Cranial Nerve Computation Model

ECG STEMI AI Model: In the past, most AI systems gave people the feeling of a black box and couldn't be trusted. The team designed a mechanism that allows doctors to adjust and observe the AI ​​model, so that the AI ​​model can be customized to the functions the doctor wants. We use LINE, the most commonly used communication software for doctors, to design an EKG Line Bot. Medical staff can upload an electrocardiogram to the EKG Line Bot to instantly identify whether the electrocardiogram is Stemi, so as to help doctors determine whether the patient has signs of myocardial infarction. We use this Line Bot to cooperate with doctors and ask them to communicate with the Line Bot. According to the heat map provided by the system, we can check whether it is consistent with the medical concept, and then help us correct the accuracy of our model. The system will train the correct data again.

To develop an AI model that doctors trustthat generalizes to as many hospitals as possible, NVIDIAMass General Brigham embarked on an initiative called strongEXAM/strong (strongE/strongMR CstrongX/strongR strongA/strongI strongM/strongodel) the largest, most diverse federated learning initiative with 20 hospitalsresearch institutions from around the world. In just two weeks, the global collaboration achieved a model with .94 area under the curve (with an AUC goal of 1.0), resulting in excellent prediction for the level of oxygen required by incoming patients.  

Textual Advertisement Generator: Given any limited specifics of any product, AI Advertisement Producer can automatically generate tons of top-quality descriptions and advertisements for the product in just one second. And not just one copy is produced. With deep learning and natural language processing technologies learned from millions of existing samples, our AI model can produce various styles of advertisements at the same time for users to select. It will be a big helper or a virtual brainstorming partner for any brands or advertisers to create their advertisements.

The application of voice assistants is becoming more and more popular, however, due to the inefficiency of artificial intelligence-based technology, current products are mostly built by using rules-based methods. Therefore, in this project, we would like to propose some corresponding solutions for different components of the dialogue system to improve the data efficiency and work efficiency of each component.

Drone delivery is a popular and emerging application at present. However, existing drone delivery systems can only deliver to outdoor open spaces via GPS, and cannot directly to the interior of recipient＇s building. In the era of covid-19 pandemic, we aim to reduce human contact and propose a drone delivery system that can deliver packages to the doorstep or the interior of buildings, and to achieve fully automatic control of the drone by developing visual positioning technique.

Development of AI Platform for Smart Drone - Intelligent Flight: Due to its high mobility and the ability to fly in the sky, the drone has inspired more and more innovative applications/services in recent years. The goal of this project is to resolve the problem of blindly flying an unmanned aerial vehicle (UAV, which a drone in our case) when it is out of human sight or the range of wireless communication, and three major research and development directions will be considered in this project. Three artificial intelligence (AI) technologies, namely, smart sensing, smart control, and smart simulation, are applied in this project. Smart sensing - a flight system is developed, which can avoid the obstacles, complete a flight mission, and land safely. Smart control - an intelligence flight control system and a light-weighted somatosensory vest are developed. Smart simulation - a cost-effective training system and a 3D model simplification method are designed.

Machines need annotations to learn, but human babies learn human languages with almost no annotations. Can machines do the same thing? To allow machines to learn human languages with only observations like human babies, a research team at Taiwan has partnered with the speech research groups in Meta, CMU, MIT, and JHU to develop a brand new self-supervised speech processing evaluation framework, Speech Processing Universal PERformance Benchmark (SUPERB).