The US Department of Justice arrested a Wisconsin man last week for generating and distributing AI-generated child sexual abuse material (CSAM). As far as we know, this is the first case of its kind as the DOJ looks to establish a judicial precedent that exploitative materials are still illegal even when no children were used to create them. "Put simply, CSAM generated by AI is still CSAM," Deputy Attorney General Lisa Monaco wrote in a press release. The DOJ says 42-year-old software engineer Steven Anderegg of Holmen, WI, used a fork of the open-source AI image generator Stable Diffusion to make the images, which he then used to try to lure an underage boy into sexual situations. The latter will likely play a central role in the eventual trial for the four counts of "producing, distributing, and possessing obscene visual depictions of minors engaged in sexually explicit conduct and transferring obscene material to a minor under the age of 16."

The Justice Department named its first-ever official focused on artificial intelligence (AI) on Thursday in anticipation of the rapidly evolving technology's impact on the criminal justice system. Jonathan Mayer, a professor at Princeton University who focuses on the "intersection of technology and law, with emphasis on national security, criminal procedure, consumer privacy, network management, and online speech," according to his online biography, was selected to serve as the DOJ's chief science and technology adviser and chief AI officer, Reuters reported. "The Justice Department must keep pace with rapidly evolving scientific and technological developments in order to fulfill our mission to uphold the rule of law, keep our country safe and protect civil rights," U.S. Attorney General Merrick Garland said in a statement. Mayer previously served as the technology adviser to Vice President Kamala Harris during her time as a U.S. senator, and as the Chief Technologist of the Federal Communications Commission Enforcement Bureau. In his new role, he is expected to advise Garland and DOJ leadership on matters related to emerging technologies, including how to responsibly integrate AI into the department's investigations and criminal prosecutions, according to Reuters.

Pricing-based mechanisms have been widely studied and developed for resource allocation in multi-agent systems. One of the main goals in such studies is to avoid envy between the agents, i.e., guarantee fair allocation. However, even the simplest combinatorial cases of this problem is not well understood. Here, we try to fill these gaps and design polynomial revenue maximizing pricing mechanisms to allocate homogeneous resources among buyers in envy-free manner. In particular, we consider envy-free outcomes in which all buyers' utilities are maximized. We also consider pair envy-free outcomes in which all buyers prefer their allocations to the allocations obtained by other agents. For both notions of envy-freeness, we consider item and bundle pricing schemes. Our results clearly demonstrate the limitations and advantages in terms of revenue between these two different notions of envy-freeness.

We consider the online version of the coalition structure generation problem, in which agents, corresponding to the vertices of a graph, appear in an online fashion and have to be partitioned into coalitions by an authority (i.e., an online algorithm). When an agent appears, the algorithm has to decide whether to put the agent into an existing coalition or to create a new one containing, at this moment, only her. The decision is irrevocable. The objective is partitioning agents into coalitions so as to maximize the resulting social welfare that is the sum of all coalition values. We consider two cases for the value of a coalition: (1) the sum of the weights of its edges, and (2) the sum of the weights of its edges divided by its size. Coalition structures appear in a variety of application in AI, multi-agent systems, networks, as well as in social networks, data analysis, computational biology, game theory, and scheduling. For each of the coalition value functions we consider the bounded and unbounded cases depending on whether or not the size of a coalition can exceed a given value α. Furthermore, we consider the case of a limited number of coalitions and various weight functions for the edges, i.e., unrestricted, positive and constant weights. We show tight or nearly tight bounds for the competitive ratio in each case.

The deep neural nets of modern artificial intelligence (AI) have not achieved defining features of biological intelligence, including abstraction, causal learning, and energy-efficiency. While scaling to larger models has delivered performance improvements for current applications, more brain-like capacities may demand new theories, models, and methods for designing artificial learning systems. Here, we argue that this opportunity to reassess insights from the brain should stimulate cooperation between AI research and theory-driven computational neuroscience (CN). To motivate a brain basis of neural computation, we present a dynamical view of intelligence from which we elaborate concepts of sparsity in network structure, temporal dynamics, and interactive learning. In particular, we suggest that temporal dynamics, as expressed through neural synchrony, nested oscillations, and flexible sequences, provide a rich computational layer for reading and updating hierarchical models distributed in long-term memory networks. Moreover, embracing agent-centered paradigms in AI and CN will accelerate our understanding of the complex dynamics and behaviors that build useful world models. A convergence of AI/CN theories and objectives will reveal dynamical principles of intelligence for brains and engineered learning systems. This article was inspired by our symposium on dynamical neuroscience and machine learning at the 6th Annual US/NIH BRAIN Initiative Investigators Meeting.

