Inaugural AI Research Week Highlights Collaboration and the Future of AI
749 attendees, 85 companies, 27 universities, 17 events — all packed into 1 amazing week of celebration, workshops, open collaboration, and fun. This was the scene at the inaugural AI Research Week, hosted by the MIT-IBM Watson AI Lab, in Cambridge, MA, October 1–5, 2018. AI Research Week was created to bring together the top minds in AI to formulate research directions, network, and share their successes in topics that are crucial to the advancement of AI.
The opening events of AI Research Week were the AI Horizons Network (AIHN) Annual Meetup and Colloquium. During this event, AIHN collaborators came together in person to validate research agendas and discuss ongoing results. The AIHN program allows IBM scientists to work in close collaboration with top faculty and students at world-class institutions to accelerate core AI research. AIHN collaborators include the University of Massachusetts Amherst, University of California San Diego, Massachusetts Institute of Technology, Rensselaer Polytechnic University, University of Maryland Baltimore County, University of Michigan, MILA, and the University of Illinois Urbana-Champaign. In addition, we are proud to announce that Indian Institute of Technology Bombay is the newest member of AIHN.
To date, the AIHN program has more than 150 publications on a wide range of topics including hardware acceleration, conversation/dialog, machine learning algorithms, application of AI to human microbiome and activity context of older adults, healthcare, cybersecurity, vision, and many other AI topics. Many of these papers were on display during AI Research Week, with a packed poster session that included 85 research posters and 6 best poster awards. In the spirit of collaboration, all of the posters from the AI Horizons Colloquium are publicly available to review online.
We are also proud to highlight the following best poster awards:
- Prior Convictions: Black-box Adversarial Attacks with Bandits and Priors. Andrew Ilyas, Logan Engstrom*, Aleksander Madry
- Learned String Similarity via Soft Alignments of Character Encodings. Nicholas Monath, Derek Tam, Ari Kobren, Aaron Traylor, Rajarshi Das, Andrew McCallum
- DNNBuilder: Building the State-of-the-Art DNN Accelerators for FPGAs. Xiaofan Zhang, Junsong Wang, Chao Zhu, Yonghua Lin, Jinjun Xiong, Wen-mei Hwu, Deming Chen
- Machine Learning and the Malware Arms Race. Abdullah Al-Dujaili, Alex Huang, Erik Hemberg, Una-May O’Reilly
- The Rensselaer Mandarin Project: A Cognitive and Immersive Language Learning Environment. David Allen, Rahul R. Divekar, Jaimie Drozdal, Lilit Balagyozyan, Shuyue Zheng, Ziyi Song, Huang Zou, Jeramey Tyler, Xiangyang Mou, Rui Zhao, Helen Zhou, Jianling Yue, Jeffrey O. Kephart,Hui Su
- Co-Designing Tech to Support Aging in Place. Shengzhi Wang, Khalisa Bolling, Camille Nebeker
We were fortunate to hear from 5 brilliant keynote speakers during the Meetup and the Colloquium.
Rob Knight’s (UCSD) keynote on “AI and the Human Microbiome” discussed how the microbiome plays a critical role for the quality of human life. Rob presented exciting opportunities of applying AI to human microbiome research which requires big data and ML, and NLP for knowledge base. (WATCH THE REPLAY)
Andrew McCallum (UMASS Amherst) gave a keynote on “AI for Representation and Reasoning in Knowledge Bases of Science.” Andrew showed several NLP advances in the areas of knowledge base completion, a new box embedding for reasoning, and multi-hop reasoning, with promising results. (WATCH THE REPLAY)
Megan Smith, CEO of Shift7 and the 3rd CTO of the United States, gave an inspirational talk about democratizing information technology, called “7 Billion Colleagues.” (WATCH THE REPLAY)
Yoshua Bengio’s (MILA) keynote, “Learning to Understand Language — Text Corpora Are Not Enough,” outlined directions that the machine learning community should explore so we can eventually construct automatic systems that display a far deeper understanding of language than current systems, which rely only on surface statistical regularities. These directions include learning algorithms that jointly learn about natural language and the world via interaction with the world and with human teachers, possibly inside a “Baby AI” game; the use of deep generative models to provide causal predictions; and approaches to learning theory that move beyond the assumption of independent and identically distributed samples by presuming that test data are generated by the same causal dynamics as training data, but with different initial conditions. (WATCH THE REPLAY)
Josh Tenenbaum (MIT) talked about how to build machines that learn and think like humans. He described his ongoing research on reverse engineering the human mind, by developing models for learning intuitive physics and theory of mind, taking inspiration from developmental psychology studies on humans, adults as well as infants. (WATCH THE REPLAY)
The collaborative approach to research allows open use of new datasets, machine learning platforms, and frameworks for those involved in AI research. Some of these collaboration opportunities include the Moments in Time short-action dataset from the MIT-IBM Watson AI Lab, an open platform that allows a user to test machine learning and deep learning models under different hardware configurations from UIUC C3SR, and an open tool for integrating, extending, and exploring provenance-supported knowledge graphs from RPI. All of these tools were on display and are available for attendees to consider using in their own AI Research.
The inaugural AI Research Week brought together hundreds of the world’s top scientists, students, industry researchers, and IBMers to engage in thoughtful and productive conversation on the science, implications, and future of AI. To learn more about AI Research Week, please see the complete list of events and replays from AI Research Week.
Next year promises even more collaboration, an expanded poster session, and another lineup of incredible speakers. Although we are just in the early stages of planning the 2019 AI Research Week, we’d love to add you to our list of interested attendees. Please email firstname.lastname@example.org and let me know you’d like to be kept in the loop!