Artificial intelligence means different things to different people. Actually to get a definition of what artificial intelligence is, it depends on whom you ask. A system can be termed to be artificially intelligent if it exhibits behaviors associated with human intelligence. These include behaviors such as planning, learning, reasoning, knowledge representation, problem-solving, perception, manipulation, motion and to some effects on social intelligence and creativity.
AI is mostly used today to recommend what you should buy nest and well as understanding what you say to virtual assistants in order to give you appropriate responses. This is more so evident in Amazon’s Alexa and Apple’s Siri which are used in various ways such as spotting spam, detecting credit card fraud among others. AI is simply a tech that appears to think the way we do.
Types of AI
At the very basic, AI can be categorized into two; that is narrow AI and general AI. Narrow AI is what we mostly see in computers today- Just intelligent systems that have been taught to learn how to understand specific tasks without having been programmed to do so. This is the type of intelligence we mostly see in the likes of Alexa and Siri. It is also very common in vision recognition systems such as self-driving as well as in recommendation systems that recommend the type of products to buy.
Machine learning deals with situations where a computer is fed with large amounts of data, which is supposed to use to learn how to undertake specific tasks. Machine learning is linked to artificial intelligence and neural networks. The key to processing machine learning instructions is neural networks. Neural networks are brain-inspired networks that comprise interconnected layers of algorithms known as neurons.
These networks feed data into each other and are trained to undertake specific tasks by making modifications of the importance that is attributed to input data as it gets passed through these layers. There is a subset of machine learning called deep learning. This is where neural networks get expanded into other sprawling networks with a number of layers which are trained using a lot of data.
Other the years, recent breakthroughs in recent are the main factor fueling the resurgence of AI. Today, there is the availability of huge amounts of data and this is what’s making research and analysis easy. The elements of machine learning are categorized into supervised and unsupervised learning. There is also reinforcement learning where a system attempts to maximize a reward that is based on its input data through a process of trial and error until the best possible outcome is achieved. The likes of Google, Apple, and Amazon are leading the race towards AI.