No one can truly have it. The world is too interconnected for nations to go it alone. Governments plan to pour $1.3 trillion into AI infrastructure by 2030 to invest in "sovereign AI," with the premise being that countries should be in control of their own AI capabilities. The funds include financing for domestic data centers, locally trained models, independent supply chains, and national talent pipelines. This is a response to real shocks: covid-era supply chain breakdowns, rising geopolitical tensions, and the war in Ukraine. But the pursuit of absolute autonomy is running into reality.

Objectives: The integration of Artificial Intelligence (AI) in healthcare promises to revolutionize patient care, diagnostics, and treatment protocols. Collaborative efforts among healthcare systems, research institutions, and industry are pivotal to leveraging AI's full potential. This study aims to characterize collaborative networks and stakeholders in AI healthcare initiatives, identify challenges and opportunities within these collaborations, and elucidate priorities for future AI research and development. Methods: This study utilized data from the Chinese Society of Radiology and the Chinese Medical Imaging AI Innovation Alliance. A national cross-sectional survey was conducted in China (N = 5,142) across 31 provincial administrative regions, involving participants from three key groups: clinicians, institution professionals, and industry representatives. The survey explored diverse aspects including current AI usage in healthcare, collaboration dynamics, challenges encountered, and research and development priorities. Results: Findings reveal high interest in AI among clinicians, with a significant gap between interest and actual engagement in development activities. Despite the willingness to share data, progress is hindered by concerns about data privacy and security, and lack of clear industry standards and legal guidelines. Future development interests focus on lesion screening, disease diagnosis, and enhancing clinical workflows. Conclusion: This study highlights an enthusiastic yet cautious approach toward AI in healthcare, characterized by significant barriers that impede effective collaboration and implementation. Recommendations emphasize the need for AI-specific education and training, secure data-sharing frameworks, establishment of clear industry standards, and formation of dedicated AI research departments.

Machine learning is an interdisciplinary field that studies how computers can learn and simulate human learning behaviour. By acquiring new knowledge, machine learning aims to reorganize existing knowledge structures to continuously improve its own performance. Machine learning was proposed in the mid-1950s, and over the next 30 years, related research in the field of machine learning continued to develop. Machine learning has interdisciplinary attributes and has been widely applied in the field of artificial intelligence. Zhang and Wang [2016] argue that the way to transform big data into more valuable knowledge is by applying machine learning techniques.

Generative Artificial Intelligence (generative AI) poses both opportunities and risks for the integrity of research. Universities must guide researchers in using generative AI responsibly, and in navigating a complex regulatory landscape subject to rapid change. By drawing on the experiences of two Australian universities, we propose a framework to help institutions promote and facilitate the responsible use of generative AI. We provide guidance to help distil the diverse regulatory environment into a principles-based position statement. Further, we explain how a position statement can then serve as a foundation for initiatives in training, communications, infrastructure, and process change. Despite the growing body of literature about AI's impact on academic integrity for undergraduate students, there has been comparatively little attention on the impacts of generative AI for research integrity, and the vital role of institutions in helping to address those challenges. This paper underscores the urgency for research institutions to take action in this area and suggests a practical and adaptable framework for so doing.

Large Language Models (LLMs) are a powerful technology that augment human skill to create new opportunities, akin to the development of steam engines and the internet. However, LLMs come with a high cost. They require significant computing resources and energy to train and serve. Inequity in their control and access has led to concentration of ownership and power to a small collection of corporations. In our study, we collect training and inference requirements for various LLMs. We then analyze the economic strengths of nations and organizations in the context of developing and serving these models. Additionally, we also look at whether individuals around the world can access and use this emerging technology. We compare and contrast these groups to show that these technologies are monopolized by a surprisingly few entities. We conclude with a qualitative study on the ethical implications of our findings and discuss future directions towards equity in LLM access.

Few-shot NER aims to identify entities of target types with only limited number of illustrative instances. Unfortunately, few-shot NER is severely challenged by the intrinsic precise generalization problem, i.e., it is hard to accurately determine the desired target type due to the ambiguity stemming from information deficiency. In this paper, we propose Superposition Concept Discriminator (SuperCD), which resolves the above challenge via an active learning paradigm. Specifically, a concept extractor is first introduced to identify superposition concepts from illustrative instances, with each concept corresponding to a possible generalization boundary. Then a superposition instance retriever is applied to retrieve corresponding instances of these superposition concepts from large-scale text corpus. Finally, annotators are asked to annotate the retrieved instances and these annotated instances together with original illustrative instances are used to learn FS-NER models. To this end, we learn a universal concept extractor and superposition instance retriever using a large-scale openly available knowledge bases. Experiments show that SuperCD can effectively identify superposition concepts from illustrative instances, retrieve superposition instances from large-scale corpus, and significantly improve the few-shot NER performance with minimal additional efforts.

Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.

Today, AcademicID has announced the official launch of its state-of-the-art, AI-powered platform designed to revolutionise the research process, help academics simplify their workload and provide new data insights to institutions and funders. Minerva, AcademicID's AI research assistant, is a tool to streamline knowledge discovery and save time on administrative tasks. With the ability to respond intelligently and swiftly to requests, Minerva can comb through more than 200 million academic papers to provide detailed answers and relevant findings. Minerva's ability to return relevant academic research ensures that all responses are quickly verifiable – a defining feature not available on other chatbots such as ChatGPT – ensuring academics can more confidently utilise the incredible power that latest-generation AI technologies offer. The platform's advanced AI technology also allows academics to find and stay across the latest research, source collaborators and use advanced data analytics to track their careers quickly and accurately.

In March 2022 the Mayo Clinic Platform announced their new accelerator program for AI powered health tech startups. The 20 week immersive Mayo Clinic Platform Accelerate Program is designed to help innovative startups establish credibility and get market ready so that they can spark innovation in healthcare. Each company receives a $200,000 benefit package that includes access to Mayo Clinic's rich de-identified data sets, validation frameworks, clinical workflow planning, and mentorship. Startups in the accelerator receive guidance on clinical, regulatory, technology, and business decisions. There are also opportunities for workflow integration and clinical collaboration for research trials, access thought leadership, and peer-reviewed publications.

The video describes Einstein's contributions to the field of physics in 1905. In 4 separate papers, he theorized the particle nature of light, a new atomic model, special relativity, and the world-famous E MC² equation. How does this relate to me? Well, I'm an aspiring researcher with a few papers to my name already (you can check those out here). As researchers, we all have hopes and aspirations to publish amazing research that revolutionizes our field (and in Einstein's case, our basic understanding of the universe). Moreover, we both even have other occupations eating away our time--he was a patent clerk and I am a student.