Videos
See AI2's full collection of videos on our YouTube channel.Viewing 201-210 of 244 videos
Learning from Zero
April 12, 2016 | Percy LiangCan we learn if we start with zero examples, either labeled or unlabeled? This scenario arises in new user-facing systems (such as virtual assistants for new domains), where inputs should come from users, but no users exist until we have a working system, which depends on having training data. I will discuss…
Leveraging Human Insights into Problem Structure for Scientific Discovery
April 6, 2016 | Ronan Le BrasMost 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…
Predictive Interaction
April 4, 2016 | Jeffrey HeerHow might we architect interactive systems that have better models of the tasks we're trying to perform, learn over time, help refine ambiguous user intents, and scale to large or repetitive workloads? In this talk I will present Predictive Interaction, a framework for interactive systems that shifts some of the…
Improving Structured Prediction With Locally Normalized Models
March 25, 2016 | Ashish VaswaniLocally normalized approaches for structured prediction, such as left-to-right parsing and sequence labeling, are attractive because of their simplicity, ease of training, and the flexibility in choosing features from observations. Combined with the power of neural networks, they have been widely adopted for NLP…
Beyond the Distributional Hypothesis: Learning Better Word Representations
March 9, 2016 | Manaal FaruquiUnsupervised learning of word representations have proven to provide exceptionally effective features in many NLP tasks. Traditionally, construction of word representations relies on the distributional hypothesis, which posits that the meaning of words is evidenced by the contextual words they occur with (Harris…
Deja Vu: The Story of Vision & AI
March 3, 2016 | Ali FarhadiAli Farhadi discusses the history of computer vision and AI.
Beyond Informational Retrieval
March 2, 2016 | Ashish SabharwalArtificial 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…
Strategies and Principles for Distributed Machine Learning
February 16, 2016 | Eric XingThe 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…
Intelligible Machine Learning Models for HealthCare
February 9, 2016 | Rich CaruanaLocally normalized approaches for structured prediction, such as left-to-right parsing and sequence labeling, are attractive because of their simplicity, ease of training, and the flexibility in choosing features from observations. Combined with the power of neural networks, they have been widely adopted for NLP…
Probabilistic Models for Learning a Semantic Parser Lexicon
January 27, 2016 | Jayant KrishnamurthyLexicon 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…