Probing the ethical implications surrounding the use of AI, Franklin Ritch's debut feature hinges on a high-concept premise: an entirely digital avatar of a young girl named Cherry (Tatum Matthews) is used as bait to trap paedophiles in online chatrooms. Without the signature spectacle of the sci-fi genre, The Artifice Girl is a markedly low-key and small-scale endeavour, steeped in philosophical musings that ultimately seem stagey rather than cinematic. It starts in a police interrogation room where Ritch's Gareth, Cherry's creator, is questioned by Deena (Sinda Nichols) and Amos (David Girard), members of a taskforce combatting child sexual abuse. Once Gareth reveals Cherry is a virtual being, concerns arise as to whether she can meaningfully consent to interacting with men on a daily basis. As Cherry grows increasingly sentient, the same talking points are reiterated in the second section of the film, as Gareth advocates to transfer Cherry's intelligence into a physical form.

In front of a suburban house on the outskirts of the northern Germany city of Hamburg, a single word -- "LAION" -- is scrawled in pencil across a mailbox. It's the only indication that the home belongs to the person behind a massive data gathering effort central to the artificial intelligence boom that has seized the world's attention. That person is high schoolteacher Christoph Schuhmann, and LAION, short for "Large-scale AI Open Network," is his passion project. When Schuhmann isn't teaching physics and computer science to German teens, he works with a small team of volunteers building the world's biggest free AI training data set, which has already been used in text-to-image generators such as Google's Imagen and Stable Diffusion. Databases like LAION are central to AI text-to-image generators, which rely on them for the enormous amounts of visual material used to deconstruct and create new images.

How might we use cognitive modeling to consider the ways in which antiblackness, and racism more broadly, impact the design and development of AI systems? We provide a discussion and an example towards an answer to this question. We use the ACT-R/{\Phi} cognitive architecture and an existing knowledge graph system, ConceptNet, to consider this question not only from a cognitive and sociocultural perspective, but also from a physiological perspective. In addition to using a cognitive modeling as a means to explore how antiblackness may manifest in the design and development of AI systems (particularly from a software engineering perspective), we also introduce connections between antiblackness, the Human, and computational cognitive modeling. We argue that the typical eschewing of sociocultural processes and knowledge structures in cognitive architectures and cognitive modeling implicitly furthers a colorblind approach to cognitive modeling and hides sociocultural context that is always present in human behavior and affects cognitive processes.

Recent progress in deep learning, a special form of machine learning, has led to remarkable capabilities machines can now be endowed with: they can read and understand free flowing text, reason and bargain with human counterparts, translate texts between languages, learn how to take decisions to maximize certain outcomes, etc. Today, machines have revolutionized the detection of cancer, the prediction of protein structures, the design of drugs, the control of nuclear fusion reactors etc. Although these capabilities are still in their infancy, it seems clear that their continued refinement and application will result in a technological impact on nearly all social and economic areas of human activity, the likes of which we have not seen before. In this article, I will share my view as to how AI will likely impact asset management in general and I will provide a mental framework that will equip readers with a simple criterion to assess whether and to what degree a given fund really exploits deep learning and whether a large disruption risk from deep learning exist.

Language Models (LMs) have been shown to leak information about training data through sentence-level membership inference and reconstruction attacks. Understanding the risk of LMs leaking Personally Identifiable Information (PII) has received less attention, which can be attributed to the false assumption that dataset curation techniques such as scrubbing are sufficient to prevent PII leakage. Scrubbing techniques reduce but do not prevent the risk of PII leakage: in practice scrubbing is imperfect and must balance the trade-off between minimizing disclosure and preserving the utility of the dataset. On the other hand, it is unclear to which extent algorithmic defenses such as differential privacy, designed to guarantee sentence- or user-level privacy, prevent PII disclosure. In this work, we introduce rigorous game-based definitions for three types of PII leakage via black-box extraction, inference, and reconstruction attacks with only API access to an LM. We empirically evaluate the attacks against GPT-2 models fine-tuned with and without defenses in three domains: case law, health care, and e-mails. Our main contributions are (i) novel attacks that can extract up to 10$\times$ more PII sequences than existing attacks, (ii) showing that sentence-level differential privacy reduces the risk of PII disclosure but still leaks about 3% of PII sequences, and (iii) a subtle connection between record-level membership inference and PII reconstruction. Code to reproduce all experiments in the paper is available at https://github.com/microsoft/analysing_pii_leakage.

