The statistical analysis of Randomized Numerical Linear Algebra (RandNLA) algorithms within the past few years has mostly focused on their performance as point estimators. However, this is insufficient for conducting statistical inference, e.g., constructing confidence intervals and hypothesis testing, since the distribution of the estimator is lacking. In this article, we develop an asymptotic analysis to derive the distribution of RandNLA sampling estimators for the least-squares problem. In particular, we derive the asymptotic distribution of a general sampling estimator with arbitrary sampling probabilities. The analysis is conducted in two complementary settings, i.e., when the objective of interest is to approximate the full sample estimator or is to infer the underlying ground truth model parameters. For each setting, we show that the sampling estimator is asymptotically normally distributed under mild regularity conditions. Moreover, the sampling estimator is asymptotically unbiased in both settings. Based on our asymptotic analysis, we use two criteria, the Asymptotic Mean Squared Error (AMSE) and the Expected Asymptotic Mean Squared Error (EAMSE), to identify optimal sampling probabilities. Several of these optimal sampling probability distributions are new to the literature, e.g., the root leverage sampling estimator and the predictor length sampling estimator. Our theoretical results clarify the role of leverage in the sampling process, and our empirical results demonstrate improvements over existing methods.

In safety-critical but computationally resource-constrained applications, deep learning faces two key challenges: lack of robustness against adversarial attacks and large neural network size (often millions of parameters). While the research community has extensively explored the use of robust training and network pruning \emph{independently} to address one of these challenges, we show that integrating existing pruning techniques with multiple types of robust training techniques, including verifiably robust training, leads to poor robust accuracy even though such techniques can preserve high regular accuracy. We further demonstrate that making pruning techniques aware of the robust learning objective can lead to a large improvement in performance. We realize this insight by formulating the pruning objective as an empirical risk minimization problem which is then solved using SGD. We demonstrate the success of the proposed pruning technique across CIFAR-10, SVHN, and ImageNet dataset with four different robust training techniques: iterative adversarial training, randomized smoothing, MixTrain, and CROWN-IBP. Specifically, at 99\% connection pruning ratio, we achieve gains up to 3.2, 10.0, and 17.8 percentage points in robust accuracy under state-of-the-art adversarial attacks for ImageNet, CIFAR-10, and SVHN dataset, respectively. Our code and compressed networks are publicly available at https://github.com/inspire-group/compactness-robustness

Fighting Fantasy is a popular recreational fantasy gaming system worldwide. Combat in this system progresses through a stochastic game involving a series of rounds, each of which may be won or lost. Each round, a limited resource (`luck') may be spent on a gamble to amplify the benefit from a win or mitigate the deficit from a loss. However, the success of this gamble depends on the amount of remaining resource, and if the gamble is unsuccessful, benefits are reduced and deficits increased. Players thus dynamically choose to expend resource to attempt to influence the stochastic dynamics of the game, with diminishing probability of positive return. The identification of the optimal strategy for victory is a Markov decision problem that has not yet been solved. Here, we combine stochastic analysis and simulation with dynamic programming to characterise the dynamical behaviour of the system in the absence and presence of gambling policy. We derive a simple expression for the victory probability without luck-based strategy. We use a backward induction approach to solve the Bellman equation for the system and identify the optimal strategy for any given state during the game. The optimal control strategies can dramatically enhance success probabilities, but take detailed forms; we use stochastic simulation to approximate these optimal strategies with simple heuristics that can be practically employed. Our findings provide a roadmap to improving success in the games that millions of people play worldwide, and inform a class of resource allocation problems with diminishing returns in stochastic games.

In a recent post, we described what it would take to build a sustainable machine learning practice. By "sustainable," we mean projects that aren't just proofs of concepts or experiments. A sustainable practice means projects that are integral to an organization's mission: projects by which an organization lives or dies. These projects are built and supported by a stable team of engineers, and supported by a management team that understands what machine learning is, why it's important, and what it's capable of accomplishing. Finally, sustainable machine learning means that as many aspects of product development as possible are automated: not just building models, but cleaning data, building and managing data pipelines, testing, and much more. Machine learning will penetrate our organizations so deeply that it won't be possible for humans to manage them unassisted. Organizations throughout the world are waking up to the fact that security is essential to their software projects. Nobody wants to be the next Sony, the next Anthem, or the next Equifax. But while we know how to make traditional software more secure (even though we frequently don't), machine learning presents a new set of problems. Any sustainable machine learning practice must address machine learning's unique security issues. We didn't do that for traditional software, and we're paying the price now.

