AI Use Cases 

Vision systems that enable risk and reality recognition are key to a host of autonomous and semi-autonomous vehicle use cases. Typically these rely on a battery of AI ranging from image recognition to predictive responses.

Use communication data - written and spoken - to construct a digital personality for ongoing remote interaction. Whilst largely a gimmick at this stage - able to capture tone of interactive style but little of any underlying thought processes - the potential for building out artificial constructs of departed loved ones, or simulacra of advisers or leaders may soon no longer be simply in the realm of science fiction.

Modify driver experience based on emotion tracking through facial recognition system. This will for example lead to different music being played, lighting set up and driving advice offered.

AI is used to generate the computer game experience - building larger worlds and ever more impressive challenges for gamers.

Optimise blend mixture and process timing of raw materials being used in refining and similar processes. Sustained small marginal improvements can have a significant cost impact - especially where human involvement can be costly or potentially hazardous.

Optimise mine investment and plans based on drilling samples, past sites, and other data. This may include publicly available data and specially collected private data.

Specify crop growth techniques based on individual plot characteristics and relevant real time data. Scaling out best practice in agriculture will be potentially transformative when applied to the world's nutrition challenges.

Predicting risk of health issues can be complex - especially when the creatures in question are not mammals. One example solution is using an app to listen out for the health of a colony of bees. The system has been trained to recognise sounds indicating stress or disease that may have afflicted a beehive.

Construct detailed map of farm characteristics based on aerial video or image capture. This can be of assistance for a series of tasks ranging from planning to valuation to deployment of automated farming tools.

Increasing production yields through optimising system-long inputs from team, machine, supplier and customer requirements through machine learning.

Optimise pricing (ideally in real time) based on factors such as future market, weather, and predicted crop yields. Clarity on pricing can be of especial assistance to those at the production end of the market chain.

Yield management by taking sensor data on soil quality - common in newer John Deere et al truck models and determining what seed varieties, seed spacing to use etc. Also used for checking the right moment to bring livestock to market - using cameras for example.

Deploy robots to do physical tasks in the agricultural process, for example planting, watering and harvesting. Continuing the culture of automating agriculture started in the 18th Century.

Environmental control system for automated greenhouse plant habitats is trained on a dynamic climate model. This then enables it to optimise inputs of energy, moisture and nutrients to maximise plant growth productivity. The need to optimise space utilisation for urban farming helps justify the investment required.

Predict yield for farming or production leveraging IoT sensor and other relevant data. This can work for both plant and animal yield and should also help with planning responses to disappointing predictions,.

Mobile testing of soil and water for chemicals radically speeds up the process and can enable local decisions on critical matters in a time-sensitive fashion. There will be a library of chemical indicators that expands over time. The application might work in conjunction with a physical testing device or product and the AI will interpret the signalled outcome.

Optimise farming production process, often in real time, determining where to dedicate resources to reduce bottlenecks, cycle time and error rates. Interventions might range from automated fertiliser or pesticide deployment to alerting for human involvment.

Using images to identify plant type will enable wider access to up to date agricultural knowledge - whether requiRed remotely (for example to track disease spread) or locally (to support better farming, gardening or rambling).

Rather like humans animal health and welfare is strongly related to the exercise and diet that they experience. Hence using data generated by wearables (attachables...) and other sensors to predict animal outcomes should help produce better farming yields (in both quantity and quality terms).

Declines in key populations of bees have triggered multiple attempts to deliver pollination via miniature robots or drones. This remains at a research level - a process which may also be used to learn more about these vital but endangered creatures.

Visually recreating people in detail to show them moving and/or speaking in video. The application can have positive results, such as bring artists who have passed away back in life in a digital form, but also negative ones, such as deepfakes, who have been used for spreading fake news.

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By looking at patterns, machine learning is able to track individuals as they age and predict the appearance of physical features, such as fingertips.