AI Research Week Watch replays Schedule and registration Speakers Travel

AI Research Week includes notable speakers, panels, workshops, networking and mentorship opportunities.


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Schedule of events | October 1-5 2018


October 1, 2018

AI Horizons Network Meetup (invitation only)

AI Horizons Network Meetup conversation

01:00 PM - 03:30 PM

@ The MIT Samberg Conference Center

The AI Horizons Network is a network of world-class AI faculty and top graduate students who collaborate with IBM Research on on advanced research projects and experiments. Projects are designed to create and apply AI to some of the world’s most enduring challenges, ranging from disease and the environment to transportation and education. The annual AIHN meetup will enable all AIHN faculty, postdocs, and students to network with other members and their IBM Research collaborators, present new research results, learn about important AIHN program updates, hear inside perspectives on IBM Research's AI strategy, and much more, before presenting their project posters at the AI Horizons Colloquium Poster Reception. Invite-only, please refer to EventBrite invite for registration information.

AI Horizons Network Meetup schedule

1:00 PM

Welcome address

Lisa Amini (Director, IBM Research Cambridge)

1:20 PM

Keynote: AI and the Human Microbiome

Rob Knight (Founding Director of the Center for Microbiome Innovation and Professor of Pediatrics and Computer Science & Engineering at UC San Diego)

1:50 PM

Keynote: AI for Representation and Reasoning in Knowledge Bases of Science

Andrew McCallum (Distinguished Professor of Computer Science at UMassAmherst)

2:20 PM

Collaboration Opportunities

  • Moments in Time: One Million Videos for Event Understanding
  • ML ModeScope: The Next Go-to Platform for the ML/DL Community
  • Whyis: A Framework for Provenance-Driven Knowledge Graphs

Aude Oliva (Principal Research Scientist, MIT CSAIL)

Wen Mei-Hwu (AMD Jerry Sanders Chair of Electrical and Computer Engineering Acting Department Head, Electrical and Computer Engineering University of Illinois Urbana-Champaign)

Jim McCusker (Director, Data Operations, Rensselaer Polytechnic Institute)

2:50 PM

Panel: Inside View of IBM’s AI Strategy

Brent Hailpern (Scientific Director of the AI Horizons Network)

Rania Khalaf (Director, AI Engineering, IBM Research)

Aya Soffer (VP, AI Tech, IBM Research)

Alex Gray (VP of AI Science, IBM Research)

3:30 PM

Adjourn (all except AIHN Faculty)

3:35 PM

Faculty Roundtable (room DR 1)


AI Horizons Colloquium Poster Session (open event)

AI Horizons Colloquium Poster Session presentation

04:00 PM - 06:00 PM

@ The MIT Samberg Conference Center

Come see cutting-edge research from the MIT-IBM Watson AI Lab and AI Horizons Network's world-class universities, network with the researchers, and discuss how their work will shape the future of AI. Posters from the over 60 collaborative AI research projects will be presented in a social setting with food and drinks provided to promote networking and collaboration building. All AI Week participants are welcome to join! Projects cover a variety of topics in AI including fundamental advances in machine learning and reasoning algorithms (deep learning, reinforcement learning, generative adversarial networks, novel NN techniques for program induction, causal structure learning and inference, and many more); AI for healthcare, life sciences, cybersecurity; mapping AI algorithms to quantum and analog architectures; and AI for social good, including ethics and avoiding bias in AI, economics and workforce implications of AI, and AI applied to broad societal challenges. All are welcome! Please register.

IBM Cambridge Open House

IBM Cambridge Building

06:00 PM - 09:00 PM

IBM Research Cambridge, 75 Binney Street 

Tour the IBM Cambridge Lab, home to the MIT-IBM Watson AI Lab. Check out AI technology demos and presentations, apply for jobs, and take home some swag. Talk to like-minded researchers, students and faculty in a social setting with food and drinks provided.


AI Horizons Colloquium - Day 2 (invitation only)

AI Horizons Colloquium image

8:00 AM - 03:00 PM

@ The MIT Samberg Conference Center

Join us for our full day marquee event, where leading AI researchers will cover the most compelling issues, questions, and capabilities in AI today. The theme of this event is AI for Shared Prosperity, and is intended to bring awareness and understanding of the frontiers of AI, machine learning, deep learning and machine reasoning research; the growing ethical responsibilities as AI scientists and engineers; societal challenges that can benefit from AI; and the pressing needs for AI in industries critical to the global economy, such as healthcare, life sciences, finance, and cybersecurity. Presentations will include inside views of research projects from our MIT-IBM Watson AI Lab and AI Horizons Network universities by the principal investigators leading those projects.

