Artificial intelligence is the dominant topic in companies worldwide. Whether it's about marketing, Industry 4.0, or software development, AI’s reference cannot be missing. But what are we actually talking about here? And what is really behind the term? We explain what AI, machine learning, and deep learning are all about.
The first approaches to artificial intelligence through machine learning already existed in the 50s and 60s, but the results were not particularly promising due to the lack of computing power. In the following years, the focus was exclusively on rule-based systems, which proved to be a dead-end in the 90s. In the early 2000s, Machine Learning experienced a renaissance due to the high computing power and new algorithms now available. Deep Learning and Reinforcement Learning are essential components of Machine Learning and are used, for example, for text and image processing.
Machine Learning is very simple. The goal is for a system to learn independently based on existing data and improve in the process. This independent learning requires mathematical calculations and appropriate development of algorithms in advance to build machine learning. In machine learning, computing systems receive data. Algorithms then analyze this data. This analysis primarily involves pattern recognition.
Machine learning is already widely used in practice today. For example, self-driving cars are controlled based on machine learning. Voice assistants also use this form of AI, for example, for personalized wording suggestions in messages.
In sales and marketing, machine learning plays an important role. AI analyzes sales performance and customer behavior to use the results to optimize purchasing or marketing measures. In online advertising, machine learning is indispensable today, for example, to minimize wastage with personalized advertising.
Deep Learning is a method of machine learning. In this process, so-called "neural networks" resemble the synaptic connections in the human brain. In the first layer, the so-called "input layer", raw data is processed. In further layers of the network, the data is processed to represent more abstract concepts.
The special feature of machine learning is that the system makes its own decisions and learns as the volume of data increases. Deep Learning can also handle unstructured data such as text, images, audio, or video. However, Deep Learning needs more data than other Machine Learning algorithms. But in return, more complex relationships are learned.
With artificial neural networks, Deep Learning is used to analyze and prepare all kinds of unstructured data. For example, Deep Learning enables image recognition or speech recognition. The technique works in a similar way to human learning. Deep Learning is often used in IT security and for the provision of digital assistants such as Apple's Siri or Amazon's Alexa.
Reinforcement learning is part of machine learning. In contrast to other methods, it does not rely on existing data. Instead, the model learns about its environment by performing experiments. In such experiments, strategies are optimized by trial and error. The goal of reinforcement learning is to develop the best strategies to solve a given task. The system learns by itself and improves its strategies over time.
Reinforcement learning, for example, optimizes traffic flow in cities by improving traffic light control. Thus, AI can help save energy and reduce emissions. In the financial world, reinforcement learning now plays an important role. In some areas, AI decides independently within milliseconds, whether to hold or sell shares. In the healthcare sector, reinforcement learning is already being used to suggest or analyze treatment methods.
It will be years before an AI can perform the same computational steps as, or even better than, a human brain - if it ever gets that far. Nevertheless, the evolution toward a self-learning system has succeeded in the last decade alone. As computing power increases, AI will continue to improve.