Personal electronic devices such as smartphones give access to a broad range of behavioral signals that can be used to learn about the characteristics and preferences of individuals. In this study we explore the connection between demographic and psychological attributes and digital records for a cohort of 7,633 people, closely representative of the US population with respect to gender, age, geographical distribution, education, and income. We collected self-reported assessments on validated psychometric questionnaires based on both the Moral Foundations and Basic Human Values theories, and combined this information with passively-collected multi-modal digital data from web browsing behavior, smartphone usage and demographic data. Then, we designed a machine learning framework to infer both the demographic and psychological attributes from the behavioral data. In a cross-validated setting, our model is found to predict demographic attributes with good accuracy (weighted AUC scores of 0.90 for gender, 0.71 for age, 0.74 for ethnicity). Our weighted AUC scores for Moral Foundation attributes (0.66) and Human Values attributes (0.60) suggest that accurate prediction of complex psychometric attributes is more challenging but feasible. This connection might prove useful for designing personalized services, communication strategies, and interventions, and can be used to sketch a portrait of people with similar worldviews.

Inverse optimization is a powerful paradigm for learning preferences and restrictions that explain the behavior of a decision maker, based on a set of external signal and the corresponding decision pairs. However, most inverse optimization algorithms are designed specifically in batch setting, where all the data is available in advance. As a consequence, there has been rare use of these methods in an online setting suitable for real-time applications. In this paper, we propose a general framework for inverse optimization through online learning. Specifically, we develop an online learning algorithm that uses an implicit update rule which can handle noisy data. Moreover, under additional regularity assumptions in terms of the data and the model, we prove that our algorithm converges at a rate of $\mathcal{O}(1/\sqrt{T})$ and is statistically consistent. In our experiments, we show the online learning approach can learn the parameters with great accuracy and is very robust to noises, and achieves a dramatic improvement in computational efficacy over the batch learning approach.

We focus on the task of multi-hop reading comprehension where a system is required to reason over a chain of multiple facts, distributed across multiple passages, to answer a question. Inspired by graph-based reasoning, we present a path-based reasoning approach for textual reading comprehension. It operates by generating potential paths across multiple passages, extracting implicit relations along this path, and composing them to encode each path. The proposed model achieves a 2.3% gain on the WikiHop Dev set as compared to previous state-of-the-art and, as a side-effect, is also able to explain its reasoning through explicit paths of sentences.

Recurrent Neural Networks (RNNs) are theoretically Turing-complete and established themselves as a dominant model for language processing. Yet, there still remains an uncertainty regarding their language learning capabilities. In this paper, we empirically evaluate the inductive learning capabilities of Long Short-Term Memory networks, a popular extension of simple RNNs, to learn simple formal languages, in particular $a^nb^n$, $a^nb^nc^n$, and $a^nb^nc^nd^n$. We investigate the influence of various aspects of learning, such as training data regimes and model capacity, on the generalization to unobserved samples. We find striking differences in model performances under different training settings and highlight the need for careful analysis and assessment when making claims about the learning capabilities of neural network models.

When answering a question, people often draw upon their rich world knowledge in addition to some task-specific context. Recent work has focused primarily on answering questions based on some relevant document or content, and required very little general background. To investigate question answering with prior knowledge, we present CommonsenseQA: a difficult new dataset for commonsense question answering. To capture common sense beyond associations, each question discriminates between three target concepts that all share the same relationship to a single source drawn from ConceptNet (Speer et al., 2017). This constraint encourages crowd workers to author multiple-choice questions with complex semantics, in which all candidates relate to the subject in a similar way. We create 9,500 questions through this procedure and demonstrate the dataset's difficulty with a large number of strong baselines. Our best baseline, the OpenAI GPT (Radford et al., 2018), obtains 54.8% accuracy, well below human performance, which is 95.3%.

That's one of the surprising -- and unsettling -- questions Israeli historian Yuval Noah Harari asks in his much-quoted new book, 21 Lessons for the 21st Century. Whereas 20th-century technology favored democracies as they were able to distribute power to make decisions among many people and institutions, according to Harari, artificial intelligence (AI) might make centralized systems that concentrate all information and power far more efficient as machine learning works better with more information to analyze. "If you disregard all privacy concerns and concentrate all the information relating to a billion people in one database," Harari writes, "you'll wind up with much better algorithms than if you respect individual privacy and have in your database only partial information on a million people." The rise of AI swinging the pendulum from democracies toward authoritarian regimes is just one of the feared adverse impacts of technologies: Others include job displacement, concentration of power, diminishing privacy, rising income inequality and losing our "free will." Yet most people have little or no knowledge about how AI, blockchain, the Internet of Things or genetic engineering could affect their lives.

While artificial intelligence (AI) is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn. Watch this video to better understand the relationship between AI and machine learning. The rising popularity of Machine learning is because of the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage. With Machine learning, it's possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results even on a very large scale.

Instructors often teach a class of 30 or more students, making it difficult to give proper help and attention to each student. Every student learns at a different pace and in their own way. Many instructors use computer programs to allow students to work at a pace they find comfortable. However, these programs make it easy for students to "beat the system," not truly learn. Students often develop an extrinsic attitude toward learning in this setting.

Instructors often teach a class of 30 or more students, making it difficult to give proper help and attention to each student. Every student learns at a different pace and in their own way. Many instructors use computer programs to allow students to work at a pace they find comfortable. However, these programs make it easy for students to "beat the system," not truly learn. Students often develop an extrinsic attitude toward learning in this setting.

External collaboration: This program is designed in collaboration with SAS.