Harnessing the Power of GPT-3 in Scientific Research

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Since its launch in 2020, Generative Pre-trained Transformer 3 (GPT-3) has been making waves. The powerful Large Language Model (LLM) trained on 45TB of textual data was used to develop new tools across the spectrum – from getting code suggestions and building websites to performing research meaning-oriented. The best part? Just enter the commands in plain language.

The emergence of GPT-3 also marked the beginning of a new era in scientific research. Since the LLM can process large amounts of information quickly and accurately, it has opened up a wide range of possibilities for researchers: generating hypotheses, extracting information from large datasets, detecting patterns, simplifying literature searches , facilitate the learning process and much more. /p>

In this article, we will see how this is reshaping scientific research.

Over the past few years, the use of AI in research has grown at a breakneck pace. A CSIRO report suggests that nearly 98% of scientific fields have implemented AI to some degree. Want to know who the best adopters are? In the top five you have math, decision science, engineering, neuroscience, and health. Additionally, approximately 5.7% of all peer-reviewed research papers published globally focus on AI.

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As for GPT-3, more than 300 applications worldwide use the model. They use it for research, conversation, text completion and more. The creator of GPT-3, OpenAI, claims that the model generates over 4.5 billion words every day.

Is this the future of scientific research? You could say it's a little too early to suggest that. But one thing is certain: the new range of AI-powered apps are helping many researchers connect the dots faster. And GPT-3 has a huge hand in that. Labs and businesses around the world use GPT-3's open API to build systems that not only automate mundane tasks, but also provide intelligent solutions to complex problems. Let's take a look at a few.

In the life sciences, GPT-3 is used to gather information about patient behavior in order to provide more effective and safer treatments. For example, InVibe, a voice search company, uses GPT-3 to understand patient speech and behavior. Pharmaceutical companies then use this information to make informed decisions about drug development.

LLMs like GPT-3 have also been used in genetic programming. A recently published article, "Evolution Through Large Models", explains how LLMs can be used to automate the process of mutation operators in genetic programming.

Solving math problems is always a work in progress. A team of MIT researchers found that you can get GPT-3 to solve math problems with a few tap learning and chain-of-thought prompting. The study also found that to solve college-level math problems...