AI Use Cases 

Detecting, identifying and monitoring animal populations is especially useful when they are in the wild and potentially at risk. Typically it requires a network of static camera traps or acoustic recorders but can also be extended to include mobile sensors.

Identify security-related targets from analysis of sensor feeds (e.g. drone cameras) typically matched with image recognition technology. Increasing debate about the extent to which this is tied to lethal capability and the role of a trained human in the decision loop.

Predict risk of illicit activity or terrorism using historical crime data, intelligence data and other available sources (e.g. predictive policing). Largely based on tracking network traffic, most obviously social media and telecoms activity, to predict likelihood and risk of potential terror-related criminal activity. The techniques will also reveal potential suspects. Similar techniques may be used to track other "undesirables" - including pro-democracy protesters in more repressive regimes.

Growing field of military vehicle automation. A key question is the degree of autonomous decision-making on fire / no-fire lethal decisions. The key benefit of reduced risk to military personnel is not likely to be equally true of opponents. Growing legal and moral questions likely an area of rapidly diverging decision-making across nation states and, potentially, non-state actors.

Predict likelihood of traffic accidents or queues and optimise traffic system to reduce the risk. This will require the deployment of sensors and 'smart' traffic lights.

Use of video footage potentially combined with "lie detector" analysis to make preliminary screening decisions on travellers entering protected borders. The aim is both to speed up the process and - more controversially - to achieve greater accuracy in screening results.

Espionage related cracking of opponents secure communications. Techniques used by many actors - but states still retain critical edge.

Deployed military installations will increasingly use AI for autonomous decision-making - often with a lethal impact. Examples include sentry guns (deployed on the Korean border) and so-called 'intelligent mines'.

Vast amounts of data has been generated by an array of human sensors like telescopes or exploratory spacecraft examining the mysteries of space. Analysing data at this scale is beyond human abilities - especially when part of the challenge is to find things that we did not know existed - so machine learning plays a vital role. Ultimately this may lead to the discovery of something as exciting as extra-terrestrial beings - but that is probably at least as distant as the creation of Artificial General Intelligence....

Improve weather forecasting accuracy using machine learning applied to multiple data sources. The aim is to improve an existing process that is typically already a sophisticated user of computer modelling.

Personalise public services to target individual citizens based on multiple data sources (mobile, social media, location, etc). Building up long term data sets on individuals is a growing trend - and one that raises potentially signficant issues alongside the opportunities presented.

Analysing conversation to find words or word combinations that predict likely need for urgent interventions in mental health situations - for example, when suicide risk is at a heightened level. This triage support works best in conjunction with trained human personnel.

Predict individualised educational and career paths to maximise engagement and success - both on an economy-wide basis but also for individuals. Ideally this will help inform better life decisions.

Optimise pricing for government provided goods and services (e.g. road pricing, park or museum entrance fees)

Modelling populations using wide variety of demographic, polling, socio-economic and online activity data to predict the likelihood that individuals / segments will be receptive to political (or related) messaging. May model appropriate type, mode and style of messaging as well as optimal influencers / messengers,.

Analysing and predicting the trajectory of disease propogation in a population is critical to allow better mobilisation and targeting of scarce resources to combat the spread in a time-sensitive and high pressure situation. Using machine learning to support the process enables modelled predictions to be speedier and less resource-intensive - which can also lead to greater precision and specificity in outcomes.

Using social media to compile information to identify service disruptions. Angry or worried tweeting (or similar) by customers or announcements by operators will trigger consolidated public service alerts on service disruptions. This is most advanced in transport networks but could be applied to other services.

Using satellite, drone and other data feeds to monitor potential risk factors on a macro scale across a potential battlespace. This might be a country - North Korea - and it might involve specific issues - in this case deployment of nuclear missiles. The potential scale, and opportunities for spoofing and mistakes, is huge.

Radar is used to scan individuals to identify shape and metallic content of devices being carried, including those concealed under clothing. Machine learning is then applied to the data to identify potential weapons - be they guns, knives or even bombs. Inevitably there is, at this stage, a high risk of false positives that will potentially impact on human responses to triggered alerts.

Law firms use software templates to create filled out documents based on data input from unstructured data sources

Predict risk of failure for physical assets (e.g., military, infrastructure) and recommend proactive maintenance

Identify design problems in pre-production to reduce ramp-up time to maximum output. Modelling potential scenarios will help establish capacity planning constraints,

Identify root causes for low product yield (for example input, tool or machine specific issues) in manufacturing. This analytical work will likely require working closely with human analysts.

Predict failure and recommend proactive maintenance for production equipment

Detect defects and quality issues during production using visual and other data. This process will potentially be impacted by unexpected issues - e.g. a change in the quality of the lighting on a production line.

Optimise complex manufacturing process in real time, for example to determine where to dedicate resources to reduce bottlenecks and cycle time. Resources might include automated factor input, machine re-alignment or signal for necessary human intervention.

Predict future demand trends and potential constraints in supply chain

Optimise medical product (devices, robots, drugs etc) launch strategy based on past launches and relevant data

Measuring key physical indicators - for example motion and gate - to diagnose potential medical complications

Monitoring of body (eg iris photos or skin conditions) to predict potential for issues of medical concern. May be time relapse dependent (eg size of mole over several days) or point-in-time diagnosis (e..g iris photos). Can be deployed as an app in some circumstances.