Large language models (LLMs) are excellent in-context learners. However, the sensitivity of data contained in prompts raises privacy concerns. Our work first shows that these concerns are valid: we instantiate a simple but highly effective membership inference attack against the data used to prompt LLMs. To address this vulnerability, one could forego prompting and resort to fine-tuning LLMs with known algorithms for private gradient descent. However, this comes at the expense of the practicality and efficiency offered by prompting. Therefore, we propose to privately learn to prompt.

An accidental leak revealed that Flock, which has cameras in thousands of US communities, is using workers in the Philippines to review and classify footage. Flock, the automatic license plate reader and AI-powered camera company, uses overseas workers from Upwork to train its machine learning algorithms, with training material telling workers how to review and categorize footage including images people and vehicles in the United States, according to material reviewed by 404 Media that was accidentally exposed by the company. The findings bring up questions about who exactly has access to footage collected by Flock surveillance cameras and where people reviewing the footage may be based. Flock has become a pervasive technology in the US, with its cameras present in thousands of communities that cops use every day to investigate things like carjackings. Local police have also performed numerous lookups for ICE in the system.

Plus: A secret FBI anti-ransomware task force gets exposed, the mystery of the CIA's Kryptos sculpture is finally solved, North Koreans busted hiding malware in the Ethereum blockchain, and more. In a stunning new study, researchers at UC San Diego and the University of Maryland revealed this week that satellites are leaking a wealth of sensitive data completely unencrypted, from calls and text messages on T-Mobile to in-flight Wi-Fi browsing sessions, to military and police communications. And they did this with just $800 in off-the-shelf equipment. Face recognition systems are seemingly everywhere. But what happens when this surveillance and identification technology doesn't recognize your face as a face?

Flock Safety is pitching its police-style drone program to private businesses. It could bring aerial surveillance to shopping centers, warehouses, and hospitals. Flock Safety, whose drones were once reserved for police departments, is now offering them for private-sector security, the company announced today, with potential customers including including businesses intent on curbing shoplifting. Companies in the US can now place Flock's drone docking stations on their premises. If the company has a waiver from the Federal Aviation Administration to fly beyond visual line of sight (these are becoming easier to get), its security team can fly the drones within a certain radius, often a few miles. "Instead of a 911 call [that triggers the drone], it's an alarm call," says Keith Kauffman, a former police chief who now directs Flock's drone program.

Why do collectives outperform individuals when solving some problems? Fundamentally, collectives have greater computational resources with more sensory information, more memory, more processing capacity, and more ways to act. While greater resources present opportunities, there are also challenges in coordination and cooperation inherent in collectives with distributed, modular structures. Despite these challenges, we show how collective resource advantages lead directly to well-known forms of collective intelligence including the wisdom of the crowd, collective sensing, division of labour, and cultural learning. Our framework also generates testable predictions about collective capabilities in distributed reasoning and context-dependent behavioural switching. Through case studies of animal navigation and decision-making, we demonstrate how collectives leverage their computational resources to solve problems not only more effectively than individuals, but by using qualitatively different problem-solving strategies.

Fine-tuning the large language models (LLMs) are prevented by the deficiency of centralized control and the massive computing and communication overhead on the decentralized schemes. While the typical standard federated learning (FL) supports data privacy, the central server requirement creates a single point of attack and vulnerability to poisoning attacks. Generalizing the result in this direction to 70B-parameter models in the heterogeneous, trustless environments has turned out to be a huge, yet unbroken bottleneck. This paper introduces FLock, a decentralized framework for secure and efficient collaborative LLM fine-tuning. Integrating a blockchain-based trust layer with economic incentives, FLock replaces the central aggregator with a secure, auditable protocol for cooperation among untrusted parties. We present the first empirical validation of fine-tuning a 70B LLM in a secure, multi-domain, decentralized setting. Our experiments show the FLock framework defends against backdoor poisoning attacks that compromise standard FL optimizers and fosters synergistic knowledge transfer. The resulting models show a >68% reduction in adversarial attack success rates. The global model also demonstrates superior cross-domain generalization, outperforming models trained in isolation on their own specialized data.

