Probabilistic graphical modeling (PGM) provides a framework for formulating an interpretable generative process of data and expressing uncertainty about unknowns, but it lacks flexibility. Deep learning (DL) is an alternative framework for learning from data that has achieved great empirical success in recent years. DL offers great flexibility, but it lacks the interpretability and calibration of PGM. This thesis develops deep probabilistic graphical modeling (DPGM.) DPGM consists in leveraging DL to make PGM more flexible. DPGM brings about new methods for learning from data that exhibit the advantages of both PGM and DL. We use DL within PGM to build flexible models endowed with an interpretable latent structure. One model class we develop extends exponential family PCA using neural networks to improve predictive performance while enforcing the interpretability of the latent factors. Another model class we introduce enables accounting for long-term dependencies when modeling sequential data, which is a challenge when using purely DL or PGM approaches. Finally, DPGM successfully solves several outstanding problems of probabilistic topic models, a widely used family of models in PGM. DPGM also brings about new algorithms for learning with complex data. We develop reweighted expectation maximization, an algorithm that unifies several existing maximum likelihood-based algorithms for learning models parameterized by neural networks. This unifying view is made possible using expectation maximization, a canonical inference algorithm in PGM. We also develop entropy-regularized adversarial learning, a learning paradigm that deviates from the traditional maximum likelihood approach used in PGM. From the DL perspective, entropy-regularized adversarial learning provides a solution to the long-standing mode collapse problem of generative adversarial networks, a widely used DL approach.

The study of the social impact of automated decision making has focused largely on issues of fairness at the point of decision, evaluating the fairness (with respect to a population) of a sequence or pipeline of decisions, or examining the dynamics of a game between the decision-maker and the decision subject. What is missing from this study is an examination of precarity: a term coined by Judith Butler to describe an unstable state of existence in which negative decisions can have ripple effects on one's well-being. Such ripple effects are not captured by changes in income or wealth alone or by one decision alone. To study precarity, we must reorient our frame of reference away from the decision-maker and towards the decision subject; away from aggregates of decisions over a population and towards aggregates of decisions (for an individual) over time. An individual who lives with higher precarity is more affected and less able to recover by the same negative decision than another with low precarity. Thus including only the direct impact of a single decision or a few decisions is insufficient to judge if that system was fair. However, precarity is not an attribute of an individual; it is a result of being subject to greater risks and fewer supports, in addition to starting off at a less secure position. Precarity is impacted by racism, sexism, ableism, heterosexism, and other systems of oppression, and an individual's intersectional identity may put one at greater risk in society, subject to a lower income for the same job, less able to build wealth even at the same income level, and less able to recover from harm.

The European Union's lead data protection supervisor has called for remote biometric surveillance in public places to be banned outright under incoming AI legislation. The European Data Protection Supervisor's (EDPS) intervention follows a proposal, put out by EU lawmakers on Wednesday, for a risk-based approach to regulating applications of artificial intelligence. The Commission's legislative proposal includes a partial ban on law enforcement's use of remote biometric surveillance technologies (such as facial recognition) in public places. But the text includes wide-ranging exceptions, and digital and humans rights groups were quick to warn over loopholes they argue will lead to a drastic erosion of EU citizens' fundamental rights. And last week a cross-party group of MEPs urged the Commission to screw its courage to the sticking place and outlaw the rights-hostile tech.

What may appear to be an image of Tacoma is, in fact, a simulated one, created by transferring visual patterns of Beijing onto a map of a real Tacoma neighborhood. A fire in Central Park seems to appear as a smoke plume and a line of flames in a satellite image. Colorful lights on Diwali night in India, seen from space, seem to show widespread fireworks activity. Both images exemplify what a new University of Washington-led study calls "location spoofing." The photos -- created by different people, for different purposes -- are fake but look like genuine images of real places. And with the more sophisticated AI technologies available today, researchers warn that such "deepfake geography" could become a growing problem.

