Natural Language Processing research is increasingly reliant on large scale data and computational power. Many achievements in the past decade resulted from collaborations with the tech industry. But an increasing entanglement of academic research and industry interests leads to conflicts of interest. We assessed published NLP research from 2000-2024 and labeled author affiliations as academic or industry-affiliated to measure conflicts of interest. Overall 27.65% of the papers contained at least one industry-affiliated author. That figure increased substantially with more than 1 in 3 papers having a conflict of interest in 2024. We identify top-tier venues (ACL, EMNLP) as main drivers for that effect. The paper closes with a discussion and a simple, concrete suggestion for the future.

With advancement of robotics and artificial intelligence, applications for robotics are flourishing. Human-robot interaction (HRI) is an important area of robotics as it allows robots to work closer to humans (with them or for them). One crucial factor for the success of HRI research is transferability, which refers to the ability of research outputs to be adopted by industry and provide benefits to society. In this paper, we explore the potentials and challenges of transferability in HRI research. Firstly, we examine the current state of HRI research and identify various types of contributions that could lead to successful outcomes. Secondly, we discuss the potential benefits for each type of contribution and identify factors that could facilitate industry adoption of HRI research. However, we also recognize that there are several challenges associated with transferability, such as the diversity of well-defined job/skill-sets required from HRI practitioners, the lack of industry-led research, and the lack of standardization in HRI research methods. We discuss these challenges and propose potential solutions to bridge the gap between industry expectations and academic research in HRI.

Text data can pose a risk of harm. However, the risks are not fully understood, and how to handle, present, and discuss harmful text in a safe way remains an unresolved issue in the NLP community. We provide an analytical framework categorising harms on three axes: (1) the harm type (e.g., misinformation, hate speech or racial stereotypes); (2) whether a harm is \textit{sought} as a feature of the research design if explicitly studying harmful content (e.g., training a hate speech classifier), versus \textit{unsought} if harmful content is encountered when working on unrelated problems (e.g., language generation or part-of-speech tagging); and (3) who it affects, from people (mis)represented in the data to those handling the data and those publishing on the data. We provide advice for practitioners, with concrete steps for mitigating harm in research and in publication. To assist implementation we introduce \textsc{HarmCheck} -- a documentation standard for handling and presenting harmful text in research.

Artificial intelligence could eventually help to award scores to the tens of thousands of papers submitted to the Research Excellence Framework by UK universities.Credit: Yuichiro Chino/Getty Researchers tasked with examining whether artificial intelligence (AI) technology could assist in the peer review of journal articles submitted to the United Kingdom's Research Excellence Framework (REF) say the system is not yet accurate enough to aid human assessment, and recommend further testing in a large-scale pilot scheme. The team's findings, published on 12 December, show that the AI system generated identical scores to human peer reviewers up to 72% of the time. When averaged out over the multiple submissions made by some institutions across a broad range of the 34 subject-based'units of assessment' that make up the REF, "the correlation between the human score and the AI score was very high", says data scientist Mike Thelwall at the University of Wolverhampton, UK, who is a co-author of the report. In its current form, however, the tool is most useful when assessing research output from institutions that submit a lot of articles to the REF, Thelwall says. It is less useful for smaller universities that submit only a handful of articles.

CERN, Europe's particle-physics laboratory, produces vast amounts of data, which are stored at its computer centre (pictured) and analysed with the help of artifical intelligence (AI). UK funders want to know whether AI could also assist in peer reviewing thousands of research outputs for nationwide quality audits.Credit: Dean Mouhtaropoulos/Getty Efforts to ease the workloads of peer reviewers by using artificial intelligence (AI) are gathering pace -- with one country's main research-evaluation exercise actively looking into ways of harnessing the technology. A study commissioned by the United Kingdom's main public research-funding bodies is examining how algorithms can assist in conducting peer review on journal articles submitted to the UK's Research Excellence Framework (REF). The REF, a national quality audit that measures the impact of research carried out at UK higher-education institutions, is a huge undertaking. In the latest iteration, the results of which were published in May 2022, more than 185,000 research outputs were evaluated from more than 76,000 academics based at 157 UK institutions.

Artificial intelligence poses new questions for intellectual property (IP) law. Can machines be inventors for purposes of patent law? Is new legislation required to govern AI creativity? Courts, IP offices, and legislators in multiple jurisdictions are considering these questions. By now, there is a well-developed and comprehensive academic literature which analyses the interface between IP and AI. And while there will always be room for further analysis of such questions as technology progresses, there is diminishing marginal returns to such inquiries at this point in time.

Linkitup is a web-based dashboard for enrichment of research output published via industry grade data repository services. It takes metadata entered through Figshare.com

Smart Harvesting brings artificial intelligence and sophisticated automation to the process of creating comprehensive research repositories and updating researcher profiles, which saves time and reduces manual work. This technology makes it possible for institutions to showcase the work of affiliated researchers completely and accurately, in one place and across all disciplines. Matching research output to the right researcher, and ensuring you included everything, is often a complicated exercise. Smart Harvesting accomplishes this goal with unique machine learning algorithms for tackling two key tasks: matching scholars to their work; and populating the research information hub with this information at scale. For effective Smart Harvesting, machine learning identifies and assesses three main data properties: individual names; general information from the outputs; and semantic content.

A da Vinci surgical robot system performs heart surgery in 2017 at a hospital in Hefei, China.Credit: Shutterstock In 2006, China highlighted the importance of robotics in its 15-year plan for science and technology. In 2011, the central government fleshed out these ambitions in its 12th five-year plan, specifying that robots should be used to support society in a wide range of roles, from helping emergency services during natural disasters and firefighting, to performing complex surgery and aiding in medical rehabilitation. Guang-Zhong Yang, head of the Institute of Medical Robotics at Shanghai Jiao Tong University, says that China's robotics research output has been growing steadily for two decades, driven by three major factors: "The clinical utilization of robotics; increased funding levels driven by national planning needs; and advances in engineering in areas such as precision mechatronics, medical imaging, artificial intelligence and new materials for making robots." Yang points out that funding levels for medical robotics from the National Natural Science Foundation of China and the Ministry of Science and Technology began to increase more sharply in 2011 compared to the previous decade. The accompanying rises in research output are closely related to the introduction of specialized robotics equipment in medical-research facilities, says Yao Li, a research scientist at Stanford Robotics Laboratory in California and founder of the company Borns Medical Robotics, based in both Chengdu, China, and Silicon Valley, California.

I have a bachelor degree in CS and I've 5 years experience of working in multiple tech companies. I have mostly worked in machine learning and artificial intelligence including deep neural nets. Alongside, I am a huge music addict and a self learned amateur piano player and sometimes, I love to compose music out of an experiment. Now, I wish to pursue masters. But, I want to give myself a platform where my crave for technology and passion for music will find the perfect blend.