While the possibility of reaching human-like Artificial Intelligence (AI) remains controversial, the likelihood that the future will be characterized by a society with a growing presence of autonomous machines is high. Autonomous AI agents are already deployed and active across several industries and digital environments and alongside human-human and human-machine interactions, machine-machine interactions are poised to become increasingly prevalent. Given these developments, I argue that criminology must begin to address the implications of this transition for crime and social control. Drawing on Actor-Network Theory and Woolgar's decades-old call for a sociology of machines -- frameworks that acquire renewed relevance with the rise of generative AI agents -- I contend that criminologists should move beyond conceiving AI solely as a tool. Instead, AI agents should be recognized as entities with agency encompassing computational, social, and legal dimensions. Building on the literature on AI safety, I thus examine the risks associated with the rise of multi-agent AI systems, proposing a dual taxonomy to characterize the channels through which interactions among AI agents may generate deviant, unlawful, or criminal outcomes. I then advance and discuss four key questions that warrant theoretical and empirical attention: (1) Can we assume that machines will simply mimic humans? (2) Will crime theories developed for humans suffice to explain deviant or criminal behaviors emerging from interactions between autonomous AI agents? (3) What types of criminal behaviors will be affected first? (4) How might this unprecedented societal shift impact policing? These questions underscore the urgent need for criminologists to theoretically and empirically engage with the implications of multi-agent AI systems for the study of crime and play a more active role in debates on AI safety and governance.

The proliferation of autonomous AI agents marks a paradigm shift toward complex, emergent multi-agent systems. This transition introduces systemic security risks, including control-flow hijacking and cascading failures, that traditional cybersecurity paradigms are ill-equipped to address. This paper introduces the Aegis Protocol, a layered security framework designed to provide strong security guarantees for open agentic ecosystems. The protocol integrates three technological pillars: (1) non-spoofable agent identity via W3C Decentralized Identifiers (DIDs); (2) communication integrity via NIST-standardized post-quantum cryptography (PQC); and (3) verifiable, privacy-preserving policy compliance using the Halo2 zero-knowledge proof (ZKP) system. We formalize an adversary model extending Dolev-Yao for agentic threats and validate the protocol against the STRIDE framework. Our quantitative evaluation used a discrete-event simulation, calibrated against cryptographic benchmarks, to model 1,000 agents. The simulation showed a 0 percent success rate across 20,000 attack trials. For policy verification, analysis of the simulation logs reported a median proof-generation latency of 2.79 seconds, establishing a performance baseline for this class of security. While the evaluation is simulation-based and early-stage, it offers a reproducible baseline for future empirical studies and positions Aegis as a foundation for safe, scalable autonomous AI.

"The Big Weekend Show" analyzes the possibilities of artificial intelligence when it comes to influencing voters. If you haven't heard the buzz about Manus yet, it's the new AI model unveiled by a Singapore-based company called Butterfly Effect. It's one of the first truly autonomous AI agents, able to do its own research, make decisions and even carry out plans, all with barely any human oversight. But here's the thing: While all this innovation opens up exciting possibilities, it also brings some serious privacy and security questions. Whether you're eager to try out the latest AI or you'd rather steer clear, it's worth understanding what Manus could mean for your personal data and digital safety.

Large Language Models (LLMs) have shown remarkable capabilities in natural language tasks requiring complex reasoning, yet their application in agentic, multi-step reasoning within interactive environments remains a difficult challenge. Traditional supervised pre-training on static datasets falls short in enabling autonomous agent capabilities needed to perform complex decision-making in dynamic settings like web navigation. Previous attempts to bridge this ga-through supervised fine-tuning on curated expert demonstrations-often suffer from compounding errors and limited exploration data, resulting in sub-optimal policy outcomes. To overcome these challenges, we propose a framework that combines guided Monte Carlo Tree Search (MCTS) search with a self-critique mechanism and iterative fine-tuning on agent interactions using an off-policy variant of the Direct Preference Optimization (DPO) algorithm. Our method allows LLM agents to learn effectively from both successful and unsuccessful trajectories, thereby improving their generalization in complex, multi-step reasoning tasks. We validate our approach in the WebShop environment-a simulated e-commerce platform where it consistently outperforms behavior cloning and reinforced fine-tuning baseline, and beats average human performance when equipped with the capability to do online search. In real-world booking scenarios, our methodology boosts Llama-3 70B model's zero-shot performance from 18.6% to 81.7% success rate (a 340% relative increase) after a single day of data collection and further to 95.4% with online search. We believe this represents a substantial leap forward in the capabilities of autonomous agents, paving the way for more sophisticated and reliable decision-making in real-world settings.

In recent weeks, the field of autonomous AI agents has grown exponentially, and the idea of AGI (artififcial general intelligence) has become an increasingly popular topic. An AI project inspired by AutoGPT and BabyAGI, provides a unique opportunity to explore the potential of these agents firsthand. In this article, we'll delve into the exciting features of free demo, discuss the importance of AI alignment projects, and reveal how you can try this groundbreaking technology for free. Question: "How do I increase Substack subscribers?" The AI agent can perform a variety of tasks, such as conducting market analysis, finding and negotiating a lease, or growing a Substack Subscriber Base.

LLMs have long been a topic of fascination and speculation, but what if I told you that we're on the cusp of a breakthrough that could change the game forever? A new frontier in digital artistry, fueled by independent creators and cutting-edge technology, is about to unleash a wave of innovation that could redefine what it means to be an autonomous AI artist/agent in the digital age. In a world where technology is advancing at breakneck speed, the AI landscape is about to be revolutionized. Imagine a future where artificial intelligence can not only perform tasks on its own but also learn and improve itself in the process. This might sound like science fiction, but that future is closer than you think.

Blockchain can also facilitate access control for any self-directed AI entity based on the status of that sovereign agent. In any case, perhaps the only approval that you can relate to a non-human autonomous AI entity is to stop it; or if you do not have that authority, eliminate access rights to the community. You could think this a modern day "excommunication." Generally, when we envision a future where standards are defined around what Artificial Intelligence has to put down to the Blockchain as well as the access control method that fundamentally looks at the "status" of the autonomous AI entity, and then we have machinery that can craft a control that is unfeasible to get around by any autonomous AI agent. The immutability of this Blockchain guarantees no AI agent can interfere with it, consequently establishing a reputation ledger for every autonomous AI agents.

The biggest threat for our future, according to Elon Musk, is the development of artificial intelligence (AI). "I think we should be very careful about artificial intelligence. If I had to guess at what our biggest existential threat is, it's probably that. So we need to be very careful. I'm increasingly inclined to think that there should be some regulatory oversight, maybe at the national and international level, just to make sure that we don't do something very foolish."

The biggest threat for our future, according to Elon Musk, is the development of artificial intelligence (AI). Musk addresses a couple of key issues. The first is how we should take care when it comes to the implementation of AI. The second, and perhaps the most important challenge, is how can you regulate and oversee this technology? In today's rush to adopt machine learning and AI techniques to gain a competitive advantage, there is a real danger that the technology will be deployed in an unsuitable field.

The biggest threat for our future, according to Elon Musk, is the development of artificial intelligence (AI). "I think we should be very careful about artificial intelligence. If I had to guess at what our biggest existential threat is, it's probably that. So we need to be very careful. I'm increasingly inclined to think that there should be some regulatory oversight, maybe at the national and international level, just to make sure that we don't do something very foolish."