Artificial intelligence (AI) and machine learning (ML) models hold great promise in transforming knowledge and better knowledge management. In this series, participants will learn how can this be accomplished, What software, tools, and technologies are behind it, How accurate is the outcome or prediction? In this series participants will learn how artificial intelligence and machine learning methods superseding human intelligence can be applied to day-to-day applications.
Artificial intelligence is impacting the future of virtually every industry and every human being. Artificial intelligence has acted as the main driver of emerging technologies like big data, robotics and IoT, and it will continue to act as a technological innovator for the foreseeable future. Artificial Intelligence and Machine Learning have reached a critical tipping point and will increasingly augment and extend virtually every technology enabled service, thing, or application. Self-driving vehicles, digital assistants, robotic factory staff, and smart cities have proven that intelligent machines are possible. This talk will address the differences between AI and ML, different use cases of AI and ML and unique and different applications such as predict and prevent player injuries, language and translation, insurance claims, AI for fashion design, swarm intelligence etc.
Swarm intelligence refers to collective intelligence. Biologists and natural scientists have been studying the behavior of social insects due to their efficiency of solving complex problems such as finding the shortest path between their nest and food source or organizing their nests. The collective behavior of such ecosystems, and their artificial counterpart of SI, is not encoded within the set of rules that determines the movement of each isolated agent, but it emerges through the interaction of multiple agents. This talk will provide an overview of some of the most widely used bio-inspired algorithms and related topics.
Recommender systems are algorithms aimed at suggesting relevant items to users, items being movies to watch, text to read, products to buy or anything else depending on industries. Recommender systems also help in improvement of sales and are really critical in some industries as they can generate a huge amount of income when they are efficient. In this session, participants will learn how recommender systems work, describe their theoretical basis and discuss their strengths and weaknesses.