In this position paper, we argue that careless reliance on AI to answer our questions and to judge our output is a violation of Grice's Maxim of Quality as well as a violation of Lemoine's legal Maxim of Innocence, performing an (unwarranted) authority fallacy, and while lacking assessment signals, committing Type II errors that result from fallacies of the inverse. What is missing in the focus on output and results of AI-generated and AI-evaluated content is, apart from paying proper tribute, the demand to follow a person's thought process (or a machine's decision processes). In deliberately avoiding Neural Networks that cannot explain how they come to their conclusions, we introduce logic-symbolic inference to handle any possible epistemics any human or artificial information processor may have. Our system can deal with various belief systems and shows how decisions may differ for what is true, false, realistic, unrealistic, literal, or anomalous. As is, stota AI such as ChatGPT is a sorcerer's apprentice.

The recent blizzard of warnings about artificial intelligence and how it is transforming learning, upending legal, financial and organizational functions, and reshaping social and cultural interaction, have mostly left out the role it is already playing in governance. Governments in the US at every level are attempting the transition from a programmatic model of service delivery to a citizen-focused model. Los Angeles, the US's second largest city, is a pioneer in the field, unveiling technologies to help streamline bureaucratic functions from police recruitment to paying parking tickets to filling potholes or locating resources at the library. For now, AI advances are limited to automation. When ChatGPT was asked recently about how it might change how people deal with government, it responded that "the next generation of AI, which includes ChatGPT, has the potential to revolutionize the way governments interact with their citizens."

Eugenia Kuyda defended AI companion bots during the interview with Fox News Digital and argued that dating app Replika is just one of many possible solutions to loneliness. Tech giants are ill-prepared to combat "hallucinations" generated by artificial intelligence platforms, industry experts warned in comments to Fox News Digital, but corporations themselves say they're taking steps to ensure accuracy within the platforms. AI chatbots, such as ChatGPT and Google's Bard, can at times spew inaccurate misinformation or nonsensical text, referred to as "hallucinations." "The short answer is no, corporation and institutions are not ready for the changes coming or challenges ahead," said AI expert Stephen Wu, chair of the American Bar Association Artificial Intelligence and Robotics National Institute, and a shareholder with Silicon Valley Law Group. Often, hallucinations are honest mistakes made by technology that, despite promises, still possess flaws.

An AI art lecturer said he believes the U.S. government would encounter difficulty if it attempted to establish a watermark system for AI-generated content. The U.S. will likely have a tough time trying to regulate AI-generated content, such as requiring watermarks on computer-made media, a university art lecturer told Fox News. "[F]or us to enforce it would be a lot more difficult," Tyler Coleman, who teaches University of Texas classes focused on AI, said. "I think it will be harder to achieve in the U.S. than it would be in China." China's government announced regulations in December 2022 requiring any AI-generated content to include a flag such as a watermark to indicate its origin.

Individuals involved in gang-related activity use mainstream social media including Facebook and Twitter to express taunts and threats as well as grief and memorializing. However, identifying the impact of gang-related activity in order to serve community member needs through social media sources has a unique set of challenges. This includes the difficulty of ethically identifying training data of individuals impacted by gang activity and the need to account for a non-standard language style commonly used in the tweets from these individuals. Our study provides evidence of methods where natural language processing tools can be helpful in efficiently identifying individuals who may be in need of community care resources such as counselors, conflict mediators, or academic/professional training programs. We demonstrate that our binary logistic classifier outperforms baseline standards in identifying individuals impacted by gang-related violence using a sample of gang-related tweets associated with Chicago. We ultimately found that the language of a tweet is highly relevant and that uses of ``big data'' methods or machine learning models need to better understand how language impacts the model's performance and how it discriminates among populations.