Edge AI: The Revolution Within GenAI

As artificial intelligence becomes an integral part of our lives, a new frontier called Edge AI is emerging, promising to enhance AI capabilities even further. This development marks a significant step in the GenAI revolution, bringing advanced AI functionalities directly to mobile devices.  Let’s explore how Edge AI will revolutionize our interaction with technology, making it more personalized, efficient, and privacy-centric.

The Rise of Large Language Models on Mobile Devices

Large Language Models (LLMs) have quickly become essential in AI, known for their ability to understand and generate human-like text. Traditionally, these models required powerful backend servers for data processing. However, this is quickly changing as the phone eco-system manufacturers and OS vendors are already integrating LLMs directly into mobile devices.

• Apple: Ajax LLM has been integrated into the iPhone 15 Pro, enhancing Siri and other features with on-device processing for privacy and efficiency​.
• Samsung: Galaxy S24 series uses Qualcomm’s Snapdragon 8 Gen 3 to run Meta’s Llama 2 on-device, and Google Cloud’s Gemini Pro and Imagen 2 for advanced generative AI tasks​.
• Google: Pixel 8 series incorporates Gemini Nano for on-device AI tasks, enhancing features like Google Assistant and text generation.
• Qualcomm: Collaborates with Meta to bring Llama 2 to mobile devices, enabling on-device AI for better privacy and performance​​. Qualcomm’s vision for Edge AI appears to be a core of its future strategy.

Other apps are already introducing LLMs working offline on mobile phones. These implementations highlight the growing trend of embedding LLMs in mobile phones, with increasing advancements expected by the end of 2024.

The new emergence of SLM (Small Language Model) is also contributing to the migration of GenAI into mobile handsets. The main difference between LLMs and SLMs is the number of parameters in their neural networks. While SLMs are models that fit the 500 million to 20 billion parameter range, LLMs hit the 20 billion mark.

Edge AI: Creating First-Party Data on the Device

With LLMs running on mobile devices, with no need for online connection, a critical question arises: where will the personal data for these models come from? 

The new generation of Edge AI engines offers a solution by leveraging on-device capabilities to gather and process vast amounts of first-party data directly on the phone. For example, Intent HQ Edge AI is an SDK that integrates into existing mobile apps to empower them with a privacy-first user personalization and real-time context. It can process up to 16,000 data points from 50 different on-device signals and translate them into over 500 actionable insights. This data collection happens entirely on the device, ensuring that personal data remains on the device, private and secure​​. 

Edge AI systems incorporate numerous on-device models designed to personalize user experiences and predict behavior. In practice, this technology creates a new behavioral layer based on first-party data found within the device itself. For instance, an on-device model can accurately determine a user’s age within seconds of app installation by analyzing various data points. These may range from raw data such as font size and screen on/off rates to more detailed information like the user’s car model. While no single data point is sufficient on its own, collectively, they enable the model to achieve a high level of accuracy in age detection.

Personalizing User Experience with Prompt Data Augmentation

Edge AI enables a level of personalization previously unattainable with traditional GenAI models. Currently, GenAI responses are often generic, lacking the nuance required for truly personalized user experiences. With Edge AI, every GenAI prompt can be tailored to the devices user persona and context, based on the rich, highly personal firstparty data retrieved from the wealth of on-device sensors. This is a dramatic shift from the current ‘one-size-fits-all’ solutions.

Consider this simple example: A user asks for the best places in town to visit. An Edge AI-enabled Agent would not only provide general recommendations but also consider the user’s preferences and behaviors. A golfer might receive suggestions for the best golf courses, while a parent to a toddler might be directed to family-friendly parks and attractions. 

This level of personalization can extend to various scenarios, such as recommending fitness routines based on exercise habits or suggesting relevant offers for heavy drivers considering personal behavioral preferences.

Understanding Real-Time User Context

One of the standout benefits of Edge AI is its ability to understand real-time user context, significantly enhancing the relevance and timeliness of services provided.

