IB Computer Science Topic A4.1: Machine Learning Fundamentals Key Definitions

An application-specific integrated circuit (ASIC) is a chip designed and fabricated to carry out one fixed task. It cannot be reprogrammed after manufacture, which is what makes it fast and power-efficient on that task and useless if the task changes.

Machine learning that uses artificial neural networks with multiple layers between the input and the output.

Edge computing processes data on or beside the device that produced it, rather than sending it to a central data centre first. Removing that round trip is what allows a response in milliseconds.

A field-programmable gate array (FPGA) is a chip whose internal logic can be reconfigured after manufacture by loading a new design onto it. One board can be rewired for a new model, at the cost of being slower and less power-efficient than an ASIC doing the same job.

Inference is the stage where a trained model is given a new input and returns a prediction. Each input passes through the model once, so inference needs far less processing and storage than training the same model did.

A branch of artificial intelligence in which a program improves its performance on a task by processing data, rather than by following rules a programmer has written out in full.

An agent takes actions in an environment and receives a reward or a penalty in return.

The ability of a system to handle more data and more users by adding further nodes rather than by replacing existing hardware with something more powerful.

It trains a model on a labelled data set, in which every input is paired with the correct output.

A tensor processing unit (TPU) is a processor built for the tensor arithmetic that neural network layers are made of. It beats a general-purpose GPU on that specific work, and is usually rented through a cloud platform rather than owned.

Takes a model already trained on a large, general data set and retrains part of it on a smaller data set for a related task.

Deals with unlabeled data, aiming to find hidden patterns or structures.