I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Please give it a try by subscribing below:

我最近开始了一份有关AI教育的新时事通讯。 TheSequence是无BS(意味着没有大肆宣传，没有新闻等)，它是专注于AI的新闻通讯，需要5分钟才能阅读。 目标是让您了解机器学习项目，研究论文和概念的最新动态。 请通过以下订阅尝试一下：

The brain has always been considered the main inspiration for the field of artificial intelligence(AI). For many AI researchers, the ultimate goal of AI is to emulate the capabilities of the brain. That seems like a nice statement but its an incredibly daunting task considering that neuroscientist are still struggling trying to understand the cognitive mechanism that power the magic of our brains. Despite the challenges, more regularly we are seeing AI research and implementation algorithms that are inspired by specific cognition mechanisms in the human brain and that have been producing incredibly promising results. In 2017, the DeepMind team published a paper about neuroscience-inspired AI that summarizes the circle of influence between AI and neuroscience research.

大脑一直被认为是人工智能(AI)领域的主要灵感来源。 对于许多AI研究人员而言，AI的最终目标是模仿大脑的功能。 这似乎是一个很好的陈述，但考虑到神经科学家仍在努力理解驱动大脑魔术的认知机制，这是一项艰巨的任务。 尽管面临挑战，但更经常地，我们看到AI研究和实现算法受到人类大脑中特定的认知机制的启发，并产生了令人难以置信的令人鼓舞的结果。 2017年，DeepMind团队发表了一篇关于神经科学启发的AI的论文 ，总结了AI与神经科学研究之间的影响圈。

You might be wondering what’s so new about this topic? Everyone knows that most foundational concepts in AI such as neural networks have been inspired by the architecture of the human brain. However, beyond that high level statement, the relationship between the popular AI/deep learning models we used everyday and neuroscience research is not so obvious. Let’s quickly review some of the brain processes that have a footprint in the newest generation of deep learning methods.

您可能想知道有关此主题的最新信息？ 众所周知，人工智能中的大多数基础概念(例如神经网络)都受到人脑结构的启发。 然而，除了高水平的陈述外，我们每天使用的流行AI /深度学习模型与神经科学研究之间的关系还不是很明显。 让我们快速回顾一下最新一代深度学习方法中具有足迹的一些大脑过程。

注意 (Attention)

Attention is one of those magical capabilities of the human brain that we don’t understand very well. What brain mechanisms allow us to focus on a specific task and ignore the rest of the environment? Attentional mechanisms have become a recent source of inspiration in deep learning models such as convolutional neural networks(CNNs) or deep generative models. For instance, modern CNN models have been able to get a schematic representation of the input and ignore irrelevant information improving their ability of classifying objects in a picture.

注意是人类大脑无法理解的神奇功能之一。 什么样的大脑机制可以使我们专注于特定任务而忽略其余环境？ 注意机制已成为诸如卷积神经网络(CNN)或深度生成模型之类的深度学习模型中最新的灵感来源。 例如，现代的CNN模型已经能够获得输入的示意图，并忽略不相关的信息，从而提高了它们对图片中的对象进行分类的能力。

情景记忆 (Episodic Memory)

When you remember autobiographical events such as events or places we are using a brain function known as episodic memory. This mechanism is most often associated with circuits in the medial temporal lobe, prominently including the hippocampus. Recently, AI researchers have try to incorporate methods inspired by episodic memory into reinforcement learning(RL) algorithms to episodic control. These networks store specific experiences (e.g., actions and reward outcomes associated with particular Atari game screens) and select new actions based on the similarity between the current situation input and the previous events stored in memory, taking the reward associated with those previous events into account.

