Artificial Intelligence/Robots – Page 2

“Exploring Emerging Technologies in Archiving and Preservation: Leveraging 3D Models, Interactive Environments, and AI Tools”

This article. . . explores how cultural heritage practitioners can leverage emerging technologies to enhance their work. . . . This article highlights AI applications and emerging technologies that can generate scripts without needing coding experience, create 3D models that increase accessibility and engagement, and develop virtual exhibits that extend the lifespan and reach of physical exhibits while providing additional interactive elements.

https://doi.org/10.1177/18758789251336085

“ProQuest – New Ebook Central AI Tool Helps Users Engage Deeper with Scholarly Ebooks”

The Ebook Central Research Assistant simplifies book exploration by identifying key concepts and providing contextual explanations to enhance understanding. With chapter-level insights and key term definitions drawn directly from the text, it keeps users engaged in their research. Powered by Retrieval-Augmented Generation (RAG), it ensures accuracy by sourcing insights directly from the book’s content. Additionally, it helps users discover related titles within their library’s Ebook Central collection, seamlessly expanding their research scope. Features at launch include:

Key Takeaways: Chapter-level insights help users assess content relevance efficiently

Concept Highlights: Identification and explanation of key terms, with one-click searches, deepen research

https://tinyurl.com/49kwvajd

“New STM Draft Report: Classifying AI Use in Manuscript Preparation”

While publishers have long offered guidance on disclosing human assistance—such as language editing—recent advances in generative AI have significantly expanded the ways in which machine tools can support manuscript preparation. From writing and editing to generating images and diagrams, the use of AI in scholarly publishing is evolving rapidly. . . .

This lack of consistent standards risks undermining trust in scholarly communication. To support the integrity of the academic record, the draft classification offers a clear framework to help publishers define, evaluate, and guide the transparent use of AI in manuscript preparation in the context of their individual editorial processes.

https://tinyurl.com/2druekkw

“Clarivate Releases EndNote 2025 with AI-powered Research Tools”

The new EndNote 2025 features are available for all users, and include:

Key Takeaway — A new, generative AI-powered tool that expedites research discovery by extracting key insights and takeaways from individual papers.

Find a Journal publishing tool — An enhanced machine learning tool available directly in Cite While You Write that allows researchers to find the best journal match using their paper.

Cite from PDF — Quickly insert both a highlighted quote from a PDF and its corresponding citation into the document with a click of a button. . . .

Web of Science citing articles and related records — Curate a more comprehensive reference library by viewing relevant articles and finding papers that have cited existing references.

https://tinyurl.com/4vzcthjy

“Where There’s a Will There’s a Way: ChatGPT Is Used More for Science in Countries Where It Is Prohibited”

Regulating AI is a key societal challenge, but effective methods remain unclear. This study evaluates geographic restrictions on AI services, focusing on ChatGPT, which OpenAI blocks in several countries, including China and Russia. If restrictions were effective, ChatGPT usage in these countries should be minimal. We measured usage with a classifier trained to detect distinctive word choices (e.g., “delve”) typical of early ChatGPT outputs. The classifier, trained on pre- and post-ChatGPT “polished” abstracts, outperformed GPTZero and ZeroGPT on validation sets, including papers with self-reported AI use. Applying our classifier to preprints from Arxiv, BioRxiv, and MedRxiv revealed ChatGPT use in approximately 12.6% of preprints by August 2023, with usage 7.7% higher in restricted countries. This gap emerged before China’s first major domestic LLM became widely available. To address whether high demand could have driven even greater use without restrictions, we compared Asian countries with high expected demand (where English is not an official language) and found higher usage in countries with restrictions. ChatGPT use correlated with increased views and downloads but not with citations or journal placement. Overall, geographic restrictions on ChatGPT appear ineffective in science and potentially other domains, likely due to widespread workarounds.

https://doi.org/10.1162/qss_a_00368

“Intelligence Prompt Engineering to Enhance Information Retrieval for Medical Librarians”

Prompt engineering, an emergent discipline at the intersection of Generative Artificial Intelligence (GAI), library science, and user experience design, presents an opportunity to enhance the quality and precision of information retrieval. An innovative approach applies the widely understood PICO framework, traditionally used in evidence-based medicine, to the art of prompt engineering. This approach is illustrated using the “Task, Context, Example, Persona, Format, Tone” (TCEPFT) prompt framework as an example. TCEPFT lends itself to a systematic methodology by incorporating elements of task specificity, contextual relevance, pertinent examples, personalization, formatting, and tonal appropriateness in a prompt design tailored to the desired outcome. Frameworks like TCEPFT offer substantial opportunities for librarians and information professionals to streamline prompt engineering and refine iterative processes. This practice can help information professionals produce consistent and high-quality outputs. Library professionals must embrace a renewed curiosity and develop expertise in prompt engineering to stay ahead in the digital information landscape and maintain their position at the forefront of the sector.

https://doi.org/10.5195/jmla.2025.2022

Paywall: “An AI Is Going to Art School — and Might Earn a Diploma. Meet Flynn.”

