The rise of Generative Artificial Intelligence systems (``AI systems'') has
created unprecedented social engagement. AI code generation systems provide
responses (output) to questions or requests by accessing the vast library of
open-source code created by developers over decades. However, they do so by
allegedly stealing the open-source code stored in virtual libraries, known as
repositories. How all this happens and whether there is a solution short of
years of litigation that can protect innovation is the focus of this article.
We also peripherally touch upon the array of issues raised by the relationship
between AI and copyright. Looking ahead, we propose the following: (a)
immediate changes to the licenses for open-source code created by developers
that will allow access and/or use of any open-source code to humans only; (b)
we suggest revisions to the Massachusetts Institute of Technology (``MIT'')
license so that AI systems procure appropriate licenses from open-source code
developers, which we believe will harmonize standards and build social
consensus for the benefit of all of humanity rather than profit-driven centers
of innovation; (c) We call for urgent legislative action to protect the future
of AI systems while also promoting innovation; and (d) we propose that there is
a shift in the burden of proof to AI systems in obfuscation cases.

本文旨在探讨如何保护开源代码的知识产权，特别是与 AI 生成代码的关系，提出改变开源代码许可证，鼓励立法行动等措施来保障 AI 系统未来发展并促进创新。

ChatGPT 和其他相似系统是 AI 的现代勒尔纳水蛇吗？

Are ChatGPT and Other Similar Systems the Modern Lernaean Hydras of AI?

An imperfect-information game is a type of game with asymmetric information.
It is more common in life than perfect-information game. Artificial
intelligence (AI) in imperfect-information games, such like poker, has made
considerable progress and success in recent years. The great success of
superhuman poker AI, such as Libratus and Deepstack, attracts researchers to
pay attention to poker research. However, the lack of open-source code limits
the development of Texas hold'em AI to some extent. This article introduces
DecisionHoldem, a high-level AI for heads-up no-limit Texas hold'em with safe
depth-limited subgame solving by considering possible ranges of opponent's
private hands to reduce the exploitability of the strategy. Experimental
results show that DecisionHoldem defeats the strongest openly available agent
in heads-up no-limit Texas hold'em poker, namely Slumbot, and a high-level
reproduction of Deepstack, viz, Openstack, by more than 730 mbb/h
(one-thousandth big blind per round) and 700 mbb/h. Moreover, we release the
source codes and tools of DecisionHoldem to promote AI development in
imperfect-information games.

本文介绍了 DecisionHoldem，一种高级 AI，可通过安全深度限制子游戏解决来降低对手的可能手牌范围，以减少策略的可利用性，实验结果表明，DecisionHoldem 战胜了 heads-up no-limit Texas hold'em 扑克中最强的公开可用代理 Slumbot 和 Deepstack 的高水平繁殖，即 OpenStack，超过了 730 mbb/h 和 700mbb/h。此外，我们公开了 DecisionHoldem 的源代码和工具，以促进不完全信息游戏中的人工智能发展。

DecisionHoldem: 不完全信息游戏的安全深度限制求解与多样对手

DecisionHoldem: Safe Depth-Limited Solving With Diverse Opponents for Imperfect-Information Games

Since 2012, Deep learning has revolutionized Artificial Intelligence and has
achieved state-of-the-art outcomes in different domains, ranging from Image
Classification to Speech Generation. Though it has many potentials, our current
architectures come with the pre-requisite of large amounts of data. Few-Shot
Learning (also known as one-shot learning) is a sub-field of machine learning
that aims to create such models that can learn the desired objective with less
data, similar to how humans learn. In this paper, we have reviewed some of the
well-known deep learning-based approaches towards few-shot learning. We have
discussed the recent achievements, challenges, and possibilities of improvement
of few-shot learning based deep learning architectures. Our aim for this paper
is threefold: (i) Give a brief introduction to deep learning architectures for
few-shot learning with pointers to core references. (ii) Indicate how deep
learning has been applied to the low-data regime, from data preparation to
model training. and, (iii) Provide a starting point for people interested in
experimenting and perhaps contributing to the field of few-shot learning by
pointing out some useful resources and open-source code. Our code is available
at Github: this https URL

本文为深度学习架构在小数据训练中的应用进行了综述，提供了关键参考和开源代码，为希望在小数据场景下进行 Few-shot 学习的人提供了起点。

Few-Shot Learning 领域中深度学习框架概述

An Overview of Deep Learning Architectures in Few-Shot Learning Domain

We assess the performance of two techniques in the context of nonlinear
transform coding with artificial neural networks, Sadam and GDN. Both
techniques have been successfully used in state-of-the-art image compression
methods, but their performance has not been individually assessed to this
point. Together, the techniques stabilize the training procedure of nonlinear
image transforms and increase their capacity to approximate the (unknown)
rate-distortion optimal transform functions. Besides comparing their
performance to established alternatives, we detail the implementation of both
methods and provide open-source code along with the paper.

该研究评估了 Sadam 和 GDN 两种技术在非线性转换编码中的性能表现，结果表明这两种技术可以稳定训练过程并提高逼近未知速率失真最优转换函数的能力，同时作者详细介绍了它们的实现方法并提供开源代码。