Strong AI requires the learning engine to be task non-specific and to
automatically construct a dynamic hierarchy of internal features. By hierarchy,
we mean, e.g., short road edges and short bush edges amount to intermediate
features of landmarks; but intermediate features from tree shadows are
distractors that must be disregarded by the high-level landmark concept. By
dynamic, we mean the automatic selection of features while disregarding
distractors is not static, but instead based on dynamic statistics (e.g.
because of the instability of shadows in the context of landmark). By internal
features, we mean that they are not only sensory, but also motor, so that
context from motor (state) integrates with sensory inputs to become a
context-based logic machine. We present why strong AI is necessary for any
practical AI systems that work reliably in the real world. We then present a
new generation of Developmental Networks 2 (DN-2). With many new novelties
beyond DN-1, the most important novelty of DN-2 is that the inhibition area of
each internal neuron is neuron-specific and dynamic. This enables DN-2 to
automatically construct an internal hierarchy that is fluid, whose number of
areas is not static as in DN-1. To optimally use the limited resource
available, we establish that DN-2 is optimal in terms of maximum likelihood,
under the condition of limited learning experience and limited resources. We
also present how DN-2 can learn an emergent Universal Turing Machine (UTM).
Together with the optimality, we present the optimal UTM. Experiments for
real-world vision-based navigation, maze planning, and audition used DN-2. They
successfully showed that DN-2 is for general purposes using natural and
synthetic inputs. Their automatically constructed internal representation
focuses on important features while being invariant to distractors and other
irrelevant context-concepts.

本文论述了强人工智能的必要性，介绍了一种名为 DN-2 的新一代发展型网络，该网络能够自动构建一个流动的内部层次结构，以最大限度地利用有限的资源并具有最大似然性优势，同时通过实验证明了其通用性。

发展网络二，其最优性和 emergent Turing Machines

Developmental Network Two, Its Optimality, and Emergent Turing Machines

Current machine learning systems operate, almost exclusively, in a
statistical, or model-free mode, which entails severe theoretical limits on
their power and performance. Such systems cannot reason about interventions and
retrospection and, therefore, cannot serve as the basis for strong AI. To
achieve human level intelligence, learning machines need the guidance of a
model of reality, similar to the ones used in causal inference tasks. To
demonstrate the essential role of such models, I will present a summary of
seven tasks which are beyond reach of current machine learning systems and
which have been accomplished using the tools of causal modeling.

为了实现强人工智能，机器学习系统需要现有因果推断任务所使用的现实模型等形式来进行指导，以克服当前系统以统计或无模型方式运行时的理论性局限和性能障碍。该论文总结了现有系统无法完成的七个任务，并说明了现实模型对机器学习系统的必要性。