President Biden appeared to wander off at the G7 summit in Italy, with officials needing to pull him back to focus.

'The Five' co-hosts discuss how President Biden appeared to freeze again at a recent fundraiser. White House press secretary Karine Jean-Pierre faced a wave of criticism Monday after claiming a series of viral videos of President Biden appearing frail in the past week were "deepfakes." Numerous controversial videos have emerged of Biden during his visits to commemorate D-Day in France, attend the G-7 summit in Italy, and attend a recent fundraiser for his campaign that have raised questions about his age. Jean-Pierre was asked about "a rash of videos that have been edited to make the president appear especially frail or mentally confused," and responded by calling them "cheap fakes," a phrase she attributed to the Washington Post, "pushing misinformation, disinformation." "It tells you everything that we need to know about how desperate Republicans are here," Jean-Pierre said.

UPenn Wharton School Associate Professor Ethan Mollick weighs in on the Biden White House's new guidelines for artificial intelligence in the workplace on'Fox News Live.' A consortium of NATO allies has confirmed the first tranche of companies awarded funding as part of the group's 1.1 billion innovation fund. The alliance unveiled the fund in the summer of 2022, months after the Russian invasion of Ukraine, promising to invest in technologies that would enhance its defenses. The fund is backed by 24 of NATO's 32 member states, including Finland and Sweden, which joined the alliance earlier this year. On Tuesday, the NATO Innovation Fund (NIF) confirmed it had directly invested in four European tech companies, which it said would help address challenges in defense, security, and resilience.

For the past three months, a small encrypted group chat of Latin American officials who investigate online child-exploitation cases has been lighting up with reports of raids, arrests, and rescued minors in half a dozen countries. The successes are the result of a recent trial of a facial-recognition tool given to a group of Latin American law-enforcement officials, investigators, and prosecutors by the American company Clearview AI. During a five-day operation in Ecuador in early March, participants from 10 countries including Argentina, Brazil, Colombia, the Dominican Republic, El Salvador, and Peru were given access to Clearview's technology, which allows them to upload images and run them through a database of billions of public photos scraped from the Internet. "Normally it takes at least several days for a child to be identified, and sometimes there are victims that have not been identified for years," says Guillermo Galarza Abizaid, the vice president in charge of partnerships and law enforcement at the Virginia-based nonprofit International Centre for Missing and Exploited Children (ICMEC), which organized the event. The group used the facial-recognition tool to analyze a total of 2,198 images and 995 videos, hundreds of them from cold cases.

The Russian invasion of Ukraine has seen so many landmines deployed across the country that clearing them would take 700 years, say researchers. To make the task more manageable, Ukrainian scientists are turning to artificial intelligence to identify which regions are a priority for de-mining, though they expect some may simply have to be left as a permanent "scar" on the country. Russia's minelaying and Ukrainian efforts to remove the explosives began with the initial invasion of Crimea in 2014, which saw a few hundred square kilometres of land contaminated.

Dynamic elements: A 50cm x 50cm x 20cm drone constituent is a software component (running on some arrives on the hand left side of the surveillance area piece of hardware) that takes as input images provided (with orientation = (10, 25, 3)) at a distance from a camera and generates as outputs data representing of 450m from the system, moving with a straight bounding boxes of objects detected in the image along with trajectory, in the direction of the system, at a constant their classification. The ML constituent, figure 3, contains speed of 1m/s. Sun is visible (on the left hand side of three main software components (the pre/post-processing the image).

