AI Institute: Planning: From Biological Intelligence to Human Intelligence to Artificial General Intelligence (B2A)

AI研究院：规划：从生物智能到人类智能再到通用人工智能（B2A）

• 批准号: 2020312
• 负责人: Konrad Kording
• 金额: $ 50万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2020312&HistoricalAwards=false
• 关键词: AI; Institute; Planning; Biological; Intelligence

Artificial Intelligence (AI) is driving considerable parts of the US economy, influencing almost every industrial and scientific sector. Yet, the "intelligence" of these systems still cannot match the flexibility and breadth of even simple biological systems. The objective of this project is to develop a revolutionary new class of AI by focusing on four insights from the biological intelligence (BI) of animals that, unlike current AI agents, (1) do not start as blank slates (2) do not forget when they learn new things (3) have curiosity, and (4) interpret the world in terms of cause and effect. This project brings together outstanding scientists from a wide variety of disciplines and diverse backgrounds to tackle this problem. Through planning meetings and collaborative exercises, the team will generate preliminary data, and preparatory work for a future center focused on reframing the fundamental question of "intelligence." Outreach efforts will engage many diverse participants through large-scale online teaching.At the founding of AI, Alan Turing proposed a test to determine when an AI behaves like a BI, and specifically human intelligence (HI). This test set the stage for the following 70 years of AI development. It has largely been replaced by narrow competitions aimed at imitating humans at specific tasks, such as playing certain games, identifying objects, or translating languages. But neither the Turing test nor today’s AI competitions utilize modern conceptualizations of what it means to be intelligent. It has become clear that intelligence evolved to incorporate several complex capabilities, which are critically missing from today’s AI: (1) preprogramming biases and baseline behaviors, (2) continually leveraging of many previous experiences to improve decision making in newly encountered tasks, (3) actively seeking out information that is useful for future decisions even if these differ from past decisions, and, finally, (4) constructing causal models relevant to decisions and communicating these models. This project brings together a unique group that truly understands both AI and BI/HI to address this gap. This group aims to define what is missing in AI relative to BI/HI, and to determine which research paths can enhance future approaches. In year one, the project will develop a test to measure a specific aspect of intelligence found in animals, but not current AI. This test will be sufficiently simple that it can be performed by several different biological taxa, humans, as well as AI. In year two, the participants will conduct pilot experiments to quantify current levels of performance on these tests and distill insights from BI/HI for AI. The resulting benchmarks will provide explicit and quantitative milestones for the eventual institute, whose goal will be to develop AI that matches HI on the new tests of intelligence.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人工智能（AI）正在推动美国经济的相当大一部分，影响着几乎所有的工业和科学部门。然而，这些系统的“智能”仍然无法与甚至简单的生物系统的灵活性和广度相匹配。该项目的目标是通过专注于动物生物智能（BI）的四个见解来开发一种革命性的新型AI，与当前的AI代理不同，（1）不要从白板开始（2）当他们学习新事物时不要忘记（3）有好奇心，（4）从因果关系方面解释世界。该项目汇集了来自各种学科和不同背景的杰出科学家来解决这个问题。通过规划会议和协作练习，该团队将生成初步数据，并为未来的中心做准备工作，重点是重新定义“情报”的基本问题。“推广工作将通过大规模的在线教学吸引许多不同的参与者。在人工智能的创立之初，艾伦·图灵提出了一个测试，以确定人工智能何时表现得像BI，特别是人类智能（HI）。这次测试为接下来70年的人工智能发展奠定了基础。它在很大程度上被旨在模仿人类完成特定任务的狭隘竞争所取代，例如玩某些游戏，识别物体或翻译语言。但图灵测试和今天的人工智能竞赛都没有利用现代概念来理解智能的含义。很明显，智能的发展包含了几种复杂的能力，而这些能力是当今人工智能所严重缺乏的：（1）预先编程偏见和基线行为，（2）不断利用许多以前的经验来改善新遇到的任务的决策，（3）积极寻找对未来决策有用的信息，即使这些信息与过去的决策不同，最后，（4）构建与决策相关的因果模型并传达这些模型。该项目汇集了一个真正了解AI和BI/HI的独特团队，以解决这一差距。该小组旨在定义AI相对于BI/HI缺少什么，并确定哪些研究路径可以增强未来的方法。在第一年，该项目将开发一种测试，以测量动物中发现的特定方面的智力，但不是当前的人工智能。这项测试将足够简单，可以由几种不同的生物分类群，人类以及AI进行。在第二年，参与者将进行试点实验，以量化这些测试的当前性能水平，并从BI/HI中提取AI的见解。由此产生的基准将为最终的研究所提供明确和量化的里程碑，其目标将是开发在新的智力测试中与HI相匹配的人工智能。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。