Have you ever wondered? How can AI learn, process, adapt and improve? The answer lies in neural networks: a sophisticated mechanism of algorithms inspired by human brain. Here, starting from their history, we try to understand how they work in a simple and direct way, without going into too much detail reserved for professionals. So if you don't know the related techniques, don't worry.
Whether we like it or not, in fact, artificial intelligences are and will be increasingly packaged and distributed to become accessible to everyone. Having a basic idea therefore becomes fundamental both to use them, to reject them, and to interpret their products and results.
Let's get started then.
Content index
What are neural networks? When are they born?
Neural networks are computational models designed to simulate the behavior of the human brain. Their main purpose is to process information similar to how our neurons do. Each network is made up of layers of nodes, called artificial neurons or units, connected to each other. Each connection has a weight that affects the strength of the transmitted signal. As data passes through the network, these weights are adjusted through a learning process, allowing the network to recognize patterns and make predictions.
Neural networks aren't exactly new today. Indeed already in the 40s scientists like Warren McCulloch e Walter Pitts proposed the first theoretical models, but only today thanks to advances in calculations and the greater availability of data have they become possible.
Process and types of learning
Neural networks learn through training, during which they are usually exposed to large amounts of labeled data and used to identify patterns and improve predictive capabilities. This process relies on machine learning algorithms that adjust the weights of connections between neurons to minimize errors in predictions.
In other words, neural networks try and try again, continually adapting until satisfactory results are obtained.
However, they are not always guided in the process. Sometimes, in fact, the data may not be labeled. This other type of learning allows the neural network to identify patterns autonomously, i.e. without external inputs.
Another type of learning is based on a system of rewards and punishments, where the network learns through interaction with the environment.
Practical applications of neural networks
Among the most common uses of neural networks are the image recognition and computer vision. These systems can identify objects, faces and even diagnose diseases from medical images.
Another fundamental application is natural language processing, i.e understanding of human language. This field of AI is the basis of the virtual assistants, generative artificial intelligences, automatic translators and to the analysis tools for Social.
Other applications include weather forecasts, markets, fraud detection, supply chain optimization, advanced chatbots and industrial automation.
Utopia or dystopia?
To conclude we offer you two opposite paintings about the future prospects of neural networks in light of their practical applications. To simplify, we propose extreme scenarios: one utopian, the other dystopian. However, as almost always happens, the truth is in the middle.
In a utopian vision, neural networks significantly improve the quality of life, leading to extraordinary advances in medicine, education and environmental sustainability. In fact, medical diagnoses become very precise and timely. Personalized educational technologies make learning accessible to all. Innovative solutions for natural resource management help fight climate change.
In reverse, in the dystopian vision, the uncontrolled use of neural networks leads to privacy issues, economic inequality and job losses due to automation. Mass surveillance becomes an everyday reality, AI-based decisions amplify existing biases, and reliance on advanced technologies creates new vulnerabilities and security threats.
What do you think could happen?
