AI Concepts
Artificial intelligence (AI) is intelligence demonstrated by computing devices, as opposed to the natural intelligence displayed by humans. AI is used to describe computing devices that mimic and display cognitive skills that are associated with the human mind, such as learning and problem-solving. The learning aspect of AI is a complex enough field of study and development to be separated into what we call Machine Learning.
Often, AL and ML are complementary solutions, since, for a computing device to be considered capable of AI, it also must demonstrate the ability to learn. Without learning, it would only be able to solve a defined set of problems, which is not much better than what normal computing devices and algorithms do.
Several solutions can be categorised as AI/ML applications:
- Recommendation systems that suggest products and services you may like based on your profile or your previous activities or purchases.
- Speech recognition systems (such as Siri and Alexa) that recognise voice commands and can respond.
- Self-driving cars that can follow roads and traffic signs and detect objects to avoid collisions.
- Strategic game systems (such as chess)
Interestingly, as computing devices and AI/ML algorithms become increasingly capable, tasks initially considered to require cognitive skills or intelligence are now considered to be normal computing capabilities, a phenomenon known as the AI effect. For instance, optical character recognition (OCR) which came into use many years ago and has now matured, actually does mimic the human ability to read and recognise written text but is no longer considered AI or ML.
It is important to understand that implementing AI/ML algorithms in any high-level language is a very complex task. Thanks to the advancement of unrelated technology such as cloud computing (SaaS and PaaS), AI/ML developers have been able to deploy their complex algorithms in forms that many app developers can use, without requiring high-end skills to develop the algorithms themselves.