We explore the AI2050 "hard problems" that block the promise of AI and cause
AI risks: (1) developing general capabilities of the systems; (2) assuring the
performance of AI systems and their training processes; (3) aligning system
goals with human goals; (4) enabling great applications of AI in real life; (5)
addressing economic disruptions; (6) ensuring the participation of all; (7) at
the same time ensuring socially responsible deployment; (8) addressing any
geopolitical disruptions that AI causes; (9) promoting sound governance of the
technology; and (10) managing the philosophical disruptions for humans living
in the age of AI. For each problem, we outline the area, identify significant
recent work, and suggest ways forward. [Note: this paper reviews literature
through January 2023.]

AI2050 年的 “硬问题” 会阻止 AI 的发展，并引发 AI 的风险。其中包括系统的全面能力开发、确保 AI 系统和训练过程的性能、使系统目标与人类目标相一致、在实际生活中实现出色的 AI 应用以及应对经济颠覆。

人工智能中的十个难题

Ten Hard Problems in Artificial Intelligence We Must Get Right

Can deep neural networks learn to solve any task, and in particular problems
of high complexity? This question attracts a lot of interest, with recent works
tackling computationally hard tasks such as the traveling salesman problem and
satisfiability. In this work we offer a different perspective on this question.
Given the common assumption that $\textit{NP} \neq \textit{coNP}$ we prove that
any polynomial-time sample generator for an $\textit{NP}$-hard problem samples,
in fact, from an easier sub-problem. We empirically explore a case study,
Conjunctive Query Containment, and show how common data generation techniques
generate biased datasets that lead practitioners to over-estimate model
accuracy. Our results suggest that machine learning approaches that require
training on a dense uniform sampling from the target distribution cannot be
used to solve computationally hard problems, the reason being the difficulty of
generating sufficiently large and unbiased training sets.

本文指出，基于密集均匀样本的训练集是学习复杂任务的限制，因为实践者会高估模型的准确性；通过一项案例研究解决 “共谓查询包含” 问题，我们证明了任何 $	extit {NP}$ 难问题多项式时间样本生成器的采样都是从一个更容易的子问题中进行的。

机器学什么不重要，我们无法教的才是关键

It's Not What Machines Can Learn, It's What We Cannot Teach

We consider stochastic gradient descent (SGD) for least-squares regression
with potentially several passes over the data. While several passes have been
widely reported to perform practically better in terms of predictive
performance on unseen data, the existing theoretical analysis of SGD suggests
that a single pass is statistically optimal. While this is true for
low-dimensional easy problems, we show that for hard problems, multiple passes
lead to statistically optimal predictions while single pass does not; we also
show that in these hard models, the optimal number of passes over the data
increases with sample size. In order to define the notion of hardness and show
that our predictive performances are optimal, we consider potentially
infinite-dimensional models and notions typically associated to kernel methods,
namely, the decay of eigenvalues of the covariance matrix of the features and
the complexity of the optimal predictor as measured through the covariance
matrix. We illustrate our results on synthetic experiments with non-linear
kernel methods and on a classical benchmark with a linear model.

本篇论文研究了随机梯度下降算法在多次迭代时对于最小二乘回归的预测能力，理论分析表明虽然单次迭代可以实现统计上的最优，但对于难题来说，多次迭代可以实现统计上的最优。我们的研究基于无限维模型，关注特征协方差矩阵的特征值衰减和最优预测器的复杂度等基于核方法的概念，研究结果得到了人工合成和真实数据上的验证。