The future of artificial intelligence (AI) is here: self-driving cars, grocery-delivering drones and voice assistants like Alexa that control more and more of our lives, from the locks on our front doors to the temperatures of our homes. For example, should an autonomous vehicle swerve into a pedestrian or stay its course when facing a collision? These questions plague technology companies as they develop AI at a clip outpacing government regulation, and have led Seattle University to develop a new ethics course for the public. Launched last week, the free, online course for businesses is the first step in a Microsoft-funded initiative to merge ethics and technology education at the Jesuit university. Seattle U senior business-school instructor Nathan Colaner hopes the new course will become a well-known resource for businesses "as they realize that [AI] is changing things," he said.

Researchers at the Tampa veterans' hospital are training computers to diagnose cancer. It's one example of how the Department of Veterans Affairs is expanding artificial intelligence development. Inside a laboratory at the James A. Haley Veterans' Hospital in Tampa, Fla., machines are rapidly processing tubes of patients' body fluids and tissue samples. Pathologists examine those samples under microscopes to spot signs of cancer and other diseases. But distinguishing certain features about a cancer cell can be difficult, so Drs.

In 2020, we will see US governments shift the conversation from who implements AI fastest to how we can implement most responsibly. While China is already using AI to measure students' brain waves with IoT sensors during class to help teachers provide more customizable content to achieve better retention and results, it's likely that the U.S. government will focus heavily in the coming year on privacy regulations to ensure AI use cases like this are fully vetted before being allowed. Federal regulations on privacy when it comes to the use of AI will take center stage in 2020. We've already seen the beginnings of this with two instances of the U.S. government taking action to prevent AI overstepping in states California and Massachusetts. This past May, the San Francisco Board of Supervisors banned the use of facial recognition technology by police and all other municipal agencies under the Stop Secret Surveillance Ordinance.

We consider problems for which one has incomplete binary matrices that evolve with time (e.g., the votes of legislators on particular legislation, with each year characterized by a different such matrix). An objective of such analysis is to infer structure and inter-relationships underlying the matrices, here defined by latent features associated with each axis of the matrix. In addition, it is assumed that documents are available for the entities associated with at least one of the matrix axes. By jointly analyzing the matrices and documents, one may be used to inform the other within the analysis, and the model offers the opportunity to predict matrix values (e.g., votes) based only on an associated document (e.g., legislation). The research presented here merges two areas of machine-learning that have previously been investigated separately: incomplete-matrix analysis and topic modeling.

The International Monitoring System (IMS) is a global network of sensors whose purpose is to identify potential violations of the Comprehensive Nuclear-Test-Ban Treaty (CTBT), primarily through detection and localization of seismic events. We report on the first stage of a project to improve on the current automated software system with a Bayesian inference system that computes the most likely global event history given the record of local sensor data. The new system, VISA (Vertically Integrated Seismological Analysis), is based on empirically calibrated, generative models of event occurrence, signal propagation, and signal detection. VISA exhibits significantly improved precision and recall compared to the current operational system and is able to detect events that are missed even by the human analysts who post-process the IMS output. Papers published at the Neural Information Processing Systems Conference.

We develop a probabilistic model of legislative data that uses the text of the bills to uncover lawmakers' positions on specific political issues. Our model can be used to explore how a lawmaker's voting patterns deviate from what is expected and how that deviation depends on what is being voted on. We derive approximate posterior inference algorithms based on variational methods. Across 12 years of legislative data, we demonstrate both improvement in heldout predictive performance and the model's utility in interpreting an inherently multi-dimensional space. Papers published at the Neural Information Processing Systems Conference.

Applications such as robot control and wireless communication require planning under uncertainty. Partially observable Markov decision processes (POMDPs) plan policies for single agents under uncertainty and their decentralized versions (DEC-POMDPs) find a policy for multiple agents. The policy in infinite-horizon POMDP and DEC-POMDP problems has been represented as finite state controllers (FSCs). We introduce a novel class of periodic FSCs, composed of layers connected only to the previous and next layer. Our periodic FSC method finds a deterministic finite-horizon policy and converts it to an initial periodic infinite-horizon policy.

Problem: The problem we intend to solve is modelled as a binary classification problem. We intend to find the relation in the words and the context in which the words appear within the text and how it could be used to classify texts as real (negative cases) or fake (positive). High-level description: Many news sources contain false information and are therefore "fake news." Because there is a lot of "fake news" articles and fabricated, misleading information on the web, we would like to determine which texts are legitimate (real) and which are illegitimate (fake). To solve this as a binary classification problem, we investigate the effectiveness of different Natural Language Processing models which are used to convert character based texts into numeric representations such as TFIDF, CountVectorizer and Word2Vec models and find out which model is able to preserve most of the contextual information about the text used in a fake news data set and how helpful and effective it is in detecting whether the text is a fake news or not.

Current reading comprehension models generalise well to in-distribution test sets, yet perform poorly on adversarially selected inputs. Most prior work on adversarial inputs studies oversensitivity: semantically invariant text perturbations that cause a model's prediction to change when it should not. In this work we focus on the complementary problem: excessive prediction undersensitivity, where input text is meaningfully changed but the model's prediction does not, even though it should. We formulate a noisy adversarial attack which searches among semantic variations of the question for which a model erroneously predicts the same answer, and with even higher probability. Despite comprising unanswerable questions, both SQuAD2.0 and NewsQA models are vulnerable to this attack. This indicates that although accurate, models tend to rely on spurious patterns and do not fully consider the information specified in a question. We experiment with data augmentation and adversarial training as defences, and find that both substantially decrease vulnerability to attacks on held out data, as well as held out attack spaces. Addressing undersensitivity also improves results on AddSent and AddOneSent, and models furthermore generalise better when facing train/evaluation distribution mismatch: they are less prone to overly rely on predictive cues present only in the training set, and outperform a conventional model by as much as 10.9% F1.

A new methodology is developed for joint analysis of a matrix and accompanying documents, with the documents associated with the matrix rows/columns. The documents are modeled with a focused topic model, inferring latent binary features (topics) for each document. A new matrix decomposition is developed, with latent binary features associated with the rows/columns, and with imposition of a low-rank constraint. The matrix decomposition and topic model are coupled by sharing the latent binary feature vectors associated with each. The model is applied to roll-call data, with the associated documents defined by the legislation.