From Cloud to Edge: How LLMs & Edge Computing drive real time decisions.

I’ve recently been involved in testing some LLMs on edge devices. This led me to think about how Gen AI deployed on edge devices can impact business processes. But first, why would you deploy an LLM on an edge device? Here’s why.

Processing power is shifting closer to the action, to the edge where valuable data is generated. This convergence of edge computing and Large Language Models (LLMs) enables real-time decision-making across industries.

Imagine powerful AI analyzing data locally, in real-time, on factory floors or in retail stores. LLMs can:

??Speed Up Decisions: Analyze sensor data, customer inquiries, or operational logs for immediate responses and actions.

? Reduce Latency: Get insights on-site, minimizing delays and enabling faster decision-making.

? Improve Security: Keep sensitive data local, reducing the risk of breaches and enhancing data privacy.

?Here are specific examples of how AI on the edge can transform businesses:

?1. Predictive Maintenance on Steroids for Automotive Industry:

?Imagine a car factory experiencing bottlenecks in the paint shop. Traditionally, engineers analyze data to identify improvement strategies, a time-consuming process.

?LLMs deployed on edge devices (robotic arms, shop floor) can be trained on production data, robot movement logs, and best practices to analyze real-time sensor readings. When it detects an anomaly, it doesn't just raise an alert. It generates a clear explanation for engineers in natural language, including:

??? Specific sensor readings outside the normal range for paint application.

??References to relevant sections from paint protocols for potential causes of the issue.

?? Suggestions for adjustments to the robotic arm or paint application parameters.

This allows the engineers to quickly understand the problem, explore solutions suggested by the LLM, and potentially test them before implementation.

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2. Real-Time Threat Detection with Explanations (Security Systems)

?Imagine a large commercial building with an extensive network of security cameras. An LLM on the edge device continuously analyzes video feeds for suspicious activity. Trained on video footage, police reports, and security protocols, the LLM acts as a virtual guard. If it detects an anomaly, it initiates a chat with security personnel, including:

??? A description of the suspicious activity with details like the number of people involved and their direction of movement.

?? References to relevant sections from security protocols for handling such situations.

?This allows the security personnel to quickly assess the situation, determine the level of threat, and take appropriate action.

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3. Proactive Safety with Contextual Awareness for Oil & Gas, Construction or Mining Industry:

?In large oil & gas rigs or on massive construction or mining sites, there are massive safety risks due to complex construction sites with numerous workers, heavy machinery operating concurrently & seismic movements.

?LLMs on edge devices continuously analyze the following real-time data to build a real-time threat profile & proactively intervene.

• Real-time Video Feeds: Identify unsafe situations like workers approaching excavations without fall protection.
• Sensor Data from Wearables: Understand worker location data to assess proximity to hazards.

?Beyond simply raising alarms, the LLM leverages its understanding of the construction environment to:

• ?Identify Specific Safety Risks: Recognize the specific hazard based on video and sensor data (e.g., fall risk near excavation).
• ?Provide Clear and Actionable Warnings: Communicate with workers through voice alerts or wearable displays in their native language. It can provide clear warnings about the specific hazard detected and the recommended action for the worker to take (e.g., "Warning: Fall risk detected. Please secure your fall protection harness").

?Benefits:

???? Proactive safety management system: Identify and address potential risks before accidents occur.

?? Improved communication and awareness: Natural language-based warnings ensure workers understand the nature of the hazard and how to mitigate it.

??? Reduced accidents and injuries: Proactive interventions lead to a safer work environment for construction crews.

?4. Personalized & Proactive Support for Customers in High End Fashion Retail:

?Imagine a high-end clothing boutique struggling to personalize the customer experience. Sales associates might lack in-depth knowledge about every designer or garment.

An LLM on an edge device can become a valuable asset. Trained on historical sales data, customer preferences, and detailed product information, the LLM can analyze customer behavior through in-store cameras.

Personalized Interaction: When a customer lingers near a particular garment, the LLM can initiate a discreet chat on a dressing room tablet, including:

???? Highlighting the garment's unique features and designer heritage, tailored to the customer's past purchases and browsing history.

??? Offering styling suggestions and recommending complementary pieces from the inventory based on the customer's body type and fashion preferences.

??? Discreetly prompting a sales associate to approach the customer with additional information or personalized recommendations.

?This personalized approach can significantly improve the customer experience, fostering a sense of exclusivity and leading to higher sales conversions.

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LLMs at the edge are revolutionizing businesses by offering real-time insights, proactive actions, and personalized experiences. As LLM technology continues to evolve, we can expect even more innovative use cases for Gen AI at the edge.

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