Plus: AI reportedly caused ICE to send agents into the field without training, Palantir's app for targeting immigrants gets exposed, and more. As Immigration and Customs Enforcement continues its "Operation Metro Surge" infiltration of Minnesota, more than 2,000 ICE operatives and about 1,000 other federal agents have made more than 2,400 arrests since the operation began in late 2025, and tear gassed protesters. Last week, an ICE agent shot and killed local resident Renee Nicole Good, a 37-year-old US citizen. In response, the state of Minnesota and the Twin Cities' local governments sued the US government and several officials this week to stop the operation . WIRED reported on a contract justification published in a federal register on Tuesday that says 31 ICE vehicles currently operating in Minnesota "lack the necessary emergency lights and sirens" to be "compliant" with regulations.

The middle and working classes have seen a steady decline in their fortunes. Sending jobs to foreign countries, the hollowing out of the manufacturing sector, pivoting toward a service economy and the weakening of unions have been blamed for the challenges faced by a majority of Americans. According to a new academic research study, automation technology has been the primary driver in U.S. income inequality over the past 40 years. The report, published by the National Bureau of Economic Research, claims that 50% to 70% of changes in U.S. wages, since 1980, can be attributed to wage declines among blue-collar workers who were replaced or degraded by automation. Artificial intelligence, robotics and new sophisticated technologies have caused a wide chasm in wealth and income inequality.

Increasingly sophisticated killer AI robots and machines could accidentally start a war and lead to mass atrocities, an ex-Google worker has told The Guardian. Laura Nolan resigned from Google last year in protest at being assigned to Project Maven, which was aimed at enhancing U.S. military drone technology. She has called for all unmanned autonomous weapons to be banned. AI killer robots have the potential to do "calamitous things that they were not originally programmed for," Nolan explained to the Guardian. She is part of a growing group of experts that are showing concern over the development of artificial intelligence programmed into war machines.

Artificial intelligence (AI) is not a new concept, with its origins dating back to the 1950s. Yet AI has only recently emerged to the forefront of investment interest from industry, government, and venture capital after decades of relative dormancy. What contributed to the thawing of AI's winter and current boom? AI is booming largely due to advancements in pattern recognition capabilities resulting from deep learning--a subset of machine learning where an artificial neural network consists of over two layers of processing. Machine learning is a subset of AI that involves algorithms that are able to learn from data, versus a programmer having to explicitly code instructions.

Artificial Intelligence (AI) is redefining industries by offering personalization, automating processes, and disrupting how we work. In modern times, AI is embraced by every industry from healthcare to government. Here are the 10 industries where AI has caused a disruption. The most popular applications of AI in agriculture range from robotics to crop and soil monitoring to predictive analytics. Agriculture majors are developing autonomous robots programmed to handle routine agricultural tasks such as crop harvesting at a higher volume human labourers.

It has been argued that causal rules are necessary for representing both implicit side-effects of actions and action qualifications, and there have been a number different approaches for representing causal rules in the area of formal theoriesof actions. These different approaches in general agree on rules without cycles. However, they differ on causal rules with mutual cyclic dependencies, both in terms of how these rules are supposed to be represented and their semantics. In this paper we show that by adding one more minimization to Lin's circumscriptive causal theory in the situation calculus, we can have a uniform representation of causal rules including those with cyclic dependencies. We also demonstrate that sometimes causal rules can be compiled into logically equivalent successor state axioms even in the presence of cyclical dependencies between fluents.

We describe a system for specifying the effects of actions. Unlike those commonly used in AI planning, our system uses an action description language that allows one to specify the effects of actions using domain rules, which are state constraints that can entail new action effects from old ones. Declaratively, an action domain in our language corresponds to a nonmonotonic causal theory in the situation calculus. Procedurally, such an action domain is compiled into a set of logical theories, one for each action in the domain, from which fully instantiated successor state-like axioms and STRIPS-like systems are then generated. We expect the system to be a useful tool for knowledge engineers writing action specifications for classical AI planning systems, GOLOG systems, and other systems where formal specifications of actions are needed.

It has been argued that causal rules are necessary for representing both implicit side-effects of actions and action qualifications, and there have been a number different approaches for representing causal rules in the area of formal theories of actions. These different approaches in general agree on rules without cycles. However, they differ on causal rules with mutual cyclic dependencies, both in terms of how these rules are supposed to be represented and their semantics. In this paper we show that by adding one more minimization to Lin's circumscriptive causal theory in the situation calculus, we can have a uniform representation of causal rules including those with cyclic dependencies. We also demonstrate that sometimes causal rules can be compiled into logically equivalent (under a proposed semantics) successor state axioms even in the presence of cyclical dependencies between fluents.

We show how the situation calculus can be reformulated in terms of the first-order stable model semantics. A further transformation into answer set programs allows us to use an answer set solver to perform propositional reasoning about the situation calculus. We also provide an ASP style encoding method for Reiter's basic action theories, which tells us how the solution to the frame problem in ASP is related to the solution in the situation calculus.

We describe a system for specifying the effects of actions. Unlike those commonly used in AI planning, our system uses an action description language that allows one to specify the effects of actions using domain rules, which are state constraints that can entail new action effects from old ones. Declaratively, an action domain in our language corresponds to a nonmonotonic causal theory in the situation calculus. Procedurally, such an action domain is compiled into a set of logical theories, one for each action in the domain, from which fully instantiated successor state-like axioms and STRIPS-like systems are then generated. We expect the system to be a useful tool for knowledge engineers writing action specifications for classical AI planning systems, GOLOG systems, and other systems where formal specifications of actions are needed.