Viewing 14 videos in Distinguished Lecture Series See AI2’s full collection of videos on our YouTube channel.
    • November 12, 2019

      Dr. Asma Ben Abacha

      Consumer health questions pose specific challenges to automated answering. Two of the salient aspects are the higher linguistic and semantic complexity when compared to open domain questions, and the more pronounced need for reliable information. In this talk I will present two main approaches to deal with the increased complexity by recognizing question entailment and by question summarization, recently published respectively in BMC Bioinformatics and ACL 2019. In particular, our question entailment approach to question answering (QA) showed that restricting the answer sources to only reliable resources led to an improvement of the QA performance and our summarization experiments showed the relevance of data augmentation methods for abstractive question summarization. I’ll also talk about the MEDIQA shared task on question entailment, textual inference and medical question answering that we recently organized at ACL-BioNLP. In the second part of the talk, I will address more specifically questions about medications and present our last study and dataset on medication QA. Finally, I’ll describe our recent endeavors in visual question answering (VQA) from radiology images and the medical VQA challenge (VQA-Med) editions for 2019 and 2020 that we organize in the scope of ImageCLEF.

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    • July 23, 2019

      Jeff Hammerbacher

      Many promising cancer immunotherapy treatment protocols rely on efficient and increasingly extensive methods for manipulating human immune cells. T cells are a frequent target of the laboratory and clinical research driving the development of such protocols as they are most often the effector of the cytotoxic activity that makes these treatments so potent. However, the cytokine signaling network that drives the differentiation and function of such cells is complex and difficult to replicate on a large scale in model biological systems. Abridged versions of these networks have been established over decades of research but it remains challenging to define their global structure as the classification of T cell subtypes operating in these networks, the mechanics of their formation, and the purpose of the signaling molecules they excrete are all controversial, with a slowly expanding understanding emerging in literature over time.

      To aid in the quantification of this understanding, we are developing a methodology for identifying references to well known cytokines, transcription factors, and T cell types in literature as well as classifying the relationships between the three in an attempt to determine what cytokines initiate the transcription programs that lead to various cell states in addition to the secretion profiles associated with those states. Entity recognition for this task is performed using SciSpacy and classification of the relations between these entities is based on an LSTM trained using Snorkel, where weak supervision is established through a variety of classification heuristics and distant supervision is provided via previously published immunology databases.

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    • April 24, 2019

      Peter Anderson

      From robots to cars, virtual assistants and voice-controlled drones, computing devices are increasingly expected to communicate naturally with people and to understand the visual context in which they operate. In this talk, I will present our latest work on generating and comprehending visually-grounded language. First, we will discuss the challenging task of describing an image (image captioning). I will introduce captioning models that leverage multiple data sources, including object detection datasets and unaligned text corpora, in order to learn about the long-tail of visual concepts found in the real world. To support and encourage further efforts in this area, I will present the 'nocaps' benchmark for novel object captioning. In the second part of the talk, I will describe our recent work on developing agents that follow natural language instructions in reconstructed 3D environments using the R2R dataset for vision-and-language navigation.

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    • September 19, 2018

      Kevin Gimpel

      A key challenge in natural language understanding is recognizing when two sentences have the same meaning. I'll discuss our work on this problem over the past few years, including the exploration of compositional functional architectures, learning criteria, and naturally-occurring sources of training data. The result is a single sentence embedding model that outperforms all systems from the 2012-2016 SemEval semantic textual similarity competitions without training on any of the annotated data from those tasks.

      As a by-product, we developed a large dataset of automatically-generated paraphrase pairs by using parallel text and neural machine translation. We've since used the dataset, which we call ParaNMT-50M, to impart a notion of meaning equivalence to controlled text generation tasks, including syntactically-controlled paraphrasing and textual style transfer.

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    • August 28, 2018

      Dan Weld

      Since AI software uses techniques like deep lookahead search and stochastic optimization of huge neural networks, it often results in complex behavior that is difficult for people to understand. Yet organizations are deploying AI algorithms in many mission-critical settings. To trust their behavior, we must make AI intelligible, either by using inherently interpretable models or by developing new methods for explaining and adjusting otherwise overwhelmingly complex decisions using local approximation, vocabulary alignment, and interactive explanation. This talk argues that intelligibility is essential, surveys recent work on building such systems, and highlights key directions for research.

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    • November 6, 2017

      Gary Marcus

      All purpose, all-powerful AI systems, capable of catering to our every intellectual need, have been promised for six decades, but thus far still not arrived. What will it take to bring AI to something like human-level intelligence? And why haven't we gotten there already? Scientist, author, and entrepreneur Gary Marcus (Founder and CEO of Geometric Intelligence, recently acquired by Uber) explains why deep learning is overrated, and what we need to do next to achieve genuine artificial intelligence.

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    • September 15, 2017

      Horacio Saggion

      In the current online Open Science context, scientific data-sets and tools for deep text analysis, visualization and exploitation play a major role. I will present a system developed over the past three years for “deep” analysis and annotation of scientific text collections. After a brief overview of the system and its main components, I will present our current work on the development of a bi-lingual (Spanish and English) fully annotated text resource in the field of natural language processing that we have created with our system. Moreover, a faceted-search and visualization system to explore the created resource will be also discussed.

