Human-centric computer vision (HCCV) data curation practices often neglect privacy and bias concerns, leading to dataset retractions and unfair models. HCCV datasets constructed through nonconsensual web scraping lack crucial metadata for comprehensive fairness and robustness evaluations. Current remedies are post hoc, lack persuasive justification for adoption, or fail to provide proper contextualization for appropriate application. Our research focuses on proactive, domain-specific recommendations, covering purpose, privacy and consent, and diversity, for curating HCCV evaluation datasets, addressing privacy and bias concerns. We adopt an ante hoc reflective perspective, drawing from current practices, guidelines, dataset withdrawals, and audits, to inform our considerations and recommendations.

If a week is a long time in politics, a year is an eternity in tech. Just over 12 months ago, the industry was humming along in its usual way. The big platforms were deep into what Cory Doctorow calls "enshittification" – the process in which platforms go from being initially good to their users, to abusing them to make things better for their business customers and finally to abusing those customers in order to claw back all the value for themselves. Elon Musk was ramping up his efforts to alienate advertisers on Twitter/X and accelerate the death spiral of his expensive toy. TikTok was monopolising every waking hour of teenagers.

Artificial Intelligence (AI) is already revolutionising the way we work and conduct research, and its future impact is challenging to predict. Concerning qualitative content analysis, recent studies demonstrate its usefulness for coding research interviews, a fundamental tool for data collection across numerous academic disciplines (Lopezosa and Codina, 2023; Zhang et al., 2023). However, its use is incipient and there are still not many experiences in the scientific literature, despite the need to analyse and closely monitor the development of tools with the potential to bring about such profound changes. Consequently, this paper illustrates its practical application in a real case where interviews were initially manually coded using expert criteria. These interviews were carried out as part of a broader study aimed at evaluating the changes in landscape quality that occurred on a small island in Cuba (Cayo Santa María) as a result of tourism development (Burgui et al., 2018).

Care and Feeding is Slate's parenting advice column. Have a question for Care and Feeding? My husband and I share one child together, as well as my child from a previous marriage and his child from a previous marriage. We are a wonderfully blended family and are fortunate to share our lives. Our blended bliss has had one major challenge, though.

Manila, Philippines – Leila de Lima was released from detention last month into what the former Philippines senator calls "a whole new world". In 2016, then-President Rodrigo Duterte promised to "destroy" de Lima, one of the loudest critics of his deadly drug war. The president's supporters began targeting the first-term senator and former human rights commissioner – ridiculing her for an alleged romantic affair with her driver, and accusing her of involvement in drug trafficking. In February 2017, she was arrested on drug charges she denies and that international observers have said are politically motivated. "I had this deep sense of disbelief," de Lima told Al Jazeera. "I never thought that Mr Duterte would go to that extent, that length, of jailing me. I thought it would just be daily vilification, personal attacks, attacks against my womanhood."

In recent years, groundbreaking advancements in natural language processing have culminated in the emergence of powerful large language models (LLMs), which have showcased remarkable capabilities across a vast array of domains, including the understanding, generation, and translation of natural language, and even tasks that extend beyond language processing. In this report, we delve into the performance of LLMs within the context of scientific discovery, focusing on GPT-4, the state-of-the-art language model. Our investigation spans a diverse range of scientific areas encompassing drug discovery, biology, computational chemistry (density functional theory (DFT) and molecular dynamics (MD)), materials design, and partial differential equations (PDE). Evaluating GPT-4 on scientific tasks is crucial for uncovering its potential across various research domains, validating its domain-specific expertise, accelerating scientific progress, optimizing resource allocation, guiding future model development, and fostering interdisciplinary research. Our exploration methodology primarily consists of expert-driven case assessments, which offer qualitative insights into the model's comprehension of intricate scientific concepts and relationships, and occasionally benchmark testing, which quantitatively evaluates the model's capacity to solve well-defined domain-specific problems. Our preliminary exploration indicates that GPT-4 exhibits promising potential for a variety of scientific applications, demonstrating its aptitude for handling complex problem-solving and knowledge integration tasks. Broadly speaking, we evaluate GPT-4's knowledge base, scientific understanding, scientific numerical calculation abilities, and various scientific prediction capabilities.

