In this blog, we’ll explore various interesting facts about human brain, then compare those facts with recent advancements in AI and see where are we currently in developing an artificial (vs human level) general intelligence.

在此博客中，我们将探索有关人脑的各种有趣的事实，然后将这些事实与AI的最新进展进行比较，并了解我们目前在开发人工(相对于人类水平) 通用智能的领域 。

Before we start, here’s a quick introduction about myself: I started my career as an AI Engineer and been fortunate enough in building various real-time AI systems (Computer Vision and NLP). I was fascinated by the fact that human brain is the “most complex thing in the universe” and developed a great interest in neuroscience (understanding human brain) and I hope it’s the same interest that brought you here.

在开始之前，这里有一个关于我自己的快速介绍：我以AI工程师的身份开始了我的职业生涯，并且幸运地构建了各种实时AI系统(Computer Vision和NLP)。 人脑是“宇宙中最复杂的事物”这一事实使我着迷，并且对神经科学(了解人脑)产生了浓厚的兴趣，我希望它带给您同样的兴趣。

Let’s get started with our topics:

让我们开始讨论我们的主题：

1. Understanding the relation between human brain and computers了解人脑与计算机之间的关系
2. Interesting facts about Human brain and Decision Making有关人脑和决策的有趣事实
3. Human intelligence and Artificial Intelligence: The Differences人工智能与人工智能：差异
4. Will AI take over Humans?人工智能会取代人类吗？

了解人脑与计算机之间的关系： (Understanding the relation between Human brain and Computers:)

In Classical Neurology, Brain is considered as a machine / a computer that performs Classification and Categorization. But wait there’s something special about human brain, the “personal” (i.e) the ability to feel things and continuously judge it’s world(environment) and thereby continuously learning. Removing this personal from one’s brain, makes him defective and he’’ll just be a computer / machine.

在古典神经病学中，大脑被视为执行分类和分类的机器/计算机。 但是，请等待，关于人类大脑有一些特殊之处，即“个人”(即)感知事物并不断判断世界(环境)并由此不断学习的能力。 把这个人从大脑中移开，会使他变得有缺陷，他将只是一台计算机/机器。

Dr. Oliver Sacks in his book “The Man who mistook his wife for a hat”, explains about a patient(old man) who can recognize Einstein from a picture but not his friends or family members. And the same patient when experimented with a rose flower. He was able to describe it’s(rose) features like six inches in length, a convoluted red form with a linear green attachment but unable to map this to his memory of rose flower. Even a kid could do this, so what made him defective? Was he lost in the world of lifeless abstraction?This is how a brain without it’s self(personal) behave.

奥利弗·萨克斯(Oliver Sacks)博士在他的著作“把妻子误戴帽子的男人”中解释了一个病人(老人)可以从照片中识别爱因斯坦，但不能识别他的朋友或家人。 而同一位患者在尝试玫瑰花时。 他能够描述它的特征，如玫瑰长约六英寸，呈红色，带有线性绿色附件，但无法映射到他对玫瑰花朵的记忆。 甚至一个孩子都能做到这一点，那么是什么使他有缺陷呢？ 他在迷茫的抽象世界中迷失了吗？这就是没有自我(个人)的大脑的行为方式。

Are we building an AI system that’s as defective as this patient? We’ll see more about this in the later part of the article.

我们是否正在构建一个与该患者一样有缺陷的AI系统？ 我们将在本文的后面部分中看到有关此内容的更多信息。

有关人脑和决策的有趣事实： (Interesting facts about Human brain and Decision Making:)

To keep things simple, I’ll try not to use any biological terms for brain regions or parts rather use some experiments or examples from V S Ramachandran and Oliver Sacks

为简单起见，我将尽量不要对大脑区域或部位使用任何生物学术语，而应使用VS Ramachandran和Oliver Sacks的一些实验或示例

• Intuitive mind vs Rational mind: As explained by Daniel Kahneman in his book “Thinking Fast and Slow”, there are two primary modes of thinking to process information and make decisions. Intuitive mind (System 1) which is fast, unconscious, emotional and it’s responsible for 90% of our decision making. Rational mind (System 2) which is slow, conscious, logical and it comes into play when intuitive mind doesn’t have an immediate answer or to validate the thoughts of intuitive mind.