The AI program, which is attending classes ahead of its enrollment in the fall [at the University of Applied Arts Vienna], is treated like any other student, university instructors said. It attends lectures, collaborates with classmates and will receive grades on submitted work. Flynn could, in theory, progress toward a diploma.

https://tinyurl.com/4wyz345u

“The Argument for Near-Term Human Disempowerment through AI”

Many researchers and intellectuals warn about extreme risks from artificial intelligence. However, these warnings typically came without systematic arguments in support. This paper provides an argument that AI will lead to the permanent disempowerment of humanity, e.g. human extinction, by 2100. It rests on four substantive premises which it motivates and defends: first, the speed of advances in AI capability, as well as the capability level current systems have already reached, suggest that it is practically possible to build AI systems capable of disempowering humanity by 2100. Second, due to incentives and coordination problems, if it is possible to build such AI, it will be built. Third, since it appears to be a hard technical problem to build AI which is aligned with the goals of its designers, and many actors might build powerful AI, misaligned powerful AI will be built. Fourth, because disempowering humanity is useful for a large range of misaligned goals, such AI will try to disempower humanity. If AI is capable of disempowering humanity and tries to disempower humanity by 2100, then humanity will be disempowered by 2100. This conclusion has immense moral and prudential significance.

https://doi.org/10.1007/s00146-024-01930-2

“OpenAI’s New Image Generator Aims to Be Practical Enough for Designers and Advertisers”

Example images from OpenAI show progress here. The model is able to generate 12 discrete graphics within a single image—like a cat emoji or a lightning bolt—and place them in proper order. Another shows four cocktails accompanied by recipe cards with accurate, legible text. More images show comic strips with text bubbles, mock advertisements, and instructional diagrams. The model also allows you to upload images to be modified, and it will be available in the video generator Sora as well as in GPT-4o.

https://tinyurl.com/msnch7z5

“Evaluating AI Language Models for Reference Services: A Comparative Study of ChatGPT, Gemini, and Copilot”

The descriptive statistics indicate that Google Gemini outperformed the other GenAI chatbots, by scoring high on “accuracy,” relevancy,” “friendliness” and “instruction” resulting in a higher mean score followed by public ChatGPT, commercial ChatGPT-4.0, and Microsoft Copilot.

https://doi.org/10.1080/10875301.2025.2478861

“Scaffolding AI Literacy: An Instructional Model for Academic Librarianship”

As artificial intelligence (AI) becomes integral to academic, professional, and societal contexts, the demand for AI literacy in higher education is growing. Academic librarians, with their expertise in information literacy and critical pedagogy, are well-equipped to address this need. This article introduces a scaffolded model to advance AI literacy through progressive skill development across four tiers: foundational awareness, applied problem-solving, critical evaluation, and ethical advocacy. Each tier builds on the previous, fostering a comprehensive understanding of AI concepts, tools, and societal implications. Adapted from traditional information literacy workshops, this instructional model empowers students to navigate, critique, and responsibly engage with AI technologies. Tier 1 introduces basic AI concepts and tools. Tier 2 examines AI’s role in research and problem-solving, addressing practical applications and limitations. Tier 3 emphasizes the critical evaluation of AI-generated content and tools. Tier 4 focuses on ethical decision-making and advocacy, encouraging students to consider AI’s broader societal impacts. This article discusses the proposed model’s pedagogical design, details its application through workshop plans, and explores its implications for academic librarians’ roles in fostering AI literacy. By implementing this approach, librarians can equip students to become critical consumers of AI technologies.

https://doi.org/10.1016/j.acalib.2025.103041

“NVIDIA Announces DGX Spark and DGX Station Personal AI Computers”

DGX Spark — formerly Project DIGITS — and DGX Station™, a new high-performance NVIDIA Grace Blackwell desktop supercomputer powered by the NVIDIA Blackwell Ultra platform, enable AI developers, researchers, data scientists and students to prototype, fine-tune and inference large models on desktops. Users can run these models locally or deploy them on NVIDIA DGX Cloud or any other accelerated cloud or data center infrastructure. . . .

NVIDIA DGX Station brings data-center-level performance to desktops for AI development. The first desktop system to be built with the NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip, DGX Station features a massive 784GB of coherent memory space to accelerate large-scale training and inferencing workloads. The GB300 Desktop Superchip features an NVIDIA Blackwell Ultra GPU with latest-generation Tensor Cores and FP4 precision — connected to a high-performance NVIDIA Grace™ CPU via NVLink-C2C — delivering best-in-class system communication and performance.

https://tinyurl.com/2r66z523

Ars Technica reports that: “Since the systems will be manufactured by different companies, Nvidia did not mention pricing for the units. However, in January, Nvidia mentioned that the base-level configuration for a DGX Spark-like computer would retail for around $3,000.”

