Tupac Shakur's estate is none too happy about Drake cloning the late hip-hop legend's voice in a Kendrick Lamar diss track. Billboard reported Wednesday that attorney Howard King, representing Mr. Shakur's estate, sent a cease-and-desist letter calling Drake's use of Shakur's voice "a flagrant violation of Tupac's publicity and the estate's legal rights." Drake (Aubrey Drake Graham) dropped the diss track "Taylor Made Freestyle" last Friday, the latest chapter of the artist's simmering decade-long feud with Pulitzer and 17-time Grammy award winner Kendrick Lamar. "Kendrick, we need ya, the West Coast savior / Engraving your name in some hip-hop history," an AI-generated 2Pac recreation raps in Drake's track. "If you deal with this viciously / You seem a little nervous about all the publicity."

Recent successes in Generative Artificial Intelligence (GenAI) have led to new technologies capable of generating high-quality code, natural language, and images. The next step is to integrate GenAI technology into products, a task typically conducted by software developers. Such product development always comes with a certain risk of liability. Within this article, we want to shed light on the current state of two such risks: data protection and copyright. Both aspects are crucial for GenAI. This technology deals with data for both model training and generated output. We summarize key aspects regarding our current knowledge that every software developer involved in product development using GenAI should be aware of to avoid critical mistakes that may expose them to liability claims.

Large language models (LLMs) show early signs of artificial general intelligence but struggle with hallucinations. One promising solution to mitigate these hallucinations is to store external knowledge as embeddings, aiding LLMs in retrieval-augmented generation. However, such a solution risks compromising privacy, as recent studies experimentally showed that the original text can be partially reconstructed from text embeddings by pre-trained language models. The significant advantage of LLMs over traditional pre-trained models may exacerbate these concerns. To this end, we investigate the effectiveness of reconstructing original knowledge and predicting entity attributes from these embeddings when LLMs are employed. Empirical findings indicate that LLMs significantly improve the accuracy of two evaluated tasks over those from pre-trained models, regardless of whether the texts are in-distribution or out-of-distribution. This underscores a heightened potential for LLMs to jeopardize user privacy, highlighting the negative consequences of their widespread use. We further discuss preliminary strategies to mitigate this risk.

Explainable AI (xAI) methods are important for establishing trust in using black-box models. However, recent criticism has mounted against current xAI methods that they disagree, are necessarily false, and can be manipulated, which has started to undermine the deployment of black-box models. Rudin (2019) goes so far as to say that we should stop using black-box models altogether in high-stakes cases because xAI explanations "must be wrong". However, strict fidelity to the truth is historically not a desideratum in science. Idealizations -- the intentional distortions introduced to scientific theories and models -- are commonplace in the natural sciences and are seen as a successful scientific tool. Thus, it is not falsehood qua falsehood that is the issue. In this paper, I outline the need for xAI research to engage in idealization evaluation. Drawing on the use of idealizations in the natural sciences and philosophy of science, I introduce a novel framework for evaluating whether xAI methods engage in successful idealizations or deceptive explanations (SIDEs). SIDEs evaluates whether the limitations of xAI methods, and the distortions that they introduce, can be part of a successful idealization or are indeed deceptive distortions as critics suggest. I discuss the role that existing research can play in idealization evaluation and where innovation is necessary. Through a qualitative analysis we find that leading feature importance methods and counterfactual explanations are subject to idealization failure and suggest remedies for ameliorating idealization failure.

Ensuring the resilience of Large Language Models (LLMs) against malicious exploitation is paramount, with recent focus on mitigating offensive responses. Yet, the understanding of cant or dark jargon remains unexplored. This paper introduces a domain-specific Cant dataset and CantCounter evaluation framework, employing Fine-Tuning, Co-Tuning, Data-Diffusion, and Data-Analysis stages. Experiments reveal LLMs, including ChatGPT, are susceptible to cant bypassing filters, with varying recognition accuracy influenced by question types, setups, and prompt clues. Updated models exhibit higher acceptance rates for cant queries. Moreover, LLM reactions differ across domains, e.g., reluctance to engage in racism versus LGBT topics. These findings underscore LLMs' understanding of cant and reflect training data characteristics and vendor approaches to sensitive topics. Additionally, we assess LLMs' ability to demonstrate reasoning capabilities. Access to our datasets and code is available at https://github.com/cistineup/CantCounter.

