在科学领域，数据的处理和分析已经变得越来越复杂，需要专业知识和大量的时间。为了简化这一过程，越来越多的公司开始尝试利用大型语言模型（LLM）将科幻小说中的幻想变为现实。LLM，作为功能强大的人工智能工具，让研究人员能够用自然语言向数据提出问题，例如“对照组和实验组有什么区别？”这样的问题。然而，尽管这些AI工具的出现让数据的解读变得更加便捷，其给出的答案仍需经过仔细检查和验证，以确保其准确性。
以ChatGPT为代表的人工智能工具在科学领域的应用，旨在通过提供快速且易于理解的分析结果，帮助研究人员节省时间和精力，无需深入了解数据的复杂性或编程技能。理论上，这样的工具可以实现数据的深入挖掘、理解和解释，让科研人员能够从数据中提取有价值的信息和见解，而无需具备编程或数据科学的专业知识。
在生物科学领域，数据的规模和复杂性正在迅速增长。例如，Enable Medicine公司正在使用LLM构建空间基因表达和蛋白质定位数据图谱，以支持药物开发工作。科学经理Alexandro Trevino表示，面对数据集的规模和复杂性，如何有效地挖掘、理解和解释数据已经成为一项挑战。
然而，尽管LLM在简化数据处理方面显示出巨大潜力，它们仍处于开发的初级阶段，存在产生不准确或错误信息的风险。因此，研究人员在使用这类工具时需要保持谨慎，确保答案的准确性，并在必要时进行验证。
为了进一步简化与数据的互动，一些公司正在开发基于LLM的工具，旨在解决药物发现和开发过程中的复杂问题。例如，Genentech公司正在构建其基于LLM的工具，以减轻繁重的手动任务，如数据汇总和分析。类似地，Enable Medicine公司正在开发一个系统，让公司代表其客户（主要是肿瘤学和自身免疫性疾病领域的制药公司）查询生物图谱，以提出诸如“患者对治疗有反应吗？对治疗有反应的患者与没有反应的患者有何区别？”或“哪些生物标记会影响或预测疾病进展？”等具体问题。
尽管这些工具在简化科研数据处理方面显示出巨大的潜力，但它们的准确性和可靠性仍需要进一步验证。研究团队正在不断实验，以探索这些界面是否具有科学有效性和价值。随着技术的不断发展和完善，未来可能会看到更多高效、准确的LLM工具在科学领域发挥重要作用。
英语如下：
News Title: “AI Assistants Revolutionize Research: Bridging the Gap Between Natural Language and Data with ChatGPT and the Future of Science”
Keywords: Scientific Applications, Large Models, Data Query
News Content: In the realm of science, the processing and analysis of data have become increasingly complex, requiring specialized knowledge and substantial time. To streamline this process, more companies are turning to large language models (LLMs) to turn science fiction into reality. As powerful AI tools, LLMs enable researchers to pose questions in natural language to data, such as asking about the differences between control and experimental groups. However, while these AI tools make data interpretation more accessible, their answers still require careful scrutiny and verification to ensure accuracy.
Artificial intelligence tools, exemplified by ChatGPT, are being applied in scientific fields to provide quick and easily understandable analysis results, saving researchers time and effort without delving into the complexity of data or programming skills. Theoretically, such tools can facilitate deep data mining, understanding, and explanation, allowing research personnel to extract valuable information and insights from data without needing expertise in programming or data science.
In the biomedical sciences, the scale and complexity of data are growing rapidly. For instance, Enable Medicine is utilizing LLMs to construct spatial gene expression and protein localization data maps to support drug development efforts. Science manager Alexandro Trevino notes that the challenge of effectively mining, understanding, and interpreting large data sets has become a hurdle.
While LLMs demonstrate significant potential in simplifying data processing, they are still in their early stages of development and carry the risk of producing inaccurate or erroneous information. Thus, researchers using such tools must exercise caution, ensuring the accuracy of answers and validating them when necessary.
To further simplify interactions with data, some companies are developing tools based on LLMs aimed at addressing complex issues in drug discovery and development. For example, Genentech is building its LLM-based tools to alleviate cumbersome manual tasks, such as data aggregation and analysis. Similarly, Enable Medicine is developing a system that allows company representatives to query biological maps on behalf of their clients (primarily pharmaceutical companies in oncology and autoimmune diseases) with questions like “Does the patient respond to treatment? What are the differences between patients who respond and those who do not?” or “Which biomarkers affect or predict disease progression?”
Although these tools show great potential in simplifying research data processing, their accuracy and reliability require further validation. Research teams are continuously experimenting to explore whether these interfaces have scientific validity and value. As technology continues to advance and improve, it is anticipated that more efficient and accurate LLM tools will play a significant role in the scientific field.
【来源】https://www.jiqizhixin.com/articles/2024-07-25-6
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