Fundamental Research
Deep Learning
With access to the extraordinarily large data, we focus on modeling the underlying structure for both structured and unstructured data. Our research covers fundamental and theoretical work in the field of deep learning, including mathematics, statistics, learning and algorithms. The most critical mission at IIAI is to advance on the understanding of underlying mecanismes giving rise to intelligence. we will be dedicating more efforts on exploring high-level reasoning and thinking skills that can eventually create human-imitative intelligence. Theses advances in the development of fundamental machine learning algorithm research and it application to real world scenarios will accelerate innovation and eventually be benefits to the society. Our primary research areas include but not limit to: deep neural network understanding, unsupervised learning (e.g., large- scale data unsupervised clustering, unsupervised/weakly-supervised data representation learning and unsupervised data knowledge association), adversarial learning (e.g.,Generative Adversarial Networks and its derivatives).
Reinforcement Learning
Reinforcement learning (RL) is an important area of machine learning inspired by behaviourist psychology, concerned with an agent learn how to behave in a environment by performing actions so as to maximize some notion of cumulative reward. Inception Institute focuses on reinforcement learning including both policy-search and temporal-difference based, as well as closely related methods for sample-based decision theoretic planning. Our efforts will be fully-dedicated to develop AI agents which are able to learn in an interactive environment by exploring solutions and automatically determine the ideal behaviour. Basically, the AI agents operate by getting a feedback information from trial and error using its own actions and experiences. For RL research, the techniques such as Monte Carlo tree search, swarm intelligence, control theory, operations research, information theory, optimisation theory and deep learning are expected to play key roles. Additionally, we apply RL in some real-word applications including automatic natural language processing, speech analysis, healthcare, image processing, video analytics.
Computer Vision
Computer vision is an interdisciplinary field that seeks to build a human-imitative visual understanding system to allow machines to obtain high-level understanding from multiple visual data forms (e.g., images and video sequences). From a scientific perspective, we desperately promote the development of computer vision theories behind the artificial visual understanding system, and target at driving excellence in a wide range of unsolved fundamental computer vision problems, including but not limited to human action recognition, gait recognition, person re-identification. From a technological perspective, we heavily promote the transferability of our research findings for enhancing the performance of existing visual understanding systems.
Natural Language Processing
Language is an advanced and complex system of human communication. Over the past decades, natural language process (NLP) has been a research area for developing machines’ capability in terms of how to use humans’ communication language. Based on the latest advancement of NLP theories, IIAI targets at developing innovative text input and understanding solutions as a universal and flexible interface that allows machines to intelligently and accurately understand voices and texts. In addition, IIAI also targets at developing highly effective and efficient NLP techniques that are capable of dealing with large volumes of natural language data in multiple documentation forms. Eventually, we aim at building the artificial language system that is the interface of machines to not only humans, but also the whole textual and linguistic world.
Applied Research
Intelligent video understanding
We aim at building a world-leading intelligent video understanding platform that can unlock a new era of how to effectively utilize the public and private monitoring and control devices with highly sophisticated video analytics in face detection and annotation, pedestrian identification and tracking, scene analysis and tagging, human behavior analysis and content enhancement. For example, with access to the national database that stores a vast amount of photo IDs, a fully-integrated person detection, tracking and recognition system can rapidly identify a target with deep learning techniques applied to the massive nationwide video system. Even more mission-critically, we can go from “unknown” to “unknown”, developing a self-learning AI system for detecting unknown human behavior from real-time surveillance camera feed, which can identify, classify and report abnormal activities in unlimited behavioral forms. Self-taught learning algorithms can study abnormal human patterns across the board and correspondingly perform inferences on the future patterns not yet known to us.
Medical imaging
Medical image computing is an interdisciplinary field at the intersection of computer science, data science, mathematics and have a wide range of applications in medicine and bioscience. In IIAI, researchers fully dedicate on applying cutting-edge machine learning and computer vision techniques to revolutionize the healthcare industry through medical image computation and analysis, computer-assisted interventional systems and robotics, bioscience applications and computer-aided diagnosis and clinical visualization and feedback systems. All these applications require the manipulation and integration of medical image information. Many applications also depend on integration of the image information with sensor data (e.g. from tracking systems), and/or effector control systems (e.g. robots or positioning devices), and/or visual displays or other feedback systems. In fact, our researcher put efforts on a broad range of medical imaging applications. For instance, One of our key applications will be destined to Mammography; which is medical imaging technique that uses a low-dose x-ray system to aid in the early detection and diagnosis of breast diseases in women. Advanced deep learning techniques can be applied to much more accurately and efficiently detect cancerous lesions than the traditional examination by expert radiologists. Another challenging target is Gastrointestinal Diseases (GI), where we can deploy AI-based medical imaging to attain highly effective detection and classification. Additionally, medical image computing techniques are utilized in eye fundus scan for diabetes early detection and cardiac disease diagnosis in our institute as well.
Healthcare
AI has extensive use cases in healthcare. We aim at redesigning healthcare by utilizing the latest advancement of AI research in drug development, computer assisted interventional systems and robotics, bioscience applications and computer-aided diagnosis. For example: Artificial intelligence is providing advice that could help more than 30 million Americans suffering from diabetes. The advice is coming from algorithms which are designed to help patients with diabetes control their glucose levels by using data around how foods, exercise, and insulin affect their glucose levels under different conditions. In addition, a care manager might use the office-based platform to ask for a trends analysis of when blood glucose levels for an individual patient were high or low during certain days, weeks, or months. Those results might be used to adjust a patient’s overall care plan.