The Pentagon revealed its new B-21 nuclear stealth bomber Friday in Palmdale, California. DEADLY STEALTH: This US bomber is why China suddenly wants to talk about nukes and AI. HIGH-TECH HEALTH: AI could help predict lung cancer risks in non-smokers. FORCE MULTIPLIER: US accelerates race for new military tech against China. PAYCHECK PROBLEMS: AI may be greater threat to wages than jobs, European study finds.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Michigan is joining an effort to curb deceptive uses of artificial intelligence and manipulated media through state-level policies as Congress and the Federal Elections Commission continue to debate more sweeping regulations ahead of the 2024 elections. Campaigns on the state and federal level will be required to clearly say which political advertisements airing in Michigan were created using artificial intelligence under legislation expected to be signed in the coming days by Gov. Gretchen Whitmer, a Democrat. It also would prohibit use of AI-generated deepfakes within 90 days of an election without a separate disclosure identifying the media as manipulated.

Ukraine will forge ahead in the battle against Russia's invasion, Kyiv's top diplomat has pledged as he seeks to rally Western support. Ukraine will not "back down" in fighting against the Russian invasion, Foreign Minister Dmytro Kuleba told a summit of NATO foreign ministers in Brussels on Wednesday, even as its military locked into a bloody stalemate with enemy forces amid signs that the provision of weapons and funding is slowing and that the war in Gaza is limiting global attention. "We have to continue, we have to keep fighting. Ukraine is not going to back down," Kuleba said. "The issue here is not just Ukraine's security, it is the security and safety of the entire Euro-Atlantic space."

House Armed Services Committee holds hearing on the Department of Defense using AI. The Pentagon is planning to field thousands of artificial intelligence-enabled autonomous vehicles by 2026 in a bid to keep pace with the Chinese military. The plan, which has been called Replicator, will seek to "galvanize progress in the too-slow shift of U.S. military innovation to leverage platforms that are small, smart, cheap and many," Deputy Secretary of Defense Kathleen Hicks said, according to a report by The Associated Press. While the report notes few details, including how the program will be funded and how fast the Pentagon will truly be able to accelerate the development of the new vehicles, the program represents an ongoing shift in how the U.S. views the future of warfare, especially as China continues to forge ahead with AI programs of its own. Phil Siegel, the founder of the Center for Advanced Preparedness and Threat Response Simulation (CAPTRS), believes the rapid push toward AI weapons is similar to that of a nuclear arms race.

In our study, we delve into the realm of evaluating Despite large language models (Arkoudas, 2023; large language models' (LLMs) ability to generate OpenAI, 2022) perform strikingly in plenty of reasoning counterfactually coherent thoughts. Specifically, benchmarks (Cobbe et al., 2021; Hendrycks we proposed an innovative evaluation system et al., 2021a), late studies observe an internal inconsistency that quantitatively measures the evolution of information in their reasoning processes (Saparov and in statement pairs, ensuring that they adhere He, 2023; Arkoudas, 2023). The inconsistency is to a specified logical relationship. Our approach attributed to misunderstanding and misapplication includes designing a specialized task where models of logical relations. However, logical relations in are presented with mismatched argument-premise complex language reasoning are not yet properly pairs bound by a specific logical relation. The objective quantified and evaluated.

We consider a multi-class queueing model of a telephone call center, in which a system manager dynamically allocates available servers to customer calls. Calls can terminate through either service completion or customer abandonment, and the manager strives to minimize the expected total of holding costs plus abandonment costs over a finite horizon. Focusing on the Halfin-Whitt heavy traffic regime, we derive an approximating diffusion control problem, and building on earlier work by Han et al. (2018), develop a simulation-based computational method for solution of such problems, one that relies heavily on deep neural network technology. Using this computational method, we propose a policy for the original (pre-limit) call center scheduling problem. Finally, the performance of this policy is assessed using test problems based on publicly available call center data. For the test problems considered so far, our policy does as well as the best benchmark we could find. Moreover, our method is computationally feasible at least up to dimension 100, that is, for call centers with 100 or more distinct customer classes.

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron. We will make our code available online upon publication.

With the rapid development of artificial intelligence and breakthroughs in machine learning and natural language processing, intelligent question-answering robots have become widely used in government affairs. This paper conducts a horizontal comparison between Guangdong Province's government chatbots, ChatGPT, and Wenxin Ernie, two large language models, to analyze the strengths and weaknesses of existing government chatbots and AIGC technology. The study finds significant differences between government chatbots and large language models. China's government chatbots are still in an exploratory stage and have a gap to close to achieve "intelligence." To explore the future direction of government chatbots more deeply, this research proposes targeted optimization paths to help generative AI be effectively applied in government chatbot conversations.

Contrastive learning (CL) has recently gained significant popularity in the field of recommendation. Its ability to learn without heavy reliance on labeled data is a natural antidote to the data sparsity issue. Previous research has found that CL can not only enhance recommendation accuracy but also inadvertently exhibit remarkable robustness against noise. However, this paper identifies a vulnerability of CL-based recommender systems: Compared with their non-CL counterparts, they are even more susceptible to poisoning attacks that aim to promote target items. Our analysis points to the uniform dispersion of representations led by the CL loss as the very factor that accounts for this vulnerability. We further theoretically and empirically demonstrate that the optimization of CL loss can lead to smooth spectral values of representations. Based on these insights, we attempt to reveal the potential poisoning attacks against CL-based recommender systems. The proposed attack encompasses a dual-objective framework: One that induces a smoother spectral value distribution to amplify the CL loss's inherent dispersion effect, named dispersion promotion; and the other that directly elevates the visibility of target items, named rank promotion. We validate the destructiveness of our attack model through extensive experimentation on four datasets. By shedding light on these vulnerabilities, we aim to facilitate the development of more robust CL-based recommender systems.

Caution: this paper may include material that could be offensive or distressing. The advent of Large Language Models (LLMs) necessitates the development of training approaches that mitigate the generation of unethical language and aptly manage toxic user queries. Given the challenges related to human labor and the scarcity of data, we present KoTox, comprising 39K unethical instruction-output pairs. This collection of automatically generated toxic instructions refines the training of LLMs and establishes a foundational framework for improving LLMs' ethical awareness and response to various toxic inputs, promoting more secure and responsible interactions in Natural Language Processing (NLP) applications.