The Federal Trade Commission ( FTC) has proposed a formal ban on fake reviews and testimonials. Companies would also be prohibited from using phony followers and views to inflate their social media metrics if the rule takes effect as it stands. This isn't the first time the agency has trained its sights on fake reviews. In its first such case in 2019, it fined a third-party Amazon seller for paying for fake reviews (Amazon itself has sued phony review providers). Earlier this year, the FTC levied a $600,000 penalty against the owner of a vitamin brand for "review hijacking" on Amazon.

Nestled within yesterday's Supreme Court decision declaring that race-conscious admissions programs, like those at Harvard and the University of North Carolina, are unconstitutional is a crucial carveout: Colleges are free to consider "an applicant's discussion of how race affected his or her life." In other words, they can weigh a candidate's race when it is mentioned in an admissions essay. Observers had already speculated about personal essays becoming invaluable tools for candidates who want to express their racial background without checking a box--now it is clear that the end of affirmative action will transform not only how colleges select students, but also how teenagers advertise themselves to colleges. For essays and statements to provide a workaround for pursuing diversity, applicants must first cast themselves as diverse. The American Council on Education, a nonprofit focused on the impacts of public policy on higher education, recently convened a panel dedicated to planning for the demise of affirmative action; admissions directors and consultants emphasized the need "to educate students about how to write about who they are in a very different way," expressing their "full authentic story" and "trials and tribulations."

Center for A.I. Safety Director Dan Hendrycks explains concerns about how the rapid growth of artificial intelligence could impact society. House lawmakers are urging federal agencies to quickly and aggressively adopt artificial intelligence technology, at a time when the push from civil rights and industry groups for new AI regulations is still waiting to get off the ground. The House Appropriations Committee, led by Rep. Kay Granger, R-Texas, released several spending bills this week that encourage the government to incorporate AI into everything from national security functions to routine office work to the detection of pests and diseases in crops. Several of those priorities are not just encouraged but would get millions of dollars in new funding under the legislation still being considered by the committee. And while comprehensive AI regulations are likely still months away and are unlikely to be developed this year, lawmakers seem keen on making sure the government is deploying AI where it can. The bills are backed by the GOP majority, and Rep. Don Beyer, D-Va., the vice chair of the Congressional Artificial Intelligence Caucus, said agencies shouldn't have to wait to start using AI.

Large documents written in juridical language are difficult to interpret, with long sentences leading to intricate and intertwined relations between the nouns. The present paper frames this problem in the context of recent European security directives. The complexity of their language is here thwarted by automating the extraction of the relevant information, namely of the parts of speech from each clause, through a specific tailoring of Natural Language Processing (NLP) techniques. These contribute, in combination with ontology development principles, to the design of our automated method for the representation of security directives as ontologies. The method is showcased on a practical problem, namely to derive an ontology representing the NIS 2 directive, which is the peak of cybersecurity prescripts at the European level. Although the NLP techniques adopted showed some limitations and had to be complemented by manual analysis, the overall results provide valid support for directive compliance in general and for ontology development in particular.

Sentence-level relation extraction (RE) aims to identify the relationship between 2 entities given a contextual sentence. While there have been many attempts to solve this problem, the current solutions have a lot of room to improve. In this paper, we approach the task of relationship extraction in the financial dataset REFinD. Our approach incorporates typed entity markers representations and various models finetuned on the dataset, which has allowed us to achieve an F1 score of 69.65% on the validation set. Through this paper, we discuss various approaches and possible limitations.

In contrast, transformers employ a "Scaled Dot-Product Attention" mechanism that is parallelizable. This unique attention mechanism allows for large-scale pretraining. Additionally, self-supervised pretraining paradigm such as masked language modeling onlarge unlabeled datasets enabled transformers to be trained without costly annotations. Transformer model, although originally designed for the NLP [3] domain, Transformers have witnessed adaptations in various domains such as computer vision [5, 6], remote sensing [7], time series [8], speech processing [9] and multimodal learning [10]. Consequently, modality specific surveys emerged, focusing on medical imaging [11-13] and biomedical language models [14] in the medical domain. This paper aims to provide comprehensive overview of Transformer models utilized across multiple modalities of data to address healthcare objectives. We discuss pre-training strategies to manage the lack of robust and annotated healthcare datasets. The rest of the paper is organized as follows: Section 2 discusses the strategy to search for relevant citations; Section 3 describes the architecture of the original transformer; Section 4 describes the two primary Transformer variants: the Bidirectional Encoder Representations from Transformers (BERT) and the Vision Transformer (ViT). Section 5 describes advancements in large language models (LLM), and section 6 through 12 provides a review of Transformers in healthcare.

The ability to conduct retrospective analyses of attacks on human rights defenders over time and by location is important for humanitarian organizations to better understand historical or ongoing human rights violations and thus better manage the global impact of such events. We hypothesize that NLP can support such efforts by quickly processing large collections of news articles to detect and summarize the characteristics of attacks on human rights defenders. To that end, we propose a new dataset for detecting Attacks on Human Rights Defenders (HRDsAttack) consisting of crowdsourced annotations on 500 online news articles. The annotations include fine-grained information about the type and location of the attacks, as well as information about the victim(s). We demonstrate the usefulness of the dataset by using it to train and evaluate baseline models on several sub-tasks to predict the annotated characteristics.

We introduce MABe22, a large-scale, multi-agent video and trajectory benchmark to assess the quality of learned behavior representations. This dataset is collected from a variety of biology experiments, and includes triplets of interacting mice (4.7 million frames video+pose tracking data, 10 million frames pose only), symbiotic beetle-ant interactions (10 million frames video data), and groups of interacting flies (4.4 million frames of pose tracking data). Accompanying these data, we introduce a panel of real-life downstream analysis tasks to assess the quality of learned representations by evaluating how well they preserve information about the experimental conditions (e.g. strain, time of day, optogenetic stimulation) and animal behavior. We test multiple state-of-the-art self-supervised video and trajectory representation learning methods to demonstrate the use of our benchmark, revealing that methods developed using human action datasets do not fully translate to animal datasets. We hope that our benchmark and dataset encourage a broader exploration of behavior representation learning methods across species and settings.

Sam Altman, the CEO of artificial intelligence lab OpenAI, told a Senate panel he welcomes federal regulation on the technology'to mitigate' its risks. A class-action complaint filed Wednesday in the northern district of California alleges tech leaders OpenAI and Microsoft Corp. used "stolen and misappropriated" information from hundreds of millions of internet users without their knowledge to train and develop its artificial intelligence tech like chatbot ChatGPT. The 16 plaintiffs, who are represented by the Clarkson Law Firm and listed with initials, claimed the defendants "continue to unlawfully collect and feed additional personal data from millions" worldwide to that end and that they systematically scraped 300 billion words from the internet without consent. "Once trained on stolen data, defendants saw the immediate profit potential and rushed the products to market without implementing proper safeguards or controls to ensure that they would not produce or support harmful or malicious content and conduct that could further violate the law, infringe rights and endanger lives," Clarkson continued. "Without these safeguards, the products have already demonstrated their ability to harm humans, in real ways."

A first-of-its-kind law in New York City aims to make the use of AI in hiring and promotion both clearer and fairer. New York's Local Law 144, which goes into effect on 5 July, requires employers to get an independent audit of their automated employment decision tools to ensure that they do not demonstrate significant bias based on sex, race or ethnicity – though it does not cover discrimination based on factors such as …