These are three terms that are heard all the time now, but often people still get confused about what each one really entails. Below is a quick rundown of each that will hopefully things out a little and give you a real insight as to what these interchangeable terms mean.
, or for short, is the broadest way in which to describe computer intelligence. Back in1956 it was described as “Every aspect of or any other feature of intelligence can in principle is that a machine can be made to simulate it” at the Dartmouth Conference. can come in various forms including game-playing computer programs and In voice recognition an algorithm is able to listen to what a human or machine says and write it down and understand its meaning. Image Recognition describes the process of a machine knowing that you are currently looking at cat pictures. Consequently, video recognition denotes the act of a machine watching a video, and being able to understand what it 'sees'. systems. To further break down the definition of it can be split into three subgroups: Narrow , artificial general intelligence (AGI), and superintelligent . Narrow ’s are that is only skilled at one particular task such as IBM’s Deep Blue or Google DeepMind ‘s Alpha Go. Whereas artificial general intelligence (AGI) can perform a range of tasks and is considered to be human-level. Superintelligent goes one step further and is basically the day when machines become smarter than we are.
Machine () is a subdivision of that involves machines deciphering data and for themselves. It’s used a lot throughout the businesses of today as is very efficient when used in areas such as , object, and facial recognition, translation, and other tasks. Programs that use machine can learn to recognize patterns on their own and make predictions based on what it’s learned. An example of this would be Google’s DeepMind.
That then leads us to , which is a subdivision of . It too makes use of certain techniques by tapping into Neural Networks are simplified abstract models of the human brain. Usually they have different layers and many nodes. Each layer receives input on which it carries out simple computations, and passes on the result to the next layer, by the final layer the answer to whatever problem will be produced. that mimic a human’s decision making. The problem with is that it requires an enormous amount of data to train itself with which can often be very expensive. However, that hasn’t deterred scientists at all, and just last year a major victory was seen for as DeepMind’s AlphaGo program used its techniques to beat the world champion Go, player, Lee Sedol. is also heavily integrated with various business applications too including fraud and spam detection, text-based searches, recognition, and image searching.