If you've been wondering when you'll be able to order the flame-throwing robot that Ohio-based Throwflame first announced last summer, that day has finally arrived. The Thermonator, what Throwflame bills as "the first-ever flamethrower-wielding robot dog" is now available for purchase. Thermonator is a quadruped robot with an ARC flamethrower mounted to its back, fueled by gasoline or napalm. It features a one-hour battery, a 30-foot flame-throwing range, and Wi-Fi and Bluetooth connectivity for remote control through a smartphone. It also includes a Lidar sensor for mapping and obstacle avoidance, laser sighting, and first-person-view navigation through an onboard camera.

Meta's drive to integrate artificial intelligence into its products yielded strong financial results for the second quarter in a row. The company plans to spend even more on AI in the coming months, though, and its share price slumped more than 12% as the company reported earnings Wednesday. A weak sales forecast and higher spending guidance rattled investors. Revenue at the world's largest social media business increased 27% to 36.46bn during the first quarter in contrast to analyst expectations of 36.16bn. Earnings per share more than doubled to 4.71, surpassing expectations on Wall Street of 4.32.

The world is facing the collapse of the 1948 international order established in the wake of World War II, amid the brutal wars in Gaza and Ukraine, while authoritarian policies continue to spread, Amnesty International has warned. The report accused the world's most powerful governments, including China, Russia and the United States, of leading the global disregard for international rules and values enshrined in the Universal Declaration of Human Rights of December 1948. The war in Gaza, which began on October 7, was a "descent into hell", Secretary-General Agnes Callamard wrote in her preface to the report, where "the'never again' moral and legal lessons [of 1948] were torn into a million pieces". Noting that Hamas had committed "horrific crimes" in its assault on communities in southern Israel on October 7, Callamard said Israel's "campaign of retaliation" had become a "campaign of collective punishment". Amnesty said while Israel continued to disregard international human rights law, the US, its foremost ally, and other countries including the United Kingdom and Germany were guilty of "grotesque double standards" given their willingness to back Israeli and US authorities over Gaza while condemning war crimes by Russia in Ukraine.

Taking inspiration from Set Theory, we introduce SetCSE, an innovative information retrieval framework. SetCSE employs sets to represent complex semantics and incorporates well-defined operations for structured information querying under the provided context. Within this framework, we introduce an inter-set contrastive learning objective to enhance comprehension of sentence embedding models concerning the given semantics. Furthermore, we present a suite of operations, including SetCSE intersection, difference, and operation series, that leverage sentence embeddings of the enhanced model for complex sentence retrieval tasks. Throughout this paper, we demonstrate that SetCSE adheres to the conventions of human language expressions regarding compounded semantics, provides a significant enhancement in the discriminatory capability of underlying sentence embedding models, and enables numerous information retrieval tasks involving convoluted and intricate prompts which cannot be achieved using existing querying methods.

Linking a claim to grounded references is a critical ability to fulfill human demands for authentic and reliable information. Current studies are limited to specific tasks like information retrieval or semantic matching, where the claim-reference relationships are unique and fixed, while the referential knowledge linking (RKL) in real-world can be much more diverse and complex. In this paper, we propose universal referential knowledge linking (URL), which aims to resolve diversified referential knowledge linking tasks by one unified model. To this end, we propose a LLM-driven task-instructed representation compression, as well as a multi-view learning approach, in order to effectively adapt the instruction following and semantic understanding abilities of LLMs to referential knowledge linking. Furthermore, we also construct a new benchmark to evaluate ability of models on referential knowledge linking tasks across different scenarios. Experiments demonstrate that universal RKL is challenging for existing approaches, while the proposed framework can effectively resolve the task across various scenarios, and therefore outperforms previous approaches by a large margin.