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Viewing 18 videos in Talks by AI2 Team Members See AI2’s full collection of videos on our YouTube channel.
    • 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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    • 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 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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    • November 19, 2016

      Oren Etzioni

      Artificial Intelligence advocate Oren Etzioni makes a case for the life-saving benefits of AI used wisely to improve our way of life. Acknowledging growing fears about AI’s potential for abuse of power, he asks us to consider how to responsibly balance our desire for greater intelligence and autonomy with the risks inherent in this new and growing technology. Less

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    • May 23, 2016

      Oren Etzioni

      Oren Etzioni, CEO of the Allen Institute for AI, shares his vision for deploying AI technologies for the common good.

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    • April 6, 2016

      Ronan Le Bras

      Most problems, from theoretical problems in combinatorics to real-world applications, comprise hidden structural properties not directly captured by the problem definition. A key to the recent progress in automated reasoning and combinatorial optimization has been to automatically uncover and exploit this hidden problem structure, resulting in a dramatic increase in the scale and complexity of the problems within our reach. The most complex tasks, however, still require human abilities and ingenuity. In this talk, I will show how we can leverage human insights to effectively complement and dramatically boost state-of-the-art optimization techniques. I will demonstrate the effectiveness of the approach with a series of scientific discoveries, from experimental designs to materials discovery.

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    • March 3, 2016

      Ali Farhadi

      Ali Farhadi discusses the history of computer vision and AI.

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    • March 2, 2016

      Ashish Sabharwal

      Artificial intelligence and machine learning communities have made tremendous strides in the last decade. Yet, the best systems to date still struggle with routine tests of human intelligence, such as standardized science exams posed as-is in natural language, even at the elementary-school level. Can we demonstrate human-like intelligence by building systems that can pass such tests? Unlike typical factoid-style question answering (QA) tasks, these tests challenge a student’s ability to combine multiple facts in various ways, and appeal to broad common-sense and science knowledge. Going beyond arguably shallow information retrieval (IR) and statistical correlation techniques, we view science QA from the lens of combinatorial optimization over a semi-formal knowledge base derived from text. Our structured inference system, formulated as an Integer Linear Program (ILP), turns out to be not only highly complementary to IR methods, but also more robust to question perturbation, as well as substantially more scalable and accurate than prior attempts using probabilistic first-order logic and Markov Logic Networks (MLNs). This talk will discuss fundamental challenges behind the science QA task, the progress we have made, and many challenges that lie ahead.

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    • January 27, 2016

      Jayant Krishnamurthy

      Lexicon learning is the first step of training a semantic parser for a new application domain, and the quality of the learned lexicon significantly affects both the accuracy and efficiency of the final semantic parser. Existing work on lexicon learning has focused on heuristic methods that lack convergence guarantees and require significant human input in the form of lexicon templates or annotated logical forms. In contrast, the proposed probabilistic models are trained directly from question/answer pairs using EM and the simplest model has a concave objective function that guarantees that EM converges to a global optimum. An experimental evaluation on a data set of 4th grade science questions demonstrates that these models improve semantic parser accuracy (35-70% error reduction) and efficiency (4-25x more sentences per second) relative to prior work, despite using less human input. The models also obtain competitive results on Geoquery without any dataset-specific engineering.

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    • December 10, 2015

      Chandra Bhagavatula

      In this talk, I will describe two systems designed to extract structured knowledge from unstructured and semi-structured data. First, I'll present an entity linking system for Web tables. Next, I'll talk about a key phrase extraction system that extracts a set of key concepts from a research article. Towards the end of the talk, I will briefly introduce an underlying common problem which connects these two seemingly distinct tasks. I will also present an approach, based on topic modeling, to solve this common underlying problem.

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    • November 3, 2015

      Hanie Sedghi

      Learning with big data is akin to finding a needle in a haystack: useful information is hidden in high dimensional data. Optimization methods, both convex and nonconvex, require new thinking when dealing with high dimensional data, and I present two novel solutions.

