Artificial Intelligence (AI) is revolutionizing industries, products, and core capabilities by delivering dramatically enhanced experiences. However, this is just the start of the AI revolution. The field of AI, especially deep learning, is still in its infancy with tremendous opportunity for exploration and improvement. For instance, deep neural networks of today are rapidly growing in size and use too much memory, compute, and energy. To make AI truly ubiquitous, it needs to run on the end device within a tight power and thermal budget. New approaches and fundamental research in AI, as well as applying that research, is required to advance machine learning further and speed up adoption.
That’s where Qualcomm AI Research comes in. Qualcomm Technologies has a rich history of foundational research across technologies that have led to breakthrough innovations. Qualcomm AI Research brings together machine learning researchers across the organization to investigate a wide range of machine learning topics from fundamental deep learning research to applied AI. In this blog post, I’ll briefly discuss notable topics that show the breadth of our research, from quantization and unsupervised learning to fundamental long-term research like quantum AI. For more in-depth discussion, please attend my upcoming webinar.
His webinar from SAI Conference is embedded below. The slides are available here.
The Unsupervised learning from RF for precise positioning picked my interest immensely. He mentions in the blog post:
AI is a powerful tool, but the key is to intelligently apply AI to solve the right challenges – for example, challenges that are difficult to solve with traditional methods but much easier to solve with AI. One such challenge is determining a receiver’s precise position from radio frequency (RF) signals. Radio waves are all around us, and there is an opportunity to learn from them. In this research area, we are applying unsupervised learning to the RF signals to achieve centimeter-accurate positioning.
Consider this auto assembly line in the image below where GPS and other techniques are infeasible. The environment is complex with many irregular shapes and moving equipment. If we wanted to know the precise location of an assembly line worker (from the RF the smartphone receives), it would be very complex to model the indoor RF propagation using traditional methods. In other words, it is hard to precisely know the location of the worker since the RF signals that we are measuring could be coming from different paths due to reflections, diffraction, and scattering from walls and various irregular objects like robot arms.
For this type of complex environment or any type of indoor positioning, we thought that AI coupled with domain knowledge of physics would be a good tool to learn the complex physics of propagation from the unlabeled RF. We call this hybrid approach “neural augmentation,” a technology that augments neural networks with human knowledge and algorithms, or vice versa. One benefit of a neural network learning the RF environment is that it can estimate the precise position of the RF receiver, and thus the location of the person.
The neural network we created uses a generative auto-encoder plus conventional channel modeling (based on physics of propagation) to train on unlabeled channel state information (CSI) observations and learn the environment. Our initial results from implementing neural unsupervised learning from RF for positioning are promising. The neural network learns the virtual transmitter locations up to rigid body transformations (shifts, reflections, rotations) completely unsupervised. With a few labeled measurements, map ambiguity is resolved to achieve cm-level positioning.
You can read more about Qualcomm AI Research here.
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