研讨会包括 SERC的 最新种子基金获得者关于空气污染预测和负责任的计算机视觉部署, 人工智能调整和人工智能教育, 专题小组的研究报告，以及康奈尔大学蒂施大学计算机科学和信息科学教授 Jon Kleinberg 博士 ’96, 的主题演讲。该活动还设有海报会议,，学生研究人员展示了他们作为 SERC 学者全年开展的项目。
The symposium included research talks by SERC的 latest seed grant recipients on topics such as air pollution forecasting and responsible computer vision deployment, panels on AI alignment and AI in education, and a keynote address by Jon Kleinberg PhD ’96, the Tisch University Professor of Computer Science and Information Science at Cornell University. The event also featured a poster session, where student researchers showcased projects they worked on throughout the year as SERC Scholars.
“A 计算和人工智能越来越嵌入到社会的几乎每个层面, SERC的使命是帮助确保道德反思和技术进步共同进步,” SERC 联席副院长兼 J.C. Penney 管理学教授 Nikos Trichakis, 表示。 “今年的的研讨会强调了麻省理工学院,正在进行的一系列非凡工作，并为我们的社区创建了一个论坛，让他们深入承担塑造计算未来的责任。”
“As computing and AI become increasingly embedded in nearly every dimension of society, SERC的 mission is to help ensure that ethical reflection and technical progress advance together,” said Nikos Trichakis, co-associate dean of SERC and the J.C. Penney Professor of Management. “This year的 symposium highlights the extraordinary range of work underway across MIT, and creates a forum for our community to engage deeply with the responsibilities that come with shaping the future of computing.”
让人工智能与人类价值观保持一致 — 以及这些价值观可能是什么
Aligning AI with human values — and what values those might be
人工智能联盟和道德融合的挑战在于如何将“人类价值观”灌输到非常强大且快速变化的技术中的道德问题。谁决定道德框架中包含哪些价值观和合理性? 将这些价值观从用户转换为机器时如何解释失真?
The challenges with AI alignment and moral meshing lie in the ethical questions of how to instill “human values” onto a very powerful and rapidly changing technology. Who makes the decision on what values and rationalities are included in an ethical framework? How does one account for distortion when translating these values from user to machine?
这些问题, 以及其他, 是由 EECS, Dylan Hadfield-Menell, 副教授在他主持的一个由跨学科演讲者组成的小组讨论中提出的。
These questions, among others, were posed by Dylan Hadfield-Menell, associate professor of EECS, during a panel he moderated that brought together an interdisciplinary group of speakers.
Iason Gabriel, 是 Google DeepMind, 的哲学家和研究科学家，他用法官的例子来说明他的观点。 “您希望法官具有良好的品格,，但仍能解释规则。一个通情达理的人,，但不一定是有史以来最好的人。当谈到 AI, 时，的 不适合将其建模为完美。 AI 应该按照我们的指示去做,，同时利用其性格根据我们的道德价值观进行解释。”
Iason Gabriel, a philosopher and research scientist at Google DeepMind, used the example of a judge to illustrate his point. “You want a judge to have good character, but to still interpret the rules. A reasonable person, though not necessarily the best person who ever lived. When it comes to AI, it的 not appropriate to model it as perfect. AI should be doing what we tell it to do, while using its character to interpret according to our moral values.”
加入 Flanigan 小组的还有 Bernado Zacka, 政治学副教授。鉴于人工智能的发展势头和复杂的制度设计, Zacka 表示, �%9最紧迫的问题是了解我们正在取代的系统中包含的智慧, 以及它们为何以这种方式运行。”
Joining Flanigan on the panel was Bernado Zacka, associate professor of political science. Given the momentum of AI and complex institutional designs, Zacka expressed, “one of the most urgent problems is understanding the wisdom contained in the systems we are replacing, and why they function the way they do.”
随着部署压力的增加,，人们常常会感觉人们在驾驶飞机,时正在建造飞机，尽管小组成员总体上对人工智能调整的轨迹持乐观态度,，强调人类因素对于塑造这些系统的重要性。
As deployment pressure increases, it can often feel like people are building the plane as they fly it, although the panelists overall seemed optimistic about the trajectory of AI alignment, emphasizing how crucial human components are to shaping these systems.
随着各级教育的学生开始使用 AI,，问题出现了：是否有一种方法可以在保持学术准确性和严谨性的同时，合乎道德地整合 AI 工具。在人工智能和教育, 麻省理工学院教师小组和 Gemini for Education, 总监 Marta McAlister, 上，探讨了人工智能如何在课堂中使用，并讨论了人工智能如何支持学习，同时与教学和课程目标保持一致。
As students across all levels of education begin to use AI, questions arise on whether there的 a way to ethically incorporate AI tools while maintaining academic accuracy and rigor. At a panel on AI and education, MIT faculty and Marta McAlister, the director of Gemini for Education, explored how AI is already being used in their classrooms and discussed ways it can support learning while remaining aligned with instructional and curricular goals.
