Last edited by @suen 2024-10-09T05:20:40Z
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BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Chemistry with one half to David Baker “for computational protein design” and the other half jointly to Demis Hassabis and John M. Jumper “for protein structure prediction.”
This year’s chemistry laureate David Baker created the first protein that was entirely different to all known existing proteins.
The protein, Top7, was a bolt from the blue for the researchers working on protein design. Those who had previously created new proteins had only been able to imitate existing structures. Top7’s unique structure did not exist in nature. Also, with its 93 amino acids, the protein was larger than anything previously produced using de novo design.
The 2024 #NobelPrize laureates in chemistry Demis Hassabis and John Jumper have successfully utilised artificial intelligence to predict the structure of almost all known proteins. In 2020, Hassabis and Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic. Read more about their story: https://bit.ly/3XI7KK3
Learn more about the 2024 #NobelPrize in Chemistry Press release: https://bit.ly/4eFLmHX Popular information: https://bit.ly/3XI7KK3 Advanced information: https://bit.ly/3XXgXiT
Work that once took years now takes just a few minutes thanks to this year’s chemistry laureates. 2024 #NobelPrize laureates Demis Hassabis and John Jumper have used their AI model AlphaFold2 to calculate the structure of all human proteins. They also predicted the structure of virtually all the 200 million proteins that researchers have so far discovered when mapping Earth’s organisms. Google DeepMind has also made the code for AlphaFold2 publicly available, and anyone can access it. The AI model has become a gold mine for researchers. By October 2024, AlphaFold2 had been used by more than two million people from 190 countries. The picture shows a few of the many examples of how AlphaFold2 helps researchers.
2024 #NobelPrize laureate in chemistry David Baker has succeeded with the almost impossible feat of building entirely new kinds of proteins. In recent years, one incredible protein creation after the other has emerged from Baker’s laboratory. They range from new nanomaterials where up to 120 proteins spontaneously link together… Animation: ©Terezia Kovalova/The Royal Swedish Academy of Sciences
This year’s chemistry laureates Demis Hassabis and John Jumper have developed an AI model, AlphaFold2, to solve a 50-year-old problem: predicting proteins’ complex structures.
Check out two examples of protein structures determined using AlphaFold2. First up, a bacterial enzyme that causes antibiotic resistance. The structure is important for discovering ways of preventing antibiotic resistance.
Animation: ©Terezia Kovalova/The Royal Swedish Academy of Sciences
Let’s take a closer look at this protein structure determined using AlphaFold2. This protein structure is part of a huge molecular structure in the human body. More than a thousand proteins form a pore through the membrane surrounding the cell nucleus.
Animation: ©Terezia Kovalova/The Royal Swedish Academy of Sciences
今天提問的就直接把AI貼臉開問了：）
@Creative 概述上述信息中關鍵點，使用中文，同時點評官方頒獎詞： BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Chemistry with one half to David Baker “for computational protein design” and the other half jointly to Demis Hassabis and John M. Jumper “for protein structure prediction.”
2024 年诺贝尔化学奖关键信息概述及颁奖词点评
关键信息：
- 获奖者：
- David Baker：一半奖金，“因计算蛋白质设计”
- Demis Hassabis 和 John M. Jumper：共享另一半奖金，“因蛋白质结构预测”
- 主要成就：
- David Baker：创造了第一个与所有已知蛋白质完全不同的蛋白质 Top7，开创了蛋白质从头设计的先河。
- Demis Hassabis 和 John M. Jumper：开发了名为 AlphaFold2 的 AI 模型，成功预测了几乎所有已知蛋白质的结构（约 2 亿个），极大地加速了蛋白质研究进程。
- AlphaFold2 的影响：
- 已被全球 190 个国家的 200 多万人使用。
- 应用广泛，包括理解抗生素耐药性、创造可分解塑料的酶图像等。
- 代码已公开，成为研究人员的宝贵工具。
颁奖词点评：
官方颁奖词 BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Chemistry with one half to David Baker “for computational protein design” and the other half jointly to Demis Hassabis and John M. Jumper “for protein structure prediction.” 简洁明了地概括了获奖者的成就和贡献。
- 优点：
- 准确： 精准地概括了三位科学家的主要贡献领域：计算蛋白质设计和蛋白质结构预测。
- 简洁： 语言精炼，没有冗余信息，直接点明获奖原因。
- 清晰： 明确了奖金分配方式，以及每个获奖者的贡献。
- 可改进之处：
- 缺乏细节： 颁奖词仅点明了获奖领域，但没有提及具体的成就和影响，例如 Top7 蛋白质的突破性意义以及 AlphaFold2 对科学界的巨大贡献。
- 略显枯燥： 语言较为正式，缺乏一些能够引发读者兴趣和好奇心的元素。
总而言之，官方颁奖词准确地传达了获奖信息，但可以考虑在未来加入更多细节和生动的描述，更全面地展现获奖者的成就和贡献，并提高公众对科学的兴趣。
學術之路被顛覆的先聲。且不談世界本無學科，培養一個人到本學科領域的巔峰，生有涯而識無涯，無AI不學術，這兩場頒獎，正式確認了？
您说得很有洞察力。这两场颁奖（诺贝尔生理学或医学奖和化学奖）的确可以被视作学术之路被颠覆的先声，预示着“无AI不学术”时代的到来。
1. 跨学科研究成为主流：
今年诺贝尔奖得主们的研究成果横跨多个学科领域，例如生物学、计算机科学、人工智能等。这表明，未来的学术研究将更加注重跨学科的交叉融合，单纯依靠单一学科的知识和方法难以取得突破性进展。AI 的强大能力恰好能够打破学科壁垒，整合不同领域的知识，促进新的研究方向和方法的产生。
2. AI 成为科研的重要工具：
无论是蛋白质结构预测还是 mRNA 疫苗研发，AI 都扮演着至关重要的角色。它能够处理海量数据、发现潜在规律、进行模拟和预测，极大地提高了科研效率和准确性。未来，AI 将被更广泛地应用于各个学科的科研工作中，成为科学家们不可或缺的工具。
3. 人才培养模式需要转变：
“无AI不学术”时代的到来对人才培养模式提出了新的挑战。未来的科研人才需要具备跨学科的知识背景，熟练掌握 AI 工具和技术，同时拥有批判性思维和创新能力。传统的学科划分和教学模式需要进行改革，以适应新的时代需求。
4. 学术评价体系需要调整：
AI 的应用也对学术评价体系提出了新的挑战。传统的学术评价标准可能难以衡量 AI 辅助研究的成果和贡献。我们需要探索新的评价指标和方法，例如数据共享、代码开源、模型的可重复性等，以确保学术评价的公平性和科学性。
总而言之，这两场诺贝尔奖的颁发标志着 AI 正式进入学术研究的核心领域，对未来的学术发展方向和人才培养模式都将产生深远影响。 “无AI不学术”的时代已经到来，我们必须积极拥抱 AI，迎接新的机遇和挑战。
当然，我们也需要警惕 AI 的潜在风险，例如数据隐私、算法偏见等问题，并制定相应的伦理规范和监管措施，确保 AI 技术的健康发展。
Congrats David Baker, @demishassabis, and John Jumper 👏👏👏
— Yann LeCun (@ylecun) October 9, 2024
We knew this was coming.
The impact of Rosetta, AlphaFold, and other ML-powered efforts in protein structure prediction and protein design is already tremendous and promises to have an even bigger impact in the future. https://t.co/ZUVRGUicHY
