The adoption of generative AI technologies is swiftly expanding. Services employing both linguistic and mul-timodal models are evolving, offering users increasingly precise responses. Consequently, human reliance on these technologies is expected to grow rapidly. With the premise that people will be impacted by the output of AI, we explored approaches to help AI output produce better results. Initially, we evaluated how contemporary AI services competitively meet user needs, then examined society's depiction as mirrored by Large Language Models (LLMs). We did a query experiment, querying about social conventions in various countries and eliciting a one-word response. We compared the LLMs' value judgments with public data and suggested an model of decision-making in value-conflicting scenarios which could be adopted for future machine value judgments. This paper advocates for a practical approach to using AI as a tool for investigating other remote worlds. This re-search has significance in implicitly rejecting the notion of AI making value judgments and instead arguing a more critical perspective on the environment that defers judgmental capabilities to individuals. We anticipate this study will empower anyone, regardless of their capacity, to receive safe and accurate value judgment-based out-puts effectively.

Reinforcement Learning (RL) methods are typically sample-inefficient, making it challenging to train and deploy RL-policies in real world robots. Even a robust policy trained in simulation requires a real-world deployment to assess their performance. This paper proposes a new approach to evaluate the real-world performance of agent policies prior to deploying them in the real world. Our approach incorporates a simulator along with real-world offline data to evaluate the performance of any policy using the framework of Marginalized Importance Sampling (MIS). Existing MIS methods face two challenges: (1) large density ratios that deviate from a reasonable range and (2) indirect supervision, where the ratio needs to be inferred indirectly, thus exacerbating estimation error. Our approach addresses these challenges by introducing the target policy's occupancy in the simulator as an intermediate variable and learning the density ratio as the product of two terms that can be learned separately. The first term is learned with direct supervision and the second term has a small magnitude, thus making it computationally efficient. We analyze the sample complexity as well as error propagation of our two step-procedure. Furthermore, we empirically evaluate our approach on Sim2Sim environments such as Cartpole, Reacher, and Half-Cheetah. Our results show that our method generalizes well across a variety of Sim2Sim gap, target policies and offline data collection policies. We also demonstrate the performance of our algorithm on a Sim2Real task of validating the performance of a 7 DoF robotic arm using offline data along with the Gazebo simulator.

Measures of power grid vulnerability are often assessed by the amount of damage an adversary can exact on the network. However, the cascading impact of such attacks is often overlooked, even though cascades are one of the primary causes of large-scale blackouts. This paper explores modifications of transmission line protection settings as candidates for adversarial attacks, which can remain undetectable as long as the network equilibrium state remains unaltered. This forms the basis of a black-box function in a Bayesian optimization procedure, where the objective is to find protection settings that maximize network degradation due to cascading. Notably, our proposed method is agnostic to the choice of the cascade simulator and its underlying assumptions. Numerical experiments reveal that, against conventional wisdom, maximally misconfiguring the protection settings of all network lines does not cause the most cascading. More surprisingly, even when the degree of misconfiguration is limited due to resource constraints, it is still possible to find settings that produce cascades comparable in severity to instances where there are no resource constraints.

The authors are concerned about the safety, health, and rights of the European citizens due to inadequate measures and procedures required by the current draft of the EU Artificial Intelligence (AI) Act for the conformity assessment of AI systems. We observe that not only the current draft of the EU AI Act, but also the accompanying standardization efforts in CEN/CENELEC, have resorted to the position that real functional guarantees of AI systems supposedly would be unrealistic and too complex anyways. Yet enacting a conformity assessment procedure that creates the false illusion of trust in insufficiently assessed AI systems is at best naive and at worst grossly negligent. The EU AI Act thus misses the point of ensuring quality by functional trustworthiness and correctly attributing responsibilities. The trustworthiness of an AI decision system lies first and foremost in the correct statistical testing on randomly selected samples and in the precision of the definition of the application domain, which enables drawing samples in the first place. We will subsequently call this testable quality functional trustworthiness. It includes a design, development, and deployment that enables correct statistical testing of all relevant functions. We are firmly convinced and advocate that a reliable assessment of the statistical functional properties of an AI system has to be the indispensable, mandatory nucleus of the conformity assessment. In this paper, we describe the three necessary elements to establish a reliable functional trustworthiness, i.e., (1) the definition of the technical distribution of the application, (2) the risk-based minimum performance requirements, and (3) the statistically valid testing based on independent random samples.