AI Horizons Colloquium schedule

8:00 AM

Registration and Breakfast

8:30 AM

Welcome and Vision

Dario Gil (COO, IBM Research and VP, AI and Quantum, IBM Research)

8:45 AM

Keynote: 7 Billion Colleagues

Megan Smith (CEO, shift7; 3rd Chief Technology Officer of the United States)

9:15 AM

Panel: Beyond Fairness in Machine Learning: Social Justice and Causal Inference

Joi Ito (Director of the MIT Media Lab.)

Chelsea Barabas (Research scientist, MIT)

Francesca Rossi (Distinguished Research Staff Member, and AI Ethics Global Leader IBM Research)

Miguel Hernan (Kolokotrones Professor of Biostatistics and Epidemiology, Harvard)

Vikash Mansinghka (Research Scientist, MIT)

10:00 AM


10:30 AM

Keynote: Building machines that learn and think like people

Josh Tenenbaum (Professor, Department of Brain and Cognitive Sciences,Massachusetts Institute of Technology)

11:00 AM

MIT-IBM Watson AI Lab Spotlight Talks

David Cox (IBM Director of the MIT-IBM Watson AI Lab)

Antonio Torralba (Assistant Professor of Electrical Engineering and Computer Science, MIT)

Regina Barzilay (Delta Electronics Professor, EECS MacArthur Fellow, MIT Computer Science & Artificial Intelligence Lab)

Tommi Jaakkola (Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society, Massachusetts Institute of Technology)

Ian Molloy (Principal Research Staff Member, and Manager, Cognitive Security Services IBM Research)

Daby Sow (Principal Research Staff Member, and Manager, Biomedical Analytics and Modeling, IBM Research)

12:00 PM


1:00 PM

Keynote: Learning to Understand Language: Text Corpora are not Enough

Yoshua Bengio (Head of the Montreal Institute for Learning Algorithms (MILA))

1:40 PM

What can Quantum do for AI?

Yoshua Bengio (Head of the Montreal Institute for Learning Algorithms (MILA))
Aram Harrow (Associate Professor of Physics, MIT)
Peter Shor (Professor of Applied Mathematics, MIT)
Kristan Temme (Research Staff Member, IBM Research)

2:30 PM

From AI Research to Industries

David Cox (Director, MIT-IBM Watson AI Lab, IBM)
Antonio Torralba (Professor, MIT CSAIL)
Ruchir Puri (CTO & Chief Architect, IBM Watson; IBM Fellow)
Kathryn Guarini (Vice President, IBM Industry Research)

3:00 PM



October 3, 2018

AI Systems Day

AI Horizons Colloquium image

09:00 AM - 05:30 PM

1 Rogers Street

Louis Mandel

Local organizer:
Hendrik Strobelt

AI Systems Day is a regional, 1-day, open workshop about both Systems for AI and AI for systems. Topics of interest include, but are not limited to: AI platforms, algorithm toolkits, AI programming languages, data structures, distributed learning, GPU processing, data visualization, AI lifecycle acceleration, AI application composition, automated ML and synthesis, HCI of AI, security and ethics, and hardware for AI. Presentations will include invited talks by Soumith Chintala (Facebook) on "Usable while Performant: the challenges building PyTorch" as well as research talks.

MIT-IBM Watson AI Lab briefing

AI Horizons Colloquium image

09:00 AM - 01:00 PM

@ MIT Samberg Center

Adam Bogue

Join us to learn about the MIT-IBM Watson AI Lab and the new model for collaboration forged between AI research leaders from industry and academia. We will provide an overview of projects managed under the themes of AI Algorithms, Physics of AI, Applications of AI to industries (healthcare, cyber, financial services), and Advancing prosperity through AI. We will highlight a few selected projects and the results of work completed since the founding of the lab a year ago. We will also describe our plan to expand the MIT-IBM Watson AI Lab through membership. Speakers will include the MIT and IBM Directors of the lab, principal investigators from MIT and IBM, and others.

