Engineers World

ARTIFICIAL INTELLIGENCE

  Seminar Topics 

Artificial Intelligence in Power Station
Detailed investigations and developments are in progress on power distribution systems and the monitoring of apparatus. These are on (1) “digital technology” based on the application of semiconductor high-speed elements, (2) intelligent substations applying IT (information technology), and (3) system configurations aimed at high-speed communication. Incorporated in these are demands for the future intelligent control of substations, protection, monitoring, and communication systems that have advantages in terms of high performance, functional distribution, information-sharing and integrated power distribution management.

Intelligent Agent Paradigm
An intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. The simplest intelligent agents are programs that solve specific problems. More complicated agents include human beings and organizations of human beings (such as firms). The paradigm gives researchers license to study isolated problems and find solutions that are both verifiable and useful, without agreeing on one single approach. An agent that solves a specific problem can use any approach that works – some agents are symbolic and logical, some are sub-symbolic neural networks and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields—such as decision theory and economics—that also use concepts of abstract agents.
Exploratory Visualization
Visualization is a key enabling technology in this endeavor: it helps people explore and explain data through software systems that provide a static or interactive visual representation. Despite the promise that visualization can serve as an effective enabler of advances in other disciplines, the application of visualization technology is non-trivial. The design of effective visualizations is a complex process that requires understanding of existing techniques and how they relate to human cognition. For a visualization to be insightful, it needs to be both effective and efficient. This requires a combination of design and science to reveal information that is otherwise obscured.

Tera-scale deep learning 
Deep learning and unsupervised feature learning offer the potential to transform many domains such as vision, speech, and NLP. However,these methods have been fundamentally limited by our computational abilities, and typically applied to small-sized problems. In this talk, I describe the key ideas that enabled scaling deep learning algorithms to train a very large model on a cluster of 16,000 CPU cores (2000 machines). This network has 1.15 billion parameters, which is more than 100x larger than the next largest network reported in the literature.Such network, when applied at the huge scale, is able to learn abstract concepts in a much more general manner than previously demonstrated. Specifically, we find that by training on 10 million unlabeled images, the network produces features that are very selective for high-level concepts such as human faces and cats. Using these features, we also obtain significant leaps in recognition performance on several large-scale computer vision tasks.
Machine Learning for Dynamic Difficulty Adjustment in Computer Games
Most computer games only have static difficulty settings and computer game researchers have proposed a number of heuristic approaches. Slides should cover (i) formalize dynamic difficulty adjustment as a learning problem on partially ordered sets, (ii) propose an exponential update algorithm for this setting, (iii) show a bound on the number of wrong difficulty settings relative to the best static setting chosen in hindsight, and (iv) demonstrate the empirical performance of the algorithm.