As we keep rapidly advancing toward an era where artificial intelligence is a
constant and normative experience for most of us, we must also be aware of what
this vision and this progress entail. By first approximating neural connections
and activities in computer circuits and then creating more and more
sophisticated versions of this crude approximation, we are now facing an age to
come where modern deep learning-based artificial intelligence systems can
rightly be called thinking machines, and they are sometimes even lauded for
their emergent behavior and black-box approaches. But as we create more
powerful electronic brains, with billions of neural connections and parameters,
can we guarantee that these mammoths built of artificial neurons will be able
to forget the data that we store in them? If they are at some level like a
brain, can the right to be forgotten still be protected while dealing with
these AIs? The essential gap between machine learning and the RTBF is explored
in this article, with a premonition of far-reaching conclusions if the gap is
not bridged or reconciled any time soon. The core argument is that deep
learning models, due to their structure and size, cannot be expected to forget
or delete a data as it would be expected from a tabular database, and they
should be treated more like a mechanical brain, albeit still in development.

在人工智能快速发展的时代，必须意识到深度学习模型的结构和大小使得其难以像传统数据库那样删除或遗忘数据，从而探讨了机器学习和被遗忘权之间的实质差距以及其可能带来的深远影响。

机械心灵的永恒阳光：机器学习与被遗忘权之不可调和

Eternal Sunshine of the Mechanical Mind: The Irreconcilability of  Machine Learning and the Right to be Forgotten

The original vision of AI was re-articulated in 2002 via the term 'Artificial
General Intelligence' or AGI. This vision is to build 'Thinking Machines' -
computer systems that can learn, reason, and solve problems similar to the way
humans do. This is in stark contrast to the 'Narrow AI' approach practiced by
almost everyone in the field over the many decades. While several large-scale
efforts have nominally been working on AGI (most notably DeepMind), the field
of pure focused AGI development has not been well funded or promoted. This is
surprising given the fantastic value that true AGI can bestow on humanity. In
addition to the dearth of effort in this field, there are also several
theoretical and methodical missteps that are hampering progress. We highlight
why purely statistical approaches are unlikely to lead to AGI, and identify
several crucial cognitive abilities required to achieve human-like adaptability
and autonomous learning. We conclude with a survey of socio-technical factors
that have undoubtedly slowed progress towards AGI.

人工通用智能（AGI）是构建 “思考机器” 的愿景，与 “狭义人工智能” 方法形成鲜明对比。本研究指出了纯统计方法无法实现 AGI，同时确定了实现类似人类适应能力和自主学习所需的关键认知能力，并总结了阻碍 AGI 进展的社会技术因素。

为何我们尚未拥有通用人工智能

Why We Don't Have AGI Yet

Generative AI techniques have opened the path for new generations of machines
in diverse domains. These machines have various capabilities for example, they
can produce images, generate answers or stories, and write codes based on the
"prompts" only provided by users. These machines are considered 'thinking
minds' because they have the ability to generate human-like responses. In this
study, we have analyzed and explored the capabilities of artificial
intelligence-enabled machines. We have revisited on Turing's concept of
thinking machines and compared it with recent technological advancements. The
objections and consequences of the thinking machines are also discussed in this
study, along with available techniques to evaluate machines' cognitive
capabilities. We have concluded that Turing Test is a critical aspect of
evaluating machines' ability. However, there are other aspects of intelligence
too, and AI machines exhibit most of these aspects.

本文探讨了生成式人工智能技术的应用领域和能力，重温了图灵关于 “思考机器” 的概念，分析了机器智能测试的重要性，同时指出人工智能机器展示了智能的多个方面，但也存在相应的疑虑和后果。