What is On-Device Intelligence?
In recent years, machine learning and deep learning have driven a new wave of automation and intelligence across industries, showcasing the progress that artificial intelligence and computing have made. However, the limitations of traditional computing for AI are becoming increasingly apparent as more and more data is generated, processed, and stored. That’s where on-device intelligence comes into play, with the ability of devices to perform tasks locally without relying on cloud-based services or remote servers.
Recent technological advancements have made the emergence of on-device intelligence possible, as organizations can now train AI models and deploy them in production on devices, thanks to the advancement of neural networks.
Moreover, the development of highly parallel GPUs that can execute neural networks has contributed to the cause. With the widespread adoption of IoT devices, big data from various sources, such as industrial sensors, smart cameras, and robots, can be collected and combined with the devices necessary to deploy AI models on devices. This has enabled organizations to leverage the power of on-device intelligence.
The potential of on-device intelligence has also been boosted by the advent of 5G, which provides faster, more stable, and secure connectivity for IoT devices. Therefore, the emergence of on-device intelligence has been driven by recent technological advancements that have made it possible to deploy AI models on devices, the advances in compute infrastructure, and the widespread adoption of IoT devices, making it possible to collect big data from various sources.
How to Develop On-Device Intelligence?
On-device intelligence needs advanced hardware like GPUs, accelerators, and AI chips. Typically, AI models are trained and optimized on the cloud using lots of data. Then, these models are transformed into a format that works on devices, using frameworks like Tensorflow Lite, Pytorch Mobile, and ML Kit by Google. Examples of some of the processors that support on-device intelligence are Samsung Exynos and Qualcomm Snapdragon.
Next, these models are incorporated into devices in factories, hospitals, cars, satellites, and homes. They process data locally, providing real-time insights. But AI models may still face issues during processing. When this happens, problematic data is sent to the cloud to train the original AI model further.
This feedback loop is critical because it helps refine the AI models, boosting their performance. After deployment, on-device AI models keep getting smarter as they continuously improve over time. Besides training models on clouds, there are some applications that perform training and inference on devices. Popular examples of these apps are the “Hey Siri” feature, the “Now Playing” feature on a Google Pixel phone which recognizes sounds around you, and Face ID on iPhone.
Benefits of On-Device Intelligence
On-device intelligence is a decentralized and reliable technology that does not require internet access to process data, making it ideal for mission-critical, production-grade AI applications. It provides faster and more efficient processing of data by eliminating the need to send data to remote servers for processing. This reduces latency, improves overall device performance, and lowers data usage.
Moreover, on-device intelligence provides greater privacy and security by processing data locally on the device, reducing the risk of sensitive information being exposed to third parties. With the growing number of connected devices, on-device intelligence is becoming increasingly important in industries such as healthcare, finance, and government, where data privacy and security are crucial.
As on-device AI models train on more data, they become increasingly accurate. When an application faces data that it cannot accurately process, it typically uploads it to allow the AI to retrain and learn from it. This results in better performance and accuracy over time.
On-device intelligence also enables new use cases and applications, such as powering voice assistants, real-time translation, and augmented reality applications. As more devices become capable of processing data locally, we can expect to see even more innovative use cases emerge.
Applications of On-Device Intelligence
On-device intelligence is revolutionizing various industries with its real-world applications. Here are some examples:
- Smartphones: On-device intelligence is widely used in smartphones. Virtual assistants like Siri and Google Assistant rely on on-device intelligence to process voice commands instantly without the need for an internet connection. Moreover, on-device intelligence enables features like facial recognition for secure authentication and unlocking of the phone.
- Autonomous Vehicles: On-device intelligence is also transforming the automotive industry by powering autonomous vehicles. By processing data from sensors and cameras locally on the vehicle, on-device intelligence can identify potential dangers and make quick decisions to avoid accidents.
- Healthcare: On-device intelligence is making healthcare more accessible and efficient by enabling remote monitoring and diagnosis of patients. Wearable devices like smartwatches and fitness trackers use on-device intelligence to track vital signs and alert patients and doctors to potential health issues. It can also analyze medical images to improve the accuracy and speed of diagnoses.
- Manufacturing: On-device intelligence is being used in manufacturing to reduce downtime and improve efficiency. By analyzing sensor data from machines in real-time, on-device intelligence can predict issues before they cause breakdowns, enabling predictive maintenance and reducing the need for costly repairs.
- Retail: On-device intelligence is transforming the retail industry by offering personalized shopping experiences. It analyzes customer data and offers personalized product recommendations and targeted promotions, improving customer satisfaction and loyalty.
To sum up, on-device intelligence is revolutionizing industries by providing faster data processing, increased security and privacy, and new applications. The future of on-device intelligence looks promising with the continual growth in hardware capabilities, the expansion of the Internet of Things, and the rising demand for real-time data processing. With this, on-device intelligence is expected to pave the way for further innovation and development.