In this paper, we introduce an enhanced textual adversarial attack method, known as Saliency Attention and Semantic Similarity driven adversarial Perturbation (SASSP). The proposed scheme is designed to improve the effectiveness of contextual perturbations by integrating saliency, attention, and semantic similarity. Traditional adversarial attack methods often struggle to maintain semantic consistency and coherence while effectively deceiving target models. Our proposed approach addresses these challenges by incorporating a three-pronged strategy for word selection and perturbation. First, we utilize a saliency-based word selection to prioritize words for modification based on their importance to the model's prediction. Second, attention mechanisms are employed to focus perturbations on contextually significant words, enhancing the attack's efficacy. Finally, an advanced semantic similarity-checking method is employed that includes embedding-based similarity and paraphrase detection. By leveraging models like Sentence-BERT for embedding similarity and fine-tuned paraphrase detection models from the Sentence Transformers library, the scheme ensures that the perturbed text remains contextually appropriate and semantically consistent with the original. Empirical evaluations demonstrate that SASSP generates adversarial examples that not only maintain high semantic fidelity but also effectively deceive state-of-the-art natural language processing models. Moreover, in comparison to the original scheme of contextual perturbation CLARE, SASSP has yielded a higher attack success rate and lower word perturbation rate.

The critical inquiry pervading the realm of Philosophy, and perhaps extending its influence across all Humanities disciplines, revolves around the intricacies of morality and normativity. Surprisingly, in recent years, this thematic thread has woven its way into an unexpected domain, one not conventionally associated with pondering "what ought to be": the field of artificial intelligence (AI) research. Central to morality and AI, we find "alignment", a problem related to the challenges of expressing human goals and values in a manner that artificial systems can follow without leading to unwanted adversarial effects. More explicitly and with our current paradigm of AI development in mind, we can think of alignment as teaching human values to non-anthropomorphic entities trained through opaque, gradient-based learning techniques. This work addresses alignment as a technical-philosophical problem that requires solid philosophical foundations and practical implementations that bring normative theory to AI system development. To accomplish this, we propose two sets of necessary and sufficient conditions that, we argue, should be considered in any alignment process. While necessary conditions serve as metaphysical and metaethical roots that pertain to the permissibility of alignment, sufficient conditions establish a blueprint for aligning AI systems under a learning-based paradigm. After laying such foundations, we present implementations of this approach by using state-of-the-art techniques and methods for aligning general-purpose language systems. We call this framework Dynamic Normativity. Its central thesis is that any alignment process under a learning paradigm that cannot fulfill its necessary and sufficient conditions will fail in producing aligned systems.

Large Language Models (LLMs) possess the potential to exert substantial influence on public perceptions and interactions with information. This raises concerns about the societal impact that could arise if the ideologies within these models can be easily manipulated. In this work, we investigate how effectively LLMs can learn and generalize ideological biases from their instruction-tuning data. Our findings reveal a concerning vulnerability: exposure to only a small amount of ideologically driven samples significantly alters the ideology of LLMs. Notably, LLMs demonstrate a startling ability to absorb ideology from one topic and generalize it to even unrelated ones. The ease with which LLMs' ideologies can be skewed underscores the risks associated with intentionally poisoned training data by malicious actors or inadvertently introduced biases by data annotators. It also emphasizes the imperative for robust safeguards to mitigate the influence of ideological manipulations on LLMs.

Facts extraction is pivotal for constructing knowledge graphs. Recently, the increasing demand for temporal facts in downstream tasks has led to the emergence of the task of temporal fact extraction. In this paper, we specifically address the extraction of temporal facts from natural language text. Previous studies fail to handle the challenge of establishing time-to-fact correspondences in complex sentences. To overcome this hurdle, we propose a timeline-based sentence decomposition strategy using large language models (LLMs) with in-context learning, ensuring a fine-grained understanding of the timeline associated with various facts. In addition, we evaluate the performance of LLMs for direct temporal fact extraction and get unsatisfactory results. To this end, we introduce TSDRE, a method that incorporates the decomposition capabilities of LLMs into the traditional fine-tuning of smaller pre-trained language models (PLMs). To support the evaluation, we construct ComplexTRED, a complex temporal fact extraction dataset. Our experiments show that TSDRE achieves state-of-the-art results on both HyperRED-Temporal and ComplexTRED datasets.