In the early nineteen-eighties, when Kazuo Ishiguro was starting out as a novelist, a brief craze called Martian poetry hit our literary planet. It was launched by Craig Raine's poem "A Martian Sends a Postcard Home" (1979). The poem systematically deploys the technique of estrangement or defamiliarization--what the Russian formalist critics called ostranenie--as our bemused Martian wrestles into his comprehension a series of puzzling human habits and gadgets: "Model T is a room with the lock inside-- / a key is turned to free the world / for movement." Or, later in the poem: "In homes, a haunted apparatus sleeps, / that snores when you pick it up." For a few years, alongside the usual helpings of Hughes, Heaney, and Larkin, British schoolchildren learned to launder these witty counterfeits: "Caxtons are mechanical birds with many wings / And some are treasured for their markings-- / they cause the eyes to melt / or the body to shriek without pain. Teachers liked Raine's poem, and ...

Though not as venerable as novels based on movies, novelizations of video games have been a sturdy publishing genre for decades. Assassin's Creed, Halo, Donkey Kong Country--all have been rendered in what technically counts as prose. You wouldn't call these novels of ideas, probably, but that's what the Swedish gaming company Mojang got, in 2016, when it approved Max Brooks to write a book based on Minecraft, widely considered the best-selling video game of all time. In most iterations, Minecraft players enter a Lego-like universe where they must learn how to shelter and feed themselves, marshal resources, build stuff, and otherwise survive while coping with nightly mobs of zombies, skeletons, and other bad actors. There is an often ignored way to "win" Minecraft, but for most players the game is more a world to invent.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Authorities in Wichita, Kan., said Sunday that they are investigating a shooting that injured three police officers this weekend and working to determine if the shotgun was rigged to the door. A "modified, loaded shotgun" discharged as the officers entered a home in the city on Saturday, according to a release by Wichita Police Department spokesman Officer Trevor Macy. "Apparently there were several modifications made to this one," Macy told The Wichita Eagle.

Paul Lipman has worked in cybersecurity for 10-plus years. The onset of Covid-19 necessitated a work-from-home environment on an unprecedented scale. Large and small companies raced to reframe and reevaluate cybersecurity measures within a massive BYOD environment and amid increased Covid-19-related phishing scams and cyberattacks like the recent ransomware attacks against the Clark County School District (CCSD) in Las Vegas and United Health Services. Regulations like GDPR and CCPA helped make the collection of consumer data and privacy a matter of law instead of just good practice. However, consumers remain skeptical of businesses that continue to put profit ahead of privacy after breaches, like Facebook, TikTok and YouTube.

Artificial Intelligence (AI) is playing an increasingly dominant role in the lives of people around the world. As yet, however, it is subject to remarkably little governance, let alone global governance. There is a strong case for the establishment of a UN regime on Artificial Intelligence. Such a regime should include a supervisory body that can provide a democratic input. Artificial Intelligence has immense value to offer to humanity, such as improved efficiency, new capabilities, and solutions for complex problems.

Artificial intelligence (AI) has become a buzzword in technology in both civilian and military contexts. With interest comes a radical increase in extravagant promises, wild speculation, and over-the-top fantasies, coupled with funding to attempt to make them all possible. In spite of this fervor, AI technology must overcome several hurdles: it is costly, susceptible to data poisoning and bad design, difficult for humans to understand, and tailored for specific problems. No amount of money has eradicated these challenges, yet companies and governments have plunged headlong into developing and adopting AI wherever possible. This has bred a desire to determine who is "ahead" in the AI "race," often by examining who is deploying or planning to deploy an AI system.

Forecasting economic developments during crisis time is problematic since the realizations of the variables are far away from their average values, while econometric models are typically better at explaining and predicting values close to the average, particularly so in the case of linear models. The situation is even worse for the Covid-19 induced recession, when typically well performing econometric models such as Bayesian VARs with stochastic volatility have troubles in tracking the unprecedented fall in real activity and labour market indicators -- see for example for the US Carriero et al. (2020) and Plagborg-Møller et al. (2020), or An and Loungani (2020) for an analysis of the past performance of the Consensus Forecasts. As a partial solution, Foroni et al. (2020) employ simple mixed-frequency models to nowcast and forecast US and the rest of G7 GDP quarterly growth rates, using common monthly indicators, such as industrial production, surveys, and the slope of the yield curve. They then adjust the forecasts by a specific form of intercept correction or estimate by the similarity approach, see Clements and Hendry (1999) and Dendramis et al. (2020), showing that the former can reduce the extent of the forecast error during the Covid-19 period. Schorfheide and Song (2020) do not include COVID periods in the estimation of a mixed-frequency VAR model because those observations substantially alter the forecasts. An alternative approach is the specification of sophisticated nonlinear / time-varying models. While this is not without perils when used on short economic time series, it can yield some gains, see e.g.

Modeling real-world phenomena is a focus of many science and engineering efforts, such as ecological modeling and financial forecasting, to name a few. Building an accurate model for complex and dynamic systems improves understanding of underlying processes and leads to resource efficiency. Towards this goal, knowledge-driven modeling builds a model based on human expertise, yet is often suboptimal. At the opposite extreme, data-driven modeling learns a model directly from data, requiring extensive data and potentially generating overfitting. We focus on an intermediate approach, model revision, in which prior knowledge and data are combined to achieve the best of both worlds. In this paper, we propose a genetic model revision framework based on tree-adjoining grammar (TAG) guided genetic programming (GP), using the TAG formalism and GP operators in an effective mechanism to incorporate prior knowledge and make data-driven revisions in a way that complies with prior knowledge. Our framework is designed to address the high computational cost of evolutionary modeling of complex systems. Via a case study on the challenging problem of river water quality modeling, we show that the framework efficiently learns an interpretable model, with higher modeling accuracy than existing methods.

Gaussian processes (GPs) provide a gold standard for performance in online settings, such as sample-efficient control and black box optimization, where we need to update a posterior distribution as we acquire data in a sequential fashion. However, updating a GP posterior to accommodate even a single new observation after having observed $n$ points incurs at least $O(n)$ computations in the exact setting. We show how to use structured kernel interpolation to efficiently recycle computations for constant-time $O(1)$ online updates with respect to the number of points $n$, while retaining exact inference. We demonstrate the promise of our approach in a range of online regression and classification settings, Bayesian optimization, and active sampling to reduce error in malaria incidence forecasting. Code is available at https://github.com/wjmaddox/online_gp.

This paper discusses the problem of estimation and inference on time-varying treatments. We propose a method for inference on treatment histories, by introducing a \textit{dynamic} covariate balancing method. Our approach allows for (i) treatments to propagate arbitrarily over time; (ii) non-stationarity and heterogeneity of treatment effects; (iii) high-dimensional covariates, and (iv) unknown propensity score functions. We study the asymptotic properties of the estimator, and we showcase the parametric convergence rate of the proposed procedure. We illustrate in simulations and an empirical application the advantage of the method over state-of-the-art competitors.