AI scientific research: Machine learning's true purpose

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AI scientific research: Machine learning's true purpose

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AI scientific research: Machine learning's true purpose

Subheading text
Researchers are testing artificial intelligence's capacity to evaluate vast quantities of data which can lead to breakthrough discoveries.
    • Author:
    • Author name
      Quantumrun Foresight
    • May 11, 2023

    Developing hypotheses has traditionally been considered a solely human activity, as it requires creativity, intuition, and critical thinking. However, with technological advancements, scientists are increasingly turning to machine learning (ML) to generate novel discoveries. Algorithms can analyze large amounts of data quickly and identify patterns that humans may not be able to see.


    Rather than depending on human preconceptions, researchers have constructed neural network ML algorithms with a design inspired by the human brain, suggesting new hypotheses based on data patterns. As a result, many areas may soon turn to ML to accelerate scientific discovery and decrease human biases. In the case of unexplored battery materials, scientists have traditionally relied on database search techniques, modeling, and their chemical sense to identify viable molecules. A team from the UK-based University of Liverpool employed ML to simplify the creative process. 

    First, the researchers created a neural network that prioritized chemical combinations based on their likelihood of producing a valuable new material. The scientists then utilized these rankings to guide their laboratory studies. As a result, they found four viable battery material choices without testing everything on their list, sparing them months of trial and error. New materials are not the only field where ML may aid research. Researchers also use neural networks to solve more significant technological and theoretical concerns. For example, a physicist at Zurich's Institute for Theoretical Physics, Renato Renner, hopes to develop a cohesive explanation of how the world works using ML. 

    Additionally, more sophisticated generative AI models like OpenAI's ChatGPT allow researchers to automatically generate new data, models, and hypotheses. This feat is achieved through techniques such as generative adversarial networks (GANs), variational autoencoders (VAEs), and transformer-based language models (such as Generative Pre-trained Transformer-3 or GPT-3). These AI models can be used to generate synthetic data sets, design and optimize new ML architectures, and develop new scientific hypotheses by identifying patterns and relationships in data that were previously unknown.

    Disruptive impact

    Scientists may increasingly use generative AI to aid with research. With the ability to analyze patterns and predict outcomes based on that knowledge, these models might solve complex theories of science that have remained unsolved by humankind. Not only will this save time and money, but it will also help the human understanding of science to extend far beyond its current boundaries. 
    A research and development (R&D) venture will likely find it easier to gather appropriate funding because ML can process data faster. As a result, scientists will seek more assistance by hiring new employees or collaborating with well-known businesses and companies to produce better results. The overall impact of this interest will be positive, not just for scientific advancements but also for professionals within the scientific fields. 
    However, a potential roadblock is that solutions from these adaptive models are frequently challenging for humans to grasp, especially the reasoning involved. Due to the machines only giving out answers and not explaining the reason behind the solution, scientists may remain uncertain about the process and conclusion. This obscurity weakens confidence in the results and reduces the number of neural networks that can help with analysis. Therefore, it will be necessary for researchers to develop a model that can explain itself.

    Implications of AI scientific research

    Wider implications of AI scientific research may include:


    • Changes in authorship standards for research papers, including giving intellectual property credit to AI. Similarly, AI systems one day be awarded as potential Nobel Prize recipients, which can cause intense debates on whether these algorithms should be acknowledged as inventors.
    • AI-generated research may lead to new forms of liability and further legal and ethical questions related to using AI and autonomous systems in scientific discoveries.
    • Scientists working with various generative AI tools to fast-track medical developments and testing.
    • Increasing energy usage caused by the high computing power needed to run these elaborate algorithms.
    • Future scientists being trained to use AI and other ML tools in their workflows.
    • Governments creating global standards on the limitations and requirements of conducting AI-generated scientific experiments.

    Questions to consider


    • If you’re a scientist, how is your institution or laboratory planning to incorporate AI-assisted research?
    • How do you think AI-generated research will impact the job market for scientists and researchers?


    Insight references

    The following popular and institutional links were referenced for this insight: