What Can Neural Networks Do?

Deep learning, based on artificial neural networks, is now a core technology across industries. It solves complex tasks that were previously impossible or required heavy manual effort. Neural networks are widely used in many domains:

• Image recognition: used for identifying objects in photos, automatic tagging on social media, and medical image analysis (MRI, X-rays).

• 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?

Despite their versatility, neural networks still have important limitations:

• Artificial general intelligence (AGI): current models cannot match human-level reasoning, adaptability, or broad understanding. A neural network performs only the task it was trained for.

• Data-poor tasks: deep learning needs large datasets. With too little data, models either fail to learn patterns (underfitting) or memorize examples (overfitting).

• High-interpretability requirements: neural networks are often “black boxes.” In fields like healthcare or finance, where decisions must be transparent, this low interpretability becomes a major barrier.

• Strict rule-based tasks: neural networks learn from patterns—not rigid logic. They are not well-suited for tasks requiring precise rule following, such as solving equations or executing deterministic algorithms.

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.