Research by members of the Department of Computer Science at Rutgers, and by their collaborators, is organized within the Laboratory for Computer Science research(LCSR). AI and AI-related applications are the major area of research within LCSR, with about forty people-faculty, staff and graduate students-currently involved in various aspects of AI research.
The birth of artificial-intelligence research as an autonomous discipline is generally thought to have been the monthlong Dartmouth Summer Research Project on Artificial Intelligence in 1956, which convened 10 leading electrical engineers -- including MIT's Marvin Minsky and Claude Shannon -- to discuss "how to make machines use language" and "form abstractions and concepts." A decade later, impressed by rapid advances in the design of digital computers, Minsky was emboldened to declare that "within a generation ... the problem of creating'artificial intelligence' will substantially be solved." The problem, of course, turned out to be much more difficult than AI's pioneers had imagined. In recent years, by exploiting machine learning -- in which computers learn to perform tasks from sets of training examples -- artificial-intelligence researchers have built special-purpose systems that can do things like interpret spoken language or play Jeopardy with great success. But according to Tomaso Poggio, the Eugene McDermott Professor of Brain Sciences and Human Behavior at MIT, "These recent achievements have, ironically, underscored the limitations of computer science and artificial intelligence.
In recent years there has been a significant decline in the number of college students choosing majors in computer science or technology related fields. Although this trend is beginning to turn around at the undergraduate level, there remains disparity in the number of under-represented minority students who earn graduate degrees as compared to majority students. Additionally, within the United States, there is an achievement gap between under-represented minority students and majority students at a time when underrepresented groups are becoming an increasing proportion of the national labor force. This reluctance to study Science, Technology, Engineering, and Mathematics (STEM) disciplines must be confronted and changed if the United States is to maintain a competitive position within the global market. Effective use of learning technologies is vital to solving many of our current STEM learning challenges. Robotics is a growing research area in computer science education. We use robotics as a technology tool captivate and engage students in research in robotics.
Computer vision has become one of the largest computer science research communities. We have made tremendous progress in recent years over a wide range of areas, including object recognition, image understanding, video analysis, 3D reconstruction, etc. It has also become one of the largest computer science research community. However, despite the expansion of our field, the percentage of female faculty members and researchers both in academia and in industry is still relatively low. As a result, many female researchers working in computer vision may feel isolated and do not have a lot of opportunities to meet with other women.
Faculty at liberal arts colleges are often challenged to offer a quality education to their students, complete with opportunities for undergraduate research. To guard against a curriculum that is too theoretical, students want to see applications of their course work and tangible results of their efforts. Like all computer science educators, we want to attract students to our discipline. The use of robotics can often be part of the answer in each of these realms.