That was the score Mary Louis was given by an AI-powered tenant screening tool. The software, SafeRent, didn't explain in its 11-page report how the score was calculated or how it weighed various factors. It didn't say what the score actually signified. It just displayed Louis's number and determined it was too low. Louis, who works as a security guard, had applied for an apartment in an eastern Massachusetts suburb.

Artificial intelligence is being used to power the personalization of popular sports betting apps to tailor experiences to users' preferences. SUITS MOUNTING: Two Texas parents filed a lawsuit this week against the makers of Character.AI, claiming the artificial intelligence chatbot is a "clear and present danger to minors," with one plaintiff alleging it encouraged their teen to kill his parents. GENERATION AT RISK: Senate lawmakers unanimously passed the bipartisan-led Take It Down Act that would force social media companies to speedily remove sexually explicit deepfakes, prevent them from being posted and criminalize the act. 'WHAT WILL BE LEFT?': Lisa Kudrow fears an uncertain future as artificial intelligence becomes more and more prevalent in Hollywood. FUTURISTIC ROBOCOP: Footage from the streets of China captured a scene straight from a science fiction novel – spherical drones alongside patrolling law enforcement.

Abstract: Timely detection and geolocation of events based on social data can provide critical information for applications such as crisis response and resource allocation. However, most existing methods are greatly affected by event detection errors, leading to insufficient geolocation accuracy. To this end, this paper proposes a novel end-to-end event geolocation method (GTOP) leveraging Hyperbolic space and toponym hierarchies. Specifically, the proposed method contains one event detection module and one geolocation module. The event detection module constructs a heterogeneous information networks based on social data, and then constructs a homogeneous message graph and combines it with the text and time feature of the message to learning initial features of nodes. Node features are updated in Hyperbolic space and then fed into a classifier for event detection. To reduce the geolocation error, this paper proposes a noise toponym filtering algorithm (HIST) based on the hierarchical structure of toponyms. HIST analyzes the hierarchical structure of toponyms mentioned in the event cluster, taking the highly frequent city-level locations as the coarsegrained locations for events. To further improve the geolocation accuracy, we propose a fine-grained pseudo toponyms generation algorithm (FIT) based on the output of HIST, and combine generated pseudo toponyms with filtered toponyms to locate events based on the geographic center points of the combined toponyms. Extensive experiments are conducted on the Chinese dataset constructed in this paper and another public English dataset. The experimental results show that the proposed method is superior to the state-of-the-art baselines.

The rapid growth of the financial sector and the rising focus on Environmental, Social, and Governance (ESG) considerations highlight the need for advanced NLP tools. However, open-source LLMs proficient in both finance and ESG domains remain scarce. To address this gap, we introduce SusGen-30K, a category-balanced dataset comprising seven financial NLP tasks and ESG report generation, and propose TCFD-Bench, a benchmark for evaluating sustainability report generation. Leveraging this dataset, we developed SusGen-GPT, a suite of models achieving state-of-the-art performance across six adapted and two off-the-shelf tasks, trailing GPT-4 by only 2% despite using 7-8B parameters compared to GPT-4's 1,700B. Based on this, we propose the SusGen system, integrated with Retrieval-Augmented Generation (RAG), to assist in sustainability report generation. This work demonstrates the efficiency of our approach, advancing research in finance and ESG.

In recent years, the rise of AI-assisted code-generation tools has significantly transformed software development. While code generators have mainly been used to support conventional software development, their use will be extended to powerful and secure AI systems. Systems capable of generating code, such as ChatGPT, OpenAI Codex, GitHub Copilot, and AlphaCode, take advantage of advances in machine learning (ML) and natural language processing (NLP) enabled by large language models (LLMs). However, it must be borne in mind that these models work probabilistically, which means that although they can generate complex code from natural language input, there is no guarantee for the functionality and security of the generated code. However, to fully exploit the considerable potential of this technology, the security, reliability, functionality, and quality of the generated code must be guaranteed. This paper examines the implementation of these goals to date and explores strategies to optimize them. In addition, we explore how these systems can be optimized to create safe, high-performance, and executable artificial intelligence (AI) models, and consider how to improve their accessibility to make AI development more inclusive and equitable.

