Kashtanova, who uses a gender-neutral honorific and pronouns, used a series of written prompts to guide the AI software Midjourney to create the images in the book, which describes the voyage of a young person through several futuristic worlds and was the subject of an article in The Wall Street Journal last month.

What happened: U.S. and EU regulators have increasingly opened up to the possibility of regulating artificial intelligence (AI). AI poses an interesting challenge as regulators look to define what it is and the reasonable balance between regulation and the progression of the technology. On one hand, AI can drastically improve quality of life by running complex algorithms to help create cures for diseases or invent new technology. On the other hand, it can -- and is -- replacing jobs and careers around the globe. People like Tesla Inc. CEO Elon Musk have warned it will eventually replace every job, making the need to work obsolete.

DALL-E 2, Stable Diffusion, and others transformed the image generation space. We saw more powerful language models, PaLM, and of course ChatGPT. We saw foundation models being developed for speech, music, proteins, and many other data modalities. And, for the first time, these models are now being widely deployed and utilized by consumers to accomplish a wide breadth of useful tasks. What is clear is that while foundation models have opened up unprecedented new possibilities, they are also still raw, imperfect research artifacts that we do not entirely understand. In 2021, we founded the Center for Research on Foundation Models (CRFM), recognizing the critical role of foundation models. CRFM's mission is to understand and improve foundation models from both a technical and societal perspective.

Editor's note: "Artificial Intelligence Just Had Its Very Own'iPhone Moment'" was previously published in January 2023. It has since been updated to include the most relevant information available. Three months ago, the world changed forever. And if you're able to embrace it, you could put yourself in a position to make fortunes over the next decade as the biggest technological revolution since the internet sweeps across America. Specifically, on Nov. 30, 2022, small tech startup OpenAI launched a brand-new conversational chatbot – ChatGPT.

Efforts to promote equitable public policy with algorithms appear to be fundamentally constrained by the "impossibility of fairness" (an incompatibility between mathematical definitions of fairness). This technical limitation raises a central question about algorithmic fairness: How can computer scientists and policymakers support equitable policy reforms with algorithms? In this article, I argue that promoting justice with algorithms requires reforming the methodology of algorithmic fairness. First, I diagnose the problems of the current methodology for algorithmic fairness, which I call "formal algorithmic fairness." Because formal algorithmic fairness restricts analysis to isolated decision-making procedures, it leads to the impossibility of fairness and to models that exacerbate oppression despite appearing "fair." Second, I draw on theories of substantive equality from law and philosophy to propose an alternative methodology, which I call "substantive algorithmic fairness." Because substantive algorithmic fairness takes a more expansive scope of analysis, it enables an escape from the impossibility of fairness and provides a rigorous guide for alleviating injustice with algorithms. In sum, substantive algorithmic fairness presents a new direction for algorithmic fairness: away from formal mathematical models of "fair" decision-making and toward substantive evaluations of whether and how algorithms can promote justice in practice.

State leaders are sounding the alarm about the emergence of dangerous "Frankenstein" opioids that are more potent than fentanyl and quickly spreading across the United States. Florida Attorney General Ashley Moody is pushing new legislation to add "nitazene compounds," also known as Frankenstein opioids, to the Schedule I controlled substance list in the state, which would categorize the drugs as having a high potential for abuse with no acceptable medical use. "Last year, I signed an emergency rule temporarily adding these deadly nitazene compounds to the Schedule I controlled substance list. I am proud to announce my support for SB 736, which will permanently add these incredibly deadly drugs to the Schedule I list," Moody told Fox News Digital this week. Florida Attorney General Ashley Moody is pushing new legislation to add these "nitazine compounds" to the Schedule I controlled substance list in the state.

Natural language processing based on large language models (LLMs) is a booming field of AI research. After neural networks have proven to outperform humans in games and practical domains based on pattern recognition, we might stand now at a road junction where artificial entities might eventually enter the realm of human communication. However, this comes with serious risks. Due to the inherent limitations regarding the reliability of neural networks, overreliance on LLMs can have disruptive consequences. Since it will be increasingly difficult to distinguish between human-written and machine-generated text, one is confronted with new ethical challenges. This begins with the no longer undoubtedly verifiable human authorship and continues with various types of fraud, such as a new form of plagiarism. This also concerns the violation of privacy rights, the possibility of circulating counterfeits of humans, and, last but not least, it makes a massive spread of misinformation possible.

This paper introduces HADES, a novel tool for automatic comparative documents with similar structures. HADES is designed to streamline the work of professionals dealing with large volumes of documents, such as policy documents, legal acts, and scientific papers. The tool employs a multi-step pipeline that begins with processing PDF documents using topic modeling, summarization, and analysis of the most important words for each topic. The process concludes with an interactive web app with visualizations that facilitate the comparison of the documents. HADES has the potential to significantly improve the productivity of professionals dealing with high volumes of documents, reducing the time and effort required to complete tasks related to comparative document analysis. Our package is publically available on GitHub.

Human cognition has compositionality. We understand a scene by decomposing the scene into different concepts (e.g., shape and position of an object) and learning the respective laws of these concepts, which may be either natural (e.g., laws of motion) or man-made (e.g., laws of a game). The automatic parsing of these laws indicates the model's ability to understand the scene, which makes law parsing play a central role in many visual tasks. This paper proposes a deep latent variable model for Compositional LAw Parsing (CLAP), which achieves the human-like compositionality ability through an encoding-decoding architecture to represent concepts in the scene as latent variables. CLAP employs concept-specific latent random functions instantiated with Neural Processes to capture the law of concepts. Our experimental results demonstrate that CLAP outperforms the baseline methods in multiple visual tasks such as intuitive physics, abstract visual reasoning, and scene representation. The law manipulation experiments illustrate CLAP's interpretability by modifying specific latent random functions on samples. For example, CLAP learns the laws of position-changing and appearance constancy from the moving balls in a scene, making it possible to exchange laws between samples or compose existing laws into novel laws.

The recent (re)popularization of deep learning methods increased the importance and need for the data even further. Similarly, the other subfields of theoretical and applied linguistics have also seen a shift towards more data-driven methods. As a result, availability of large and high-quality language data is essential for both linguistic research and practical NLP applications. In this paper, we present a comprehensive and critical survey of linguistic resources for Turkish.