This analysis is likely to yield insights into how different people phrase requests and offers for resources, in various languages. Next, we plan to utilize the insights obtained from the linguistic analysis, to build systems that will help coordinate the resource requests and offerings.
Specifically, we envision building an automated bot that responds appropriately to resource requests and offers, and then matches corresponding requests and offers.
Follow us:. Share this page:. 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 from Microsoft Research Redmond, which keeps track of users attention and engagement through visual cues such as gaze tracking, head orientation etc. Further, it can also make use of hesitation e. 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 [] and chatbots with ASI, models of pragmatics, politeness, multilingual interactions, etc.
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! Update: We have shortlisted the final proposals along with the proposers faculty and the students for the Summer workshop. Proposals are invited from faculty members in Indian universities and Indian start-ups in topics including but not limited to: Computational Social Science Socially and culturally aware agents Speech and Language Systems for ASI Multi-modality and ASI Proposals could suggest building a Socially Intelligent Agent or generic platforms for architecting such agents; we also invite proposals that seek to conduct large-scale socio-cultural studies using computational methods that will guide architecting ASI systems.
Submission Guidelines Proposals are invited from faculty in Indian institutions in the following format: Name, Affiliation and contact of the faculty submitting the proposal Name of collaborators if any : Up to ONE collaborator could be a faculty member from outside India or a researcher in some non-academic lab or company, including start-ups can be suggested.
Along with the proposal, please also send A brief CV of the proposer highlighting the expertise in the proposed area. Computational Infrastructure Garage style workstations will be provided to each team in Microsoft Research India premises. Best Project Award All projects will be presented and there will be a competition at the end of the workshop to choose the winning project. Proposer — Prof.
Mitesh Khapra, IIT Madras Abstract: Most of the AI systems today are driven by three key components i data ii common sense knowledge and iii powerful learning algorithms which can harness this data and knowledge to learn task specific meaningful patterns. Detection of Aggressive Behavior on Social Media. Proposer: Prof.
Ritesh Kumar, Ambedkar University Abstract: As the interaction over the web has increased, incidents of aggression and related events like trolling, cyberbullying, flaming, hate speech, etc. Saptarshi Ghosh, IIT Kharagpur Abstract: Effective coordination of post-disaster relief operations depends critically on the availability of reliable situational information, as well as on citizen participation in the operations. Her long-term goal is to further her educational journey by attending graduate school.
During the Fall semester, she is set to complete an internship focused on bioinformatics, plant genomics, and machine learning at a Department of Energy national laboratory.
She was the recipient of the Richard J. Carey Medal for Economics, which is awarded annually to the top student in the department. She has many interests — startups, data science and politics, but is primarily fascinated with artificial intelligence. She hopes that technology will change the world to be a happier and more equal place, and aspires to work to make that happen in the future.
She is an avid corgi fan, although she owns no pets because she lives in between Malaysia and Australia. Abraham Neuwirth is an undergraduate student at Touro College where he is majoring in Computer Science and minoring in Mathematics. He is passionate about fields where these two disciplines intersect such as data science and machine learning. His favorite R package is dplyr and his operator of choice is the pipe. Jai Punjwani is a rising junior at Adelphi University studying computer science.
In the future, he wishes to join the field of cryptography so that he can strengthen security in a world with more data than ever. She has been writing code since high school, and finds working with data using code very interesting because of the variety of possibilities and applications. She plans to attend graduate school for Computer Science in Fall of After obtaining her high school diploma in Senegal, Marieme came back to the US for college education.
As an undergrad, the invisible forces that shape our world fascinate her. Why does one company succeed and another fail? Is it possible to predict which idea will be the next big thing? She is planning to go to graduate school and get a Master degree in Quantitative Finance and work in the most prestigious firms.
As a big sport fan, Marieme is supporting the American team for the Olympics games. In her free time, she enjoys watching T.
Glenda Ascencio, an undergraduate student with an entrepreneur, mathematical, and software development skill living in New York City. She loves challenging her mind by finding solutions to different programming problems. Shannon Evans was born in St. An Applied Mathematics major with a concentration in finance, his ultimate goal is to become a financial analyst. He lives for the opportunity to become a world changer; to design financial systems that improves our lives.
