In the mid90s, the chess computer “Deep Blue” beat the world champion, Garry Kasparov, heralding a new era of Artificial Intelligence. The inconceivable thought of a machine beating a man in an extremely complex board game was now a reality. This chess game was the turning point in the history of AI and adaptive computer systems.
We have come a long way since the 1996 Deep Blue that was based on a powerful but conventional IT architecture. The focus of machine learning today has shifted to newer sub-disciplines such as artificial neural networks. An artificial neural network is a type of self-learning system that does not depend on fixed, predetermined prediction models. It defines, reviews, and refines its decision patterns based on training data by simulating the way the human brain analyzes and processes information.
Initially, this kind of self-learning system was an extremely complex and expensive solution that could only be implemented in specialized data centers. Due to the continuous development of hardware and increased efficiency through research, these innovations have gradually found their way into our everyday applications. In particular, increasingly powerful graphic cards with multi-core technology are used to provide the necessary computing power for more demanding applications.
What can neural networks do in practice?
One clear strength of neural networks lies in the field of image recognition. Primary training data is key for optimal results. With an adequate amount of training data, neural networks can recognize patterns and elements in the image. Depending on the quality of the data, systems can evolve the ability to detect complex objects like faces or street signs and classify them according to their unique application.
Although neural network performance in chess is already evident, we can go a lot further. There are no limits to the possible implementation scenarios.
New applications available indicate the real possibilities this technology has to offers.
In the medical industry, neural networks help doctors to evaluate x-rays to detect certain types of cancer early. Recently, scientist have designed a computer model to detect COVID-19 in x-rays. The strength of neural networks is also evident when we look at robotics, self-driving vehicles and other applications that need to identify patterns and use them in large data sets. Ultimately, virtual reconstruction of the human brain is a vision that some see as possible.
You can benefit from the latest technology
Our experts are constantly monitoring and contributing to the latest developments in the field of machine learning and neural networks. We are working to make the benefits of this technology available to the economy and for SMEs. If you are looking for applications that suit your individual requirements, contact us. Together we can specify how your company can benefit from neural networks.