Paywall: “Bridging the AI Gap: Comparative Analysis of AI Integration, Education, and Outreach in Academic Libraries”

This study examines AI integration, education, and outreach in academic libraries across Europe, North America (Canada and USA), Sub-Saharan Africa, Latin America and the Caribbean. An environmental scan of 40 academic library websites from the Times Higher Education 10 highest-ranked libraries in each region was conducted. Results show that more than 50% of the libraries offered educational materials and 42.5% conducted educational activities, while only 12.5% included AI policies.

https://doi.org/10.1177/03400352251325274

“Elsevier Launches ScienceDirect AI to Transform Research with Rapid Mission-Critical Insights from Trusted Content”

Researchers grapple with an ever-growing and overwhelming volume of information and need to quickly get accurate insights they can rely on. Studies show that they spend 25%-35% of their time sifting through literature. ScienceDirect AI helps address this challenge by drawing on the broadest and deepest content set of millions of peer-reviewed full-text research articles and book chapters to generate instant accurate summaries and highlight key findings, while providing references to support reproducibility and integrity of research.

https://tinyurl.com/2s3m2hwp

“Can LLMs Categorize the Specialized Documents from Web Archives in a Better Way?”

The explosive growth of web archives presents a significant challenge: manually curating specialized document collections from this vast data. Existing approaches rely on supervised techniques, but recent advancements in Large Language Models (LLMs) offer new possibilities for automating collection creation. Large Language Models (LLMs) are demonstrating impressive performance on various tasks even without fine-tuning. This paper investigates the effectiveness of prompt design in achieving results comparable to fine-tuned models. We explore different prompting techniques for collecting specialized documents from web archives like UNT.edu, Michigan.gov, and Texas.gov. We then analyze the performance of LLMs under various prompt configurations. Our findings highlight the significant impact of incorporating task descriptions within prompts. Additionally, including the document type as justification for the search scope leads to demonstrably better results. This research suggests that well-crafted prompts can unlock the potential of LLMs for specialized tasks, potentially reducing reliance on resource-intensive fine-tuning. This research paves the way for automating specialized collection creation using LLMs and prompt engineering.

https://dl.acm.org/doi/10.1145/3677389.3702591

“AI Search Engines Cite Incorrect Sources at an Alarming 60% Rate, Study Says”

A new study from Columbia Journalism Review’s Tow Center for Digital Journalism finds serious accuracy issues with generative AI models used for news searches. The research tested eight AI-driven search tools equipped with live search functionality and discovered that the AI models incorrectly answered more than 60 percent of queries about news sources.

https://tinyurl.com/5ym7mc92

“Elsevier Launches Sciencedirect AI to Transform Research with Rapid Mission-Critical Insights from Trusted Content”

ScienceDirect AI includes the following features:

Ask ScienceDirect AI – search and summaries of full-text articles and book chapters

Users can search and get answers from within the full-text of 14 million articles and book chapters, using their own words to describe what they need and why. ScienceDirect AI will search across the millions of documents in its index to provide a Summary Response with references, Source Snippets for each reference, and short Related Insights summaries while linking back to the original document.

Reading Assistant – chat with a document in ScienceDirect

This conversational feature answers questions about the content of a specific full-text article or book chapter and allows researchers to ask further questions of the document. Users can click on references within the summaries to jump to locations in the article where the answer comes from, it also suggests research questions.

Compare Experiments – experiment summary table

Comparing and synthesizing literature can be very time-consuming. ScienceDirect AI’s unique Compare Experiments tool takes a set of articles and creates a table breaking down each experiment within them, drawing out the key aspects of each including goals, methods and results.

https://tinyurl.com/mwnkar8u

“Tracking the AI Evolution in Research Libraries: Findings from ARL’s Third AI Quick Poll”

Optimism about generative AI is evident among respondents. Over a quarter (28%) described their outlook as “very positive,” envisioning significant enhancements to library services in the next year. The majority (63%) expressed a “somewhat positive” view, acknowledging the potential of AI while being mindful of challenges. Only 10% maintained a neutral stance, reflecting a general trend toward growing confidence in AI’s role within libraries. . . .

