algorithms allow software to learn from the patterns and regularities of the training data, generating knowledge from experience. This process is called machine learning. In simple terms, AI is a software that enables computers or robots to solve complex problems and tasks based on data.
This type of AI is called weak AI, as it only learns from algorithms. Strong AI can solve all kinds of problems, like robots we see in science fiction movies, but strong AI has not yet been achieved in reality.
Industrial use of AI
Artificial intelligence is more prevalent in our daily lives than we realize, such as navigation systems, recommendation systems (individualized advertising on platforms like Amazon or Netflix), chatbots, voice assistants, and more.
In industry, AI is already widely used, such as with intelligent software, robots in production, or machines that can notify when they require maintenance. For example, the Salzburg ski manufacturer ORIGINAL+ uses software that determines the best ski for a user based on their data. The Salzburg IT company Porsche Informatik uses AI software to simplify routine processes, such as maintaining the online platforms of car dealers. The Salzburg software company Blumatix has developed a solution for automated invoice recognition with its AI.
No artificial intelligence without data
The basis of every artificial intelligence is data. The advancing digitization makes access to data easier. Large amounts of data from the internet and mobile phones, social media, credit and customer cards, and many other sources are stored, which also leads to the term “big data.” The data volume is too large and complex for humans to process, so digital solutions are used.
Important points for companies in data processing include having access to the data they want to process, such as customer data or machine data. The quality of data is crucial to the success of AI. A data strategy and “clean” master data create the foundation for this.
AI in Austria and Salzburg
AI applications are strongly trending in Austria. A 2020 study by Interxion showed that 5.3 percent of Austrian study participants already use AI in various application fields. 18 percent use the technology in a first application scenario, while another 34 percent are currently testing AI. In addition, 12.7 percent plan to use artificial intelligence in the short term. Looking ahead to the next two years, 30 percent plan to use AI for a first application case by then.
According to Austrian companies, the biggest obstacles to implementation are costs (66 percent), lack of technical expertise (56 percent), and a lack of integration into the corporate strategy (41.3 percent).
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