Throughout their history, homo sapiens have used technologies to better
satisfy their needs. The relation between needs and technology is so
fundamental that the US National Research Council defined the distinguishing
characteristic of technology as its goal "to make modifications in the world to
meet human needs". Artificial intelligence (AI) is one of the most promising
emerging technologies of our time. Similar to other technologies, AI is
expected "to meet [human] needs". In this article, we reflect on the
relationship between needs and AI, and call for the realisation of needs-aware
AI systems. We argue that re-thinking needs for, through, and by AI can be a
very useful means towards the development of realistic approaches for
Sustainable, Human-centric, Accountable, Lawful, and Ethical (HALE) AI systems.
We discuss some of the most critical gaps, barriers, enablers, and drivers of
co-creating future AI-based socio-technical systems in which [human] needs are
well considered and met. Finally, we provide an overview of potential threats
and HALE considerations that should be carefully taken into account, and call
for joint, immediate, and interdisciplinary efforts and collaborations.

本文讨论了人工智能与人类需求之间的关系，并呼吁实现有意识的 AI 系统以满足人类需求。文章讨论了人类需求在可持续、以人为中心、可问责、合法和道德（HALE）的 AI 系统中的重要性及其发展中的难点，并提出了人工智能系统面临的潜在威胁和需要注意的 HALE 问题，呼吁进行紧急的跨学科合作。

需求和人工智能

Needs and Artificial Intelligence

AI is undergoing a paradigm shift with the rise of models (e.g., BERT,
DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a
wide range of downstream tasks. We call these models foundation models to
underscore their critically central yet incomplete character. This report
provides a thorough account of the opportunities and risks of foundation
models, ranging from their capabilities (e.g., language, vision, robotics,
reasoning, human interaction) and technical principles(e.g., model
architectures, training procedures, data, systems, security, evaluation,
theory) to their applications (e.g., law, healthcare, education) and societal
impact (e.g., inequity, misuse, economic and environmental impact, legal and
ethical considerations). Though foundation models are based on standard deep
learning and transfer learning, their scale results in new emergent
capabilities,and their effectiveness across so many tasks incentivizes
homogenization. Homogenization provides powerful leverage but demands caution,
as the defects of the foundation model are inherited by all the adapted models
downstream. Despite the impending widespread deployment of foundation models,
we currently lack a clear understanding of how they work, when they fail, and
what they are even capable of due to their emergent properties. To tackle these
questions, we believe much of the critical research on foundation models will
require deep interdisciplinary collaboration commensurate with their
fundamentally sociotechnical nature.

AI 基础模型的兴起，具有广泛数据训练和可适应下游任务的特点，其机制、应用和社会影响需要更多的跨学科合作来研究。

基础模型的机遇与风险

On the Opportunities and Risks of Foundation Models

HCI and NLP traditionally focus on different evaluation methods. While HCI
involves a small number of people directly and deeply, NLP traditionally relies
on standardized benchmark evaluations that involve a larger number of people
indirectly. We present five methodological proposals at the intersection of HCI
and NLP and situate them in the context of ML-based NLP models. Our goal is to
foster interdisciplinary collaboration and progress in both fields by
emphasizing what the fields can learn from each other.

介绍了五种方法论提案，并将其位于 ML-based 的 NLP 模型的背景下，以在 HCI 和 NLP 交叉的位置上培育跨学科合作和进步。