Turing meets Freud: Therapy-Inspired Dialog Modeling 

Therapy-Inspired Dialog Modeling

09:00 AM - 12:00 PM

75 Binney St

Satrajit Ghosh (MIT), Irina Rish (IBM), Guillermo Cecchi (IBM)

Dialogs are now integral to many human-machine interactions. This workshop is inspired by a large body of knowledge in therapy, psychology, and psychiatry on how to lead a good dialog with specific purposes by better understanding the dynamics of mental states in a person you communicate with (from an opponent in an argument to a patient in therapy). Our focus here is on the requirements for the development of generative models for effective dialogs. Specifically, we will consider the challenges to solve in estimating mental state dynamics from dialogue, and the importance of multi-modal information channels in dialog (such as, content, voice characteristics and prosody, facial expression, gestures, and environmental and historic context).

Causal Inference for Business Decision Making 

Causal Inference for Business Decision Making image

01:00 PM - 06:00 PM

MIT Samberg Center

Karthikeyan Shanmugam

Local organizer:
Kristjan Greenewald

Causal Inference is attracting a lot of attention in recent years due to its potential in addressing many challenges in Machine Learning and AI we face today. Interpretation of ML models, Fairness of ML processes/models and decision making based on data going beyond mere prediction involves causal inference principles. Existing ML models are good at prediction. However, to enable many other AI goals like interpretability/ Fairness/ decision making etc, algorithms must be equipped with counterfactual reasoning - answer the "what if" questions. Causal understanding is quite important for answering these questions. There are a number of research areas within Machine Learning that use causal inference principles and they are increasingly becoming important.

We will bring speakers from academia, researchers in industry for a workshop focused on two areas in causal inference: a) Causal Inference for Healthcare and b) Learning causal dynamics of a system from trace/time series generated by it. This workshop will also have a poster session highlighting the work done at IBM in this space and also contributed posters from universities in the Boston Area.

* Workshop now at capacity

Lifelong Learning, Stability vs. Plasticity, Catastrophic Interference 

Lifelong Learning, Stability vs. Plasticity, Catastrophic Interference image

02:00 PM - 05:00 PM

43 Vassar Street
Building 46 - Room 3189 (McGovern Auditorium)

Robert Ajemian (MIT), Irina Rish (IBM)

Today’s state-of-the-art deep neural networks have proven remarkably successful in many important problems, such as image recognition, speech processing and natural language understanding. However, these methods have steadfastly exhibited a very clear set of limitations. Many, if not most, of these limitations can be intuitively described as the networks being “overly specialized”, “brittle”, and depending too much on human programming – that is, they work well under certain tightly controlled operating conditions, but are unable to self-organize to improve their level of performance and/or expand the domain over which they can function successfully.

Perhaps the clearest example of this functional deficiency is the phenomenon of “catastrophic forgetting, whereby a network will fail to retain old information while learning new information. This workshop will explore different solutions and the various strengths and weaknesses inherent in each. The six categories explored in the workshop include: 1) memory-based rehearsal methods; 2) dual network architectures, 3) semi-distributed representations, 4) latent synaptic dynamics, 5) feedback-based methods, and 6) noise-based exploration methods.


October 4, 2018

AI Fairness for People with Disabilities

AI Horizons Colloquium image

09:00 AM - 04:00 PM

75 Binney St

Shari Trewin

This workshop will bring together people with disabilities, AI researchers and accessibility experts to identify areas where an early focus on AI fairness for people with disabilities is most important. For example, autonomous vehicles must properly identify people using wheelchairs, career advancement opportunities should be offered equally to people who use accommodations to perform their work, and AI-based interaction and authentication methods such as voice, face or gesture recognition need to accommodate 'outlier' individuals not seen in training. The outcome of the workshop will be a white paper outlining the challenges and identifying stakeholder priorities for AI fairness research in this area.

Machine Learning Meets Knowledge Representation

Machine Learning Meets Knowledge Representation image

9:00 AM to 5:00 PM

MIT Samberg Conference Center, Chang Building (E52)
Room: Dining Room 4

Michael Witbrock (IBM), Jim Hendler (RPI) 

Kartik Talamadupula

The incredible progress in machine learning over the past few years has led to solving problems that have challenged AI researchers for decades. The surge of enthusiasm for these ML breakthroughs have caused some to declare (yet again) that it is the end of “knowledge representation” as AI moves into a world dominated by neural networks, data mining and the knowledge graph. This workshop is focused on exploring the intersection/union of these breakthrough methods and the outstanding problems that require a level of knowledge and reasoning beyond what is currently available in these learning and representation techniques. Topics of interest include: problems that require integration of learning and symbolic knowledge; how ML might take advantage of complex, interconnected background knowledge; how knowledge representation systems might be built that can have as input the results of modern learning technologies; and how those problems that remain unsolved might be tackled.