Optimizing modern production plants using the job-shop principle is a known hard problem. For very large plants, like semiconductor fabs, the problem becomes unsolvable on a plant-wide scale in a reasonable amount of time using classical linear optimization. An alternative approach is the use of swarm intelligence algorithms. These have been applied to the job-shop problem before, but often in a centrally calculated way where they are applied to the solution space, but they can be implemented in a bottom-up fashion to avoid global result computation as well. One of the problems in semiconductor production is that the production process requires a lot of switching between machines that process lots one after the other and machines that process batches of lots at once, often with long processing times. In this paper, we address this switching problem with the ``boids'' flocking algorithm that was originally used in robotics and movie industry. The flocking behavior is a bio-inspired algorithm that uses only local information and interaction based on simple heuristics. We show that this algorithm addresses these valid considerations in production plant optimization, as it reacts to the switching of machine kinds similar to how a swarm of flocking animals would react to obstacles in its course.

-- Recent advances in multi - agent systems manipulation have demonstrated a rising demand for the implementation of multi - UAV systems in urban areas, which are always subjected to the presence of static and dynamic obstacles. Inspired by the collective behavior of tilapia fish and pigeons, the focus of the presented research is on the introduction of a nature - inspired collision - free formation control for a multi - UAV system, considering the obstacle avoidance maneuvers. The developed framework in this study utilizes a semi - distributed control approach, in which, based on a probabilistic Lloyd's algorithm, a centralized guidance algorithm works for optimal positioning of the UAVs, while a distributed control approach has been used for the intervehicle collision and obstacle avoidance. Further, the presented framework has been extended to the 3D space with a novel definition of 3D maneuvers. Collision Avoidance, Centroidal Voronoi Tessellation, Distributed Control, Formation Control, Multi - Agent System, Obstacle Avoidance . From an engineering perspective, swarm intelligence shows how decentralized systems, composed of numerous simple agents, can achieve complex collective behaviors.

The synchronized flight of bird flocks, exemplified by starling murmurations, is perhaps the most striking example of collective behavior in natural systems, which fascinated scholars for quite a long time [1]. Evolutionary biologists, for instance, have long debated the advantages of living in groups [2], which should offer increased protection from predation by diluting the individual risk and 1 possibly confusing the attackers by the sheer size of the assembly. Flocking behavior involves a high degree of order in the individual directions of motion [3], and has been reproduced by minimal models of self-propelling particles (SPPs), such as Craig Reynolds Boids [4] or the celebrated Vicsek model [5] that has long captivated the attention of statistical physicists and played a pivotal role in the birth of the active matter research field. The essential ingredient of these models is the tendency of individual particles to align their direction of motion with those of their local neighbours, which is enough to promote long range order in systems with finite density (even in two spatial dimensions, due to the non-equilibrium nature of self-propelled particles) such as in toy models with periodic boundary conditions. In open systems, constituted by a finite number of individuals in an open, infinite space, purely alignment interactions are however not enough to maintain group cohesion.

The orchestration of agents to optimize a collective objective without centralized control is challenging yet crucial for applications such as controlling autonomous fleets, and surveillance and reconnaissance using sensor networks. Decentralized controller design has been inspired by self-organization found in nature, with a prominent source of inspiration being flocking; however, decentralized controllers struggle to maintain flock cohesion. The graph neural network (GNN) architecture has emerged as an indispensable machine learning tool for developing decentralized controllers capable of maintaining flock cohesion, but they fail to exploit the symmetries present in flocking dynamics, hindering their generalizability. We enforce rotation equivariance and translation invariance symmetries in decentralized flocking GNN controllers and achieve comparable flocking control with 70% less training data and 75% fewer trainable weights than existing GNN controllers without these symmetries enforced. We also show that our symmetry-aware controller generalizes better than existing GNN controllers. Code and animations are available at http://github.com/Utah-Math-Data-Science/Equivariant-Decentralized-Controllers.