AI technology: a lawyer's guide
Artificial neural networks
Artificial neural networks are computer systems that try to emulate certain characteristics of biological neural networks – in other words, a brain.
In artificial neural networks, a piece of code will represent a ‘node’, which is fed a certain input and gives a certain output. Generally, decisions made by these nodes will be fairly simple, such as a binary outcome based on whether the input met a certain threshold value. These computer nodes are connected to multiple other nodes, so the output of one will be the input of others. It is this feature that makes the network mimic a brain; the nodes are equivalent to neurons, and the connections mimic the dendrites and axons in a human brain that connect neurons to other neurons.
There are two other unique features of artificial neural networks. First, each artificial neuron can both store and process information at the same time. In contrast, a traditional computer’s computer processing unit (CPU) can only process data. Therefore it has to send the results of that processing to a computer’s data storage if the result is to be kept, so storage and processing are separate.
Second, because storage and processing are done at the same time in each artificial neuron, all the neurons can be processing information simultaneously. This increases the processing power immensely. In contrast, in a traditional computer system each piece of information that is input into the CPU has to be processed sequentially.
Because of these features, artificial neural networks are generally run on graphics processing units (GPUs), which have the physical architecture to run multiple processes simultaneously. Quantum computing may also be well suited to running artificial neural networks because of its particular ability to process large amounts of data in parallel. Further development is needed, though, before practical use cases become available.
Author
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Giles Pratt Partner
London