Chinese Named Entity Recognition (NER) is an important task in information extraction, which has a significant impact on downstream applications. Due to the lack of natural separators in Chinese, previous NER methods mostly relied on external dictionaries to enrich the semantic and boundary information of Chinese words. However, such methods may introduce noise that affects the accuracy of named entity recognition. To this end, we propose a character relation enhanced Chinese NER model (CRENER). This model defines four types of tags that reflect the relationships between characters, and proposes a fine-grained modeling of the relationships between characters based on three types of relationships: adjacency relations between characters, relations between characters and tags, and relations between tags, to more accurately identify entity boundaries and improve Chinese NER accuracy. Specifically, we transform the Chinese NER task into a character-character relationship classification task, ensuring the accuracy of entity boundary recognition through joint modeling of relation tags. To enhance the model's ability to understand contextual information, WRENER further constructed an adapted transformer encoder that combines unscaled direction-aware and distance-aware masked self-attention mechanisms. Moreover, a relationship representation enhancement module was constructed to model predefined relationship tags, effectively mining the relationship representations between characters and tags. Experiments conducted on four well-known Chinese NER benchmark datasets have shown that the proposed model outperforms state-of-the-art baselines. The ablation experiment also demonstrated the effectiveness of the proposed model.

OpenAI published receipts, in the form of a long timeline of emails, texts and legal filings, illustrating that Elon Musk's injunction to prevent OpenAI from converting into a for-profit company runs counter to what he wanted in 2017. Essentially, OpenAI is providing even more evidence to the fact that its former co-founder wanted the AI startup to become a for-profit company and make him CEO. You should read the whole blog to get all of the details (and get a sense for how billionaires email) but the gist is that in 2017, Musk and OpenAI came to an understanding that the then non-profit needed to become a for-profit to "advance its mission" and seemingly capitalize on the public interest earned from its AI beating professional Dota 2 players in one-on-one matches. According to OpenAI, Musk proposed a new board structure where he "would unequivocally have initial control of the company," which OpenAI was opposed to. That led to the disagreements between Musk and OpenAI leadership, and him ultimately leaving the nonprofit's board in 2018.

OpenAI CEO Sam Altman expects AGI, or artificial general intelligence--AI that outperforms humans at most tasks--around 2027 or 2028. Elon Musk's prediction is either 2025 or 2026, and he has claimed that he was "losing sleep over the threat of AI danger." As the limitations of current AI become increasingly clear, most AI researchers have come to the view that simply building bigger and more powerful chatbots won't lead to AGI. This story is from the WIRED World in 2025, our annual trends briefing. However, in 2025, AI will still pose a massive risk: not from artificial superintelligence, but from human misuse.

New Jersey residents frustrated with a lack of answers regarding dozens of potential drone sightings in the skies above their homes are threatening to take action on their own if the government doesn't start providing answers. James Ward, a Jersey Shore Realtor, shared video on Facebook that he said shows "SUV-size drones" above Island Beach State Park taken Sunday. It's difficult to judge their size in the clip, which shows a number of lights hovering in the sky. "Dozens of SUV-size drones in all directions," the caption says. "Emerging at same time and flying over the ocean and then heading in different directions – what do you think?" "A good shotgun will fix that problem," one commenter replied.

The security of Large Language Models (LLMs) has become an important research topic since the emergence of ChatGPT. Though there have been various effective methods to defend against jailbreak attacks, prefilling attacks remain an unsolved and popular threat against open-sourced LLMs. In-Context Learning (ICL) offers a computationally efficient defense against various jailbreak attacks, yet no effective ICL methods have been developed to counter prefilling attacks. In this paper, we: (1) show that ICL can effectively defend against prefilling jailbreak attacks by employing adversative sentence structures within demonstrations; (2) characterize the effectiveness of this defense through the lens of model size, number of demonstrations, over-defense, integration with other jailbreak attacks, and the presence of safety alignment. Given the experimental results and our analysis, we conclude that there is no free lunch for defending against prefilling jailbreak attacks with ICL. On the one hand, current safety alignment methods fail to mitigate prefilling jailbreak attacks, but adversative structures within ICL demonstrations provide robust defense across various model sizes and complex jailbreak attacks. On the other hand, LLMs exhibit similar over-defensiveness when utilizing ICL demonstrations with adversative structures, and this behavior appears to be independent of model size.