1. What are the definitions of Artificial Intelligence and Machine Learning?
Artificial intelligence is the capability of a machine to emulate intelligent human behavior. Machine Learning (ML) is the science and engineering of endowing computers with the ability to learn without explicit preprogramming.
AI and ML are often conflated with one another, but it is important to understand that not all AI techniques use Machine Learning and that ML is not genuine AI.
2. What are neural networks?
Neural networks are a subset of machine learning algorithms. They are computer systems based on super-simple models of the human brain.
The ‘neuron’ is the computational component and the ‘network’ is how the neurons are connected. Neural networks pass data among themselves, accumulating patterns as the data flows.
3. What is deep learning?
Deep learning is a subset of machine learning. It refers to using multi-layered neural networks to process data in increasingly complex ways with task-specific algorithms.
This enables the machine to ‘train itself’ to accomplish certain tasks like speech- or image-recognition through exposure to vast amounts of data. Deep learning allows for continual improvement in the ability to recognize and process that information.
4. What are some examples of AI?
AI technologies are developing in many different fields with many diverse applications, some used today include:
- Computer Vision – Recognizing objects in the visual world — to provide data input for control of an autonomous car, for example.
- Speech Recognition – ‘listening’ to audio and interpreting what the words spoken are/reacting to them.
- Natural Language Processing – Taking sequences of words and trying to determine the intended meaning.