Four days before he leaves office, US president Joe Biden has issued a sweeping cybersecurity directive ordering improvements to the way the government monitors its networks, buys software, uses artificial intelligence, and punishes foreign hackers. The 40-page executive order unveiled on Thursday is the Biden White House's final attempt to kickstart efforts to harness the security benefits of AI, roll out digital identities for US citizens, and close gaps that have helped China, Russia, and other adversaries repeatedly penetrate US government systems. The order "is designed to strengthen America's digital foundations and also put the new administration and the country on a path to continued success," Anne Neuberger, Biden's deputy national security adviser for cyber and emerging technology, told reporters on Wednesday. Looming over Biden's directive is the question of whether president-elect Donald Trump will continue any of these initiatives after he takes the oath of office on Monday. None of the highly technical projects decreed in the order are partisan, but Trump's advisers may prefer different approaches (or timetables) to solving the problems that the order identifies.

It may be a new year, but the hacks, scams, and dangerous people lurking online haven't gone anywhere. Just a day before the ball dropped, the United States Treasury Department said it had been hacked. Officials believe the attackers are an as-yet-unidentified Advanced Persistent Threat group linked to China's government that exploited flaws in remote tech support software made by BeyondTrust to carry out what the Treasury Department described as a "major" breach. The company told the Treasury on December 8 that the attackers stole an authentication key, which ultimately allowed them to access department computers. While the Treasury says the attackers were only able to steal "certain unclassified documents," new details have already begun to emerge, which we'll get into more below.

Recent extensive numerical experiments in high scale machine learning have allowed to uncover a quite counterintuitive phase transition, as a function of the ratio between the sample size and the number of parameters in the model. As the number of parameters $p$ approaches the sample size $n$, the generalisation error (a.k.a. testing error) increases, but it many cases, it starts decreasing again past the threshold $p=n$. This surprising phenomenon, brought to the theoretical community attention in \cite{belkin2019reconciling}, has been thorougly investigated lately, more specifically for simpler models than deep neural networks, such as the linear model when the parameter is taken to be the minimum norm solution to the least-square problem, mostly in the asymptotic regime when $p$ and $n$ tend to $+\infty$; see e.g. \cite{hastie2019surprises}. In the present paper, we propose a finite sample analysis of non-linear models of \textit{ridge} type, where we investigate the double descent phenomenon for both the \textit{estimation problem} and the prediction problem. Our results show that the double descent phenomenon can be precisely demonstrated in non-linear settings and complements recent works of \cite{bartlett2020benign} and \cite{chinot2020benign}. Our analysis is based on efficient but elementary tools closely related to the continuous Newton method \cite{neuberger2007continuous}.

Australia's State Super has hired Neuberger Berman LLC for an equity mandate and to help the fund accelerate development of data science and machine learning tools that can complement its more traditional investment capabilities. The move reflects continued concerns that conventional approaches to managing the Sydney-based fund's A$44 billion ($30.2 billion) portfolio may not meet the moment in unconventional times. "My biggest concern is what happens if the market is behaving in an abnormal way," outside of the industry's knowledge base and modeling conventions, said Charles Wu, State Super's deputy chief investment officer and general manager, defined contribution investments, in an interview. In that regard, Mr. Wu said the emergence of negative sovereign bond yields three years ago was a warning bell. In the current environment, "a different way of thinking is required" and machine learning -- with its potential to come to a problem without prejudices or preconceptions -- can help State Super's team navigate a world growing evermore different from the one everyone has been trained to think about, he said.