* Workshop now at capacity

Women in Machine Learning

Women in machien learning image

3:00 PM to 6:00 PM

75 Binney St Auditorium

Preethi Raghavan

Calling all aspiring and inspiring Cambridge women AI researchers, and our allies! Since 2006, WiML has been creating opportunities for women in machine learning to present and promote their research, and thereby increase the number of women in machine learning. Starting with this inaugural event at AI Research Week 2018, we will begin hosting quarterly meetups for the Cambridge- and Boston-area community of women in machine learning and AI. Our objective is to encourage women who are students, post-docs or early career researchers in machine learning and AI to build and navigate compelling careers by offering seminars from thought-leading women researchers and engineers, and opportunities to present their own research, to meet and interact with other women in the field, and to find mentors, role models and colleagues. Join this inaugural event to help us launch this exciting new community in Cambridge.


October 5, 2018

Generality and Intelligence: from Biology to AI

Cambridge biology image

9:00 AM to 5:15 PM

75 Binney St Auditorium

Lisa Amini, Marta Halina, Jose Hernandez-Orallo, Dmitry Krotov, Seán Ó hÉigeartaigh, Henry Shevlin, Michael Witbrock

Although AI is becoming increasingly important in research, economics, culture, and society, an appreciable gap remains between the generality of modern AI systems and the behavioral flexibility of humans. The goal of this workshop is threefold. First, to consider various conceptualizations and definitions of generality from different disciplines and perspectives. Second, to come up with a better understanding of this diversity of interpretations, their commonalities and differences, and how entangled they are with the very notion of intelligence. Finally, to evaluate current AI/ML algorithms in the context of generality and identify computational paradigms that can be implemented in the next generation AI technologies.The workshop will bring together a diverse group of researchers who work on the cognitive and computational mechanisms of learning and the information flow in biological and artificial neural networks, looking at their current research through the prism of generality and generalization. This meeting will launch a series of more extensive workshops that will take place in Cambridge, UK, and Cambridge, MA, in the following two years, co-organised by the MIT-IBM Watson AI Lab and the Leverhulme Centre for the Future of Intelligence.

Generality and Intelligence: from Biology to AI schedule

9:00 AM - 9:30 AM


9:30 AM - 11:45 AM

Concepts of Generality in Biology and Cognitive Science




Talk 1

Elizabeth Spelke (Harvard University)
“From Core Concepts to New Systems of Knowledge”


Talk 2

James DiCarlo (MIT)
“Reverse Engineering Human Visual Intelligence: Comparing Biological and Artificial Visual Systems"


Talk 3

Marta Halina (University of Cambridge)
“Creative Intelligence in Animals and AI”


Q&A and Open Mic


11:45 AM - 1:00 PM


1:00 PM - 3:30 PM

Concepts of Generality in AI

Talk 4

Joshua Tenenbaum (MIT)
“Engineering and Reverse-engineering Common Sense”


Talk 5

Kristjan Greenwald (MIT-IBM Watson AI Lab)
“Information Flow, Neural Networks, and Generalization”


Talk 6

Dmitry Krotov (MIT-IBM Watson AI Lab)
“Biologically Inspired Learning Algorithms and Generalization”


Talk 7

Karthikeyan Shanmugam (IBM Research)
“Generalization from a Causal Inference Perspective”


Q&A and Open Mic


3:30 PM - 3:45 PM


3:45 PM - 5:15 PM

Panel Discussion

Tomaso Poggio (MIT), Seán Ó hÉigeartaigh (University of Cambridge), Karina Vold (University of Cambridge), Laura Schulz (MIT), Irina Rish (IBM Research), Gerald Tesauro (IBM Research)


Conclusion and Next Steps

Hosted by the MIT-IBM Watson AI Lab

Image of the outside of the IBM Resaerch - Cambridge office
Two members of the MIT-IBM Watson AI Lab team
IBM Researcher at work at the lab
Picture of the interior of the MIT-IBM Watson AI Lab

The MIT-IBM Watson AI Lab is focused on fundamental artificial intelligence research with the goal of propelling scientific breakthroughs that unlock the potential of AI. Located in Cambridge, MA, the lab is hosting the inaugural AI Research Week event.


Attend the IBM Cambridge Open House

When: October 1st from 6:00 to 9:00pm
Where: IBM Research Cambridge, 75 Binney Street, Cambridge, MA | Directions