Eric Klopfer 和 Samuel Madden, 教授是 MIT的 人工智能在教学, 学习, 和研究培训, 中使用特设委员会的联合主席，他们聚焦于一个核心困境：人工智能是否被用来减轻工作,，而不是被用来帮助构建所教授的概念。
Professors Eric Klopfer and Samuel Madden, co-chairs of MIT的 Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training, homed in on a central dilemma of whether AI is being used to offload work, rather than being used to help scaffold the concepts being taught.
Klopfer, 担任麻省理工学院, 谢勒教师教育项目和教育中心的主任，他也表达了类似的观点,，即批判性思维不再成为工作成果的关键步骤。关于从哪里开始让材料保持足够的挑战性, Klopfer 建议检查整个课程。 “一些核心内容必须删除。他说，我们不断添加,，而不是解析或修剪,”。
Klopfer, who serves as director of the Scheller Teacher Education Program and the Education Arcade at MIT, echoed similar sentiments, in that critical thinking is no longer becoming a crucial step in the output of the work. Regarding where to start in keeping material just challenging enough, Klopfer suggested examining the curriculum as a whole. “Some core content has to go. We keep adding, instead of parsing or pruning,” he said.
主持人 Justin Reich, 教学系统实验室主任、比较媒体研究项目副教授/Writing, 指出，虽然青少年知道 AI 不好,，但 并不一定会阻止他们使用 AI。然而，, 通过邀请他们参与关于如何实施人工智能的讨论，并与教师进行更具反思性的交流，, 学生可以更有能力选择如何使用这些工具以及为什么使用这些工具。
Moderator Justin Reich, director of the Teaching Systems Lab and an associate professor in the Comparative Media Studies Program/Writing, noted that while teens know that AI is bad, it doesn’t necessarily stop their AI usage. However, by inviting them into the discussion on how AI is implemented and incorporating a more reflective exchange with instructors, students could be more equipped to choose how they use these tools and why.
模仿人类推理和真实的一样好吗?
Is mimicking human reasoning just as good as the real thing?
幻灯片包括国际象棋特级大师和电影参考, Kleinberg的 主题演讲, 标题为 “AI的 世界模型, 和我们的,” 评估了人工智能系统因模型之间不匹配而无意中让我们失败的实例system的 世界和我们的模型。
With a slide deck that included chess grandmasters and film references, Kleinberg的 keynote address, titled “AI的 Models of the World, and Ours,” evaluated instances where AI systems have inadvertently set us up to fail due to a mismatch between the system的 model of the world and ours.
为了说明这一点, Kleinberg 使用了国际象棋,，其中现代国际象棋引擎可以以超人水平, 进行竞争，但是当与人类伙伴, 配对时，他们的策略 无法被人类对手理解或推断。这些人为交接会导致混乱。克莱因伯格使用了“指环王,”的例子，其中甘道夫,是一个强大的巫师,，将一项高度危险且重要的任务委托给一群乌合之众的冒险家。对于那些熟悉这个故事, 的人来说，这个团队出人意料地没有甘道夫的指导,，这让他们陷入了暂时的非常严重的混乱。
To illustrate this point, Kleinberg used chess, where modern chess engines can compete at superhuman levels, but when paired with human partners, their strategies aren’t understandable or inferable to their human counterpart. These human handoffs would then lead to confusion. Kleinberg used the example of “The Fellowship of the Ring,” where Gandalf, a powerful wizard, entrusts a highly dangerous and important quest to a ragtag group of adventurers. For those familiar with the story, the group is unexpectedly left without Gandalf的 guidance, sending them into a temporary bout of very serious turmoil.
当国际象棋引擎将棋盘交给其人类伙伴,时，人类很难理解引擎一直在跟踪的预测移动模式。 “人类算法团队的危险在于，当人类接管,时，算法知道下一步要做什么,，但人类不知道’t,” Kleinberg解释道。
When the chess engine hands a turn over to its human partner, the human struggles to pick up on the predictive move pattern that the engine has been following up until this point. “The danger of human-algorithm teams is that when the human takes over, the algorithm knows what it wants to do next, but the human doesn’t,” explained Kleinberg.
这些类比展示了人工智能通过预测模拟,模式识别,和约束—模仿人类推理来理解世界—的方式与人类经验,带来的先天,体现知识的差异，以及这些系统是否真正理解它们’运行的世界。但问题仍然是，如果比赛结果仍然是将死,，那还重要吗?
These analogies showcase the differences in the ways AI understands a world — through predictive simulations, pattern recognition, and constraints — to mimic human reasoning versus the innate, embodied knowledge that comes with the human experience, and whether these systems truly understand the worlds in which they’re operating. But the question remains that if the game still results in a checkmate, does it matter?