Little demonstrable progress has been made toward AGI (Artificial General
Intelligence) since the term was coined some 20 years ago. In spite of the
fantastic breakthroughs in Statistical AI such as AlphaZero, ChatGPT, and
Stable Diffusion none of these projects have, or claim to have, a clear path to
AGI. In order to expedite the development of AGI it is crucial to understand
and identify the core requirements of human-like intelligence as it pertains to
AGI. From that one can distill which particular development steps are necessary
to achieve AGI, and which are a distraction. Such analysis highlights the need
for a Cognitive AI approach rather than the currently favored statistical and
generative efforts. More specifically it identifies the central role of
concepts in human-like cognition. Here we outline an architecture and
development plan, together with some preliminary results, that offers a much
more direct path to full Human-Level AI (HLAI)/ AGI.

自从 20 年前 AGI（人工通用智能）一词诞生以来，对其几乎没有取得可观的进展。尽管统计人工智能方面取得了极大突破，如 AlphaZero、ChatGPT 和 Stable Diffusion，但这些项目都没有明确的 AGI 路径。为了加快 AGI 的发展，了解和确定人类智能与 AGI 的核心要求至关重要。通过这样可以梳理出实现 AGI 所需的特定开发步骤以及哪些是分散注意力的因素。这种分析强调了对认知人工智能方法的需求，而不是目前受青睐的统计和生成式方法。具体而言，它确定了概念在类人认知中的核心角色。在这里，我们概述了一种架构和开发计划，以及一些初步结果，提供了一条更直接的实现全面人类级 AI（HLAI）/ AGI 的路径。

概念即是一切：通往通用人工智能的更直接路径

Concepts is All You Need: A More Direct Path to AGI

Deep neural networks (DNNs) detect patterns in data and have shown
versatility and strong performance in many computer vision applications.
However, DNNs alone are susceptible to obvious mistakes that violate simple,
common sense concepts and are limited in their ability to use explicit
knowledge to guide their search and decision making. While overall DNN
performance metrics may be good, these obvious errors, coupled with a lack of
explainability, have prevented widespread adoption for crucial tasks such as
medical image analysis. The purpose of this paper is to introduce SimpleMind,
an open-source software framework for Cognitive AI focused on medical image
understanding. It allows creation of a knowledge base that describes expected
characteristics and relationships between image objects in an intuitive
human-readable form. The SimpleMind framework brings thinking to DNNs by: (1)
providing methods for reasoning with the knowledge base about image content,
such as spatial inferencing and conditional reasoning to check DNN outputs; (2)
applying process knowledge, in the form of general-purpose software agents,
that are chained together to accomplish image preprocessing, DNN prediction,
and result post-processing, and (3) performing automatic co-optimization of all
knowledge base parameters to adapt agents to specific problems. SimpleMind
enables reasoning on multiple detected objects to ensure consistency, providing
cross checking between DNN outputs. This machine reasoning improves the
reliability and trustworthiness of DNNs through an interpretable model and
explainable decisions. Example applications are provided that demonstrate how
SimpleMind supports and improves deep neural networks by embedding them within
a Cognitive AI framework.

该论文介绍了 SimpleMind，这是一种开源的用于医学图像理解的认知人工智能软件框架，结合了深度神经网络和知识库，通过自动协同优化知识库参数进行推理和决策，从而提高了深度神经网络的可靠性和可解释性。

SimpleMind 将思考能力加入深度神经网络

SimpleMind adds thinking to deep neural networks

Recent progress in deep learning is essentially based on a "big data for
small tasks" paradigm, under which massive amounts of data are used to train a
classifier for a single narrow task. In this paper, we call for a shift that
flips this paradigm upside down. Specifically, we propose a "small data for big
tasks" paradigm, wherein a single artificial intelligence (AI) system is
challenged to develop "common sense", enabling it to solve a wide range of
tasks with little training data. We illustrate the potential power of this new
paradigm by reviewing models of common sense that synthesize recent
breakthroughs in both machine and human vision. We identify functionality,
physics, intent, causality, and utility (FPICU) as the five core domains of
cognitive AI with humanlike common sense. When taken as a unified concept,
FPICU is concerned with the questions of "why" and "how", beyond the dominant
"what" and "where" framework for understanding vision. They are invisible in
terms of pixels but nevertheless drive the creation, maintenance, and
development of visual scenes. We therefore coin them the "dark matter" of
vision. Just as our universe cannot be understood by merely studying observable
matter, we argue that vision cannot be understood without studying FPICU. We
demonstrate the power of this perspective to develop cognitive AI systems with
humanlike common sense by showing how to observe and apply FPICU with little
training data to solve a wide range of challenging tasks, including tool use,
planning, utility inference, and social learning. In summary, we argue that the
next generation of AI must embrace "dark" humanlike common sense for solving
novel tasks.

本篇论文呼吁 AI 的下一代要拥抱类人的 “黑暗” 的常识，以解决新的任务，提出 “少数据用于大任务” 的范式，FPICU 是认知 AI 的五个核心领域，是类人智能的 “暗物质”