Fine-tuning large language models (LLMs) for downstream tasks can greatly improve model quality, however serving many different fine-tuned LLMs concurrently for users in multi-tenant environments is challenging. Dedicating GPU memory for each model is prohibitively expensive and naively swapping large model weights in and out of GPU memory is slow. Our key insight is that fine-tuned models can be quickly swapped in and out of GPU memory by extracting and compressing the delta between each model and its pre-trained base model. We propose DeltaZip, an LLM serving system that efficiently serves multiple full-parameter fine-tuned models concurrently by aggressively compressing model deltas by a factor of $6\times$ to $8\times$ while maintaining high model quality. DeltaZip increases serving throughput by $1.5\times$ to $3\times$ and improves SLO attainment compared to a vanilla HuggingFace serving system.

Artificial Intelligence (AI) and its associated applications are ubiquitous in today's world, making it imperative that students and their teachers understand how it works and the ramifications arising from its usage. In this study, we investigate the experiences of seven teachers following their implementation of modules from the MIT RAICA (Responsible AI for Computational Action) curriculum. Through semi-structured interviews, we investigated their instructional strategies as they engaged with the AI curriculum in their classroom, how their teaching and learning beliefs about AI evolved with the curriculum as well as how those beliefs impacted their implementation of the curriculum. Our analysis suggests that the AI modules not only expanded our teachers' knowledge in the field, but also prompted them to recognize its daily applications and their ethical and societal implications, so that they could better engage with the content they deliver to students. Teachers were able to leverage their own interdisciplinary backgrounds to creatively introduce foundational AI topics to students to maximize engagement and playful learning. Our teachers advocated their need for better external support when navigating technological resources, additional time for preparation given the novelty of the curriculum, more flexibility within curriculum timelines, and additional accommodations for students of determination. Our findings provide valuable insights for enhancing future iterations of AI literacy curricula and teacher professional development (PD) resources.

What do Taylor Swift, Andrew Tate and Robert Oppenheimer all have in common? They were the most searched articles on Wikipedia this year. The platform shared a fascinating report revealing the topics most interested people in English-speaking countries. Collectively, we racked up more than 84 billion views on Wikipedia this year. The site's page for ChatGPT was the top article, with more than 49 million views, following its breakout year that sparked curiosity and concern worldwide. Curiosity and concern also played a part in the second most viewed Wikipedia article of the year: 'Deaths in 2023' ' which HAD over 42 million views.

It was a strange Thanksgiving for Sam Altman. Normally, the CEO of OpenAI flies home to St. Louis to visit family. But this time the holiday came after an existential struggle for control of a company that some believe holds the fate of humanity in its hands. He went to his Napa Valley ranch for a hike, then returned to San Francisco to spend a few hours with one of the board members who had just fired and reinstated him in the span of five frantic days. He put his computer away for a few hours to cook vegetarian pasta, play loud music, and drink wine with his fiancé Oliver Mulherin. "This was a 10-out-of-10 crazy thing to live through," Altman tells TIME on Nov. 30. We're speaking exactly one year after OpenAI released Chat-GPT, the most rapidly adopted tech product ever. The impact of the chatbot and its successor, GPT-4, was transformative--for the company and the world. "For many people," Altman says, 2023 was "the year that they started taking AI seriously." Born as a nonprofit research lab dedicated to building artificial intelligence for the benefit of humanity, OpenAI became an $80 billion rocket ship. Altman emerged as one of the most powerful and venerated executives in the world, the public face and leading prophet of a technological revolution. On Nov. 17, OpenAI's nonprofit board of directors fired Altman, without warning or even much in the way of explanation. The surreal maneuvering that followed made the corporate dramas of Succession seem staid. So did OpenAI's powerful investors; one even baselessly speculated that one of the directors who defenestrated Altman was a Chinese spy. The company's visionary chief scientist voted to oust his fellow co-founder, only to backtrack. Two interim CEOs came and went.