  直觉思维与理性思维：正如丹尼尔·卡尼曼(Daniel Kahneman)在他的《 快速思考和缓慢思考 》一书中所解释的那样，有两种主要的思维方式来处理信息和做出决定。 直觉式思维(系统1) 快速，无意识，情绪化 ，它负责我们90％的决策。 理性思维(系统2)，它缓慢，有意识，合乎逻辑 ，当直觉思维没有即时答案或无法验证直觉思维时，它就会发挥作用。

“The intuitive mind is a sacred gift and the rational mind is a faithful servant. We have created a society that honors the servant and has forgotten the gift.” — Albert Einstein

“直觉的思想是神圣的礼物，理性的思想是忠实的仆人。 我们创建了一个尊重仆人却忘记了礼物的社会。” - 艾尔伯特爱因斯坦

• Multiple regions of the brain are responsible for single action: Earlier it was believed that for each and every action, there’s a separate part of the brain responsible for it to happen. However, later researchers found that there’s a lot of complex processing involved even for a simple action (like “smiling”). Example: You smile automatically when you see a friend and it’s completely natural but when someone holds a camera and asks you to smile, the same action becomes difficult and looks unnatural. This is because there are two different “smile circuits”.

  大脑的多个区域负责单个动作 ：早先人们认为，每个动作都由大脑的一部分负责。 但是，后来的研究人员发现，即使是简单的动作(例如“微笑”)，也涉及很多复杂的处理过程。 示例：当您看到一个朋友时，您会自动微笑，这是完全自然的，但是当有人拿着照相机并要求您微笑时，相同的操作将变得困难并且看起来不自然。 这是因为有两个不同的“微笑回路”。

• What and How pathway of the brain: Our Visual and Auditory systems have two different pathways that guides us with our respective vision and hearing actions. Let’s take vision system as an example: “How” or “Where” pathway is concerned with grasping, navigation and other spatial information while “what” pathway is concerned with recognizing objects. These two systems work together in an optimized way thereby making sense of what you see or hear.

  大脑的途径和方式： 我们的视觉和听觉系统有两种不同的途径来指导我们各自的视觉和听觉行为。 让我们以视觉系统为例：“如何”或“何处”途径与抓取，导航和其他空间信息有关，而“什么”途径与识别物体有关。 这两个系统以优化的方式协同工作，从而使您看到或听到的声音更有意义。

• Filling in: The ability of our brain to “fill in” missing information automatically is a fascinating thing, it’s faster and automatic. For Example: Just imagine seeing a cat’s tail out underneath your bed, you don’t panic that your cat’s tail is cut, instead you automatically visualize your cat underneath the bed by filling the missing image of cat.

  填充 ：我们的大脑自动“填充”缺失信息的能力是一件令人着迷的事情，它更快，更自动。 例如：想象一下看到床底下有一只猫的尾巴，您不必担心猫的尾巴被割断了，相反，您可以通过填充丢失的猫眼图像自动在床底下可视化您的猫。

• Penfield Mapping: It’s a neurological “map” of the areas and proportions of the human brain dedicated to processing motor functions, or sensory functions, for different parts of the body.

  彭菲尔德制图(Penfield Mapping) ：这是人类大脑区域和比例的神经学“地图”，专门用于处理身体不同部位的运动功能或感觉功能。

When you look at the above image, you could notice two things:

当您查看上图时，您会注意到两件事：

1. The weightage given to different parts of the body differs. Hand and Face has higher proportion.赋予身体不同部位的重量不同。 手和脸比例更高。
2. The ordering of parts varies and completely different from our physical appearance.零件的顺序各不相同，与我们的外观完全不同。

Another Interesting fact is that, When one of the part gets damaged or removed (example: removal of hand) that particular part in this map remains vacant and over the period of time the neighbouring parts occupy them. This is one of the main reasons behind “phantom limb”.

另一个有趣的事实是，当零件中的一个零件损坏或被移除(例如：手部移除)时，该地图中的该特定零件将保持空置状态，并且在一段时间内相邻零件会占据它们。 这是“ 幻肢 ”背后的主要原因之一。

Let us now relate these awesome facts about brain with current Artificial Intelligence systems and highlight the similarities and differences.

现在让我们将有关大脑的这些令人敬畏的事实与当前的人工智能系统联系起来，并强调它们的异同。

人工智能与人工智能：差异 (Human Intelligence and Artificial Intelligence: The Differences)

Any definition of Artificial Intelligence will have to be vague enough due to our inability to define Human Intelligence. — Dr. Dan Roth, University of Illinois at Urbana-Champaign

由于我们无法定义人工智能，因此对人工智能的任何定义都必须足够模糊。 —伊利诺伊大学香槟分校的Dan Roth博士

For most of AI’s history, AI research has been divided into sub-groups:

在AI的大部分历史中，AI研究已分为以下子组：

1. Artificial Narrow Intelligence(ANI) or Weak AI: This type of AI is neither conscious nor emotional. Almost all the AI systems that we encounter today are of this type. They are mostly designed to perform single task(say, classifying objects) or in some cases multiple ANIs combined to build a complex system like self-driving vehicle.

