The A-Z of AI
AI systems inspired by the human brain.
Neural networks allow for a different kind of learning than traditional AI: inspired by, but very different from, human thought.
Neural networks are made up of collections of information-processing units that work as a team, passing information between them similar to the way neurons do inside the brain. Together, these networks are able to take on greater challenges with more complexity and detail than traditional programming can handle.
Although they attempt to mimic the structure of the brain, neural networks cannot "think" like humans.
Human beings can instantly analyze thousands of tiny details to make sense of new objects and scenarios, taking in multiple characteristics simultaneously, like color, texture and size. Neural networks are adept at spotting patterns but are not capable of contextualizing those details the same way humans can.
AI design teams can assign each piece of a network to recognizing one of many characteristics. The sections of the network then work as one to build an understanding of the relationships and correlations between those elements — working out how they typically fit together and influence each other.
By studying data provided by the network’s design team, these detail-oriented systems are able to gain an acute eye for subtle patterns within this information — enabling AI to succeed at data-intensive tasks that traditional computing couldn’t even attempt.