AI: Alternative to ChatGPT for R&D and Science

1. Explanation of how Artificial Intelligence works

In a context of scientific research or monitoring, artificial intelligence has proven itself to accelerate certain processes.


However, one must understand how an artificial intelligence system works to comprehend the characteristics and limitations of using AI in a scientific context.

AI begins by gathering a large amount of data on a specific subject: In a scientific context, data collection would involve assembling a vast quantity of existing documents, articles, and research. These documents will serve as the knowledge base for the AI.


Next, the data will be prepared for analysis. This includes normalizing the format of documents, extracting key concepts, and structuring information.


A natural language processing (NLP) AI model could be utilized to understand the textual content of the documents.

The model is trained by providing it with examples of scientific documents with annotations indicating key concepts, relationships, etc.


Once trained, the model is tested with documents it hasn’t seen before to assess its ability to extract relevant information and understand the scientific context.


Once the model is ready, it can be used to analyze new scientific documents. For instance, it could automatically extract key findings from research or identify trends in a specific field.

2. Limitations of ChatGPT in the Scientific Context

ChatGPT is a generative AI that relies on general data. For this reason, when it comes to science, the use of this tool may have some limitations:

Lack of Specialized Knowledge

ChatGPT has not been trained on specific scientific data and may lack in-depth knowledge in specific scientific domains.

Risk of Providing Incorrect Information

Due to the inability to fact-check data and reliance on pre-existing models, ChatGPT may occasionally provide incorrect or unverified information.

Superficial Interpretation

It may interpret scientific terms superficially without fully grasping the context or complexity associated with them.

Difficulty Following Precise Instructions

The precise understanding of complex instructions, especially in specialized scientific domains, can pose a challenge for ChatGPT.

Lack of Critique and Judgment

ChatGPT lacks inherent critical or judgment abilities and may generate results based on the frequency of training data rather than on scientific validity.

Tendency to Generate General Content

It has a tendency to generate general information rather than specific and detailed content, which may not meet the requirements of scientific expertise.

Difficulty with Complex Concepts

Abstract scientific concepts or complex ideas may be misunderstood or excessively simplified.

Risk of Generating Generalized Responses

Responses may sometimes lack specificity, providing general information rather than in-depth scientific details.

Context Specific Requirements for R&D and Science

R&D and science are highly demanding fields in terms of the quality of information.


The requirements for the quality of information and the processing of that information are crucial considerations.


Here are the essential criteria that information circulating within R&D and scientific teams must meet:

Access to High-Quality Publications

Access to high-level scientific journals, quality articles, and specialized databases is essential to stay at the forefront of advancements in their field.


Comprehensive and Up-to-Date Data

Researchers and R&D experts require comprehensive and up-to-date data to conduct accurate analyses and make informed decisions.


Processing and Analysis Tools

Advanced data analysis and information processing tools are necessary to extract trends, patterns, and meaningful insights from complex datasets

Data Interoperability

The ability to integrate and interact with different sets of data is crucial for obtaining an overview and fostering collaboration across domains

Technological and Scientific Monitoring

The ability to track technological and scientific advancements in their field of expertise is essential for staying competitive and innovative.

Reliability of Sources

Experts seek information from reliable and reputable sources to ensure the accuracy and credibility of the data

4. AI as an alternative to ChatGPT for R&D and Science

Artificial Intelligence (AI) technologies are extremely valuable for research and development as well as science in various fields due to their advanced information processing and analysis capabilities.


Here are some ways in which AI can be beneficial for R&D and science:

Documentary Research

AI systems enable documentary research, quickly sorting through vast volumes of scientific documents to provide researchers with relevant and up-to-date information.

Opscidia’s database contains over 180 million scientific documents (patents, scientific articles, journals, etc.), updated daily.


Massive Data Analysis

AI excels in analyzing large datasets, swiftly extracting patterns and trends to inform R&D decisions.

In scientific documents, massive data analysis saves significant time in selecting and analyzing the right scientific document.

Pattern and Trend Discovery

AI algorithms can assist in discovering patterns or trends in data, which can be particularly helpful in identifying non-obvious relationships in complex datasets.

With the Opscidia App, monitor scientific domain ecosystems. Uncover hidden relationships between scientific concepts and compare the publication flows of terms of your choice.

Prediction and Monitoring

 AI can be used to forecast natural events, monitor ecosystems, and contribute to the sustainable management of natural resources.

The Opscidia App has a feature specifically dedicated to monitoring scientific ecosystems.