The audience was then split into four groups, with each faculty presenter leading the group for interactive group activities. Following student introductions about their educational background and reason for attending the workshop, each group was given a very short peer-reviewed research paper on topics ranging from natural language processing, search, human-automation interaction, and multiagent systems to read with guidance from the faculty presenter on how to read a research paper, students read each section of the paper and discussed it with their group in response to questions from the workbook. The faculty then shared a few specific research projects from their own research areas and introduced undergraduate summer research programs. The last part of the group activity time involved a discussion on how students could seek out and secure research opportunities. This included specifics on how to prepare and reach out to faculty members about opportunities to do research in their labs.

Colon Cancer is one of the most common types of cancer. The treatment is planned to depend on the grade or stage of cancer. One of the preconditions for grading of colon cancer is to segment the glandular structures of tissues. Manual segmentation method is very time-consuming, and it leads to life risk for the patients. The principal objective of this project is to assist the pathologist to accurate detection of colon cancer. In this paper, the authors have proposed an algorithm for an automatic segmentation of glands in colon histology using local intensity and texture features. Here the dataset images are cropped into patches with different window sizes and taken the intensity of those patches, and also calculated texture-based features. Random forest classifier has been used to classify this patch into different labels. A multilevel random forest technique in a hierarchical way is proposed. This solution is fast, accurate and it is very much applicable in a clinical setup.

Alibaba Cloud, the cloud-computing arm of Alibaba Group, is making artificial intelligence (AI) technology more accessible to businesses and organizations with the debut of an upgraded machine-learning platform. Called PAI 2.0, the platform, which launched in 2015, will "help customers easily deploy large-scale data mining and modeling," Alibaba Cloud said in a statement. Machine learning is a branch of AI that gives computers the ability to absorb information, discern patterns in data and adapt to new input without explicit programming. China's first publicly available machine-learning platform, PAI is a suite of tools and AI software algorithms that allows businesses without an AI background to make practical use of Alibaba Cloud's ET program, an "artificial brain" that the company is using to solve complex problems such as predicting the winner of a popular Chinese reality TV show and helping to ease traffic congestion in the city of Hangzhou, China. "In the past year, Alibaba Cloud has implemented a number of real-life AI applications for customers across industries," said Alibaba Cloud Chief Scientist Dr. Jingren Zhou.