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Viewing 41-60 of 143 videos See AI2’s full collection of videos on our YouTube channel.
    • February 27, 2018

      Rob Speer and Catherine Havasi

      We are the developers of ConceptNet, a long-running knowledge representation project that originated from crowdsourcing. We demonstrate systems that we’ve made by adding the common knowledge in ConceptNet to current techniques in distributional semantics. This produces word embeddings that are state-of-the-art at semantic similarity in multiple languages, analogies that perform like a moderately-educated human on the SATs, the ability to find relevant distinctions between similar words, and the ability to propose new knowledge-graph edges and “sanity check” them against existing knowledge.

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    • February 26, 2018

      Luheng He

      Semantic role labeling (SRL) systems aim to recover the predicate-argument structure of a sentence, to determine “who did what to whom”, “when”, and “where”. In this talk, I will describe my recent SRL work showing that relatively simple and general purpose neural architectures can lead to significant performance gains, including a over 40% error reduction over long-standing pre-neural performance levels. These approaches are relatively simple because they process the text in an end-to-end manner, without relying on the typical NLP pipeline (e.g. POS-tagging or syntactic parsing). They are general purpose because, with only slight modifications, they can be used to learn state-of-the-art models for related semantics problems. The final architecture I will present, which we call Labeled Span Graph Networks (LSGNs), opens up exciting opportunities to build a single, unified model for end-to-end, document-level semantic analysis.

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    • February 13, 2018

      Oren Etzioni

      Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence, gave the keynote address at the winter meeting of the Government-University-Industry Research Roundtable (GUIRR) on "Artificial Intelligence and Machine Learning to Accelerate Translational Research".

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    • February 12, 2018

      Richard Zhang

      We explore the use of deep networks for image synthesis, both as a graphics goal and as an effective method for representation learning. We propose BicycleGAN, a general system for image-to-image translation problems, with the specific aim of capturing the multimodal nature of the output space. We study image colorization in greater detail and develop automatic and user-guided approaches. Moreover, colorization, as well as cross-channel prediction in general, is a simple but powerful pretext task for self-supervised feature learning. Not only does the network solve the direct graphics task, it also learns to capture patterns in the visual world, even without the benefit of human-curated labels. We demonstrate strong transfer to high-level semantic tasks, such as image classification, and to low-level human perceptual judgments. For the latter, we collect a large-scale dataset of human similarity judgments and find that our method outperforms traditional metrics such as PSNR and SSIM. We also discover that many unsupervised and self-supervised methods transfer strongly, even comparable to fully-supervised methods.

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    • January 17, 2018

      Alexander Rush

      Early successes in deep generative models of images have demonstrated the potential of using latent representations to disentangle structural elements. These techniques have, so far, been less useful for learning representations of discrete objects such as sentences. In this talk I will discuss two works on learning different types of latent structure: Structured Attention Networks, a model for learning a soft-latent approximation of the discrete structures such as segmentations, parse trees, and chained decisions; and Adversarially Regularized Autoencoders, a new GAN-based autoencoder for learning continuous representations of sentences with applications to textual style transfer. I will end by discussing an empirical analysis of some issues that make latent structure discovery of text difficult.

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    • November 21, 2017

      Danqi Chen

      Enabling a computer to understand a document so that it can answer comprehension questions is a central, yet unsolved, goal of NLP. This task of reading comprehension (i.e., question answering over a passage of text) has received a resurgence of interest, due to the creation of large-scale datasets and well-designed neural network models.

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    • November 20, 2017

      Jacob Walker

      Understanding the temporal dimension of images is a fundamental part of computer vision. Humans are able to interpret how the entities in an image will change over time. However, it has only been relatively recently that researchers have focused on visual forecasting—getting machines to anticipate events in the visual world before they actually happen. This aspect of vision has many practical implications in tasks ranging from human-computer interaction to anomaly detection. In addition, temporal prediction can serve as a task for representation learning, useful for various other recognition problems.

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    • November 17, 2017

      Sun Kim

      PubMed is a biomedical literature search engine, hosting more than 27 million bibliographic records. With the abundance and diversity of information in PubMed, many queries retrieve thousands of documents, making it difficult for users to identify the information relevant to their topic of interest. Unlike more general domains, the language of biomedicine uses abundant technical jargon to describe scientific discoveries and applications. To understand the semantics of biomedical text, it is important to identify not only the meanings of individual words, but also of multi-word phrases appearing in text. Controlled vocabularies may help, but the rapid growth of PubMed makes it hard to keep up with the new information.

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    • November 7, 2017

      Mohammad Rasooli

      Transfer methods have been shown to be effective alternatives for developing accurate natural language processing systems in the absence of annotated data in the target language of interest. They are divided into two approaches: 1) annotation projection from translation data using supervised models in resource-rich languages; and 2) direct transfer from resource-rich annotated datasets. In this talk, we review our past work on improving over both of the approaches by applying scalable machine learning methods. We empirically show how our approach is practical on different natural language processing tasks including dependency parsing, semantic role labeling and sentiment analysis of the Twitter text. For our ongoing and future work, we propose to use a holistic approach to model cross-lingual recurrent representations for many languages and tasks.