Engagement with AI technologies shows steady growth. Nearly one-third of respondents (28%) reported that their libraries are actively implementing AI solutions. The largest group (53%) is in the exploratory phase, investigating potential applications, while 19% indicated plans to consider AI in the near future.

https://tinyurl.com/y7js666m

Top 100 Gen AI Consumer Apps

This is the fourth installment of the Top 100 Gen AI Consumer Apps, our bi-annual ranking of the top 50 AI-first web products (by unique monthly visits, per Similarweb) and top 50 AI-first mobile apps (by monthly active users, per Sensor Tower). Since our last report in August 2024, 17 new companies have entered the rankings of top AI-first web products

https://tinyurl.com/n3hvc8wz

“AI Literacy: A Guide For Academic Libraries”

By embracing AI literacy, libraries can lead efforts to demystify AI, offer targeted programs, and foster interdisciplinary collaborations to explore AI’s influence on research and learning. Through partnerships with faculty and campus technology units, librarians can integrate AI literacy into courses, create learning communities, and provide practical training on AI-driven tools. In doing so, academic libraries position ourselves as key players in shaping critical conversations about AI and guiding the next generation of scholars to engage thoughtfully and ethically with these technologies.

https://tinyurl.com/5hap9t7v

Ex Libris: “Summon Research Assistant Is Now Live”

Summon Research Assistant is a new AI-enhanced discovery tool that saves researchers time by summarizing the query response and citing the most relevant resources. Users will benefit from detailed, natural language search capabilities to uncover trusted library materials. Summon Research Assistant only offers content that is vetted by libraries and the Ex Libris via the Central Discovery Index. Searching the Summon Research Assistant generates in real-time a topical overview based on information from the CDI. The most significant resources are cited in the summary, enabling researchers to instantly view context. From here they can elect to explore more results, refine their original query, or pursue a suggested related research question.

https://tinyurl.com/43ast26b

“ARTificial: Why Copyright Is Not the Right Policy Tool to Deal with Generative AI”

For the sake of this discussion, let’s assume that GAI ligation is successful. How would concepts of attribution and distribution work under existing copy- right rules of compensation? Should every author whose work is present in the dataset have an equivalent claim over every single output? How would such an outcome work in practice? Here, consider again the Stable Diffusion example. The model’s training dataset, LAION 5B, is composed of “5.85 billion CLIP-fil- tered image-text pairs.”¹⁵¹ Given the massive size of the training set, it is difficult to imagine how one could trace the attribution and weight of a single work into the final end result. To do so would be like proposing that a given output image is attributable to 5.85 billion copyright interests.

https://dx.doi.org/10.2139/ssrn.5090127

“Decoding Virtual Chats: NLP Insights Into Academic Library Services.”

This research applies a machine learning (ML) tool to the complete set of transcripts from a research university’s chat reference service (2017–2022) to examine evolving trends and patron needs in the library reference service. The study has two key objectives: 1) demonstrating ML’s effectiveness in the academic library setting, and 2) assessing the impact of COVID-19 on chat reference needs. A text classification model, trained on 1.5 % of the sample, achieves a 75 % accuracy match with human annotations

https://doi.org/10.1016/j.lisr.2025.101344

“Project Alexandria: Towards Freeing Scientific Knowledge from Copyright Burdens via LLMs”

Paywalls, licenses and copyright rules often restrict the broad dissemination and reuse of scientific knowledge. We take the position that it is both legally and technically feasible to extract the scientific knowledge in scholarly texts. Current methods, like text embeddings, fail to reliably preserve factual content, and simple paraphrasing may not be legally sound. We urge the community to adopt a new idea: convert scholarly documents into Knowledge Units using LLMs. These units use structured data capturing entities, attributes and relationships without stylistic content. We provide evidence that Knowledge Units: (1) form a legally defensible framework for sharing knowledge from copyrighted research texts, based on legal analyses of German copyright law and U.S. Fair Use doctrine, and (2) preserve most (~95%) factual knowledge from original text, measured by MCQ performance on facts from the original copyrighted text across four research domains. Freeing scientific knowledge from copyright promises transformative benefits for scientific research and education by allowing language models to reuse important facts from copyrighted text. To support this, we share open-source tools for converting research documents into Knowledge Units. Overall, our work posits the feasibility of democratizing access to scientific knowledge while respecting copyright.

https://arxiv.org/abs/2502.19413

“Building Trustworthy AI Solutions: Integrating Artificial Intelligence Literacy into Records Management and Archival Systems”

This paper explores the essential role of Artificial Intelligence (AI) competencies and literacy in the fields of records management and archival practices, within the framework of the InterPARES Trust AI project. . . . The study employs two complementary approaches: (1) a detailed competency framework developed through literature reviews, interviews with archival professionals who have applied AI to the processing of records, and validation workshops with practitioners; and (2) a comprehensive AI literacy framework derived from multiple case studies and theoretical discussions. . . . Findings indicate that archival professionals can leverage AI in their work practices by acquiring basic AI literacy, practical AI skills, data-related skills, tool-testing and evaluation, adaptation of AI to their workflows, and by actively engaging in collaborative projects with information technology (IT) developers.

https://doi.org/10.48550/arXiv.2307.14852