The article artificial intelligence: What are neural networks? Maria Gramsch first appeared on Basic Thinking. You always stay up to date with our newsletter.
A neuronal network is a model of machine learning and thus a core area of artificial intelligence. But what are neural networks Exactly and how do you work?
In many ways, the human brain serves as a model for artificial intelligence. Above all, its neuronal structure is used for AI methods such as Deep Learning.
Neuronal networks are a kind of algorithms of machine learning that are inspired by the functioning of the human brain.
KI: What are neural networks?
The human brain is a highly complex organ that consists of billions of nerve cells, i.e. neurons. Research currently assumes that a human brain 86 billion neurons united. They are connected to each other via synapses and exchange electrical and chemical signals. These processes form the basis for human thinking.
Artificial intelligence has taken this highly complex organ as a model. Their neuronal networks do not consist of biological, but of artificial neurons that are organized and connected in several layers.
In the AI, neural networks are used primarily for very complex tasks due to their performance. This can be image or speech recognitions, but also natural language processing.
How is such a network built?
Neural networks process information in layers and typically consist of three layers: the input layer, the hidden layers and the output layer. You can learn to recognize patterns and solve complex problems using training.
In the Input layerthe input layer, a neuronal network takes up the raw data. This can be, for example, the words of a entered text or the pixel values of an image.
In the hidden layers, the Hidden Layersthe artificial neurons are used. These carry out mathematical calculations. It is decided which neurons are activated and how strongly a signal is passed on.
The output layer, i.e. the Output layeris what users of Chatgpt and Co. receive, for example, to a question as an answer. This layer contains the final result of the calculation.
How do neural networks differ from the human brain?
Neuronal networks are inspired by the human brain, but have far less flexibility and efficiency. You can already solve specific tasks such as image recognition better than humans. However, they lack real awareness and the ability for creative problem solutions.
The human brain and neural networks are similar in its structure based on neurons. Both systems can adapt through training or learning. In addition, both complex patterns can recognize in data.
However, neuronal networks lack flexibility. While the human brain is highly networked, flexible and adaptable, the structure of neuronal networks is strictly bound by the layer structure.
The human brain is also the neuronal networks in terms of energy efficiency. Because these often require enormous computing power and a lot of energy. This also applies to the learning style. Because while the human brain can learn intuitively from a few examples, neural networks need huge amounts of data for your training.
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As a Tech Industry expert, I believe that artificial intelligence, particularly neural networks, have revolutionized the way we approach problem-solving and data analysis. Neural networks are a key component of AI, inspired by the structure of the human brain. They consist of interconnected nodes that work together to process information and make decisions.
Neural networks can be trained to recognize patterns in data, make predictions, and even learn from experience. This makes them incredibly powerful tools for tasks like image and speech recognition, natural language processing, and autonomous decision-making.
While neural networks have shown great promise in a wide range of applications, they are not without their limitations. They require large amounts of data to be trained effectively, and their inner workings can be complex and difficult to interpret. Additionally, they are vulnerable to bias and may not always produce accurate results.
Overall, I believe that neural networks represent a major breakthrough in artificial intelligence and have the potential to drive innovation and transformation in countless industries. However, it is important for developers and users to approach them with caution and ensure that they are used responsibly and ethically.
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