Accelerating Innovation in Hundreds of Fields with Artificial Intelligence

[Nabanita1]

Although the concept and definition of artificial intelligence (AI) is still morphing as the technology matures, generally it is the idea of building machines that can think like humans. The term machine learning (ML) is used to describe the idea of teaching computers to learn in the same way humans do. It represents the leading edge of AI.

AI is not a new concept, and this concept is in use for last many decades. For instance, think about a company that used to develop industrial control systems. They could be primary industrial IoT systems. It used to take readings from sensors located inside the machines, parse them through simple processing scripts, and then send the outputs over a wire to control equipment on the factory floor. 

How times have changed! Today’s industrial IoT systems are far more capable on some dimensions:

• Sensors are much more accurate and measure different types of inputs, including pressure, friction, vibration, and appearance.
• Communications have higher capacities and reliability, enabling wired and wireless connectivity over longer distances.
• The ability to handle big data flowing in from the edge.

This last point not only refers to faster processing, larger storage volumes, and more bandwidth - it also refers to the increased ability to derive insights and take immediate action based on them automatically with Artificial intelligence (AI). This underlying technology trend has been re-ignited by the explosion of sensors’ data – Industrial IoT data - and has started to transform operations at the edge, leading to new and exciting possibilities.

Impact of AI in different verticals

AI in Health

• Self-testing leading to diet, supplement, and exercise advice 
• Hospital re-admission prediction
• Personalised drugs
• Background scanning of digital footprints to predict disease 
• Home monitoring, e.g., fall monitoring, habit monitoring

AI in Retail

• AI robots continuously track inventory on shelves 
• Computer vision + deep learning for auto-checkout 
• Machine learning to detect sources of counterfeits 
• Machine-learning personalization 
• Auto-login and facial recognition 
• Real-time store pricing

AI in Electric Utilities

• Predictive maintenance of field gear 
• Machine learning-based forecasting maximises different energy sources 
• Self-generation use or selling of power
• Drones and on-site robots look for faults 
• Sensors and machine learning micro-adjust generation efficiency 
• Virtual agents automate call centers

AI in Farming

• Drones and hi-res cameras to identify “precision” farming needs 
• Robots for precision farming – Drones, robots, and cameras for physical security 
• Machine learning to determine optimum lighting and nutrition for vertical farming 
• Robots for crop picking and processing 
• Self-driving farm machinery 
• Multi-variable prediction for optimum plant and harvest timing

AI in Insurance

• Insurance advice and customer service
• Transaction and claims processing

Nabanita Maji
Hewlett Packard Enterprise

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