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Genome editing technologies allow scientists to modify an organism’s DNA via the addition, removal, or rearrangement of genetic material at specific genomic locations. These types of techniques could potentially be used to cure genetic disorders such as hemophilia and sickle cell anemia. One popular and widely used DNA-editing research tool that could lead to safe and effective cures for genetic disorders is the CRISPR-Cas9 system. CRISPR-Cas9 stands for Clustered Regularly Interspaced...
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Machine Learning and Gene Editing at the Helm of a Societal Evolution.

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    Artificial intelligence (AI) and biotechnology, especially machine learning (ML) and gene editing (GE), offer benefits but pose risks. Proactive policy is crucial for managing these converging technologies globally.

    Keywords:
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    Area of Science:

    • Intersection of Artificial Intelligence (AI) and Biotechnology
    • Emerging technologies and their societal impact

    Background:

    • AI, particularly Machine Learning (ML), and Gene Editing (GE) are rapidly advancing.
    • The convergence of ML and GE presents both significant opportunities and substantial risks.
    • These technologies have broad implications across sectors like medicine, agriculture, and national security.

    Purpose of the Study:

    • To explore the technological and policy implications at the intersection of ML and GE.
    • To analyze advancements and policy landscapes in key regions: US, UK, China, and EU.
    • To inform policy recommendations for managing the convergence of these powerful technologies.

    Main Methods:

    • Analysis of historical and current technical developments in ML and GE.
    • Review of policy frameworks and strategies across selected geographic regions.
    • Assessment of the current state of ML and GE integration and governance.

    Main Results:

    • Identified substantial benefits and daunting risks associated with ML and GE convergence.
    • Highlighted the complexity introduced by regional policy variations and multiple organizations.
    • Provided an assessment of the current technological and policy landscape.

    Conclusions:

    • Forward-looking policy is essential to mitigate risks and leverage opportunities from ML and GE convergence.
    • Policy recommendations are provided to guide the beneficial use of these converging technologies.
    • The study serves as a resource for policymakers and technical practitioners engaging with these advancements.