Artificial Intelligence

Which is focused on creating systems or machines that can perform tasks typically requiring human intelligence.These tasks include reasoning, learning, problem solving, perception, language understanding and decision-making. Main goal is Making machines intelligence.

Human intelligence encompasses a wide range of abilities, including pattern recognition, coding, problem-solving, emotional understanding, and more. It is a complex and multifaceted capability that allows us to navigate and interact with the world in diverse ways.But, Artificial Intelligence is a specialised subset of human intelligence, primarily focused on tasks like pattern recognition, decision-making and data processing.

Symbolic AI, also called Good Old-Fashioned AI (GOFAI), is a method of artificial intelligence that uses clear rules and logic to make decisions. It relies on a lot of if-else conditions, where specific situations lead to specific actions. This approach is often used in expert systems, which are programs designed to mimic human decision-making. For example, Symbolic AI can be used to create a chess-playing program by following predefined rules for moving pieces and evaluating positions.

But there are some limitations of it. Symbolic AI struggles with tasks involving fuzzy logic or situations that can’t be defined by clear rules. For example, creating a system to recognize different dog breeds would require an infinite number of if-else conditions which is quite impossible. To solve this problem ML comes here.

Machine Learning

In ML instead of using explicit programming, statistical methods are employed to identify patterns within the given data. We start with known inputs and outputs and through training, the model learns the underlying relationships and logic needed to solve the problem. Once trained, the model can make predictions on new, unseen data.

Unlike Symbolic AI, which requires predefined rules and logic, ML systems automatically generate their own logic based on the data they are fed. By providing large datasets, the system learns to recognize patterns, enabling it to deliver accurate outputs when presented with new inputs. This ability to learn and adapt from data is what sets ML apart from traditional rule-based AI systems.

When training a ML model, it is crucial to specify which features to focus on in the data. These features guide the model in identifying relevant patterns. For example, if the task is to recognize a dog, features such as the number of ears and eyes would be important. It will follow this way to draw patterns. But what if, we don't know the features? and to solve this problem DL was invented.

Deep Learning

It is a type of machine learning that is inspired by how the human brain works. It uses math to create artificial neurons, which are similar to the neurons in our brain, though not exactly the same.

Deep Learning does not require predefined features, it automatically creates or detects features from the given data. This is especially useful when working with fuzzy logic, where it's unclear what features might exist. For example, if I want to predict which candidate is likely to get a job based on their CV, the deep learning model can identify features like the number of certifications or good grades on its own, without needing manual input.

The more layers of neurons we add to a deep learning model, the more accurate its classification and predictions become. Also increasing the amount of input data helps improve the model’s performance. But in ML models it their might be some limitations of performance.

So we can see a clear picture that Deep Learning is a sub-domain of Machine Learning. And Machine learning is a sub-domain of Artificial Intelligence.

ML and DL are both rapidly advancing fields. You might wonder why DL isn’t always used if it’s so powerful. The reason is that ML tends to perform better with smaller datasets, while DL excels with large amounts of data, like those used by companies such as Meta or YouTube.

Thank you for reading.