当您记住诸如事件或地点之类的自传事件时，我们使用的是称为情景记忆的大脑功能。 该机制最常与内侧颞叶的电路相关，主要包括海马体。 最近，AI研究人员尝试将受情节记忆启发的方法纳入用于情节控制的强化学习(RL)算法中。 这些网络存储特定的体验(例如，与特定Atari游戏屏幕相关联的动作和奖励结果)，并根据当前情况输入与存储在内存中的先前事件之间的相似性来选择新动作，并考虑与那些先前事件相关的奖励。

持续学习 (Continual Learning)

As humans we have the ability to learn new tasks without forgetting previous knowledge. Neural networks, in contrast suffer from what is known as the problem of catastrophic forgetting. This occurs, for instance, as the neural network parameters shift toward the optimal state for performing the second of two successive tasks, overwriting the configuration that allowed them to perform the first.

作为人类，我们有能力学习新任务而不会忘记以前的知识。 相反，神经网络则遭受所谓的灾难性遗忘问题。 例如，当神经网络参数移向用于执行两个连续任务中的第二个任务的最佳状态时，就会发生这种情况，从而覆盖允许他们执行第一个任务的配置。

One of the recent deep learning techniques inspired by the field of continual learning is known as ‘‘elastic’’ weight consolidation (EWC) . This new method acts by slowing down learning in a subset of network weights identified as important to previous tasks, thereby anchoring these parameters to previously found solutions. This allows multiple tasks to be learned without an increase in network capacity, with weights shared efficiently between tasks with related structure. In this way, the EWC algorithm allows deep RL networks to support continual learning at large scale.

受持续学习领域启发的最新深度学习技术之一被称为“弹性”重量合并(EWC)。 这种新方法的作用是放慢对被认为对先前任务很重要的网络权重子集中的学习，从而将这些参数锚定到先前找到的解决方案中。 这样就可以在不增加网络容量的情况下学习多个任务，并且可以在具有相关结构的任务之间有效地共享权重。 通过这种方式，EWC算法允许深度RL网络支持大规模的连续学习。

想象力与计划 (Imagination and Planning)

One of my favorite definitions of consciousness is related to the ability of humans( and other species) to forecast and think about the future. Most deep learning systems remain operate in incredibly reactive modes that makes it impossible to plan for longer term outcomes. New areas of AI research have focused on simulation-based planning applied to deep generative models. In particular, recent work has introduced new architectures that have the capacity to generate temporally consistent sequences of generated samples that reflect the geometric layout of newly experienced realistic environments, providing a parallel to the function of the hippocampus in binding together multiple components to create an imagined experience that is spatially and temporally coherent.

我最喜欢的意识定义之一与人类(和其他物种)预测和思考未来的能力有关。 大多数深度学习系统仍然以难以置信的React模式运行，这使得无法计划长期的结果。 人工智能研究的新领域集中于应用于深度生成模型的基于仿真的计划。 特别是，最近的工作引入了新的体系结构，该体系结构具有生成所生成样本的时间上一致的序列的能力，这些序列反映了新近体验的现实环境的几何布局，提供了与海马在将多个组件绑定在一起以创建想象中的功能相类似的功能。在时间和空间上是连贯的体验。

推理 (Inference)

Human cognition is notorious for its ability to learn new concepts by drawing inspiration from previous knowledge through inductive inferences. Contrary to that, deep learning systems rely on massive amounts of training data to master the simplest of tasks. Recent work in structured probabilistic methods and deep generative models have started to incorporate brain-inspired inference mechanisms in AI programs. The classes of models can make inferences about a new concept despite a poverty of data and generate new samples from a single example concept, The rapidly growing field of meta-learning is another AI area of research inspired by the inference abilities of the human brain.

人类认知因其通过归纳推理从先前知识中汲取灵感来学习新概念的能力而臭名昭著。 与此相反，深度学习系统依赖大量的训练数据来掌握最简单的任务。 结构化概率方法和深度生成模型的最新工作已开始将脑启发式推理机制纳入AI程序。 尽管数据匮乏，这些模型类别仍可对新概念进行推断，并可以从单个示例概念生成新样本。元学习的Swift发展是受人脑推理能力启发的另一个AI研究领域。