   人工智能(AI)或弱AI：这种类型的AI既无意识也无情感。 我们今天遇到的几乎所有AI系统都是这种类型。 它们主要设计用于执行单个任务(例如，对对象进行分类)，或在某些情况下将多个ANI组合在一起，以构建像自动驾驶汽车这样的复杂系统。

2. Artificial General Intelligence(AGI) or Strong AI: This refers to machines that has the ability to understand and learn any intellectual task a human being can. We have seen this type of AI in sci-fi movies like “HER”, “Ex Machina” and we are no where near to build this type of machines.

   人工智能(AGI)或强大的AI：这是指能够理解和学习人类可以完成的任何智力任务的机器。 我们已经在诸如“ HER”，“ Ex Machina”之类的科幻电影中看到了这种类型的AI，而且我们离建造这种类型的机器也不远了。

Having seen the various types of AI and facts about Human Intelligence, let’s start comparing their differences and look where we are now in solving real-world problems with AI.

在了解了各种类型的AI以及有关人类智能的事实之后，让我们开始比较它们之间的差异，并看看我们现在在解决AI现实问题方面所处的位置。

• World or Environment: The world that we are in or the environment that surrounds us is continuous and infinite. And humans by nature continuously judge and learn their environment and gets adapted to it. This is how we’ve evolved. In contrast, the AI systems that we have today works in a pre-determined world.

  世界或环境：我们所处的世界或我们周围的环境是连续无穷的。 而且人类天生就不断地判断和学习他们的环境并适应环境。 这就是我们发展的方式 。 相反，我们今天拥有的AI系统可以在预定的世界中工作。

It is not the strongest of the species that survives, not the most intelligent that survives. It is the one that is the most adaptable to change. — Charles Darwin

它不是生存下来的物种中最强的，也不是生存下来的最聪明的。 它是最适应变化的一种。 -达尔文

• Context or Scene Understanding: What we see or understand is mainly influenced by our prior-knowledge and expectations. And it’s highly impossible to program this into a computer. Let’s get deeper into this with the example below,

  上下文或场景理解：我们看到或理解的内容主要受我们的先验知识和期望影响。 而且将其编程到计算机中是完全不可能的。 下面的示例让我们更深入地了解这一点，

There’s something common in these four pictures, yes it’s the “candle”. And at the same time you’d have noticed that these pictures imply different emotions or scenes. Picture 1: shows(memorial and gratitude) children paying tribute to APJ Abdul kalam, Picture 2: shows protest against rape, Picture 3: shows(romance) candle light dinner, Picture 4: shows a child praying to god. Let us now imagine an AI system built for Image captioning. Do you think it can understand these emotions or context and write relevant captions?

这四张图片中有一些共同点，是的，这就是“蜡烛”。 同时，您会注意到这些图片暗示着不同的情感或场景。 图1： 显示(纪念和感激)儿童向APJ Abdul kalam致敬，图2： 显示抗议强奸，图3： 显示(浪漫)烛光晚餐，图4： 显示儿童向上帝祈祷。 现在让我们想象一个为图像字幕构建的AI系统。 您认为它可以理解这些情绪或背景并撰写相关标题吗？

• Decision Making: In the second section, we saw how naturally we make most of our decisions. But we’re currently building an AI system that is neither conscious nor intuitive(or emotional). Hence it’s difficult to model our decision making ability with AI, but on specific tasks, like “chess”, “Go”, Narrow AI seems to outperform humans.

  决策：在第二部分中，我们了解了我们做出大多数决策的自然程度。 但是我们目前正在构建一个既没有意识也没有直观(或情感)的AI系统。 因此，很难用AI来建模我们的决策能力，但是在诸如“棋子”，“ Go”之类的特定任务上，Narrow AI似乎要胜过人类。

• Fooling Humans and AI systems: Humans are easily fooled by optical illusions whereas AI systems are easily fooled by adversarial examples. Let’s look at the image below,

  愚弄人类和AI系统：人类容易被视错觉蒙骗，而AI系统却容易被对抗性例子蒙骗。 让我们看下面的图片，

Image in the left shows a woman wearing t-shirt which is specifically designed to fool face recognition systems, an example of Adversarial attack. Image in the right is more popular in the internet, popcorn bag she holds tricks you and at first sight you’d have thought her legs are extremely thin, as if something is not quite right. To summarize, both AI systems and human beings can be fooled easily.

