IBM just announced a collaborative research initiative with four leading universities to advance the development and deployment of cognitive computing systems -- systems like IBM Watson -- that can learn, reason, and help human experts make complex decisions involving extraordinary volumes of fast-moving data.
Faculty at the four schools -- Carnegie Mellon, MIT, Rensselaer Polytechnic, and NYU -- will study enabling technologies and methods for building a new class of systems that better enable people to interact with big data in what IBM has identified as a new era of computing.
The research initiative was announced at a colloquium held at the Thomas J. Watson Research Center attended by nearly 200 leading academics, IBM clients, and IBM researchers to begin a dialogue that deepens the understanding of cognitive systems and identifies additional areas of research to pursue.
“IBM has demonstrated with Watson that cognitive computing is real and delivering value today,” said Zachary Lemnios, vice president of strategy for IBM Research. “It is already starting to transform the ways clients navigate big data and is creating new insights in healthcare, how research can be conducted and how companies can support their customers. But much additional research is needed to identify the systems, architectures and process technologies to support a new computing model that enables systems and people to work together across any domain of expertise.”
These initial university collaborators will help lay the foundation for a Cognitive Systems Institute that IBM envisions will comprise universities, research institutes, and IBM clients.
The initial research topics for exploration announced today are:
- MIT -- How socio-technical tools and applications can boost the collective performance of moderate-sized groups of humans engaged in collaborative tasks such as decision making.
- RPI -- How advances in processing power, data availability, and algorithmic techniques can enable the practical application of a variety of artificial intelligence techniques.
- CMU -- How systems should be architected to support intelligent, natural interaction with all kinds of information in support of complex human tasks.
- NYU -– How deep learning is impacting many areas of science where automated pattern recognition is essential.
If you haven’t been keeping up with the evolution of cognitive computing, I would highly recommend you head on over to the IBM Research website on the topic and get caught up.
It’s pretty amazing stuff, and we really have only just begun!