He also loves sports, particularly soccer, table tennis, and cricket. Thomas has worked in various projects involving neighborhood improvement and urban planning. With his experience at Microsoft, Thomas hopes to bridge the gap between the technology industry and Latinos.
Thomas anticipates going to graduate school in the computer science field to utilize data and find creative solutions to community development. Nikki Hanson, known to friends as Riley, is a rising senior at Queens College interested in software engineering, gaming, Japanese and increasing diversity in tech. A bit of a latecomer to the game, they were writing code before they knew what it was, and that path inevitably led them to return to school for a Bachelor of Science in Computer Science and a Bachelor of Arts in Math.
Their favorite subject by far is Computational Theory, and they hope to get into Cryptography next. Anastassiya Neznanova is a current honor transfer student at Queens College. She recently completed her undergraduate research in mathematics and published her paper in the International Journal of Undergraduate Research and Creative Activities. Anastassiya aims to pursue her BS in Computer Science and sees her career in entrepreneurship.
Riva Tropp is from Teaneck, New Jersey. Her favorite subway station is at 14th street and Eighth avenue. Steven Vasquez was born and raised in the Bronx where he currently resides. He attends Manhattan College, and is studying Computer Science and minoring in mathematics. Steven is a brother of the fraternity Delta Kappa Epsilon and love sports as much he loves solving problems.
He is excited to learn and grow this summer. He has had an interest in music and visual art since he was a child. In his freshman year of college, he developed an interest for math and programming, and also had the opportunity to work in a biology lab at Brooklyn College doing bioinformatics work.
He is a computer science major with a minor in music at Adelphi University. People usually say that he is a guy who likes to stay positive and motivated, and he thinks that describes him very well; he always try to make the best of every situation. He has an associate degree in Computer science and now he is studying Applied Mathematics in Finance. His goal is to study data science in graduate school. Khanna Pugach is a junior at Baruch College majoring in computer information systems with a math minor.
Besides, she is an international student from Russia and this is her third year in the US. Franky Rodriguez was born in Mexico, grew up in Miami, and now is doing a double major in mathematics and computer information technology at St.
He loves challenging his mind and finding solutions and applications to many different problems. He has worked on various applications including writing a Java program that recognizes melodies by converting musical notes into relative seminotes and durations. In his spare time he indulges in playing and composing music. It was when he entered Brooklyn College, took the introductory computer science course, and entered a non-stop frenzy of hard work and love for learning.
Though relatively new to the field, Briana is a member of both the Pforzheimer Honors College and the Seidenberg Creative Lab on campus.
Her background consists mostly of web design, game design, and app development. In addition to DS3, Briana is also in the process of developing educational applications for international implementation in Senegal. She anticipates getting her PhD and working to utilize technology in developing regions. Siobhan Wilmot-Dunbar is a Junior at Pace University studying computer science and minoring in digital design. Besides that, Siobhan plays piano, acoustic guitar, and steel drums, and has high hopes of one day combining her ability in computing with her love for music and visual arts.
Students worked in groups of two and wrote their own original code with two goals in mind: first to reproduce the results published in the paper, and second to extend those results in a direction of their choosing. We suspect these differences are due to changes in the underlying datasets and to unspecified preprocessing done by the authors. The students extended the paper in several ways: examining alternative models, forecasting on longer time horizons, and evaluating the value of search data on a longer timescale.
In investigating the latter, the students found that the utility of search data has decreased since the time of the original publication, and that it recent years a simple baseline model that omits search data is, on average, more accurate than one that does. All data and code for the projects are available on Github.
Note: our program was modified due to COVID, shortened from 8 weeks to 4 weeks and run virtually. This project replicates and extends a recent paper on racial bias in police use of force.
We selected this paper because it is both widely read and also an ideal candidate for a data analysis replication. It uses relatively simple methodology that seems straightforward to implement and check, relies on two publicly available datasets, and contains more than pages between the main text and extensive appendices. Despite this nearly ideal setting, completing the data analysis replication turned out to be much more complicated than expected and took several weeks itself, mainly for reasons that centered around how the original data were cleaned and featurized.
