How AI Is Trained

AI is normally trained in five steps (input, processing, outcomes, adjustments and assessments).

Step One: In step one, lots of data and information is collected from various sources in the form of text, audio, videos and many more. Then the data is analysed and sorted into data that can be read by the algorithm and data that cannot be read by the algorithm.

Step Two: In step two, (once the data has been inputted)The team that is training the AI has to allow the AI to decide what to do with the data. Using the patterns it has been programmed to, the AI starts to sort the data until it recognises similar patterns in the data.

Step Three: After the previous step the AI can utilise the complex patterns in the dataset to predict new answers. In this step the training team programs the AI to decide if specific data is a "pass" or "fail". Basically the AI asks itself if the data matches the previous patterns. This decides the outcome that is used to make decisions.

Step Four: If the answer is a "fail" the AI learns from it and the process is repeated again under different conditions. Sometimes the algorithm's rules

and/or the algorithm itself has to be altered.

Step Five: the last step for AI to finish a task is checking its work. In this step, the AI looks at the information it has to make guesses about what will happen next. Any feedback from its changes can be added to the program before it continues.