Artificial general intelligence (AGI) does not yet exist, but given the pace
of technological development in artificial intelligence, it is projected to
reach human-level intelligence within roughly the next two decades. After that,
many experts expect it to far surpass human intelligence and to do so rapidly.
The prospect of superintelligent AGI poses an existential risk to humans
because there is no reliable method for ensuring that AGI goals stay aligned
with human goals. Drawing on publicly available forecaster and opinion data,
the author examines how experts and non-experts perceive risk from AGI. The
findings indicate that the perceived risk of a world catastrophe or extinction
from AGI is greater than for other existential risks. The increase in perceived
risk over the last year is also steeper for AGI than for other existential
threats (e.g., nuclear war or human-caused climate change). That AGI is a
pressing existential risk is something on which experts and non-experts agree,
but the basis for such agreement currently remains obscure.

人工通用智能（AGI）尚不存在，但鉴于人工智能技术发展的速度，预计在未来大约二十年内将达到人类水平的智能水平。之后，许多专家预计它将远远超过人类智能，并且迅速超过。超级智能 AGI 的前景对人类构成了存亡风险，因为目前没有可靠的方法来确保 AGI 的目标与人类目标保持一致。作者借鉴公开可用的预测者和观点数据，考察专家和非专家对 AGI 风险的感知。研究结果表明，与其他存在风险相比，人们对 AGI 造成的世界灾难或灭绝的感知风险更大。与其他存在威胁相比，AGI 的感知风险在过去一年中增加的速度也更快（例如核战争或人为引起的气候变化）。专家和非专家一致认为 AGI 是一种紧迫的存亡风险，但目前对于这种共识的基础仍然不清楚。

人工通用智能、存在风险和人类风险认知

Artificial General Intelligence, Existential Risk, and Human Risk  Perception

Current deep learning models for dynamics forecasting struggle with
generalization. They can only forecast in a specific domain and fail when
applied to systems with different parameters, external forces, or boundary
conditions. We propose a model-based meta-learning method called DyAd which can
generalize across heterogeneous domains by partitioning them into different
tasks. DyAd has two parts: an encoder which infers the time-invariant hidden
features of the task with weak supervision, and a forecaster which learns the
shared dynamics of the entire domain. The encoder adapts and controls the
forecaster during inference using adaptive instance normalization and adaptive
padding. Theoretically, we prove that the generalization error of such
procedure is related to the task relatedness in the source domain, as well as
the domain differences between source and target. Experimentally, we
demonstrate that our model outperforms state-of-the-art approaches on both
turbulent flow and real-world ocean data forecasting tasks.

通过 DyAd 元学习方法，使用编码器和预测器相结合的方式，可以帮助深度学习模型实现跨领域的泛化。

使用任务推断进行元学习动态预测

Meta-Learning Dynamics Forecasting Using Task Inference

We consider a prediction problem with two experts and a forecaster. We assume
that one of the experts is honest and makes correct prediction with probability
$\mu$ at each round. The other one is malicious, who knows true outcomes at
each round and makes predictions in order to maximize the loss of the
forecaster. Assuming the forecaster adopts the classical multiplicative weights
algorithm, we find upper and lower bounds for the value function of the
malicious expert. Our results imply that the multiplicative weights algorithm
cannot resist the corruption of malicious experts. We also show that an
adaptive multiplicative weights algorithm is asymptotically optimal for the
forecaster, and hence more resistant to the corruption of malicious experts.

本文考虑了一种带有两位专家和一位预测者的预测问题，探讨了一种基于经典乘法权重算法的自适应乘法权重算法的近似最优性，并发现了恶意专家的价值函数的上下界，结果表明乘法权重算法无法抵制恶意专家的腐败。

恶意专家挑战在线预测的乘法权重算法

Malicious Experts versus the multiplicative weights algorithm in online  prediction

Spatial and time-dependent data is of interest in many applications. This
task is difficult due to its complex spatial dependency, long-range temporal
dependency, data non-stationarity, and data heterogeneity. To address these
challenges, we propose Forecaster, a graph Transformer architecture.
Specifically, we start by learning the structure of the graph that
parsimoniously represents the spatial dependency between the data at different
locations. Based on the topology of the graph, we sparsify the Transformer to
account for the strength of spatial dependency, long-range temporal dependency,
data non-stationarity, and data heterogeneity. We evaluate Forecaster in the
problem of forecasting taxi ride-hailing demand and show that our proposed
architecture significantly outperforms the state-of-the-art baselines.

通过基于图的 Transformer 模型 Forecaster 来解决时空相关数据处理中的空间依赖、时间依赖、数据非平稳性和数据异质性问题，并在预测出租车叫车需求问题中取得了显著的优势。