Artificial Intelligence

Types of AI

Symbolic

Trying to capture intelligence in rules (Marvin Minsky, John McCarthy, Oliver Selfridge, Ray Solomonoff)

Subsymbolic

Learn to understand the world by trial and error. (Rosenblatt).

Neural Network, later called Machine Learning.

Supervised

Feed input/output pairs

Regression problems

Predict real-valued output. Find a formula to get from the input attributes to the output.

Classification

Find a discrete value from the input data. Is this of type A, B or C

• Features and labels

The system is trained with labeled text is split in features. The features get a weight.

By finding the features in text the system can determine what it is about.

• Extract entity and find intent

Entity's can be features (see above)

• Word embeddings

Words represent part of the context. Like features contexts get a value, this leads the system to the grasp of the question.

Unsupervised

Input only. The system learns from finding commonalities in the data presented.

Reinforcement

Input and Critic. Constant feeding of new data, try to evaluate and have the result checked (by humans).

Conversational AI

Important as natural language processing is increasingly important e.g. for digital assistants (Alexa, Siri).

Rule Based

Based on rules find the answer to a question. Only predefined answers can be found and only answers can found for questions matching the rules.

Self Learning

Retrieval based

The system is trained with questions and answers. It can analyze any question and finds the most relevant answer(s).

Generative

The system understands language and can generate answers itself.