Executive summary
A decision to adopt AI can raise fundamental and moral issues for society. These are complex and vital issues that are not typically the domain of lawyers.
What is AI?
Part of our Artificial Intelligence briefing
Global | Publication | July 2017
AI is a field of computer science that includes machine learning, natural language processing, speech processing, expert systems, robotics, and machine vision.
Many people assume that AI means Artificial General Intelligence (AGI) – that is, intelligence of a machine which performs any intellectual task as well as, or better than, a human can perform it. Or to put it another way, AGI is AI that can meet the so-called “Turing Test”: a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. In reality, we are some way off the emergence of AGI, although we already benefit from AI which is itself designed by AI.
Content
Is AI new?
Is AI new? Humans have been interacting with AI (and the algorithms included within AI software coding) for some time now. As we become familiar with it, technology once described as “AI” becomes known simply as “software”. Many people are already familiar with the evolution of autonomous (or driverless) vehicles (which AI systems support – see Norton Rose Fulbright, Autonomous Vehicles for more information). AI already operates opaquely and seamlessly across borders and time zones, never needing a day off work, invisibly augmenting our daily lives with the convenience and speed it can deliver.
AI encompasses a wide spectrum of technologies
Source: Bart Van der Mark, A Primer On Robotic Process Automation, digitally.cognizant.com, 27 January 2016
AI encompasses a wide scope of technologies on a spectrum between simple automation and autonomous decision-making.
Between the two ends of that spectrum lies a range of AI deployments that use inputs of varying complexities to generate outputs of equally varying degrees of sophistication. Because of such variation, the AI industry typically favours more granular technology groupings for AI - many involving a learning component.
A convenient classification for AI is to define it by reference to the methodology it uses, on the one hand, or by reference to the intended application, on the other (the following is not an exhaustive list):
Methodology
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Machine learning |
Automating decision-making using programming rules and, in some cases, training data sets. Human subject-matter experts can provide feedback on results as part of a training process. Machine learning can adapt its programming based on the training process and feedback, and the data can be represented by various graph and network structures. For example, an artificial neural network (ANN) or neural net is a system designed to process information in a way that is inspired by the framework of biological brains |
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Deep learning |
The use of multiple layers of abstract representations of data to optimise the machine learning process |
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Supervised learning |
Labelled training data examples are used to infer functions that can be used for processing new data. A computer can predict or “guess at” the meaning of new data based on the training data set, graph and network structures, and feedback |
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Unsupervised learning |
Labelling data based on inferences about its structure |
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Reinforcement learning |
Rules to control software action in an environment to maximise a reward. Such learning may not need training data examples with labelled data sets |
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Expert systems |
Inductive reasoning based mainly on “if–then” rules or logic programming |
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Multi-agent systems |
Machine learning in combination with multiple intelligent entities (for example, so-called “swarms”) |
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Computational argumentation |
A methodology that addresses computational challenges characterised by a lack of certain, consistent and complete information, and when numerical (e.g. statistical) information is only partially available, or not available at all |
Applications
| Speech processing | Conversion between speech (audio) and text |
| Natural language processing |
Deriving meaning, context, or sentiment in textual data or conversations with humans using grammars and graph structures |
| Machine vision | Detecting patterns in visual content for object tracking, audio, and face recognition |
| Robotics |
The use of AI systems to automate and mechanically control machine movements |
| AI planning |
A form of automated programming |
What is AI?
AI is a field of computer science that includes machine learning, natural language processing, speech processing, expert systems, robotics, and machine vision.
Artificial intelligence and ethics
AI will need to meet certain minimum ethical standards to achieve sufficient end user uptake, varying according to the type of AI and sector of deployment.
What are the key legal risks?
Courts in a number of countries have already had to address a range of legal questions in relation to the automatic nature of machines and systems.
Ethics risk toolkit
The key question businesses need to consider is whether deploying AI will result in a shift of ethical and legal responsibility within their supply chain
