As autonomous systems rapidly become ubiquitous, there is a growing need for a legal and regulatory framework that addresses when and how such a system harms someone. There have been several attempts within the philosophy literature to define harm, but none of them has proven capable of dealing with the many examples that have been presented, leading some to suggest that the notion of harm should be abandoned and "replaced by more well-behaved notions". As harm is generally something that is caused, most of these definitions have involved causality at some level. Yet surprisingly, none of them makes use of causal models and the definitions of actual causality that they can express. In this paper we formally define a qualitative notion of harm that uses causal models and is based on a well-known definition of actual causality [13]. The key features of our definition are that it is based on contrastive causation and uses a default utility to which the utility of actual outcomes is compared. We show that our definition is able to handle the examples from the literature, and illustrate its importance for reasoning about situations involving autonomous systems.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Contractors in Waltham, Massachusetts uncovered cannonballs during a dig late Monday morning, prompting a response from the police bomb squad and the fire department. The Waltham Fire Department notified the State Police Bomb Squad around 11 a.m. Wednesday that a construction worker had uncovered what appeared to be an "unexploded military ordinance" while excavating a trench at a job site.

AI systems are becoming increasingly intertwined with human life. In order to effectively collaborate with humans and ensure safety, AI systems need to be able to understand, interpret and predict human moral judgments and decisions. Human moral judgments are often guided by rules, but not always. A central challenge for AI safety is capturing the flexibility of the human moral mind -- the ability to determine when a rule should be broken, especially in novel or unusual situations. In this paper, we present a novel challenge set consisting of rule-breaking question answering (RBQA) of cases that involve potentially permissible rule-breaking -- inspired by recent moral psychology studies. Using a state-of-the-art large language model (LLM) as a basis, we propose a novel moral chain of thought (MORALCOT) prompting strategy that combines the strengths of LLMs with theories of moral reasoning developed in cognitive science to predict human moral judgments. MORALCOT outperforms seven existing LLMs by 6.2% F1, suggesting that modeling human reasoning might be necessary to capture the flexibility of the human moral mind. We also conduct a detailed error analysis to suggest directions for future work to improve AI safety using RBQA. Our data is open-sourced at https://huggingface.co/datasets/feradauto/MoralExceptQA and code at https://github.com/feradauto/MoralCoT

We present an approach to learn the dynamics of multiple objects from image sequences in an unsupervised way. We introduce a probabilistic model that first generate noisy positions for each object through a separate linear state-space model, and then renders the positions of all objects in the same image through a highly non-linear process. Such a linear representation of the dynamics enables us to propose an inference method that uses exact and efficient inference tools and that can be deployed to query the model in different ways without retraining.

The 18th-century Irish philosopher Bishop George Berkeley concluded that, since all we know of the universe is what our senses convey to us, things in the world exist only to the extent that we perceive them. They have no material reality, but are phenomena in and of our minds, or the mind of God. Samuel Johnson famously countered this philosophy by kicking a large stone and saying, "I refute it thus!" Two hundred years later, while American campuses roiled with protests against the Vietnam War, the philosopher, historian, and physicist Thomas Kuhn met with a grad student at Princeton's legendary Institute for Advanced Study to discuss the student's paper. The professor and student disagreed on some fundamental ideas, and the conversation grew heated.

Massachusetts Institute of Technology students this week are recreating pivotal moments leading up to the Revolutionary War. Earlier this semester, 153 students, mostly sophomores, were tasked with building robots as part of their undergraduate mechanical engineering class. On Wednesday, 137 of those students will compete in the semifinals, on a course inspired by the American Revolution, to secure one of 16 open slots in the finals Thursday. They'll join 16 other students who have already qualified for the finals. The events, held at MIT's Johnson Ice Rink, will take the robots through obstacles that include a replica dock, boat, and church steeple.

If you imagined that thought experiments were mere mental gymnastics meant to bamboozle the uninitiated, think again. Take Schrödinger's cat, perhaps the most famous example, which involves a cat that is simultaneously alive and dead. It seems bizarre – and that's the point. It was designed as a slap on the wrist for quantum theorists, to show that a theory that predicts such nonsense must be missing something. Current thinking is that perhaps nothing is missing, and quantum theory really is as weird as it seems.