Remesh's web-based platform lets institutions hold back-and-forth conversations with audiences. And responses go through an algorithm that clusters answers with similar meanings then allows participants to agree or disagree with generated results. The algorithm was fine-tuned for the U.N. to use in contexts where people speak unique dialects. The international body worked with local war zone organizations to encourage a diverse pool of people to take part.

But it can step in when companies misrepresent the capabilities of a product they are selling, which means firms that claim their facial recognition systems, predictive policing algorithms or healthcare tools are not biased may now be in the line of fire. "Where they do have power, they have enormous power," says Calo. In the blog post, the FTC warns vendors that claims about AI must be "truthful, non-deceptive, and backed up by evidence." The result may be deception, discrimination--and an FTC law enforcement action." The FTC action has bipartisan support in the Senate, where commissioners were asked yesterday what more they could be doing and what they needed to do it. But it can step in when companies misrepresent the capabilities of a product they are selling, which means firms that claim their facial recognition systems, predictive policing algorithms or healthcare tools are not biased may now be in the line of fire. "Where they do have power, they have enormous power," says Calo. In the blog post, the FTC warns vendors that claims about AI must be "truthful, non-deceptive, and backed up by evidence." The result may be deception, discrimination--and an FTC law enforcement action."

Nearly two years since its massive 1.2 trillion transistor Wafer Scale Engine chip debuted at Hot Chips, Cerebras Systems is announcing its second-generation technology (WSE-2), which its says packs twice the performance into the same 8″x8″ silicon footprint. "We're going bigger, faster and better in a more power efficient footprint," Cerebras Founder and CTO Andrew Feldman told HPCwire ahead of today's launch. With 2.6 trillion transistors and 850,000 cores, the WSE-2 more than doubles the elements on the first-gen chip (1.2 trillion transistors, 400,000 cores). The new chip, made by TSMC on its 7nm node, delivers 40 GB of on-chip SRAM memory, 20 petabytes of memory bandwidth and 220 petabits of aggregate fabric bandwidth. Gen over gen, the WSE-2 provides about 2.3X on all major performance metrics, said Feldman.

During World War II, British intelligence agents planted false documents on a corpse to fool Nazi Germany into preparing for an assault on Greece. "Operation Mincemeat" was a success, and covered the actual Allied invasion of Sicily. The "canary trap" technique in espionage spreads multiple versions of false documents to conceal a secret. Canary traps can be used to sniff out information leaks, or as in WWII, to create distractions that hide valuable information. WE-FORGE, a new data protection system designed in the Department of Computer Science, uses artificial intelligence to build on the canary trap concept.

We extend the neural basis expansion analysis (NBEATS) to incorporate exogenous factors. The resulting method, called NBEATSx, improves on a well performing deep learning model, extending its capabilities by including exogenous variables and allowing it to integrate multiple sources of useful information. To showcase the utility of the NBEATSx model, we conduct a comprehensive study of its application to electricity price forecasting (EPF) tasks across a broad range of years and markets. We observe state-of-the-art performance, significantly improving the forecast accuracy by nearly 20% over the original NBEATS model, and by up to 5% over other well established statistical and machine learning methods specialized for these tasks. Additionally, the proposed neural network has an interpretable configuration that can structurally decompose time series, visualizing the relative impact of trend and seasonal components and revealing the modeled processes' interactions with exogenous factors. To assist related work we made the code available in https://github.com/cchallu/nbeatsx.

Health insurance is a critical component of the healthcare industry with private health insurance expenditures alone estimated at $1.1 billion in 2016, according to the latest data available from the Centers for Medicare and Medicaid Services. This figure represents 34 percent of the 2016 National Health Expenditure at $3.3 trillion. In this article, we will look at four AI applications that are tackling problems of underutilization and fraud in the insurance industry. Some applications below claim that they are using artificial intelligence to help improve health insurance cost efficiency, while reducing waste of money on underutilized or preventable care. Other applications claim to detect fraudulent claims.