While reinforcement learning (RL) has shown promising performance, its sample complexity continues to be a substantial hurdle, restricting its broader application across a variety of domains. Imitation learning (IL) utilizes oracles to improve sample efficiency, yet it is often constrained by the quality of the oracles deployed. RPI draws on the strengths of IL, using oracle queries to facilitate exploration--an aspect that is notably challenging in sparse-reward RL-- particularly during the early stages of learning. As learning unfolds, RPI gradually transitions to RL, effectively treating the learned policy as an improved oracle. This algorithm is capable of learning from and improving upon a diverse set of black-box oracles. Integral to RPI are Robust Active Policy Selection (RAPS) and Robust Policy Gradient (RPG), both of which reason over whether to perform state-wise imitation from the oracles or learn from its own value function when the learner's performance surpasses that of the oracles in a specific state. Reinforcement learning (RL) has shown significant advancements, surpassing human capabilities in diverse domains such as Go (Silver et al., 2017), video games (Berner et al., 2019; Mnih et al., 2013), and Poker (Zhao et al., 2022). Despite such achievements, the application of RL is largely constrained by its substantial computational and data requirements and high sample complexity, particularly in fields like robotics (Singh et al., 2022) and healthcare (Han et al., 2023), where the extensive online interaction for trial and error is often impractical. Imitation learning (IL) (Osa et al., 2018) improves sample efficiency by allowing the agent to replace some or all environment interactions with demonstrations provided by an oracle policy.

Meta's Oversight Board is set to take on a new high-profile case ahead of next year's presidential election. The board said it planned to announce a case involving a user appeal related to an "altered" video of President Joe Biden. The board didn't disclose specifics of the case, which it said would be announced formally "in the coming days," but suggested it will touch on policies that could have far-reaching implications for Meta. "In the coming days the Oversight Board will announce a new case regarding a user-appeal to remove an altered video of President Joe Biden on Facebook," the Oversight Board said in a statement. "This case will examine issues related to manipulated media on Meta's platforms and the company's policies on misinformation, especially around elections."

Senior foreign affairs correspondent Greg Palkot reports the latest. Russia on Tuesday is claiming that so far this year, more than 335,000 people have signed up to fight in its military and volunteer units, although a further deployment to Ukraine is not coming, a report says. Reuters, citing Russian state television, quoted Defense Minister Sergei Shoigu telling top generals that there are "no plans for an additional mobilization" and that "the armed forces have the necessary number of military personnel to conduct the special military operation" in Ukraine. "Since the start of the year, more than 335,000 people have entered military service under contract and in volunteer formations," Shoigu reportedly added. "In September alone, more than 50,000 citizens signed contracts."

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Russian government said Tuesday it will be pursuing charges against high-ranking members of the Ukrainian military for "terrorist attacks." The country's Investigative Committee accused four individuals of terrorism in connection to drone strikes on Russian territory and regions of Ukraine currently being held by Russian invading forces. Russia's official statement named the following officials -- Main Directorate of Intelligence Chief Kyrylo Budanov, Ukrainian Air Force Commander Mykola Oleshchuk, Ukrainian Naval Forces Commander Oleksiy Neizhpapa and 383rd Unmanned Aviation Brigade Commander Serhiy Burdenyuk.

You can 3D-print nearly anything: rockets, mouse ovaries, and for some reason, lamps made of orange peels. Now, scientists at Monash University in Melbourne, Australia, have printed living neural networks composed of rat brain cells that seem to mature and communicate like real brains do. Researchers want to create mini-brains partly because they could someday offer a viable alternative to animal testing in drug trials and studies of basic brain function. At the start of 2023, the US Congress passed an annual spending bill pushing scientists to reduce their use of animals in federally funded research, following the signing of the US Food and Drug Administration's Modernization Act 2.0, which allowed high-tech alternatives in drug safety trials. Rather than testing new drugs on thousands of animals, pharmaceutical companies could apply them to 3D-printed mini-brains--in theory.

Soheil Feizi considers himself an optimistic person. But the University of Maryland computer science professor is blunt when he sums up the current state of watermarking AI images. "We don't have any reliable watermarking at this point," he says. "We broke all of them." For one of the two types of AI watermarking he tested for a new study-- "low perturbation" watermarks, which are invisible to the naked eye--he's even more direct: "There's no hope."