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Customised video conferencing backgrounds have gotten an artificially intelligent upgrade: real-time animated deepfakes that transform your face into that of a celebrity. Karim Iskakov at the Skolkovo Institute of Science and Technology in Moscow, and Ali Aliev, a software developer in Moscow, have developed a program that lets you create deepfakes in real time during video calls. The program, called Avatarify, works with video conferencing applications such as Zoom or Skype. All it requires is a headshot of the person you want to appear to be. Demonstrating to New Scientist via a Zoom call, Aliev used the program to appear to be speaking as several figures including Boris Johnson, Donald Trump, Albert Einstein and the Mona Lisa.

As a response to the increasing linkages between the macroeconomy and the financial sector, as well as the expanding interconnectedness of the global economy, empirical macroeconomic models have increased both in complexity and size. For that reason, estimation of modern models that inform macroeconomic decisions - such as linear and nonlinear versions of dynamic stochastic general equilibrium (DSGE) and vector autoregressive (VAR) models - many times relies on Bayesian inference via powerful Markov chain Monte Carlo (MCMC) methods. 1 However, existing posterior simulation algorithms cannot scale up to very high-dimensions due to the computational inefficiency and the larger numerical error associated with repeated sampling via Monte Carlo; see Angelino et al. (2016) for a thorough review of such computational issues from a machine learning and high-dimensional data perspective. In that respect, while Bayesian inference is a natural probabilistic framework for learning about parameters by utilizing all information in the data likelihood and prior, computational restrictions might make it less suitable for supporting real-time decision-making in very high dimensions. This paper introduces to the econometric literature the framework of factor graphs (Kschischang et al., 2001) for the purpose of designing computationally efficient, and easy to maintain, Bayesian estimation algorithms. The focus is not only on "faster" posterior inference broadly interpreted, but on designing algorithms that have such low complexity that are future-proof and can be used in high-dimensional econometric problems with possibly thousands or millions of coefficients.

We propose to use algorithms for learning deterministic finite automata (DFA), such as Angluin’s L* algorithm, for learning a DFA that describes the possible scenarios under which a given program error occurs. The alphabet of this automaton is given by the user (for instance, a subset of the function call sites or branches), and hence the automaton describes a user-defined abstraction of those scenarios. More generally, the same technique can be used for visualising the behavior of a program or parts thereof. It can also be used for visually comparing different versions of a program (by presenting an automaton for the behavior in the symmetric difference between them), and for assisting in merging several development branches. We present experiments that demonstrate the power of an abstract visual representation of errors and of program segments, accessible via the project’s web page. In addition, our experiments in this paper demonstrate that such automata can be learned efficiently over real-world programs. We also present lazy learning, which is a method for reducing the number of membership queries while using L*, and demonstrate its effectiveness on standard benchmarks.

It is becoming evident each and every day that machine learning algorithms are achieving impressive results in domains in which it is hard to specify a set of rules for their procedures. Examples of this phenomenon include industries like finance [49, 5], transportation [37], education [42, 22], health care [23] and tasks like image recognition [41, 16, 17], machine translation [43, 7], and speech recognition [46, 24, 53, 50]. Motivated by the ease of adoption and the increased availability of affordable computational power (especially cloud computing services), machine learning algorithms are being explored in almost every commercial application and are offering great promise for the future of automation. Facing such a vast adoption across multiple disciplines, some of their weaknesses are exposed and sometimes exploited by malicious actors. For example, a common challenge to these algorithms is "generalization" or "robustness", which is the ability of the algorithm to maintain performance whenever dealing with data coming from a different distribution with which it was trained. For a long period of time, the sole focus of machine learning researchers was improving the performance of machine learning systems (true positive rate, accuracy, etc.). Nowadays, the robustness of these systems can no longer be ignored; many of them have been shown to be highly vulnerable to intentional adversarial attacks.

Training methods for artificial neural networks often rely on over-parameterization and random initialization in order to avoid spurious local minima of the loss function that fail to fit the data properly. To sidestep this, one can employ convex neural networks, which combine a convex interpretation of the loss term, sparsity promoting penalization of the outer weights, and greedy neuron insertion. However, the canonical $\ell_1$ penalty does not achieve a sufficient reduction in the number of nodes in a shallow network in the presence of large amounts of data, as observed in practice and supported by our theory. As a remedy, we propose a nonconvex penalization method for the outer weights that maintains the advantages of the convex approach. We investigate the analytic aspects of the method in the context of neural network integral representations and prove attainability of minimizers, together with a finite support property and approximation guarantees. Additionally, we describe how to numerically solve the minimization problem with an adaptive algorithm combining local gradient based training, and adaptive node insertion and extraction.

Humans' unique laziness when it comes to interacting on social media could be the key to telling us apart from artificially intelligent'bots', a new study shows. US researchers have identified behavioural trends of humans on Twitter that are absent in social media bots – namely a decrease in tweet length over time. The team studied how the behaviour of humans and bots changed over the course of a session on Twitter relating to political events. While humans get lazier as sessions progress and can't be bothered typing out long tweets, bots maintain consistent levels of engagement over time. Such a behavioural difference could inform new machine learning algorithms for bot detection software.

Since the White House issued a "call to action" to AI researchers to help fight the coronavirus spread, researchers have stepped up in multiple ways. Lots of data is available. The Covid-19 Open Research Dataset (CORD-19) is a collection of research studies published in both peer-reviewed journals and non-peer-reviewed pre-print websites such as bioRxiv and medRxiv. Currently, it consists of over 13,000 full-text papers and abstracts for another 16,000 papers and is expected to be updated with new research as it becomes available, according to an account in Forbes. The account was written by Kashyap Kompella, the CEO of the technology industry analyst firm RPA2AI Research.

See also GAO, "Cloud Computing: Agencies Have Increased Usage and Realized Benefits, but Cost and Savings Need to Be Better Tracked," Report to Congressional Requesters, April 2019, https://www.gao.gov/.

Modern agriculture depends on bees. In fact, our entire ecosystem, including the food we eat and the air we breathe, counts on pollinators. But the pollinator population is declining according to Sabiha Rumani Malik, the founder and executive president of The World Bee Project. But, in an intriguing collaboration with Oracle and by putting artificial intelligence, internet of things and big data to work on the problem, they hope to reverse the trend. Why is the global bee population in decline?