Abstract:
In the first half of this talk, we will present a broad overview of applications of AI and Cognitive Computing in several industries from an IBM perspective, and present some of the common challenges faced in the field. In the second half of the talk, we will focus on making Deep Learning practical. As deep learning continues to pave way into diverse and widespread applications, many research problem are choosing to adopt deep learning as a solution approach. There is a need for simple intuitive tools to cater the needs of the diverse communities and enable them to implement and prototype deep learning models in a quicker fashion. The existing libraries such as Tensorflow, Theano, Torch, Caffe, Keras etc. requires an initial large learning curve and also provides minimum communication across them. To overcome these challenges, we have created an abstractive tool called DARVIZ which provides an easy, intuitive drag-and-drop interface for deep learning modelling. Using the abstract DARVIZ model, the execution ready source code can be generated in any language and any library of preference, making a developer's life easy. The alpha version of DARVIZ is currently available for research community to use.