Microsoft Research (MSR) India is organizing a 4 week summer workshop on Artificial Social Intelligence (ASI), which will be an intense project-based research endeavor. We will seek proposals from faculty members as well as corporate researchers and start-ups pertaining to various themes of ASI. A subset of the submitted projects will be selected by a panel of experts. The proposers of the selected projects will be teamed up with Post-Doc, PhD, PG and UG students selected through nominations or independently, along with other collaborators from MSR, who will work towards the project during the 4 weeks. Lectures and tutorials on basic as well as advanced topics will be delivered by the experts (including the proposers) at various points during the workshop. The best project will receive an unrestricted research grant of INR 700,000/- to continue the work further. All codes and data created during the school will be made publicly available.
Breakthroughs in Artificial Intelligence (AI) have typically shown that AI systems are good at solving specific tasks that have a well-defined goal, such as Speech Recognition, Image Captioning, Games like Poker/Go/Jeopardy, among others. However, as AI systems become ubiquitous, it is not enough for them to solve specific tasks; rather they will have to continuously interact with human-users as well as other AI systems in a rapidly evolving environment. These systems will have to continuously review and evolve their interaction strategies during the ongoing interaction. Goals may not be defined in advance, and might evolve dynamically. The systems have to ensure that apart from solving the primary task, the user receives a pleasant and professional experience that is »socio-culturally appropriate». In other words, we are quickly moving towards a world where AI systems have to go far beyond functional intelligence – they have to be socio-culturally adept and behaviourally intelligent. We refer to this phenomenon as “Artificial Social Intelligence” and the consequent systems as Socially Intelligent Agents.
As one can imagine, ASI is an extremely multi-disciplinary endeavor, where one needs inputs not only from AI and Machine-Learning researchers, but also linguists, social scientists, HCI, design and vision researchers.
The above figure shows a hypothetical interaction between a chatbot “botty” and a young boy “chhota bheem” in Hinglish to demonstrate the importance of ASI. While botty is able to interpret sentences and generate responses perfectly, it misses the fact that “a princess hat” is not a culturally appropriate birthday gift for chhota bheem’s mother. Thus, recommender systems (if you imagine botty had a gift recommender system embedded within it) need to take into account larger as well as user-specific socio-cultural contexts into account while making recommendations. Further, one might observe that botty has used “uske” (non-honorific pronoun) for Chhota Bheem’s mother and “unko” (the honorific pronoun) for referring to his younger sister, though the conversation etiquettes and pragmatics of Hindi demands the pronouns to be used the other way round.
ASI is an emergent field. While there has been research on some specific aspects of ASI, the parts are yet to come together and coalesce into a field or an interactive AI agent. We believe there are four fundamental sets of problems within the broad scope of ASI, which though can be dealt with independently, at the end should feed into each other:
- Discovery of Principles of Socio-cultural Interactions: Linguists, psychologists and social-scientists have been studying human behavior to understand the norms and aberrations, their biological, social and cultural origins and needs. In order to formulate the principles of socio-culturally enriching interactions between human and AI systems, it is not only necessary to gain insights from these fields, but also to conduct large scale data-driven studies that aim at validating the principles and deciphering new behavioral traits. Such studies are now possible, thanks to the large scale availability of socially grounded user data from social media, and due to advances in machine learning and other data-analysis techniques (see [1,2] for examples). Targeted Human-human and Human-machine interaction studies would also be of great importance.
- Design and Development of ASI Systems: The learnt principles could then be used to design interaction policies for ASI systems such as chatbots [9,10], recommender systems [5], search engines, self-driving cars, multimodal agents, or some completely new form of interactive agents. Developing these agents would require one to solve yet another set of engineering and research problems. One example of such a system is the virtual receptionist developed by Dr. Dan Bohus (opens in new tab) from Microsoft Research Redmond, which keeps track of users attention and engagement through visual cues (such as gaze tracking, head orientation etc.) to initiate the interaction at the most appropriate moment. Further, it can also make use of hesitation (e.g., “hmmm… uhhh”) to attract the attention of the user, buy time for processing or even to indicate uncertainty in the response [3].
- Evaluation of ASI: It is easy to evaluate systems which has a well-defined end-goal. For instance, image recognition systems can be evaluated on standard metrics like precision and recall on a certain class of images. However, it is extremely difficult to evaluate socio-cultural intelligence of a system because these traits are neither directly measurable, nor leads to any measurable outcome. We believe this is one of the most challenging open problem of ASI.
- Techniques and Resources for enabling ASI: Generic techniques such as learning of unbiased models from potentially biased data [4], platforms for prototyping dialogue systems [6-8] and chatbots with ASI, models of pragmatics, politeness, multilingual interactions, etc. are useful and important for enabling further research and system development in ASI. Large datasets of human-human and human-machine interactions are crucial for building such models and systems.
Proposals spanning any of the above sub-areas of ASI are welcome.
References
[1] Mark my words! Linguistic style accommodation in social media. (opens in new tab)
[3] Managing human-robot engagement with forecasts and … um … Hesitations. (opens in new tab)
[4] Fairness, Accountability, and Transparency in Machine Learning Workshop Series (opens in new tab)
[6] Strategy and policy learning for non-task oriented conversational bots (opens in new tab)
[9] Conversational involvement and synchronous nonverbal behaviour (opens in new tab)
[10] Towards the automatic detection of involvement in conversation (opens in new tab)
[13] ‘How about this weather?’ Social Dialog with Embodied Conversational Agents (opens in new tab)