DUBAI, UNITED ARAB EMIRATES – Al-Qaida in the Arabian Peninsula on Sunday confirmed the death of its leader, Qassim al-Rimi, and appointed a successor, weeks after the U.S. said it had "eliminated" the Islamist militant chief, SITE Intelligence group said. The announcement came in an audio speech delivered by AQAP religious official, Hamid bin Hamoud al-Tamimi, said the group, which monitors jihadi networks worldwide. "In his speech, Tamimi spoke at length about Rimi and his jihadi journey, and stated that Khalid bin Umar Batarfi is the new leader of AQAP," it said. SITE said Batarfi has appeared in many AQAP videos over the past several years and appeared to have been Rimi's deputy and group spokesman. President Donald Trump announced Rimi's death earlier this month, saying he had been killed in a U.S. "counterterrorism operation in Yemen."

Researchers at Massachusetts Institute of Technology (MIT) have harnessed a machine-learning algorithm to identify a new antibiotic compound that, in laboratory tests, killed many of the world's most challenging disease-causing bacteria, including some strains that are resistant to all known antibiotics. The new antibiotic candidate, which has been given the name halicin--after the fictional artificial intelligence system from "2001: A Space Odyssey,"--was discovered in the Drug Repurposing Hub, and is structurally different to conventional antibiotics. Initial in vivo experiments showed that halicin was effective against Clostridium difficile and pan-resistant Acinetobacter baumannii infections in two mouse models. "We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery," said James Collins, PhD, the Termeer professor of medical engineering and science in MIT's Institute for Medical Engineering and Science (IMES) and department of biological engineering. "Our approach revealed this amazing molecule which is arguably one of the more powerful antibiotics that has been discovered."

The European Union has published its European data strategy [PDF], intended to provide the framework for what it describes as human-centric artificial intelligence. The white paper, said President of the European Commission, Ursula von der Leyen, is intended to "shape Europe's digital future". She continued: "It covers everything from cybersecurity to critical infrastructures, digital education to skills, democracy to media. I want that digital Europe reflects the best of Europe - open, fair, diverse, democratic, and confident." However, while the strategy has been pitched as boosting the EU's technology sector and preparing the bloc for a shift to an ever-more data-driven economy, increasingly governed by AI, the strategy is driven by a desire to regulate artificial intelligence and data platforms before they take off.

NEWPORT NEWS, Va., Jan. 30, 2020 – More than 1,600 nuclear physicists worldwide depend on the Continuous Electron Beam Accelerator Facility for their research. Located at the Department of Energy's Thomas Jefferson National Accelerator Facility in Newport News, Va., CEBAF is a DOE User Facility that is scheduled to conduct research for limited periods each year, so it must perform at its best during each scheduled run. But glitches in any one of CEBAF's tens of thousands of components can cause the particle accelerator to temporarily fault and interrupt beam delivery, sometimes by mere seconds but other times by many hours. Now, accelerator scientists are turning to machine learning in hopes that they can more quickly recover CEBAF from faults and one day even prevent them. Anna Shabalina is a Jefferson Lab staff member and principal investigator on the project, which has been funded by the Laboratory Directed Research & Development program for the fiscal year 2020.

Tesla CEO Elon Musk wants to see all artificial intelligence better regulated, even at his own company, he tweeted Monday (via TechCrunch). He made the remark in response to a piece about OpenAI by MIT Technology Review, which claimed that the AI organization, co-founded by Musk, has shifted from its mission of developing and distributing AI safely and equitably into a secretive company obsessed with image and driven to constantly raise more money. Musk has a history of expressing serious concerns about the negative potential of AI. He tweeted in 2014 that it could be "more dangerous than nukes," and told an audience at an MIT Aeronautics and Astronautics symposium that year that AI was "our biggest existential threat," and humanity needs to be extremely careful: With artificial intelligence we are summoning the demon. In all those stories where there's the guy with the pentagram and the holy water, it's like yeah he's sure he can control the demon.

Oliver Letwin's strange and somewhat alarming new book begins at midnight on Thursday 31 December 2037. In Swindon – stay with me! – a man called Aameen Patel is working the graveyard shift at Highways England's traffic HQ when his computer screen goes blank, and the room is plunged into darkness. He tries to report these things to his superiors, but can get no signal on his mobile. Looking at the motorway from the viewing window by his desk, he observes, not an orderly stream of traffic, but a dramatic pile-up of crashed cars and lorries – at which point he realises something is seriously amiss. In the Britain of 2037, everything, or almost everything, is controlled by 7G wireless technology, from the national grid to the traffic (not only are cars driverless; a vehicle cannot even join a motorway without logging into an "on-route guidance system"). There is, then, only one possible explanation: the entire 7G network must have gone down. It sounds like I'm describing a novel – and it's true that Aameen Patel will soon be joined by another fictional creation in the form of Bill Donoghue, who works at the Bank of England, and whose job it will be to tell the prime minister that the country is about to pay a heavy price for its cashless economy, given that even essential purchases will not be possible until the network is back up (Bill's mother-in-law is also one of thousands of vulnerable people whose carers will soon be unable to get to them, the batteries in their electric cars having gone flat).