There was a point for all of us, somewhere near the beginning of the pandemic, where we said to ourselves, oh shit. One of my first oh-shit moments arrived after reading Ed Yong's sobering feature, "How the Pandemic Will End," in the Atlantic in March. The coronavirus, Yong wrote, was "unlikely to disappear entirely," and he explained that it was possible that "COVID-19 may become like the flu is today--a recurring scourge of winter." I repeat: Yong wrote this in March. The day it published, the United States had so far detected around 68,000 cases of COVID-19 and documented less than 1,000 deaths, according to the CDC. In the days ahead of its publication, California became the first state to mandate its citizens stay home; Dr. Anthony Fauci, director of the National Institute of Allergy and Infectious Diseases, said that Americans would likely need to socially distance for "at least several weeks"; and President Donald Trump said he wanted to have the country "opened up and just raring to go by Easter."

The field of Artificial Intelligence (AI) has been introducing a certain level of anxiety in research, business and also policy. Tensions are further heightened by an AI race narrative which makes many stakeholders fear that they might be missing out. Whether real or not, a belief in this narrative may be detrimental as some stakeholders will feel obliged to cut corners on safety precautions or ignore societal consequences. Starting from a game-theoretical model describing an idealised technology race in a well-mixed world, here we investigate how different interaction structures among race participants can alter collective choices and requirements for regulatory actions. Our findings indicate that, when participants portray a strong diversity in terms of connections and peer-influence (e.g., when scale-free networks shape interactions among parties), the conflicts that exist in homogeneous settings are significantly reduced, thereby lessening the need for regulatory actions. Furthermore, our results suggest that technology governance and regulation may profit from the world's patent heterogeneity and inequality among firms and nations to design and implement meticulous interventions on a minority of participants capable of influencing an entire population towards an ethical and sustainable use of AI.

As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant. We explore the problem of algorithmic fairness, taking an information-theoretic view. The maximal correlation framework is introduced for expressing fairness constraints and shown to be capable of being used to derive regularizers that enforce independence and separation-based fairness criteria, which admit optimization algorithms for both discrete and continuous variables which are more computationally efficient than existing algorithms. We show that these algorithms provide smooth performance-fairness tradeoff curves and perform competitively with state-of-the-art methods on both discrete datasets (COMPAS, Adult) and continuous datasets (Communities and Crimes).

We propose a goodness-of-fit test for degree-corrected stochastic block models (DCSBM). The test is based on an adjusted chi-square statistic for measuring equality of means among groups of $n$ multinomial distributions with $d_1,\dots,d_n$ observations. In the context of network models, the number of multinomials, $n$, grows much faster than the number of observations, $d_i$, hence the setting deviates from classical asymptotics. We show that a simple adjustment allows the statistic to converge in distribution, under null, as long as the harmonic mean of $\{d_i\}$ grows to infinity. This result applies to large sparse networks where the role of $d_i$ is played by the degree of node $i$. Our distributional results are nonasymptotic, with explicit constants, providing finite-sample bounds on the Kolmogorov-Smirnov distance to the target distribution. When applied sequentially, the test can also be used to determine the number of communities. The test operates on a (row) compressed version of the adjacency matrix, conditional on the degrees, and as a result is highly scalable to large sparse networks. We incorporate a novel idea of compressing the columns based on a $(K+1)$-community assignment when testing for $K$ communities. This approach increases the power in sequential applications without sacrificing computational efficiency, and we prove its consistency in recovering the number of communities. Since the test statistic does not rely on a specific alternative, its utility goes beyond sequential testing and can be used to simultaneously test against a wide range of alternatives outside the DCSBM family. We show the effectiveness of the approach by extensive numerical experiments with simulated and real data. In particular, applying the test to the Facebook-100 dataset, we find that a DCSBM with a small number of communities is far from a good fit in almost all cases.

Federal officials say they will allow operators to fly small drones over people and at night, potentially giving a boost to commercial use of the machines. Most drones will need to be equipped so they can be identified remotely by law enforcement officials. The final rules announced by the Federal Aviation Administration "get us closer to the day when we will more routinely see drone operations such as the delivery of packages," said FAA Administrator Stephen Dickson. Drones are the fastest-growing segment in all of transportation, with more than 1.7 million under registration, according to the Transportation Department. However, the widespread commercial use of the machines has developed far more slowly than many advocates expected.

However, while the film is light-hearted, affectionate and comedic in tone, it carries a very important and timely message about trust and the ease with which convincing misinformation can be created and spread.

America's first confirmed wrongful arrest by facial recognition technology happened in January 2020. Robert Williams, a Black man, was arrested in his driveway just outside Detroit, with his wife and young daughter watching. He spent the night in jail. The next day in the questioning room, a detective slid a picture across the table to Williams of a different Black man who had been caught on video stealing watches from the boutique Shinola. "Is this you?" he asked.

The Federal Aviation Administration announced new rules Monday that would ease restrictions on the use of drones and will likely expand commercial uses of the technology down the road. The Federal Aviation Administration announced new rules Monday that would ease restrictions on the use of drones and will likely expand commercial uses of the technology down the road. Federal regulators have issued new guidelines allowing drones to operate at night and over people -- a change in the rules that could expand the use of the machines for commercial deliveries. The new rules from the Federal Aviation Administration will also require remote identification technology so that the machines can be identifiable from the ground. The FAA said this standard will address security concerns and make drones easier to track.

The new approach, replacing stringent protections that currently bar practically all home-delivery options, goes into effect in two months, but some requirements are likely to take years to implement. The detailed regulations, which total more than 700 pages and parts of which had been in the works since the Obama administration, also aim to address concerns related to law enforcement, national security and privacy protection. "They get us closer to the day when we will more routinely see drone operations such as the delivery of packages," FAA chief Steve Dickson said in a written statement accompanying the rules. Mr. Dickson has told colleagues he intends to stay on under the Biden administration, according to people involved in the conversations, to fill out the remainder of his five-year term ending in 2024. The rules are unlikely to be affected by other personnel changes.

The ever-increasing data traffic, various delay-sensitive services, and the massive deployment of energy-limited Internet of Things (IoT) devices have brought huge challenges to the current communication networks, motivating academia and industry to move to the sixth-generation (6G) network. With the powerful capability of data transmission and processing, 6G is considered as an enabler for IoT communication with low latency and energy cost. In this paper, we propose an artificial intelligence (AI) and intelligent reflecting surface (IRS) empowered energy-efficiency communication system for 6G IoT. First, we design a smart and efficient communication architecture including the IRS-aided data transmission and the AI-driven network resource management mechanisms. Second, an energy efficiency-maximizing model under given transmission latency for 6G IoT system is formulated, which jointly optimizes the settings of all communication participants, i.e. IoT transmission power, IRS-reflection phase shift, and BS detection matrix. Third, a deep reinforcement learning (DRL) empowered network resource control and allocation scheme is proposed to solve the formulated optimization model. Based on the network and channel status, the DRL-enabled scheme facilities the energy-efficiency and low-latency communication. Finally, experimental results verified the effectiveness of our proposed communication system for 6G IoT.