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AI technology: a lawyer's guide

Machine learning

The basic premise of machine learning is that it involves programming that teaches itself.

It’s an algorithm that begins by being unable to produce the desired output but, after enough ‘training’, it learns to produce that output. The training is given by supplying the algorithm with large datasets and using a mechanism that feeds back whether the algorithm has processed each data point correctly or incorrectly.

This training can be done manually in a process called supervised learning. This generally requires the training dataset to be manually ‘labelled’ by humans. For example, humans might manually label all the images within a database that contain a road sign. The machine learns the common features of those images and can then recognise when there is a road sign in a new image. The problem with this process is that it relies on the quality of the human trainers, because it’s their classifications that the computer tries to replicate. For more complex tasks, like classifying an obligation in a contract, this might be problematic.

This training can also be done automatically, by either unsupervised learning or reinforcement learning.

In unsupervised learning, there is no human classification of the training dataset. Instead, given a large enough dataset, an algorithm can recognise ‘clusters’ of certain features. If the algorithm is given a new item, it can say that the item is likely to be similar to some other item based on the clustering of certain features that they share.

Reinforcement learning is where the algorithm improves how it processes data by trying new actions in conjunction with already well-performing actions in order to perform as well as possible by reference to a certain measurement. It will keep trying, and thus improving, through various iterations so as to achieve the best process against a target that was set for it. Reinforcement learning has been the key to many recent advances in AI, in particular in complex decision-making, like Google DeepMind’s Go-playing AI.