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Imagine if the results of clinical trials of breakthrough drugs were not accurately released for the research community to verify the efficacy of new drugs. This leaves a lot of room for uncertainty and undercuts the importance of the research. What if a statistician published a report predicting the next big financial crisis, but didn’t talk about the methodology and source data used to reach that conclusion, and the report was (mis)quoted by a major news channel? The way research is recorded—in print media and online—influences current and future advances in the field.
Nishchay Shah (CTO, Cactus Communications)
In short, science communication is the vital process by which the discoveries, observations and ideas of the scientific and research community are communicated back to humanity at large, sometimes in the form of dissemination and sometimes critical commentary and commentary from peers. worldwide. Globally, researchers now prefer Twitter and LinkedIn to other social media platforms because participation in these platforms can lead to successful collaborations, increased funding, and the likelihood of award nominations. Some other common formats for communicating science include research papers, dissertations, and scientific journals.
Effective communication is key for researchers. Being able to disseminate their findings and showcase their work to the community is important. To ensure that good research is not eliminated, researchers rely on freelance writers and companies that provide the services of writing, editing and proofreading original manuscripts. The classic dilemma faced by first-generation entrepreneurs, whether to build technology in-house or outsource it, is often found in different variants across different industries. Whether to write and edit in-house or outsource becomes a big decision for researchers and research labs, affecting their ability to expand research output and increase communication with the world.
paradigm shift
Automated abstracts, context-driven personalized research paper searches, and tools for creating visual abstracts and figures simply did not exist a decade ago. With the advent of artificial intelligence, passionate entrepreneurs and even researchers themselves are starting to solve problems for the research industry, especially to improve scientific communication. Scientific research is benefiting from the steady adoption of technologies and products in this field.
We talk about AI as a new technology, but it is already present in our daily life in the form of different software and products. Some people think of AI as an intelligent algorithm or magic sauce, while others confuse data science and analytics with AI. In general, artificial intelligence can now be defined as anything that is data-driven and intelligent enough to provide better results than existing technological solutions. AI solves the problem by bringing the missing piece of the puzzle to the table, but it’s still not the complete solution.
The scientific research community provides a good example of how embracing technological advances can minimize effort and maximize delivery to the end audience (in this case, research results); thereby enhancing the role of artificial intelligence.
In the early stages of the COVID-19 pandemic, researchers and doctors worked around the clock to develop a vaccine. With limited samples and data, it is difficult for them to test and experiment quickly.This Chief AI Officer One of the pharmaceutical companies that launched the first-generation COVID vaccine even mentioned that one of their biggest bottlenecks was manually generating mRNA sequences. With just the right amount of process automation and AI algorithms, they were able to automate this generation process and scale it up to nearly 30x with better consistency. This helps them produce quickly and significantly reduces the time to market for a vaccine from an average of 5-10 years to less than a year.
AI as an innovator
We live in an era where data is the new electricity. So many are generated every day. The same goes for research. To give some context, millions of research papers are published every year across all subject areas. Researchers face three main hurdles: searching for the right paper, accessing the paper, and spending time searching, accessing, and reading the paper only to discover that it is not relevant to them.
Searching for relevant papers becomes more efficient thanks to intelligent research paper fingerprinting based on extracting concepts and relevant context, and by using advanced algorithms such as Concept Grid and Neural Extractive Search. These algorithms are much better than traditional keyword-focused searches.
To make research accessible to everyone, open access (OA) initiatives backed by robust data warehouses and AI strategies adopted by publishers are now driving a variety of use cases to improve scientific communication. OA has greatly benefited automated summarization and information retrieval algorithms, which in turn helps overcome the third hurdle — reducing the time researchers spend browsing through hundreds of research papers.
AI as creator
We live in an era of disruption, especially in the arts, where recent advances in generative AI models like DALL-E and Stable Diffusion have the power to create stunning visualizations from simple text prompts (written ideas).Going a step further, imagine using a model like this to generate Visual abstracts, infographics and posterswhich distills your research into content for mass consumption.
Not only that, transformer-based large-scale language model products can also help people and researchers, especially from non-English-speaking backgrounds, make negligible errors in writing, and provide them with hints to become better. it is good. They also help reduce rejection rates for research papers by helping to minimize grammatical errors and adhere to the many micro-standards of different publishers.