Since Edge AI operates on the device with consent, it can continuously monitor and analyze user behavior, location, and preferences in real-time. This capability ensures that services and recommendations are not only highly personalized but also timely. For instance, a pizza brand app could suggest an order exactly when the user is leaving the office to arrive on time when they enter back home and based on the user’s past preferences.

Since Edge AI operates on the device with consent, it can continuously monitor and analyze user behavior, location, and preferences in real-time. This capability ensures that services and recommendations are not only highly personalized but also timely. For instance, a pizza brand app could suggest an order exactly when the user is leaving the office to arrive on time when they enter back home and based on the user’s past preferences.

Solving Privacy and Efficiency Challenges

Edge AI also addresses some of the most pressing challenges in AI deployment today. Privacy concerns are mitigated because personal data does not need to be transmitted to remote servers; all data processing occurs on device. This ensures that sensitive information remains within the user’s control​​.

Additionally, Edge AI eliminates latency issues and dependency on internet connectivity, providing seamless and instantaneous AI interactions. This reduces the costs associated with data transfer and storage, as there is no need for large amounts of data to be sent back and forth between devices and servers​​.

Real-World Applications and Benefits

The integration of Edge AI into GenAI promises numerous benefits across various sectors. Here are a few examples:

• Retail: Tailored shopping recommendations and promotions based on overall lifestyle, in-store behavior and purchase history.
• Finance: Enhanced fraud detection and personalized financial planning advice derived from user real-time context, spending patterns, and transaction data.
• Travel: Customized travel itineraries and suggestions that align with personal interests, activity levels and past travel experiences.
• Insurance: Tailored policy to the user that fits their lifestyle and can predict risks and guide the user on how best to avoid them in a timely manner.

Challenges of Running Edge AI on Devices

Despite its promising potential, running Edge AI on devices presents significant challenges, particularly in terms of CPU usage and battery consumption. Efficiently processing complex AI models locally requires considerable computational power, which can strain the device’s CPU and lead to increased battery drain. There are a few solutions to address this challenge. For example, the Intent HQ Edge AI operates with ultra-low battery consumption by leveraging a unique architecture that minimizes impact on battery life and CPU cycles, ensuring that AI functionalities run seamlessly without noticeable performance degradation​​. 

Bridging the AI Knowledge Gap

The direct embedding of AI into personal, mobile devices, empowered by Edge AI, not only enhances the relevance and timeliness of services but also ties GenAI’s phenomenal capabilities to the user context, delivering highly relevant and personal marketing and services. By making AI-driven features standard in mobile devices, users perceive these services as more personalized and beneficial.

The real-time contextual understanding provided by Edge AI ensures that every interaction feels tailored to an individuals needs, This creates value from advanced GenAI technology beyond the early adopters of GenAI, to the mass market.  This shift not only broadens AI’s appeal and usability but also builds trust by ensuring that data processing occurs locally on the device, enhancing privacy and security. Consequently, the integration of Edge AI into daily devices bridges the knowledge gap and transforms AI from a non-accessible technology into a practical tool that enriches everyday experiences.

Impacting GenAI with Edge AI

Edge AI represents the missing piece in the GenAI puzzle, enhancing AI capabilities while addressing privacy, efficiency, and personalization. As this technology becomes more prevalent, it will transform how we interact with our devices and each other, fulfilling the promise of AI in ways previously unimaginable. The future of AI is not just about powerful models running on distant servers but about intelligent, context-aware systems operating right in the palm of our hands.

About the author:

Ofer Tziperman is SVP of Business Development at Intent HQ and an authoritative source on the development and application of Edge AI. With extensive experience leading innovative projects that integrate artificial intelligence directly at the edge of networks, he has driven the development of advanced AI solutions that enhance consumer engagement and enable real-time data processing on mobile devices, positioning him at the forefront of Edge AI innovation.

With a strong track record in both technological leadership and strategic application, Ofer is recognized as a leading authority on the practical deployment of AI at the edge, transforming how businesses leverage data for competitive advantage.