The journey from research generation to manuscript publication presents many opportunities where AI could be used – for better or for worse.
AI in scientific publishing – new opportunities and new dilemmas
When AI-assisted technologies became increasingly mainstream early last year, they were met with both excitement and concern over their potential to reduce the burden of mundane tasks as well as their possible misuse. Particularly in academic settings, and especially in academic publishing.
The global sharing of scientific research progress is integral to the existence of science and its ability to improve our lives and our planet. Through the current publishing model, the journey from research data generation to manuscript publication presents many opportunities where AI could, hypothetically, be used – for better or for worse.
Dmytro Shevchenko, Aimprosoft's lead data scientist, and PhD student in computer science at Kharkiv National University in Ukraine, has five years’ worth of experience working with commercial large language models (LLMs). He believes that there are many beneficial applications of generative AI (GAI) in publishing: “Creating abstracts and summaries is one example. LLMs can generate summaries of research papers, which can help readers quickly understand the main findings and implications of the research.”
Shevchenko also sees LLMs as having a positive impact on the accessibility and reach of research findings, given that they could facilitate the translation of research articles into different languages. “Text checking and correction is another benefit. LLMs are trained on large datasets and can generate coherent and grammatically correct text. This can help improve the overall quality of research papers by making articles more readable and understandable,” he adds.
“I think AI is a fantastic tool to streamline and speed up the publishing process,” echoes Dr. Andrew (Andy) Stapleton, a former research chemist. Stapleton now works as a content creator developing resources, training and products that are helpful for academics. “So much of the boring and procedural can be written faster (abstracts, literature reviews, summaries and keywords etc.).”
In early 2023, it seemed as though many scientific publishers did not share Shevchenko and Stapleton’s enthusiasm for the practical applications of AI. Some limited how the tools could be adopted during manuscript preparation, while others, like Science, took an even more restrictive stance, banning their use entirely.
“The scientific record is ultimately one of the human endeavor of struggling with important questions. Machines play an important role, but as tools for the people posing the hypotheses, designing the experiments and making sense of the results. Ultimately the product must come from – and be expressed by – the wonderful computer in our heads,” Herbert Holden Thorp, the Science journals editor-in-chief, said in January 2023.
In Stapleton’s opinion, this decision was underpinned by a fear of change. He thinks that the technology moved faster than journals were able to assess best practices. Perhaps this was a motivating factor for the outright ban – but there are very real hazards posed by the use of AI in scientific research and publication.
The possibility that AI tools could “supercharge” paper mill systems is explored in Gianluca Grimaldi and Bruno Ehrler’s AI et al.: Machines Are About To Change Scientific Publishing Forever. Paper mill systems, where organizations produce and sell poor or fake journal papers, are just one of the unfortunate consequences of the publish or perish paradigm.
“A text-generation system combining speed of implementation with eloquent and structured language could enable a leap forward for the serialized production of scientific-looking papers devoid of scientific content, increasing the throughput of paper factories and making detection of fake research more time-consuming,” Grimaldi and Ehrler say. Paper mills are already abundant across the globe, and the authors fear that the situation will only worsen with the influx of AI-assisted tools.
Another concern is that, while LLMs can generate text, they can’t always produce accurate or scientifically valid content. “In most cases, LLMs may lack a proper understanding of scientific concepts and context. While they may generate text based on statistical patterns in the training data, they do not understand the meaning of the words or scientific concepts about which they are generating a particular text,” Shevchenko explains, emphasizing that researchers must carefully review and verify the text generated by LLMs to ensure its accuracy and validity.
Fabrication of data is also a potential problem. A recent Nature study used ChatGPT-3.5 and ChatGPT-4 to create short literature reviews on 42 different topics using 84 papers. The researchers found that 18–55% of references generated using ChatGPT-4 and ChatGPT-3.5 were fabricated, respectively. Discussing the work in a BMJ editorial piece, Drs. Nazrul Islam, associate professor of epidemiology and medical statistics, and Mihaela van der Schaar, the John
Humphrey Plummer Professor of machine learning, artificial intelligence and medicine at the University of Cambridge, said, “This issue is particularly pressing with the proliferation in conspiracy theories, misinformation, disinformation and skepticism towards scientific consensus, such as the antivax movement.”
“There are also ethical issues associated with using LLMs in scientific publications, including issues of plagiarism, attribution and intellectual property rights,” Shevchenko says. “Researchers must ensure that appropriate information is provided to sources and collaborators and that ethical principles of scholarly publishing are adhered to.
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AI in scientific publishing – new opportunities and new dilemmas
When AI-assisted technologies became increasingly mainstream early last year, they were met with both excitement and concern over their potential to reduce the burden of mundane tasks as well as their possible misuse. Particularly in academic settings, and especially in academic publishing.
The global sharing of scientific research progress is integral to the existence of science and its ability to improve our lives and our planet. Through the current publishing model, the journey from research data generation to manuscript publication presents many opportunities where AI could, hypothetically, be used – for better or for worse.
Dmytro Shevchenko, Aimprosoft's lead data scientist, and PhD student in computer science at Kharkiv National University in Ukraine, has five years’ worth of experience working with commercial large language models (LLMs). He believes that there are many beneficial applications of generative AI (GAI) in publishing: “Creating abstracts and summaries is one example. LLMs can generate summaries of research papers, which can help readers quickly understand the main findings and implications of the research.”
