As AI systems increasingly operate with autonomy and adaptability, the traditional boundaries of moral responsibility in techno-social systems are being challenged. This paper explores the evolving discourse on the delegation of responsibilities to intelligent autonomous agents and the ethical implications of such practices. Synthesizing recent developments in AI ethics, including concepts of distributed responsibility and ethical AI by design, the paper proposes a functionalist perspective as a framework. This perspective views moral responsibility not as an individual trait but as a role within a socio-technical system, distributed among human and artificial agents. As an example of 'AI ethical by design,' we present Basti and Vitiello's implementation. They suggest that AI can act as artificial moral agents by learning ethical guidelines and using Deontic Higher-Order Logic to assess decisions ethically. Motivated by the possible speed and scale beyond human supervision and ethical implications, the paper argues for 'AI ethical by design', while acknowledging the distributed, shared, and dynamic nature of responsibility. This functionalist approach offers a practical framework for navigating the complexities of AI ethics in a rapidly evolving technological landscape.

Robotic cloth manipulation faces challenges due to the fabric's complex dynamics and the high dimensionality of configuration spaces. Previous methods have largely focused on isolated smoothing or folding tasks and overly reliant on simulations, often failing to bridge the significant sim-to-real gap in deformable object manipulation. To overcome these challenges, we propose a two-stream architecture with sequential and spatial pathways, unifying smoothing and folding tasks into a single adaptable policy model that accommodates various cloth types and states. The sequential stream determines the pick and place positions for the cloth, while the spatial stream, using a connectivity dynamics model, constructs a visibility graph from partial point cloud data of the self-occluded cloth, allowing the robot to infer the cloth's full configuration from incomplete observations. To bridge the sim-to-real gap, we utilize a hand tracking detection algorithm to gather and integrate human demonstration data into our novel end-to-end neural network, improving real-world adaptability. Our method, validated on a UR5 robot across four distinct cloth folding tasks with different goal shapes, consistently achieves folded states from arbitrary crumpled initial configurations, with success rates of 99\%, 99\%, 83\%, and 67\%. It outperforms existing state-of-the-art cloth manipulation techniques and demonstrates strong generalization to unseen cloth with diverse colors, shapes, and stiffness in real-world experiments.Videos and source code are available at: https://zcswdt.github.io/SSFold/

China has developed a new drone that go function in air and water. A new Chinese drone is gaining attention as it looks like something straight from a Hollywood action movie. Although its capabilities look pretty cool, in the wrong hands, this device could be dispatched on some dastardly missions. CLICK TO GET KURT'S CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER The TJ-Flying Fish was developed in China. The drone, known as the TJ-FlyingFish, was developed by a team of scientists from China's Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, and the Unmanned Systems Research Group at the Chinese University of Hong Kong.

This whole topic – where (and how) should ML and rule-based verification meet – has been on my mind for a while, but I still don't have good answers. I do think it deserves significant attention from researchers and practitioners. The next three chapters will discuss why I expect ML to keep growing in dynamic verification, why there will always be an unavoidable, irreducible non-ML part, and some ideas about connecting the two. Finally, the last chapter will talk about rules in ML-based systems, explainable AI and all that. If you are not into verification, just go directly there. Please take a quick look at my Dynamic verification in one picture post.

As intelligent agents become more autonomous, sophisticated, and prevalent, it becomes increasingly important that humans interact with them effectively. Machine learning is now used regularly to acquire expertise, but common techniques produce opaque content whose behavior is difficult to interpret. Before they will be trusted by humans, autonomous agents must be able to explain their decisions and the reasoning that produced their choices. We will refer to this general ability as explainable agency. This capacity for explaining decisions is not an academic exercise. When a self-driving vehicle takes an unfamiliar turn, its passenger may desire to know its reasons. When a synthetic ally in a computer game blocks a player's path, he may want to understand its purpose. When an autonomous military robot has abandoned a high-priority goal to pursue another one, its commander may request justification. As robots, vehicles, and synthetic characters become more self-reliant, people will require that they explain their behaviors on demand. The more impressive these agents' abilities, the more essential that we be able to understand them.

The aim of the special track is to bring researchers from related but still separated areas of robotics and artificial intelligence together to study how high-level AI techniques such as action planning and knowledge representation may help in increasing capabilities of robotic systems.