Getting personal with on-device AI

Customized AI elevates user experiences by delivering personalized solutions tailored to meet their unique needs.

Step into the realm of AI on the Edge with our fresh OnQ series, where we unravel the latest insights and trends in on-device artificial intelligence. Tune in to the voices of our top subject matter experts as they navigate the dynamic landscape of AI’s continuous evolution.

Dive deeper into the world of on-device generative AI by perusing our collection of AI on the Edge posts. If there’s a specific topic you’re keen on, drop us a note, and we’ll make sure to cover it in our exploration. Welcome to the forefront of on-device AI exploration!

Personalized AI offers many benefits, including tailored experiences, intuitive interactions, improved accessibility and increased productivity.

In the mid-50s, Julie Andrews had us all singing, “Getting to know you; getting to know all about you,” and now, this classic tune could very well be the anthem for the latest tech sensation: artificial intelligence (AI). This cutting-edge technology is all about getting up close and personal, as personalization empowers AI to respond better to human requests and anticipate individual needs.

One of AI’s most exciting potentials lies in its ability to personalize interactions between computers and humans. This involves leveraging contextual information and fine-tuning standard models to cater to individual preferences.

Yet, the hitch often comes with privacy concerns when this personalized information has to travel to and from the cloud. Here’s where on-device AI takes the spotlight. With local AI models, personalization can happen without the need to share sensitive data with the cloud, thus bolstering data privacy.

To make personalized AI at the edge a reality, the AI can tap into local data about individual users and their current environment. Imagine edge devices like smartphones, tablets, and PCs utilizing data from built-in sensors such as cameras, microphones, accelerometers, gyroscopes, GPS, Wi-Fi, and Bluetooth. This local sensor data then powers on-device inference, delivering more relevant and personalized responses.

Compared to cloud-based alternatives, this local approach proves to be cost-effective, efficient, and practical. Uploading data, especially video data, to the cloud consumes bandwidth and drains batteries. On-device AI minimizes these issues by keeping the processing local.

Privacy laws often grant users the legal right to control their personal data, and on-device AI takes this a step further. Users gain more control over their data and the decision to enable personalization. By maintaining the data on the device, under the user’s control, there’s no need for additional copies stored remotely. This not only reduces the risk of data breaches but also puts users in the driver’s seat regarding their information.

Running personalized AI on edge devices eliminates the need for continuous access to cloud resources. The result? Low latency, enhanced data privacy, and reliable offline capabilities. It’s a win-win, bringing us closer to AI that not only understands us but respects our boundaries too.

Personalized AI brings a host of advantages, tailoring experiences to individual preferences and behaviors. Here are some key benefits:

1. Enhanced User Experience:

Personalized AI is all about making the user experience more enjoyable and efficient. By understanding individual preferences, the AI can deliver content, recommendations, and interactions that are highly relevant to the user, creating a more satisfying overall experience.

2. Relevance and Engagement:

Personalization allows AI to engage users more effectively. By presenting content and experiences that resonate with their interests, users are more likely to stay engaged and interact with the system. This tailored approach boosts user satisfaction and increases overall engagement.

3. Efficient Content Delivery:

Instead of bombarding users with generic content, personalized AI ensures that the information presented is precisely what the user needs or wants. This efficiency not only saves time but also makes interactions with the AI more meaningful.

4. Adaptability:

Personalized AI systems are adaptive. They learn from user interactions and adjust their responses over time. This adaptability ensures that the AI stays relevant as users’ preferences and behaviors evolve.

5. Improved Recommendations:

Whether it’s suggesting movies, music, articles, or products, personalized AI excels at providing recommendations. By analyzing user data, the system can offer highly targeted suggestions that align with individual tastes and preferences.

6. Increased User Satisfaction:

When users feel that a system understands and caters to their specific needs, it enhances overall satisfaction. Personalized AI contributes to a sense of being understood and valued, fostering a positive relationship between the user and the technology.

7. Privacy Enhancement:

On-device personalized AI, in particular, contributes to better privacy. By processing and storing data locally, there’s reduced reliance on external servers, minimizing the risk of data breaches and offering users more control over their information.

These advantages collectively contribute to a more user-centric, efficient, and enjoyable AI experience. Personalized AI is not just about technology; it’s about creating a connection between users and the tools they use, making technology an intuitive and indispensable part of their lives

Workflow optimization with personalized on-device generative AI 

Using personalized AI, users become more productive by automating repetitive tasks, providing customized workflow recommendations, and offering timely reminders or suggestions based on the user’s work habits and preferences.  

By understanding user preferences and habits, personalized AI can automate routine decisions and actions, saving users time. For example, it can automate creating shopping lists, to-do lists and provide meal recommendations based on user patterns stored on device.

Unlocking Health Advantages with Personalized On-Device Generative AI

Personalized AI doesn’t just offer convenience; it brings tangible health benefits. Health and fitness applications, powered by personalized on-device AI, deliver tailored workout plans, dietary advice, and health tracking. These recommendations are finely tuned to an individual’s goals, health history, and ongoing progress. What sets this apart is the agility of on-device AI—it responds swiftly to health shifts, operating seamlessly even without cloud access. This technology excels in monitoring biometric readings, promptly identifying anomalies related to known health conditions. In essence, it’s a proactive and personalized approach to health management.

Improved accessibility with personalized on-device generative AI 

There’s an opportunity for personalized AI to enhance accessibility features for users with disabilities by adapting interfaces, content and interactions to individual accessibility needs and preferences. Personalized AI could be used to translate speech for those people with disabilities.

Increased productivity with on-device personal assistant 

Personalized on-device generative AI could filter and prioritize information and notifications, reducing the overwhelming volume of data and content users encounter daily. This helps users focus on what matters most to them. Knowing a user’s local context, a personalized AI would be able to alert the user to important emails, texts and social media posts without setting these filters in the cloud and with a fully integrated approach to alerts.

Leveraging personalization without compromising AI privacy

With on-device AI, users may be more comfortable with using their data for personalization knowing that their data can remain on the device and not go to the cloud. Users can still choose what information to provide as input and how it’s used to personalize their experiences, providing a sense of empowerment. Privacy is still in the control of the user and security measures available on device can protect AI models, prompts, outputs and user data.

Personalization with on-device learning

Building AI models that are specific to the individual user or use case involves machine learning techniques, such as retraining from scratch, fine-tuning, reinforcement learning or transfer learning. Looking forward, on-device personalization can be achieved through on-device learning or adaptation, such as by fine-tuning a pre-trained AI model on the device with user data. 

A benefit of on-device learning is that the model can continuously improve with more user data over time and does not drift from the user’s personal preferences. As an example, a personalized AI could be fine-tuned on device to recognize a user’s particular accent or speech patterns for more accurate responses and adjust as a user’s voice changes over time.

Generative AI can also be personalized by fine-tuning large language models (LLMs) on a user’s specific data. As an example, an LLM can be fine-tuned on a user’s writings to better mimic the user in new writings and automated — but contextually-aware — responses. A user’s personal collection of images could be used to synthesize new images or videos for the user to watch. The issue today is that LLMs are extremely large and difficult to fine-tune on an edge device — however, techniques like low-rank adaptation are making it more feasible. In the future, models will be more compact and devices more powerful to close that gap. 

Overall, personalized on-device AI can enhance user satisfaction, engagement and productivity by tailoring experiences and solutions to everyone’s unique characteristics and preferences. It’s essential to balance personalization with user privacy and data protection to maintain trust and transparency in AI systems — on-device personalization provides this.