If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Trains are part-and-parcel of every day lives in countries with large, diverse, multi-lingual population like India. Consequently, an assistant which can accurately predict and explain train delays will help people and businesses alike. We present a novel conversation agent which can engage with people about train status and inform them about its delay at in-line stations. It is trained on past delay data from a subset of trains and generalizes to others.
Open data refers to data that is freely available for reuse. Although there has been rapid increase in availability of open data to public in the last decade, this has not translated into better decision-support tools for them. We propose intelligent conversation generators as a grand challenge that would automatically create data-driven conversation interfaces (CIs), also known as chatbots or dialog systems, from open data and deliver personalized analytical insights to users based on their contextual needs. Such generators will not only help bring Artificial Intelligence (AI)-based solutions for important societal problems to the masses but also advance AI by providing an integrative testbed for human-centric AI and filling gaps in the state-of-art towards this aim.
Srivastava, Biplav, Rossi, Francesca
A new wave of decision-support systems are being built today using AI services that draw insights from data (like text and video) and incorporate them in human-in-the-loop assistance. However, just as we expect humans to be ethical, the same expectation needs to be met by automated systems that increasingly get delegated to act on their behalf. A very important aspect of an ethical behavior is to avoid (intended, perceived, or accidental) bias. Bias occurs when the data distribution is not representative enough of the natural phenomenon one wants to model and reason about. The possibly biased behavior of a service is hard to detect and handle if the AI service is merely being used and not developed from scratch, since the training data set is not available. In this situation, we envisage a 3rd party rating agency that is independent of the API producer or consumer and has its own set of biased and unbiased data, with customizable distributions. We propose a 2-step rating approach that generates bias ratings signifying whether the AI service is unbiased compensating, data-sensitive biased, or biased. The approach also works on composite services. We implement it in the context of text translation and report interesting results.
Gaurav, Ramashish, Srivastava, Biplav
India runs the fourth largest railway transport network size carrying over 8 billion passengers per year. However, the travel experience of passengers is frequently marked by delays, i.e., late arrival of trains at stations, causing inconvenience. In a first, we study the systemic delays in train arrivals using n-order Markov frameworks and experiment with two regression based models. Using train running-status data collected for two years, we report on an efficient algorithm for estimating delays at railway stations with near accurate results. This work can help railways to manage their resources, while also helping passengers and businesses served by them to efficiently plan their activities.
Srivastava, Biplav (IBM Research)
Conversation interfaces (CIs), or chatbots, are a popular form of intelligent agents that engage humans in taskoriented or informal conversation. In this position paper and demonstration, we argue that chatbots working in dynamic environments, like with sensor data, can not only serve as a promising platform to research issues at the intersection of learning, reasoning, representation and execution for goal-directed autonomy; but also handle non-trivial business applications. We explore the underlying issues in the context of Water Advisor, a preliminary multi-modal conversation system that can access and explain water quality data.
Kephart, Jeffrey O. (IBM Thomas J. Watson Research Center) | Dibia, Victor C. (IBM Thomas J. Watson Research Center) | Ellis, Jason (IBM Thomas J. Watson Research Center) | Srivastava, Biplav (IBM Thomas J. Watson Research Center) | Talamadupula, Kartik (IBM Thomas J. Watson Research Center) | Dholakia, Mishal (IBM Thomas J. Watson Research Center)
We demonstrate an embodied cognitive agent that helps scientists visualize and analyze exo-planets and their host stars. The prototype is situated in a room equipped with a large display, microphones, cameras, speakers, and pointing devices. Users communicate with the agent via speech, gestures, and combinations thereof, and it responds by displaying content and generating synthesized speech. Extensive use of context facilitates natural interaction with the agent.
We demonstrate Water Advisor, a multi-modal assistant to help non-experts make sense of complex water quality data and apply it to their specific needs. A user can chat with the tool about water quality and activities of interest, and the system tries to advise using available water data for a location, applicable water regulations and relevant parameters using AI methods.
Chakraborti, Tathagata (Arizona State University) | Talamadupula, Kartik (IBM T. J. Watson Research Center) | Dholakia, Mishal (IBM T. J. Watson Research Center) | Srivastava, Biplav (IBM T. J. Watson Research Center) | Kephart, Jeffrey O. (IBM T. J. Watson Research Center) | Bellamy, Rachel K. E. (IBM T. J. Watson Research Center)
In this brief abstract we report work in progress on developing Mr.Jones — a proactive orchestrator and decision support agent for a collaborative decision making setting embodied by a smart room. The duties of such an agent may range across interactive problem solving with other agents in the environment, developing automated summaries of meetings, visualization of the internal decision making process, proactive data and resource management, and so on. Specifically, we highlight the importance of integrating higher level symbolic reasoning and intent recognition in the design of such an agent, and outline pathways towards the realization of these capabilities.We will demonstrate some of these functionalities here in the context of automated orchestration of a meeting in the CEL — the Cognitive Environments Laboratory at IBM's T. J. Watson Research Center.
Anderson, Monica (University of Alabama) | Barták, Roman (Charles University) | Brownstein, John S. (Boston Children's Hospital, Harvard University) | Buckeridge, David L. (McGill University) | Eldardiry, Hoda (Palo Alto Research Center) | Geib, Christopher (Drexel University) | Gini, Maria (University of Minnesota) | Isaksen, Aaron (New York University) | Keren, Sarah (Technion University) | Laddaga, Robert (Vanderbilt University) | Lisy, Viliam (Czech Technical University) | Martin, Rodney (NASA Ames Research Center) | Martinez, David R. (MIT Lincoln Laboratory) | Michalowski, Martin (University of Ottawa) | Michael, Loizos (Open University of Cyprus) | Mirsky, Reuth (Ben-Gurion University) | Nguyen, Thanh (University of Michigan) | Paul, Michael J. (University of Colorado Boulder) | Pontelli, Enrico (New Mexico State University) | Sanner, Scott (University of Toronto) | Shaban-Nejad, Arash (University of Tennessee) | Sinha, Arunesh (University of Michigan) | Sohrabi, Shirin (IBM T. J. Watson Research Center) | Sricharan, Kumar (Palo Alto Research Center) | Srivastava, Biplav (IBM T. J. Watson Research Center) | Stefik, Mark (Palo Alto Research Center) | Streilein, William W. (MIT Lincoln Laboratory) | Sturtevant, Nathan (University of Denver) | Talamadupula, Kartik (IBM T. J. Watson Research Center) | Thielscher, Michael (University of New South Wales) | Togelius, Julian (New York University) | Tran, So Cao (New Mexico State University) | Tran-Thanh, Long (University of Southampton) | Wagner, Neal (MIT Lincoln Laboratory) | Wallace, Byron C. (Northeastern University) | Wilk, Szymon (Poznan University of Technology) | Zhu, Jichen (Drexel University)
It is well known that the title of an article impacts how well it is discovered by potential readers and read. With both people and search engines, acting on behalf of people, accessing papers from digital libraries, it is important that the paper titles should promote discovery. In this paper, we investigate the characteristics of titles of AI papers and then propose au- tomatic ways to augment them so that they can be better in- dexed and discovered by users. A user study with researchers shows that they overwhelmingly prefer the augmented titles over the originals for being more helpful.