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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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    • October 30, 2017

      Arman Cohan

      The rapid growth of scientific literature has created a challenge for researchers to remain current with new developments. Existence of surveys summarizing the latest state of the field shows that such information is desirable, yet obtaining such summaries requires painstaking manual efforts. Scientific document summarization aims at addressing this problem by providing a compact representation of new findings and contributions of the published literature. First, I will present methods for improving text summarization of scientific literature by utilizing citations as an alternative to abstracts. In particular, I will talk about how we can address the problem of potential citation inaccuracy by providing context from the reference to the citations. Utilizing these contexts along with the scientific discourse structure, I will present an effective extractive summarization method for capturing various contributions of the target paper. In addition to the rapid growth of biomedical scientific literature, there is an increasing demand for using health-related text, including clinical notes, patient reports, and social media. I will discuss current challenges in health-care which include medical errors and mental-health. As an attempt to address some of these challenges, I will show how we can make qualitative comparison of errors in clinical care through medical narratives. Further, I will focus on mental-health and discuss our proposed approaches to perform depression and self-harm risk assessment utilizing social media data.

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    • October 16, 2017

      Chuang Gan

      The increasing ubiquity of devices capable of capturing videos has led to an explosion in the amount of recorded video content. Instead of “eyeballing” the videos for potentially useful information, it has therefore been a pressing need to develop automatic video analysis and understanding algorithms for various applications. However, understanding videos on a large scale remains challenging: large variations and complexities, time-consuming annotations, and a wide range of involved video concepts. In light of these challenges, my research towards video understanding focuses on designing effective network architectures to learn robust video representations, learning video concepts from weak supervision and building a stronger connection between language and vision. In this talk, I will first introduce a Deep Event Network (DevNet) that can simultaneously detect pre-defined events and localize spatial-temporal key evidence. Then I will show how web crawled videos and images could be utilized for learning video concepts. Finally, I will present our recent efforts to connect visual understanding to language through attractive visual captioning and visual question segmentation.

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    • October 4, 2017

      Oren Etzioni

      Does Artificial Intelligence (AI) research result in threats to society, or will it yield beneficial technology? The talk will address these issues by describing the projects and perspective at the Allen Institute for AI (AI2) in Seattle. AI2's mission is "AI for the Common Good," as exemplified by Semantic Scholar, a search engine that utilizes AI to overcome information overload in scientific search.

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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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    • August 16, 2017

      Leo Boytsov

      We explore alternatives to classic term-based retrieval. The ultimate objective is to develop a smarter candidate generation component for question answering (QA) and information retrieval (IR), which can employ similarities that are more expressive than the commonly used TF-IDF ranking function. Achieving this objective requires solving two subproblems: designing simple yet effective similarity functions and developing efficient solutions for k-NN search.

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    • August 9, 2017

      Gabi Stanovsky

      Propositions are statements for which a truth value can be assigned (e.g., “Bob loves Mary”). Since they constitute the primary unit of information conveyed in texts, proposition extraction is often used in NLP algorithms such as question answering, summarization, or recognizing textual entailment. I will begin the talk with an overview of my research, which revolves around the different aspects of proposition extraction: from formalizing requirements and evaluation metrics, through annotation and crowdsourcing techniques, to modeling and automatic prediction. I will then describe two concrete research efforts which exemplify these aspects, while making use of the recent QA-SRL paradigm.

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    • July 25, 2017

      Oren Etzioni

      This video discusses the paper: Moving Beyond the Turing Test with the Allen AI Science Challenge. The field of Artificial Intelligence has made great strides forward recently, for example AlphaGo's recent victory against the world champion Lee Sedol in the game of Go, leading to great optimism about the field. But are we really moving towards smarter machines, or are these successes restricted to certain classes of problems, leaving other challenges untouched? In 2016, the Allen Institute for Artificial Intelligence (AI2) ran the Allen AI Science Challenge, a competition to test machines on an ostensibly difficult task, namely answering 8th Grade science questions. Our motivations were to encourage the field to set its sights broader and higher by exploring a problem that appears to require modeling, reasoning, language understanding, and commonsense knowledge, to probe the state of the art on this task, and sow the seeds for possible future breakthroughs. The challenge received a strong response, with 780 teams from all over the world participating. What were the results? This article describes the competition and the interesting outcomes of the challenge.

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    • June 22, 2017

      Arvind Neelakantan

      Knowledge representation and reasoning is one of the central challenges of artificial intelligence, and has important implications in many fields including natural language understanding and robotics. Representing knowledge with symbols, and reasoning via search and logic has been the dominant paradigm for many decades. In this work, we use deep neural networks to learn to both represent symbols and perform reasoning end-to-end from data. By learning powerful non-linear models, our approach generalizes to massive amounts of knowledge and works well with messy real-world data using minimal human effort. First, we show that recurrent neural networks with an attention mechanism achieve state-of-the-art reasoning on a large structured knowledge graph. Next, we develop Neural Programmer, a neural network augmented with discrete operations that can be learned to induce latent programs with backpropagation. We apply Neural Programmer to induce short programs on a natural language question answering dataset that requires reasoning on semi-structured Wikipedia tables. We present what is to our awareness the first weakly supervised, end-to-end neural network model to induce such programs on a real-world dataset. Unlike previous learning approaches to program induction, the model does not require domain-specific grammars, rules, or annotations.

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    • June 13, 2017

      Oren Etzioni

      As computer automations is upon us and many jobs will change or be replaced by AIs, AI optimist Oren Etzioni, CEO, Allen Institute for AI, describes the social impacts we must consider as he paints a possible euphonic future state in which jobs will be more creative and fulfilling. About XPRIZE: XPRIZE is an educational (501c3) nonprofit organization whose mission is to bring about radical breakthroughs for the benefit of humanity, thereby inspiring the formation of new industries and the revitalization of markets that are currently stuck due to existing failures or a commonly held belief that a solution is not possible. XPRIZE addresses the world's Grand Challenges by creating and managing large-scale, high-profile, incentivized prize competitions that stimulate investment in research and development worth far more than the prize itself. It motivates and inspires brilliant innovators from all disciplines to leverage their intellectual and financial capital.

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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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