左图显示了一位女士穿着T恤，该恤衫专门设计用于欺骗人脸识别系统，这是对抗攻击的一个例子。 右边的图片在互联网上更为流行，她拿着爆米花包会骗你，乍一看，你会以为她的腿很细，好像有些不对劲。 综上所述，人工智能系统和人类都容易被愚弄。

• Multi-tasking: Significant efforts have been made to build AI systems that are capable of taking a single input and performing multiple tasks at a time. One such example is “DecaNLP” by Salesforce. However, the tasks must be more relevant. In contrast, Humans are naturally capable of performing multiple tasks and some people train themselves to master the art of multi-tasking.

  多任务处理：已做出巨大的努力来构建能够接受单个输入并一次执行多个任务的AI系统。 这样的示例之一是Salesforce的“ DecaNLP ”。 但是，这些任务必须更加相关。 相比之下，人类天生就有能力执行多项任务，有些人会训练自己以掌握多项任务的艺术。

• Concept of Filling In: The very first thing that popped into my mind while reading about the “concept of filling in” in human brain is how natural it is and if we can’t model this into an AI system, How are we gonna handle uncertainties?. Given that, the goal of our narrow AI systems is to model uncertainties specific to the task it performs, how are we gonna achieve it?

  填充的概念：在阅读人脑中的“填充概念”时，我想到的第一件事就是它的自然程度，如果我们无法将其建模为AI系统，我们将如何处理？不确定性？ 鉴于此，我们狭窄的AI系统的目标是对特定于其执行任务的不确定性进行建模，我们将如何实现呢？

• Computation: Computers usually have higher precision and can perform 10 billion operations per second. However, most of the things are executed sequentially whereas brain process information in massively parallel way with very less power consumption compared to computers. If you’re more interested to learn about computation comparisons, I’d highly recommend you to read this article

  计算：计算机通常具有更高的精度，并且每秒可执行100亿次操作。 但是，大多数事情是按顺序执行的，而与计算机相比，大脑以大规模并行方式处理信息，而功耗却非常低。 如果您更想了解计算比较，强烈建议您阅读这篇文章

We’ve seen lot of facts about Human Brain and Artificial Intelligence in general. Now let’s jump into the discussion on Technological Singularity.

总的来说，我们已经看到了许多有关人脑和人工智能的事实。 现在，让我们进入有关技术奇点的讨论。

人工智能会取代人类吗？ (Will AI Take over Humans?)

We have seen lot of sci-fi movies like “Eagle Eye”, “A.I. Artificial Intelligence” where AI takes over human beings but in reality we are not even close to building “Artificial General Intelligence” systems.

我们已经看过许多科幻电影，例如“鹰眼”，“ AI人工智能”，其中AI接管了人类，但实际上，我们甚至还没有建立起“ 人工智能 ”系统。

Currently we have “Narrow AI” which can only perform specific tasks in a pre-determined environment. So instead of replacing a human being in general (like “HER” movie) it can only facilitate human beings by making their work and personal lives easier with assistants like “siri”, alexa” or “surveillance” and “autonomous driving” systems or “health monitoring” systems.

当前，我们拥有“ Narrow AI ”，它只能在预定的环境中执行特定的任务。 因此，与其代替一般人(例如“ HER”电影)，不如通过“ siri”，“ alexa”或“监视”和“自动驾驶”系统等助手来简化人的工作和生活，从而为人类提供便利“健康监测”系统。

However it can replace specific professions like customer service, similar to the effect of automation.

但是，它可以代替客户服务等特定行业，类似于自动化的效果。

There are some potential threats where AI can be used by humans for cyber attacks, AI-enabled Terrorism.

人类可能会使用AI进行网络攻击和AI引发的恐怖主义，这存在一些潜在的威胁 。

Last thing that I wanted to touch upon is Bias say, Gender and Racial bias. Humans and AI are highly correlated by bias, except the fact that there have been significant efforts in addressing and reducing bias in AI.

我想谈的最后一件事是比亚斯说的“性别与种族偏见” 。 人类和人工智能之间存在着高度的偏见，除了在解决和减少人工智能方面的偏见方面做出了巨大的努力。

Four polls of AI researchers, conducted in 2012 and 2013 by Nick Bostrom and Vincent C. Müller, suggested a median probability estimate of 50% that artificial general intelligence (AGI) would be developed by 2040–2050. Let’s wait till then to witness Technological Singularity.

尼克·波斯特罗姆 ( Nick Bostrom) 和文森特·C·穆勒 ( Vincent C.Müller)在2012年和2013年对AI研究人员进行的四次民意测验显示，到2040-2050年将开发出人工通用情报 (AGI)的中值概率估计为50％。 让我们等到那时，看看技术上的奇异之处 。

Happy Learning…!

祝您学习愉快！！