Cognitive Computing and Collective Intelligence

Overview

On completion of the  Cognitive Computing and Collective Intelligence  programme at the University of Jyväskylä , our graduates will be able to:

• use, design and train complex self-managed and continuously evolving public and private industrial systems, digital ecosystems, cyber-physical systems, systems-of-systems, platforms, services and applications;

• will be able to connect their designs with publicly available Deep Learning and  Big Data analytics and Web-based Cognitive Computing capabilities as services;

• will be able to figure-out and approach various challenging aspects of complex problems world-wide, which require collective intelligence and self-managing service-based architectures for their solutions;

• understand, and professionally utilize for that purpose, knowledge on enabling technologies and tools;

• perform research training and academic doctoral level studies;

• will be skillful in international communication due to the integrated language and communication studies.

Study Contents

Cognitive Computing (the umbrella label for technologies that ingest data and then learn as their knowledge base grows) simulates human thought processes in a computerized model. It focuses on self-learning and self-managing systems that use artificial intelligence (machine learning, data mining, pattern recognition, natural language processing, etc.) to mimic the way the human brain works.

While targeting the automatic decision-making and problem-solving, the Cognitive Computing systems are able to learn their behavior through education. They support forms of expression that are more natural for human interaction, which allows them to interpret data regardless of how it is communicated. The primary value is their expertise and the ability to continuously evolve at enormous scale as they experience new information, scenarios and responses.

Cognitive Computing as a technology enables various forms of intelligence interact naturally to collaboratively address complex problems. The technology relies on advances in the study of Collective Intelligence, in regards to not only physical groups of humans, but more to the conceptual and mechanical systems we build. Cognitive Computing and Collective Intelligence is the only way nowadays to address the complexity challenges related to the Big Data and the Internet of Things. Combination of these technologies and challenges is resulting to qualitatively new and efficient Smart Cyber-Physical Systems and Industry 4.0.

Research Focus

The Faculty is engaged in active international cooperation both in the field of research and teaching. We have strong collaboration with industry both at local and national level; the vast majority of Master’s theses are completed in collaboration with industry. The programme locates itself in the core of the area of computational thinking and decision making which is one of the major development areas of the Faculty. Artificial Intelligence and Cognitive Computing research and teaching are well established and the Faculty research is aligned to strategic plans of IBM Watson; the inventor and leading global provider of Cognitive Computing services. The programme is designed to capitalize this core research to teaching.

Doctoral Study Opportunities

After graduation, you will have the opportunity to apply for Doctoral Studies in Natural Sciences. The duration of Doctoral Studies as full-time study will take approximately four years and studies include both taught elements and Doctoral Dissertation.

Career Opportunities

• Software (Cloud) Service Architects: designing the technical infrastructures of service enabled applications;

• Enterprise Architects: architecting and aligning enterprises processes, structure, data and control;

• Web Service (IT) Professionals: experts in the development and composition of Web services into enterprise applications;

• Big Data and Knowledge Engineers, Architects, Modellers and Analysts: experts in Big Data, metadata and ontology engineering, knowledge management, data and knowledge integration and evolution, in constructing data-as-a-service solutions, data-intensive applications, expert-systems and knowledge based-systems;

• Analytics Professionals: experts in decision making and support, optimization based decision making, prescriptive analytics;

• Researchers (PhD studies): graduates are well-prepared to successfully pursue a career in academia.