Artificial Intelligence (AI) has been steadily integrated into the systems and data analytics ecosystems of modern companies. Its practical applications range from automating manufacturing processes to supporting medical diagnostics.
For developers and technology enthusiasts, gaining an understanding of real-life applications of artificial intelligence and its interaction with connected devices and robotics is crucial.
Below are some of the key areas where AI is currently making a difference.
- Manufacturing: Quality Assurance and Predictive Maintenance
Manufacturers are utilizing AI algorithms for industrial quality assurance, production analytics, and predictive maintenance. In the first application, computer vision tools can be used to inspect a product and ensure it meets the required standards. When applied in manufacturing processes, AI can help identify irregularities in production and equipment failures.
Using connected sensors and processors, the technology can be employed to collect and analyse performance data from machinery and make maintenance recommendations.
- Healthcare: Imaging, Research, Admin and Analytics
AI is being leveraged in medical imaging analysis, healthcare research, administrative automation, and data management systems. While the technology can support medical professionals in making informed decisions, appropriate caution should be applied when adopting such solutions.
- Financial Services: Fraud Detection and Customer Care
In the financial industry, artificial intelligence is being integrated into systems to detect fraud, improve customer care, and transform the overall user experience. For example, banks can use algorithms to monitor transactions and mark potentially fraudulent activities for manual verification. However, as with most applications of the technology, due diligence is needed when implementing such solutions.
- Retail: Personalization and Demand Forecasting
In the retail sector, AI can be leveraged to recommend products to customers, forecast demand, and improve the supply chain. Through personalization and demand forecasting, businesses in the space can make smarter decisions on what to stock and when.
5. Logistics: Intelligent Transport and Distribution
Some of the applications of AI in the logistics and transport sector include optimizing distribution routes and managing assets. In addition, the technology can be used in analyzing supply-demand data to help make smarter distribution decisions. Combined with GPS and connected devices, AI can be used to monitor and distribute assets in the industry.
- Agriculture: Smart Farming and Asset Management
Computer vision and analytics can be used in precision farming to interpret images captured by drones and sensors. The application of the technology in smart farming can help support decision-making in areas such as irrigation and fertilization. In addition, it can be used to monitor environmental conditions and the state of machinery.
AI, IoT and Physical AI: Applications and Uses. Most of the above applications involve the use of both artificial intelligence and the Internet of Things (IoT). IoT-enabled connected devices make it possible to collect and share data for AI algorithms to analyze, interpret, and act upon. The two technologies are often referred to as AIoT.
Physical AI, on the other hand, refers to using AI technology in the physical world by leveraging augmented smart devices and robotics. Examples of such systems and devices include drones and autonomous machines that can perform visual inspections, support warehouse operations, and transform the manufacturing process.
To understand more about this exciting field, interested readers can explore https://apertureventurestudio.com/physical-ai-and-aiot-engines-overview-and-applications/, a resource that provides an overview of the Physical AI and AIoT concepts, engines and applications.
Summary
Manufacturing, healthcare, financial services, retail, logistics, and agriculture are some of the industries and sectors that are embracing artificial intelligence and machine learning to boost their operations. Developers can play a leading role in the above domains by creating end-to-end solutions that utilize smart devices and connected systems to achieve tangible business outcomes. The key consideration in all applications is having a problem to solve and a target goal to reach.