These challenges came despite the extensive documentation in the paper and its appendix, but they also helped uncover insights that might not have been obvious from simply reading the paper. In this talk we discuss the various challenges we faced in replicating the results and the insights that the replication revealed. Watch the talk for more details. Source code for this project is available on GitHub. The New York City subway is the largest rapid transit system in the world, serving approximately 5.
While these metrics provide some insight into the performance of the subway system, they fail to capture how riders experience the system. In this project we use recently released countdown clock data that logs where each train is reported to be at each minute of the day to gain a better understanding of how riders experience the subway system.
We examine rider wait times and trip times, considering not just average but also worst-case performance of the system. We also compare the subway to above ground travel, investigate how changes to the system affect rider options, and look at how commutes vary across demographic groups. We also find a correlation between income and commute times and that small changes to the system e.
New York City serves over one million public school students each year, yet relatively little is understood in terms of how students progress through the school system. In this talk we use individual-level student data over a ten year time period to explore how early test performance correlates with later success, to describe and predict which students leave the public school system, and to examine effects of the recently implemented high school choice system.
Watch the talk or read the paper for more details. The advent of the sharing economy has redefined the way firms do business. Airbnb has led this revolution. Historically, customer loyalty was based on experience with a particular firm, but now it is based on experiences with many individuals. We chose to use the Inside Airbnb dataset to further investigate the evolving idea of loyalty.
Airbnb has both hosts and guests as customers. Host loyalty is defined as a host renting consistently, and guest loyalty as guests returning frequently.
We used decision trees to look at both the loyalty of the hosts and the guests. No matter the industry, market experts stand by measures of recency frequency to predict loyalty. However, our model is able to improve upon this idea with added features, such as review text and amenities. The end result is a model that successfully predicts the return rate of hosts and guests to Airbnb with a high level of accuracy.
New York City is home to millions of people who rely on its robust transportation system. The taxi system plays a critical role in helping people navigate the city. With access to information about every single trip that occurred in a yellow taxi in , we were able to reveal patterns in how people move throughout the city. Source code for this project is available on GitHub as well as an interactive map of travel patterns across neighborhoods.
New York City is home to the largest public school system in the country, which contains some of the best and worst schools in the state. Given this diversity, which often occurs over small geographic regions, there is extremely high demand for homes in the best public schools in the city. We investigate and quantify this demand by analyzing over 10, home sales in different school zones across the city and reveal the implicit cost of purchasing a home zoned for each elementary school in the city.
Source code for this project is available on GitHub as well as an interactive map of school zone prices. Every day, the population of New York regions shrinks and swells as people travel into and around the city.
With six million daily trips, the subway system is one of the main conduits for these travelers, but relatively little is known about the flow of subway passengers throughout the day. Bike sharing is an internationally implemented system for reducing public transit congestion, minimizing carbon emissions, and encouraging a healthy lifestyle.
In response to these issues, CitiBike employees redistribute bicycles by vehicle throughout the New York City area. He was then with Bell Laboratories, Holmdel, N. His area of research is communication networking, specifically, modeling, analysis, control and optimization problems arising in communication networks and distributed systems. Recently his research has focused primarily on wireless networking. He codirects the NSF-sponsored Center for Internet Epidemiology and Defenses, which pursues a variety of research efforts in detecting and blocking network-borne attacks.
He is interested in network algorithms and has worked extensively on switching routing, bandwidth partitioning, traffic measurement and load balancing algorithms. He has recently been involved in developing a congestion control algorithm as part of the IEEE He has contributed to stochastic network theory and to the application of probability in networking, coding and compression.
Bhaskar Ramamurthi got his B. His areas of specialisation are Communications and Signal Processing. He is a founding member of the TeNeT group of IIT Madras, active in developing telecom and networking technologies, and incubating companies to develop and market products based on these. He is currently Honorary Director of the Centre of Excellence in Wireless Technology, a public-private initiative to make India a wireless technology leader.
CEWiT works on 4G wireless technology, in partnership with Indian industry, and participates actively in international 4G standardization. Jennifer received her BSE degree in electrical engineering from Princeton University in , and her MSE and PhD degrees in computer science and electrical engineering from the University of Michigan in and , respectively.
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