What Can Neural Networks Do?

Deep learning, a branch of machine learning based on the principles of artificial neural networks, has enormous potential and is already widely used across industries. It is a modern and powerful tool capable of solving many complex problems that previously lacked effective solutions.

Neural networks are applied to various real-world tasks across multiple domains. Below are several major categories of problems they address, along with examples:

• Image recognition: used for identifying and classifying images in areas such as automatic photo tagging on social media or medical diagnostics, including the analysis of MRI and X-ray images:

• Speech recognition: systems like Siri, Google Assistant, and Alexa use deep learning to process and understand human speech:

• Text analysis: deep learning helps in the analysis and classification of texts. This includes customer reviews, news articles, social media and more. An example would be sentiment analysis in tweets or product reviews:

• Recommender systems: services like Netflix or Amazon use deep learning to offer personalized recommendations based on previous user behavior;
• Self-driving cars: deep learning allows cars to recognize objects, pedestrians, other vehicles, road signs, and more, and subsequently make decisions based on the information received:

• Facial recognition: this is used in many areas, from phone unlocking to security systems and keyless entry systems:

• Generative tasks: these are used to create new data that mimics some of the original data. Examples include creating realistic images of faces that do not exist in reality, or transforming an image of a winter landscape into a summer one. This also applies to tasks related to text and audio processing.

What Can Neural Networks NOT Do?

There are still categories of problems that remain difficult or currently impossible to solve using deep learning or neural networks:

• Building artificial general intelligence (AGI): despite significant progress, modern neural networks cannot fully replicate the diversity and adaptability of human intelligence. Each network is designed to perform only the specific task it was trained for:

• Data-poor tasks: deep learning requires large amounts of data for training. If there is little data, the model may learn poorly (underfitting) or remember the data without extracting the necessary patterns (overfitting):

• High requirements for interpretability: neural networks are often called "black boxes" because it is difficult to understand how they came to a certain conclusion or prediction. For some areas, such as medicine or finance, where a high degree of transparency and explainability is required, this can be a problem:

• Tasks that require strict adherence to rules: neural networks are good at learning from data and predicting based on patterns found in the data, but they may not be able to cope with tasks where strict rules or algorithms must be strictly followed (e.g. solving the equation):

In general, deep learning is a powerful tool that can solve many problems. However, like any tool, it has its limitations and it is important to use it where it makes the most sense.