Sonam Damani

Nitya Raviprakash, Sonam Damani, Ankush Chatterjee et al.

The handicraft industry is a strong pillar of Indian economy which provides large-scale employment opportunities to artisans in rural and underprivileged communities. However, in this era of globalization, diverse modern designs have rendered traditional designs old and monotonous, causing an alarming decline of handicraft sales. For this age-old industry to survive the global competition, it is imperative to integrate contemporary designs with Indian handicrafts. In this paper, we use novel AI techniques to generate contemporary designs for two popular Indian handicrafts - Ikat and Block Print. These techniques were successfully employed by communities across India to manufacture and sell products with greater appeal and revenue. The designs are evaluated to be significantly more likeable and marketable than the current designs used by artisans.

Khyatti Gupta, Sonam Damani, Kedhar Nath Narahari

Jewelry has been an integral part of human culture since ages. One of the most popular styles of jewelry is created by putting together precious and semi-precious stones in diverse patterns. While technology is finding its way in the production process of such jewelry, designing it remains a time-consuming and involved task. In this paper, we propose a unique approach using optimization methods coupled with machine learning techniques to generate novel stone jewelry designs at scale. Our evaluation shows that designs generated by our approach are highly likeable and visually appealing.

Sonam Damani, Nitya Raviprakash, Umang Gupta et al.

Dialogue systems and conversational agents are becoming increasingly popular in the modern society but building an agent capable of holding intelligent conversation with its users is a challenging problem for artificial intelligence. In this demo, we demonstrate a deep learning based conversational social agent called "Ruuh" (facebook.com/Ruuh) designed by a team at Microsoft India to converse on a wide range of topics. Ruuh needs to think beyond the utilitarian notion of merely generating "relevant" responses and meet a wider range of user social needs, like expressing happiness when user's favorite team wins, sharing a cute comment on showing the pictures of the user's pet and so on. The agent also needs to detect and respond to abusive language, sensitive topics and trolling behavior of the users. Many of these problems pose significant research challenges which will be demonstrated in our demo. Our agent has interacted with over 2 million real world users till date which has generated over 150 million user conversations.