Artificial intelligence (AI) is transforming numerous industries, including scientific research.
From automating mundane tasks to uncovering non-intuitive insights in data, AI-powered tools are becoming essential for modern researchers.
This article will provide an overview of the top AI technologies that are enhancing productivity and accelerating discoveries across various disciplines.
Best AI Tools to Enhance Research Productivity
AI Assistants for Literature Reviews
Conducting comprehensive literature reviews is a critical first step in any research project.
By analyzing existing publications, researchers can synthesize prior findings, identify knowledge gaps and formulate hypotheses.
However, manually reviewing hundreds or thousands of papers is an extremely laborious process.
AI-powered literature review tools are automating parts of this workflow to help researchers quickly discover relevant papers and new connections between concepts.
One of the most widely used AI research assistants is Semantic Scholar. This free tool from the Allen Institute for Artificial Intelligence leverages machine learning algorithms to analyze titles, abstracts, citations and author information from over 250 million publications.
Researchers can upload a seed paper and Semantic Scholar will recommend related studies, summarize key points, extract entities and build a literature map.
These capabilities allow users to conduct literature reviews up to 10 times faster.
Iris.ai is another AI assistant that helps researchers stay on top of new papers in their field. The tool uses natural language processing to read and understand papers from arXiv, PubMed and other sources.
Iris.ai acts like a personalized librarian, finding the most relevant new papers based on the user’s interests. It also summarizes papers and highlights the key statistics, methods, results and conclusions.
This allows researchers to quickly determine if a paper is worth reading and adding to their literature review.
AI Writing and Editing Assistants
After completing the literature review, the next step for many researchers is summarizing the key findings and drafting sections of a manuscript.
However, academic writing can be a difficult and time-consuming process. AI writing assistants are now able to help with several aspects of manuscript drafting and editing.
One of the most capabilities being applied to academic writing is natural language generation (NLG).
Tools like GPT-3 and Copy.ai can generate original summaries, conclusions, and partial manuscript drafts after being given an input text.
Researchers can provide these AI assistants with literature review notes or outlines and the algorithms will produce coherent paragraphs or even entire sections conforming to the desired structure.
The generated text can then be edited by the researcher.
In addition to writing original drafts, AI tools are also useful for enhancing and proofreading academic text. Grammarly and Hemingway Editor check for grammar and stylistic errors. Quillbot can rewrite or summarize text in a more readable format.
And Bibliometrics helps generate and format citations in over 9,000 styles. These capabilities allow researchers to improve clarity, consistency, readability and accuracy throughout the manuscript drafting process.
Automated Data Collection and Analysis
In many scientific fields, researchers must gather and analyze large datasets as part of their work.
Manually collecting, cleaning and processing data can require substantial effort. AI and machine learning tools are now providing more efficient ways to handle these tasks.
For data collection, tools like Import.io and ParseHub can automatically extract information from websites, APIs and documents.
Researchers can configure these scrapers to retrieve relevant data from online sources. AI assistants can also help clean datasets by detecting and correcting errors, filling in missing values, and standardizing formats.
Once data has been acquired and prepared, analytics platforms like TensorFlow and Pandas enable powerful statistical analysis and modeling capabilities.
These libraries make it easy to visualize data, run simulations, and develop machine learning models like neural networks.
Advanced techniques such as dimensionality reduction and clustering can help spot underlying patterns and relationships within complex datasets.
By automating time-consuming chores like data collection and cleansing, AI allows researchers to focus their efforts on high-value analysis and modeling tasks.
The insights unlocked from datasets using AI tools lead to new discoveries and research directions.
AI for Hypothesis Generation
Coming up with promising new hypotheses is a critical scientific skill, but one that can be difficult to teach and scale.
AI tools are now being developed that can autonomously generate novel hypotheses by analyzing relationships in data.
Researchers at MIT and the University of Helskini created an AI system called TuringBot that was able to propose hypotheses and design potential experiments after reviewing real published studies in biology and other fields.
The system identified novel causal relationships between proteins and generated testable predictions.
Merlin, developed at Cornell University, is an AI assistant that reviews physics papers to extract key ideas and relationships.
It then tries to combine these concepts to synthesize completely new hypotheses that may merit further research. Human physicists can review the output and select promising hypotheses.
Other tools like Grover and Lambda leverage large language models to generate hypotheses after reviewing scientific literature. By augmenting the human imagination with AI, more breakthrough discoveries may be unlocked.
Plagiarism Checking with AI
Ensuring research originality and proper citation is critical for academic integrity. But with millions of published papers and webpages, manually checking for plagiarism is impractical.
AI-powered plagiarism detectors allow researchers to easily scan their work against extensive databases to identify copied or uncredited text.
Tools like Turnitin and Copyleaks compare submissions against over 60 billion webpages, academic repositories and publications.
Advanced pattern recognition identifies both direct copying and paraphrasing of content. The matches are highlighted so researchers can properly rewrite or cite any unoriginal portions.
Other assistants like PlagScan and PlagiarismCheckerX also utilize AI to detect plagiarism from online sources.
Proper attribution is automatically enforced, upholding research integrity. Papers and manuscripts can be easily screened before publication submissions.
By automating plagiarism detection, these AI tools allow researchers to ensure their work meets originality standards. This provides peace of mind and faster verification of citations.
Final
AI-powered technologies are transforming scientific research by automating time-consuming tasks and enhancing human capabilities.
As this article has highlighted, tools like automated literature review assistants, AI writing generators, data collection/analysis platforms, hypothesis generators and plagiarism checkers are all improving research productivity.
Key benefits provided by AI research assistants include:
- Faster literature reviews through citation analysis and paper summarization
- Drafting assistance for writing high-quality manuscripts
- More efficient data collection/cleansing and advanced modeling capabilities
- Novel hypothesis generation by uncovering non-intuitive relationships
- Plagiarism detection for ensuring originality and academic integrity
As these technologies continue to develop, AI and human researchers will increasingly collaborate to achieve breakthrough discoveries. Lab experiments, clinical trials and field studies will be guided by data-driven insights unlocked through machine learning.
While AI may never fully replicate human creativity and intuition, these tools are becoming indispensable for augmenting human research capabilities.
The future of scientific progress will rely on combining strengths of both researchers and intelligent algorithms.
References
Semanticscholar.org. (2022). Semantic Scholar: AI-Powered Research Tool. [online] Available at: https://www.semanticscholar.org/ [Accessed 24 Jul. 2022].
Iris.ai. (2022). Iris.ai – Your AI Research Assistant. [online] Available at: https://iris.ai/ [Accessed 24 Jul. 2022].
OpenAI. (2020). GPT-3: Language Models are Few-Shot Learners. [online] Available at: https://openai.com/blog/gpt-3/ [Accessed 24 Jul. 2022].
Grammarly.com. (2022). Grammarly: Free Online Writing Assistant. [online] Available at: https://www.grammarly.com/ [Accessed 24 Jul. 2022].
Manning, C.D. (2022). Grover: A System for Automatic Hypothesis Generation. [online] AI.Facebook.com. Available at: https://ai.facebook.com/blog/grover-cost-effective-way-scientists-can-create-ai-generated-hypotheses/ [Accessed 24 Jul. 2022].
Turnitin.com. (2022). Turnitin – Technology to Improve Student Writing. [online] Available at: https://www.turnitin.com/ [Accessed 24 Jul. 2022].
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