Artificial intelligence is unlocking tremendous economic value across various market sectors. Individual data scientists can draw from several open source frameworks and basic hardware resources during the very initial investigative phases but quickly require significant hardware and software resources to build and deploy production models. Intel offers various hardware to support a diversity of workloads and user needs. Intel built a competitive deep learning platform to make it easy for data scientists to start from the iterative, investigatory phase and take models all the way to deployment. This platform is designed for speed and scale, and serves as a catalyst for all types of organizations to benefit from the full potential of deep learning. Example of supported applications include but not limited to automotive speech interfaces, image search, language translation, agricultural robotics and genomics, financial document summarization, and finding anomalies in IoT data. This talk will detailed what Intel has done and plans to do from hardware to software to state-of-the-art algorithms in order to democratized AI.
Da-Ming Chiang, PhD. is a technical solution architect in Intel Artificial Intelligence Product Group (AIPG) Business Development Team. He is responsible for enabling Intel AI solutions into customers’ platforms. He is an algorithm developer in the field of statistical signal processing by school training. Throughout his professional work, he has been steadily gaining experience on embedded system, wireless system, server and cloud architecture and automatic speech recognition systems. Currently, he is mixing AI with these systems to make the systems smarter.