Additively separable hedonic games and fractional hedonic games have received considerable attention in the literature. They are coalition formation games among selfish agents based on their mutual preferences. Most of the work in the literature characterizes the existence and structure of stable outcomes (i.e., partitions into coalitions) assuming that preferences are given. However, there is little discussion of this assumption. In fact, agents receive different utilities if they belong to different coalitions, and thus it is natural for them to declare their preferences strategically in order to maximize their benefit. In this paper we consider strategyproof mechanisms for additively separable hedonic games and fractional hedonic games, that is, partitioning methods without payments such that utility maximizing agents have no incentive to lie about their true preferences. We focus on social welfare maximization and provide several lower and upper bounds on the performance achievable by strategyproof mechanisms for general and specific additive functions. In most of the cases we provide tight or asymptotically tight results. All our mechanisms are simple and can be run in polynomial time. Moreover, all the lower bounds are unconditional, that is, they do not rely on any computational complexity assumptions.

Truck drivers can put the brakes on their worst automation fears. Robots are not getting behind the wheel and stealing the jobs of long-haul drivers anytime soon, according to two government experts, offering a contrarian view to some of the biggest names in tech and a presidential candidate. In a study published in the Industrial and Labor Relations Review, Maury Gittleman, a research economist at the Bureau of Labor Statistics, and Kristen Monaco, an associate commissioner in the Office of Compensation and Working Conditions also at BLS, argue that there are three main reasons why the threat of automation, robots and AI to truck drivers is more fear-mongering than fearsome. "Looking at the data, we believe that, while the risk of job loss from automation is very real, the projections that often get touted are overstated," the two penned in Harvard Business Review Wednesday. The projections they're referring to are the elimination of some 2-3 million truck driving jobs, and one that recently got a boost by presidential candidate Andrew Yang, who retweeted Tuesday an article on trucking automation by Business Insider that perpetuated the projection in question.

We consider fractional hedonic games, a subclass of coalition formation games that can be succinctly modeled by means of a graph in which nodes represent agents and edge weights the degree of preference of the corresponding endpoints. The happiness or utility of an agent for being in a coalition is the average value she ascribes to its members. We adopt Nash stable outcomes as the target solution concept; that is we focus on states in which no agent can improve her utility by unilaterally changing her own group. We provide existence, efficiency and complexity results for games played on both general and specific graph topologies. As to the efficiency results, we mainly study the quality of the best Nash stable outcome and refer to the ratio between the social welfare of an optimal coalition structure and the one of such an equilibrium as to the price of stability. In this respect, we remark that a best Nash stable outcome has a natural meaning of stability, since it is the optimal solution among the ones which can be accepted by selfish agents. We provide upper and lower bounds on the price of stability for different topologies, both in case of weighted and unweighted edges. Beside the results for general graphs, we give refined bounds for various specific cases, such as triangle-free, bipartite graphs and tree graphs. For these families, we also show how to efficiently compute Nash stable outcomes with provable good social welfare.

We consider a market setting in which buyers are individuals of a population, whose relationships are represented by an underlying social graph. Given buyers valuations for the items being sold, an outcome consists of a pricing of the objects and an allocation of bundles to the buyers. An outcome is social envy-free if no buyer strictly prefers the bundles of her neighbors in the social graph. We focus on the revenue maximization problem in multi-unit markets, in which there are multiple copies of a same item being sold and each buyer is assigned a set of identical items. We consider the four different cases arising by considering different buyers valuations, i.e., single-minded or general, and by adopting different forms of pricing, that is item- or bundle-pricing. For all the above cases we show the hardness of the revenue maximization problem and give corresponding approximation results. All our approximation bounds are optimal or nearly optimal. Moreover, we provide an optimal allocation algorithm for general valuations with item-pricing, under the assumption of social graphs of bounded treewidth. Finally, we determine optimal bounds on the corresponding price of envy-freeness, that is on the worst case ratio between the maximum revenue that can be achieved without envy-freeness constraints, and the one obtainable in case of social relationships. Some of our results close hardness open questions or improve already known ones in the literature concerning the classical setting without sociality.