Generative AI is gaining a lot of traction in the world of edge devices like laptops, smartphones, wearables, cameras, etc. Its application ranges from simple use cases like correcting grammatical errors in a mail to complex ones like analysis of real-time video footage for detecting defects, threats, or suspicious activities.

Building an Edge AI Model

• Model Selection and Design: Choose a model architecture that balances performance with resource constraints, tailor it to the specific task, and prepare the data for efficient training and inference.
• Model Training: Gather sufficient training data, use data augmentation techniques, and train the model using appropriate techniques to achieve the desired performance.
• Model Optimization: Reduce model size and computational demands through techniques like quantization, pruning, and knowledge distillation, and convert the model to a format compatible with the target edge device.
• Deployment and Integration: Integrate the model into the edge device’s software framework, ensuring compatibility with the device’s operating system and API, managing resources effectively, and evaluating its performance in a real-world environment.
• Ongoing Monitoring and Refinement: Monitor the model’s performance over time, implement mechanisms for updates, and ensure data privacy and security.

Why is it good to have AI in edge devices?

By bringing the power of AI closer to the user rather than traditional cloud-based solutions, Gen AI in edge devices provides:

• Reduced Latency: The user experience will be improved exponentially when data can be processed on-device instead of taking the data to the servers for processing. This is crucial for real-time applications.
• Enhanced privacy and security: Having the power of Gen AI in edge devices helps in reducing privacy concerns and security vulnerabilities as the sensitive data is not leaving the device. This is crucial for domains like healthcare, finance, etc.
• Improved accessibility: Remote areas where internet connectivity is poor or unavailable will not limit users to enjoying the features of Edge AI.
• Lower operational costs: Edge AI reduces operational costs as it doesn’t fully rely on cloud infrastructure for its operation.

How does Edge AI transform industries and everyday life?

• Autonomous Vehicles: Edge AI helps in real-time object detection, which is crucial for effective navigation in self-driving cars through lane keeping, adaptive cruise control, etc.
• Healthcare: Faster and more accurate diagnosis can be achieved using medical imaging powered by Edge AI.
• Smart homes: Edge AI can be used in homes for a wide variety of applications like intruder detection, optimization of energy consumption, voice-enabled appliances, etc.
• Smart manufacturing: In the manufacturing world, Edge AI can predict maintenance required for machines or anticipate potential failures, helping manufacturers by reducing downtime.
• Robotics: Edge AI empowers robots with autonomous capabilities, allowing them to adapt to changing environments.
• Security and Surveillance: It has already been proven multiple times that computer vision is more effective than human efforts in security and surveillance scenarios, and Edge AI even takes it to the next level with faster object detection and facial recognition.

The list of use cases of Edge AI in day-to-day activities doesn’t end here; rather, it goes on and on from agriculture to retail to AR and VR.

The shadow side

Even though Gen AI, through its implementation in edge devices, is making the lives of the public easy in many ways, in some sectors it is an overinflated balloon. One such example is where smartphone manufacturers have increased the price of their devices having basic Gen AI capabilities. This hype is having a negative impact on other innovative implementations of Gen AI. On one side, real-time detection of wild animals in the streets near forest areas is a boon to the public, while overhyped GenAI implementations in smartphones are emptying the pockets of people. This is one of the reasons why everyone is comparing the rise of Gen AI to the dot-com bubble. If more of these mediocre features are sold for high prices, investors will lose their trust, and funding will decrease over time.

It is high time to bring in more innovative features to the end users without the “AI” badge with an inflated price, rather as subtle features that help in their day-to-day activities. It is predicted that by 2030, Gen AI features will be used in 70% of edge devices, and the power of AI will reach millions, if not billions, of hands.