Coffee shop banter: Symbolic or Deep Learning? Promising directions of AI
Sparks flew when friends, Prof. Sriraam Natarajan, UT Dallas and Prof. Kristian Kersting, TU Darmstadt decided to get together over a cup of coffee and discuss on the topic “Symbolic or Deep Learning? Promising directions of AI”. The talk started with setting up the stage with “AI is changing the world and has a huge impact in economics, drug development, agriculture etc.” Kristian believes that AI should be an engine for innovation.
3 min read
Levelling up NLP for Indian Languages
We are working towards building a better ecosystem for Indian languages while also keeping up with the recent advancements in NLP. To this end, we are releasing IndicNLPSuite, which is a collection of various resources and models for Indian languages
6 min read
Ablation-CAM: Making AI trustworthy
As machine learning is set to change every aspect of our life, a key dilemma plagues the minds of researchers and its users- Can we trust machines to make key life decisions for us? Can we rely solely on the machine to drive us safely to the destination without knowing the basis of its function or shall we allow a machine to operate us instead of a doctor?
4 min read
Deep learning in biomedical image analysis
Medical imaging, specifically radiologic imaging is the most commonly used diagnostic tool for disease diagnosis and treatment assessment for a wide variety of conditions. Over the last decades the image acquisition hardware has improved significantly and corresponding image reconstruction software has become more sophisticated. These provide increasingly complex data both in terms of size and content, making it a challenging task for radiologists to sift through and arrive at meaningful diagnosis and therapeutic assessment. The role of AI/ML techniques in this context is to act as a radiologist’s assistant to automate routine tasks and provide preliminary diagnosis. A radiologist can then use the outputs from these systems to speed up and improve accuracy of diagnosis.
3 min read