Nearly 60 years ago, then-Senate Majority Leader Lyndon B. Johnson seized his colleagues with a stark Cold War warning: Whoever wins the space race, he predicted, would gain "control, total control, over the Earth for purposes of tyranny or for the service of freedom." The United States won that race not only by reaching the moon but by inspiring the next generation of scientists, technologists, and optimists. Recently, Russian President Vladimir Putin echoed Johnson's forecast in light of the next great technological race: artificial intelligence or AI.

Nothing is as remote as yesterday's utopias. From the 1990s until the end of the last decade, the explosion in computing power was seen by wide-eyed optimists as a force for liberation that would lay low unaccountable authority. Their eyes have narrowed now. Democracy, justice, our very ability to earn a living, feel precarious. "All that is solid melts into air," said Marx of 19th-century capitalism.

Killer robots -- they're coming to get us! At least that's what the majority of otherwise credible news sources would have us believe when tackling the very serious concerns around AI safety, as well as the increasingly clichéd use of photos of Terminator robots in such articles. It's a pet peeve expressed by renowned AI researcher Eliezer Yudkowsky in a recent appearance on Sam Harris' podcast, and a reference that betrays a deep misunderstanding of the ways in which our civilization's future survival likely depends on ensuring the safe development of future artificial intelligence. For one thing, you'd think we'd be over "robots". As was graphically illustrated by the apparent Russian meddling in the 2016 US presidential election, nefarious artificial intelligence is not some far-off thing -- it's already here and running wild throughout the World Wide Web in the form of bots. And yet somehow we still fail to recognize it for what it is, because our image of malicious artificial intelligence still looks like a gleaming metal endoskeleton with glowing red eyes and an evil grin.

Deepfakes, ultrarealistic fake videos manipulated using machine learning, are getting pretty convincing. And researchers continue to develop new methods to create these types of videos, for better or, more likely, for worse. The most recent method comes from researchers at Carnegie Mellon University, who have figured out a way to automatically transfer the "style" of one person to another. "For instance, Barack Obama's style can be transformed into Donald Trump," the researchers wrote in the description of a YouTube video highlighting the outcome of this method. The video shows the facial expressions of John Oliver transferred to both Stephen Colbert and an animated frog, from Martin Luther King, Jr. to Obama, and from Obama to Trump.

Deepfakes, ultrarealistic fake videos manipulated using machine learning, are getting pretty convincing. And researchers continue to develop new methods to create these types of videos, for better or, more likely, for worse. The most recent method comes from researchers at Carnegie Mellon University, who have figured out a way to automatically transfer the "style" of one person to another. "For instance, Barack Obama's style can be transformed into Donald Trump," the researchers wrote in the description of a YouTube video highlighting the outcome of this method. The video shows the facial expressions of John Oliver transferred to both Stephen Colbert and an animated frog, from Martin Luther King, Jr. to Obama, and from Obama to Trump.

In mid-September the European Union threatened to fine the Big Tech companies if they did not remove terrorist content within one hour of appearing online. The change came because rising tensions are now developing and being played out on social media platforms. Social conflicts that once built up in backroom meetings and came to a head on city streets, are now building momentum on social media platforms before spilling over into real life. In the past, governments tended to control traditional media, with little to no possibility for individuals to broadcast hate. The digital revolution has altered everything.

Autonomous Vehicles navigating in urban areas have a need to understand and predict future pedestrian behavior for safer navigation. This high level of situational awareness requires observing pedestrian behavior and extrapolating their positions to know future positions. While some work has been done in this field using Hidden Markov Models (HMMs), one of the few observed drawbacks of the method is the need for informed priors for learning behavior. In this work, an extension to the Growing Hidden Markov Model (GHMM) method is proposed to solve some of these drawbacks. This is achieved by building on existing work using potential cost maps and the principle of Natural Vision. As a consequence, the proposed model is able to predict pedestrian positions more precisely over a longer horizon compared to the state of the art. The method is tested over "legal" and "illegal" behavior of pedestrians, having trained the model with sparse observations and partial trajectories. The method, with no training data, is compared against a trained state of the art model. It is observed that the proposed method is robust even in new, previously unseen areas.

Tasks involving the analysis of geometric (graph- and manifold-structured) data have recently gained prominence in the machine learning community, giving birth to a rapidly developing field of geometric deep learning. In this work, we leverage graph neural networks to improve signal detection in the IceCube neutrino observatory. The IceCube detector array is modeled as a graph, where vertices are sensors and edges are a learned function of the sensors' spatial coordinates. As only a subset of IceCube's sensors is active during a given observation, we note the adaptive nature of our GNN, wherein computation is restricted to the input signal support. We demonstrate the effectiveness of our GNN architecture on a task classifying IceCube events, where it outperforms both a traditional physics-based method as well as classical 3D convolution neural networks.

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the "single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes. We propose a new approach for capturing this decision-making process through counterfactual reasoning in pairwise comparisons. Our approach is model-free and does not require iterating through the state space. We demonstrate that this approach accurately learns multifaceted heuristics on a synthetic and real world data sets. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of schedule optimization. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates optimal solutions up to 9.5 times faster than a state-of-the-art optimization algorithm.

Life-expectancy is a complex outcome driven by genetic, socio-demographic, environmental and geographic factors. Increasing socio-economic and health disparities in the United States are propagating the longevity-gap, making it a cause for concern. Earlier studies have probed individual factors but an integrated picture to reveal quantifiable actions has been missing. Amidst growing concerns about the further widening of healthcare inequality and differential access created by Artificial Intelligence, it is imperative to explore it's potential for illuminating biases and enabling transparent policy decisions. In this work, we reveal actionable interventions for decreasing the longevity-gap in the United States by analyzing a County-level data resource with healthcare, socio-economic, behavioral, education and demographic features. We learn an ensemble-averaged structure, draw inferences using the joint probability distribution and extend it to a Bayesian Decision Network for identifying policy actions. We draw quantitative estimates for the positive roles of diversity, preventive-care quality and stable-families within the unified framework of our decision network. Finally, we make this analysis and dashboard available as an interactive web-application for enabling users and policy-makers to validate our insights on bridging the longevity-gap and explore the ones beyond reported in this work.