ARTIFICIAL INTELLIGENCE

Broadly speaking, artificially intelligent systems can perform tasks commonly associated with human cognitive functions — such as interpreting speech, playing games and identifying patterns. They typically learn how to do so by processing massive amounts of data, looking for patterns to model in their own decision-making. In many cases, humans will supervise an AI’s learning process, reinforcing good decisions and discouraging bad ones. But some AI systems are designed to learn without supervision — for instance, by playing a video game over and over until they eventually figure out the rules and how to win.

?Strong AI

Strong AI, also known as?artificial general intelligence, is a machine that can solve problems it’s never been trained to work on — much like a human can. This is the kind of AI we see in movies, like the robots from?Westworld?or the character Data from?Star Trek: The Next Generation. This type of AI doesn’t actually exist yet.

The creation of a machine with human-level intelligence that can be applied to any task is the Holy Grail for many AI researchers, but the quest for artificial general intelligence has been fraught with difficulty. And some believe strong AI research should be limited, due to the?potential risks?of creating a powerful AI without appropriate guardrails.

In contrast to weak AI, strong AI represents a machine with a full set of cognitive abilities — and an equally wide array of use cases — but time hasn't eased the difficulty of achieving such a feat.

Weak AI

Weak AI, sometimes referred to as narrow AI or specialized AI, operates within a limited context and is a simulation of human intelligence applied to a narrowly defined problem (like driving a car, transcribing human speech or curating content on a website).

Weak AI is often focused on performing a single task extremely well. While these machines may seem intelligent, they operate under far more constraints and limitations than even the most basic human intelligence.