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    • May 22, 2017

      Abhinav Gupta

      In 2013, we proposed NEIL (Never Ending Image Learner), a computer program to learn visual models and commonsense knowledge from the web. In its first version, NEIL ran for 2.5 years learning 8K concepts, labeling 4.5M images and learning 20K common-sense facts. But it also helped us discover the shortcomings of the current paradigm of learning and reasoning with knowledge. In this talk, I am going to describe our subsequent efforts to overcome these drawbacks.

      On the learning side, I will talk about how we scale up learning visual models to rare and compositional categories (“wet possum”). Note the web-search data for compositional categories are noisy and cannot be used “as is” for learning. The core problem in compositional categories is respecting contextuality. The meaning of primitive categories change based on concepts being composed with (red in red wine is different from red in red car). I will talk about how we can respect contextuality while composing categories.

      On the reasoning side, I will talk about how we can incorporate the learned knowledge graphs in end-to-end learning. Specifically, we will show how these “noisy” knowledge graphs can not only improve classification performance but also provide “explainability” which is crucial for AI systems. I will also show some of our recent work on using knowledge graphs for zero-shot learning (again in an end-to-end manner).

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    • February 16, 2016

      Eric Xing

      The rise of Big Data has led to new demands for Machine Learning (ML) systems to learn complex models with millions to billions of parameters that promise adequate capacity to digest massive datasets and offer powerful predictive analytics (such as high-dimensional latent features, intermediate representations, and decision functions) thereupon. In order to run ML algorithms at such scales, on a distributed cluster with 10s to 1000s of machines, it is often the case that significant engineering efforts are required — and one might fairly ask if such engineering truly falls within the domain of ML research or not. Taking the view that Big ML systems can indeed benefit greatly from ML-rooted statistical and algorithmic insights — and that ML researchers should therefore not shy away from such systems design — we discuss a series of principles and strategies distilled from our recent efforton industrial-scale ML solutions that involve a continuum from application, to engineering, and to theoretical research and development of Big ML system and architecture, on how to make them efficient, general, and with convergence and scaling guarantees.

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    • November 4, 2014

      Raymond Mooney

      Traditional logical approaches to semantics and newer distributional or vector space approaches have complementary strengths and weaknesses.We have developed methods that integrate logical and distributional models by using a CCG-based parser to produce a detailed logical form for each sentence, and combining the result with soft inference rules derived from distributional semantics that connect the meanings of their component words and phrases. For recognizing textual entailment (RTE) we use Markov Logic Networks (MLNs) to combine these representations, and for Semantic Textual Similarity (STS) we use Probabilistic Soft Logic (PSL). We present experimental results on standard benchmark datasets for these problems and emphasize the advantages of combining logical structure of sentences with statistical knowledge mined from large corpora.

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    • July 25, 2014

      Pedro Domingos

      Building very large commonsense knowledge bases and reasoning with them is a long-standing dream of AI. Today that knowledge is available in text; all we have to do is extract it. Text, however, is extremely messy, noisy, ambiguous, incomplete, and variable. A formal representation of it needs to be both probabilistic and relational, either of which leads to intractable inference and therefore poor scalability. In the first part of this talk I will describe tractable Markov logic, a language that is restricted enough to be tractable yet expressive enough to represent much of the commonsense knowledge contained in text. Even then, transforming text into a formal representation of its meaning remains a difficult problem. There is no agreement on what the representation primitives should be, and labeled data in the form of sentence-meaning pairs for training a semantic parser is very hard to come by. In the second part of the talk I will propose a solution to both these problems, based on concepts from symmetry group theory. A symmetry of a sentence is a syntactic transformation that does not change its meaning. Learning a semantic parser for a language is discovering its symmetry group, and the meaning of a sentence is its orbit under the group (i.e., the set of all sentences it can be mapped to by composing symmetries). Preliminary experiments indicate that tractable Markov logic and symmetry-based semantic parsing can be powerful tools for scalably extracting knowledge from text.

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    • May 13, 2014

      Bart Selman

      In recent years, there has been tremendous progress in solving large-scale reasoning and optimization problems. Central to this progress has been the ability to automatically uncover hidden problem structure. Nevertheless, for the very hardest computational tasks, human ingenuity still appears indispensable. We show that automated reasoning strategies and human insights can effectively complement each other, leading to hybrid human-computer solution strategies that outperform other methods by orders of magnitude. We illustrate our approach with challenges in scientific discovery in the areas of finite mathematics and materials science.

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    • March 31, 2014

      Dan Roth

      Machine Learning and Inference methods have become ubiquitous and have had a broad impact on a range of scientific advances and technologies and on our ability to make sense of large amounts of data. Research in Natural Language Processing has both benefited from and contributed to advancements in these methods and provides an excellent example for some of the challenges we face moving forward. I will describe some of our research in developing learning and inference methods in pursue of natural language understanding. In particular, I will address what I view as some of the key challenges, including (i) learning models from natural interactions, without direct supervision, (ii) knowledge acquisition and the development of inference models capable of incorporating knowledge and reason, and (iii) scalability and adaptation—learning to accelerate inference during the life time of a learning system.

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    • January 23, 2014

      Gary Marcus

      For nearly half a century, artificial intelligence always seemed as if it just beyond reach, rarely more, and rarely less, than two decades away. Between Watson, Deep Blue, and Siri, there can be little doubt that progress in AI has been immense, yet "strong AI" in some ways still seems elusive. In this talk, I will give a cognitive scientist's perspective on AI. What have we learned, and what are we still struggling with? Is there anything that programmers of AI can still learn from studying the science of human cognition? Less

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