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    • July 30, 2015

      Matt Gardner

      A lot of attention has recently been given to the creation of large knowledge bases that contain millions of facts about people, things, and places in the world. In this talk I present methods for using these knowledge bases to generate features for machine learning models. These methods view the knowledge base as a graph which can be traversed to find potentially predictive information. I show how these methods can be applied to models of knowledge base completion, relation extraction, and question answering.

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    • March 12, 2015

      Vicente Ordonez

      Recently, there has been great progress in both computer vision and natural language processing in representing and recognizing semantic units like objects, attributes, named entities, or constituents. These advances provide opportunities to create systems able to interpret and describe the visual world using natural language. This is in contrast to traditional computer vision systems, which typically output a set of disconnected labels, object locations, or annotations for every pixel in an image. The rich visually descriptive language produced by people incorporates world knowledge and human intuition that often can not be captured by other types of annotations. In this talk, I will present several approaches that explore the connections between language, perception, and vision at three levels: learning how to name objects, generating referring expressions for objects in natural scenes, and producing general image descriptions. These methods provide a framework to augment computer vision systems with linguistic information and to take advantage of the vast amount of text associated with images on the web. I will also discuss some of the intuitions from linguistics and perception behind these efforts and how they potentially connect to the larger goal of creating visual systems that can better learn from and communicate with people.

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    • February 5, 2015

      Bhavana Dalvi

      Semi-supervised learning (SSL) has been widely used over a decade for various tasks -- including knowledge acquisition-- that lack large amount of training data. My research proposes a novel learning scenario in which the system knows a few categories in advance, but the rest of the categories are unanticipated and need to be discovered from the unlabeled data. With the availability of enormous unlabeled datasets at low cost, and difficulty of collecting labeled data for all possible categories, it becomes even more important to adapt traditional semi-supervised learning techniques to such realistic settings.

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    • January 7, 2015

      Been Kim

      I will present the Bayesian Case Model (BCM), a general framework for Bayesian case-based reasoning (CBR) and prototype classification and clustering. BCM brings the intuitive power of CBR to a Bayesian generative framework. The BCM learns prototypes, the ``quintessential" observations that best represent clusters in a data set, by performing joint inference on cluster labels, prototypes and important features. Simultaneously, BCM pursues sparsity by learning subspaces, the sets of features that play important roles in the characterization of the prototypes. The prototype and subspace representation provides quantitative benefits in interpretability while preserving classification accuracy. Human subject experiments verify statistically significant improvements to participants' understanding when using explanations produced by BCM, compared to those given by prior art.

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    • December 4, 2014

      Aria Haghigi

      I discuss three problems in applied natural language processing and machine learning: event discovery from distributed discourse, document content models for information extraction, and relevance engineering for a large-scale personalization engine. The first two are information extraction problems over social media which attempt to utilize richer structure and context for decision making; these sections reflect work from the tail end of my purely academic work. The relevance section will discuss work done while at my former startup Prismatic and will focus on issues arising from productionizing real-time machine learning. Along the way, I'll share my thoughts and experience around productizing research and interesting future directions.

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    • December 3, 2014

      Roozbeh Mottaghi

      Scene understanding is one of the holy grails of computer vision, and despite decades of research, it is still considered an unsolved problem. In this talk, I will present a number of methods, which help us take a step further towards the ultimate goal of holistic scene understanding. In particular, I will talk about our work on object detection, 3D pose estimation, and contextual reasoning, and show that modeling these tasks jointly enables better understanding of scenes. At the end of the talk, I will describe our recent work on providing richer descriptions for objects in terms of their viewpoint and sub-category information.

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    • June 4, 2014

      Paul Allen

      Paul Allen discusses his vision for the future of AI and AI2 in this fireside chat moderated by Gary Marcus of New York University at the 10th Anniversary Symposium - Allen Institute for Brain Science. AI2-related discussion begins at 17:30.

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