In recent years, deep learning using neural network architecture, i.e. deep
neural networks, has been on the frontier of computer science research. It has
even lead to superhuman performance in some problems, e.g., in computer vision,
games and biology, and as a result the term deep learning revolution was
coined. The undisputed success and rapid growth of deep learning suggests that,
in future, it might become an enabler for Artificial General Intelligence
(AGI). In this article, we approach this statement critically showing five
major reasons of why deep neural networks, as of the current state, are not
ready to be the technique of choice for reaching AGI.

批判性地探讨深度神经网络 (DNN) 作为实现人工通用智能的技术选择的可行性，并给出了五个主要的原因进行支撑，表明 DNN 目前还不适合成为实现人工通用智能的选择。

深度学习与人工通用智能：仍有很长的路要走

Deep Learning and Artificial General Intelligence: Still a Long Way to Go

Prior AI successes in complex games have largely focused on settings with at
most hundreds of actions at each decision point. In contrast, Diplomacy is a
game with more than 10^20 possible actions per turn. Previous attempts to
address games with large branching factors, such as Diplomacy, StarCraft, and
Dota, used human data to bootstrap the policy or used handcrafted reward
shaping. In this paper, we describe an algorithm for action exploration and
equilibrium approximation in games with combinatorial action spaces. This
algorithm simultaneously performs value iteration while learning a policy
proposal network. A double oracle step is used to explore additional actions to
add to the policy proposals. At each state, the target state value and policy
for the model training are computed via an equilibrium search procedure. Using
this algorithm, we train an agent, DORA, completely from scratch for a popular
two-player variant of Diplomacy and show that it achieves superhuman
performance. Additionally, we extend our methods to full-scale no-press
Diplomacy and for the first time train an agent from scratch with no human
data. We present evidence that this agent plays a strategy that is incompatible
with human-data bootstrapped agents. This presents the first strong evidence of
multiple equilibria in Diplomacy and suggests that self play alone may be
insufficient for achieving superhuman performance in Diplomacy.

本文介绍了一种在组合动作空间游戏中进行动作探索和平衡逼近的算法，该算法同时执行价值迭代和学习策略建议网络。 我们使用这个算法，训练了一种名为 DORA 的代理，完全从零开始，它在人类玩家之上展现了超人类的表现，这是对 “Diplomacy” 中多个均衡的首个强有力的证据，表明单靠自我对战可能不足以达到超人类水平。

从零开始的非正式外交

No-Press Diplomacy from Scratch

Constructing agents with planning capabilities has long been one of the main
challenges in the pursuit of artificial intelligence. Tree-based planning
methods have enjoyed huge success in challenging domains, such as chess and Go,
where a perfect simulator is available. However, in real-world problems the
dynamics governing the environment are often complex and unknown. In this work
we present the MuZero algorithm which, by combining a tree-based search with a
learned model, achieves superhuman performance in a range of challenging and
visually complex domains, without any knowledge of their underlying dynamics.
MuZero learns a model that, when applied iteratively, predicts the quantities
most directly relevant to planning: the reward, the action-selection policy,
and the value function. When evaluated on 57 different Atari games - the
canonical video game environment for testing AI techniques, in which
model-based planning approaches have historically struggled - our new algorithm
achieved a new state of the art. When evaluated on Go, chess and shogi, without
any knowledge of the game rules, MuZero matched the superhuman performance of
the AlphaZero algorithm that was supplied with the game rules.

本文提出了 MuZero 算法，它通过结合基于树的搜索和学习模型实现了对一系列具有挑战性且视觉复杂的领域的超人类表现，在 57 款不同的 Atari 游戏上以及对 Go、国际象棋和象棋等游戏的测试中，都表现出了与 AlphaZero 算法相当的性能，而无需知道游戏规则。