Personalizing on-device AI
Personalized AI improves user experience by tailoring solutions to their needs.
Welcome to AI on the Edge, a new OnQ series covering on-device AI trends and insights. Our most active subject matter experts discuss AI, a dynamic, ever-growing topic.
Julie Andrews’ mid-50s hit, “Getting to know you; getting to know all about you,” may be the theme song for artificial intelligence. Personalization helps AI anticipate and respond to human wants, making this cutting-edge technology personal.
Personalizing computer-human interaction utilizing contextual knowledge and individualized fine-tuning of conventional models is one of AI’s most promising applications.
Personalization can compromise privacy when data travels to the cloud and back. On-device AI allows local AI models to give customised replies without sending data to the cloud, improving data privacy.
Local user and environment data can be used for tailored edge AI. Edge devices like smartphones, tablets, and PCs can use camera, microphone, accelerometer, gyroscope, GPS, Wi-Fi, and Bluetooth sensors to collect user data. Uploading video data to the cloud can demand a lot of bandwidth and drain the battery, making this pricey, wasteful, impractical, and often impossible. On-device inference can employ local sensor data for more relevant and tailored replies.
Many privacy laws provide consumers the freedom to regulate their personal data, but on-device AI lets them choose to personalize their data. No new copy of the data needs to be produced and stored in a distant location out of the user’s control by keeping it on the device.
Since no data leaves the device, data exposure is minimized. This prevents data breaches during transit and eliminates cloud data replicas.
Edge devices can run personalized AI without cloud access. Low latency, data privacy, and offline reliability are ensured.
Our AI on the Edge series covers on-device generative AI issues, insights, and trends.
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Advantages of tailored AI?
A personalized AI system learns and adapts to individual tastes and actions. The AI provides user-specific content, recommendations, and interactions to improve the user experience. By offering users with material and experiences that match their interests, personalized AI can boost engagement. Here are some tailored on-device AI experiences.
Optimizing workflow with tailored on-device generative AI
By automating repetitive tasks, providing customized workflow recommendations, and providing timely reminders or suggestions based on work habits and preferences, personalized AI boosts productivity.
Personalized AI automates routine tasks by knowing user preferences and behaviors, saving time. It can automate shopping, to-do, and meal recommendations based on device user habits.
Health benefits of customised on-device generative AI
Personalized AI may improve health. Health and fitness applications can tailor workouts, diets, and health tracking to an individual’s objectives, health history, and progress. Personalized on-device AI can respond faster to health changes without cloud access. Based on established health problems, the AI can detect biometric anomalies.
Accessibility improved with tailored on-device generative AI
Personalized AI can improve accessibility for disabled individuals by tailoring interfaces, content, and interactions to their needs. Personalized AI could translate speech for disabled persons.
On-device personal assistant boosts productivity
Personalized on-device generative AI might filter and prioritize notifications, decreasing daily data and content overload. This focuses consumers on their priorities. Knowing a user’s local environment, a tailored AI may alert them to crucial emails, messages, and social media updates without cloud filters and with completely integrated notifications.
Personalizing without compromising AI privacy
Knowing their data won’t move to the cloud with on-device AI may make users more comfortable personalizing. Users feel empowered by choosing what information to submit and how it’s used to customise their experiences. The user controls privacy, and device security can secure AI models, prompts, outputs, and user data.
On-device learning personalization
AI models tailored to a user or use case require machine learning approaches including retraining, fine-tuning, reinforcement learning, and transfer learning. In the future, on-device learning or adaptation can fine-tune a pre-trained AI model with user data for on-device customisation.
With additional user data, on-device learning can improve the model and stay true to the user’s preferences. A customised AI might be fine-tuned on a device to recognize a user’s accent or speech patterns for more accurate responses and adjust as their voice changes.
Users can customize generative AI by fine-tuning large language models (LLMs) on their data. An LLM can be fine-tuned to duplicate a user’s writings in fresh writings and contextually-aware responses. A user’s image library could be used to create new photos or films to view. Today, LLMs are too huge to fine-tune on an edge device, but low-rank adaptation is making it easier. Next-generation models will be smaller and more powerful to close that gap.
Personalised on-device AI can improve user pleasure, engagement, and productivity by customising experiences and solutions to individual preferences. On-device personalization balances personalization with user privacy and data protection to retain AI system confidence and transparency.