Shevchenko also sees LLMs as having a positive impact on the accessibility and reach of research findings, given that they could facilitate the translation of research articles into different languages. “Text checking and correction is another benefit. LLMs are trained on large datasets and can generate coherent and grammatically correct text. This can help improve the overall quality of research papers by making articles more readable and understandable,” he adds.
“I think AI is a fantastic tool to streamline and speed up the publishing process,” echoes Dr. Andrew (Andy) Stapleton, a former research chemist. Stapleton now works as a content creator developing resources, training and products that are helpful for academics. “So much of the boring and procedural can be written faster (abstracts, literature reviews, summaries and keywords etc.).”
In early 2023, it seemed as though many scientific publishers did not share Shevchenko and Stapleton’s enthusiasm for the practical applications of AI. Some limited how the tools could be adopted during manuscript preparation, while others, like Science, took an even more restrictive stance, banning their use entirely.
“The scientific record is ultimately one of the human endeavor of struggling with important questions. Machines play an important role, but as tools for the people posing the hypotheses, designing the experiments and making sense of the results. Ultimately the product must come from – and be expressed by – the wonderful computer in our heads,” Herbert Holden Thorp, the Science journals editor-in-chief, said in January 2023.
In Stapleton’s opinion, this decision was underpinned by a fear of change. He thinks that the technology moved faster than journals were able to assess best practices. Perhaps this was a motivating factor for the outright ban – but there are very real hazards posed by the use of AI in scientific research and publication.
The possibility that AI tools could “supercharge” paper mill systems is explored in Gianluca Grimaldi and Bruno Ehrler’s AI et al.: Machines Are About To Change Scientific Publishing Forever. Paper mill systems, where organizations produce and sell poor or fake journal papers, are just one of the unfortunate consequences of the publish or perish paradigm.
“A text-generation system combining speed of implementation with eloquent and structured language could enable a leap forward for the serialized production of scientific-looking papers devoid of scientific content, increasing the throughput of paper factories and making detection of fake research more time-consuming,” Grimaldi and Ehrler say. Paper mills are already abundant across the globe, and the authors fear that the situation will only worsen with the influx of AI-assisted tools.
Another concern is that, while LLMs can generate text, they can’t always produce accurate or scientifically valid content. “In most cases, LLMs may lack a proper understanding of scientific concepts and context. While they may generate text based on statistical patterns in the training data, they do not understand the meaning of the words or scientific concepts about which they are generating a particular text,” Shevchenko explains, emphasizing that researchers must carefully review and verify the text generated by LLMs to ensure its accuracy and validity.
Fabrication of data is also a potential problem. A recent Nature study used ChatGPT-3.5 and ChatGPT-4 to create short literature reviews on 42 different topics using 84 papers. The researchers found that 18–55% of references generated using ChatGPT-4 and ChatGPT-3.5 were fabricated, respectively. Discussing the work in a BMJ editorial piece, Drs. Nazrul Islam, associate professor of epidemiology and medical statistics, and Mihaela van der Schaar, the John
Humphrey Plummer Professor of machine learning, artificial intelligence and medicine at the University of Cambridge, said, “This issue is particularly pressing with the proliferation in conspiracy theories, misinformation, disinformation and skepticism towards scientific consensus, such as the antivax movement.”
“There are also ethical issues associated with using LLMs in scientific publications, including issues of plagiarism, attribution and intellectual property rights,” Shevchenko says. “Researchers must ensure that appropriate information is provided to sources and collaborators and that ethical principles of scholarly publishing are adhered to.
Website: International Research Scientist Awards
#researchscientist#Professor,#Lecturer,#Scientist,#Scholar,#Researcher,#Analyst,#Engineer,#Technician,#Coordinator,#Specialist,#Writer,#Assistant,#Associate,#Biologist,#Chemist,#Physicist,#Statistician,#DataScientist,#Consultant, #Coordinator,#ResearchScientist #ResearchScientist,#ScientificResearch,#ScienceInnovation,#STEM,#ScienceMatters,#ScientificBreakthrough,#FutureOfScience,#ScienceTechnology,#ScientificDiscoveries,#AcademicResearch,#ResearchExcellence,#ScienceAwards,#InnovationAwards,#ScientificAchievement,#ResearchGrants,#FundingForScience,#GlobalResearch,#ResearchRecognition,#YoungScientists,#WomenInScience,#Research,#Science,#Education,#Biology,#PhD,#University,#Technology,#Scientist,#PhDLife,#Thesis,#Health,#Innovation,#Dissertation,#Medicine,#Chemistry,#Researcher,#Engineering,#Nature,#Laboratory,#STEM#BiomedicalResearch,#AIResearch,#DataScience,#EnvironmentalResearch,#PhysicsResearch,#NeuroscienceResearch,#MedicalInnovation,#ChemistryResearch,#Genomics,#MachineLearningResearch,#ScienceCommunication,#OpenScience,#SciComm,#ScienceCommunity,#ScientificNetworking
More Details:
Tilte: International Research Scientist Awards
website: researchscientist.net
Visit Our Award nomination: researchscientist.net/award-nomina...
For Enquires : research@researchscientist.net
Get Connected Here;
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