Artificial Intelligence

Artificial Intelligence visual image


Our mission is to develop AI that is available anywhere any time, and continuously evolve to be always helpful to everyone, with the ultimate goal of creating value for the customer.
 
At Samsung Research, we are developing innovative Artificial Intelligence (AI) technologies to enhance current business and open up new business opportunities, with the ultimate goal of enhancing human life and contributing to the society.


Aligned with our goal above, we are conducting research in broad thematic areas such as Virtual Personal Assistant, Visual Understanding, On-Device AI, etc.
 
Virtual Personal Assistants (VPA): We are developing VPA technologies to enable natural and engaging conversation between our devices/services and the user. The goal of this project is to allow our agents to understand human dialog through voice recognition, carry on natural conversation, answer questions, translate and interpret various languages, and generate realistic speech through voice synthesis. Applications include mobile phones, consumer electronics, and various online services.
 
Visual Understanding: For AI with strong visual understanding capabilities, we are developing visual understanding technologies to extract meaningful descriptions from images, with the goal of providing users with timely and accurate information, and to allow natural user interaction. We are developing deep-learning-based image understanding methods for use in our products and services. Our algorithms can recognize large number of objects and understand the semantic content of images. The technology has many uses, such as product recognition on mobile phones, user recognition in consumer electronics, and visual inspection for manufacturing.
 
On-Device AI: Most of the current AI technologies depend on cloud services. However, to reduce latency, ensure privacy, and provide AI services anywhere, any time, we need on-device AI capability. On-device AI has many challenges, such as meeting power constraints, on-device learning requirements, model reduction and optimization, and SDK and tool chain for on-device AI development and deployment. We are developing new algorithms and tools to meet these challenges