ai人工智能
“Artificial Intelligence began with an ancient wish to forge the Gods.” -Pamela McCorduck
“人工智能始于古老的铸就众神的愿望。” -帕梅拉·麦考德克(Pamela McCorduck)
Artificial General Intelligence (AGI) refers to the ability of artificial agents/programs to display human-level proficiency in reasoning about and performing tasks in their environment. AGI has long been a mainstay of science fiction movies and books, famously embodied in likeable characters such as Tony Stark’s assistant JARVIS from the Iron Man series and the humanoid robot C3PO in Star Wars.
人工智能 (AGI)指的是人工代理/程序在其环境中推理和执行任务时表现出人类水平的能力。 长期以来，AGI一直是科幻电影和书籍的中流，柱，其著名之处在于可爱的角色，例如托尼·史塔克(Tony Stark)的《钢铁侠》系列中的助手JARVIS和《星球大战》中的人形机器人C3PO。
Crucially, AGI is also considered the holy grail for most AI research today. In a sense, the formulation of most reinforcement learning problems — problems that require an agent to interact with the environment to maximize reward— is a subclass of the larger problem of general intelligence. Instead of focusing on precisely defining AGI, I will leave it at the reader’s intuition that it is a system that displays ‘human-like’ intellectual capacity.
至关重要的是，AGI也被认为是当今大多数AI研究的圣杯。 从某种意义上说，大多数强化学习问题的提出(要求行为人与环境互动以最大化报酬的问题)是广义智力较大问题的子类。 我不会专注于精确定义 AGI，而是让读者直觉，它是一个显示“类人”知识能力的系统。
Billions of dollars are being spent on AGI research today. For example, Elon-Musk-backed OpenAI’s mission is to achieve or facilitate the development of AGI (and also ensure that it is responsibly used). Similarly Google’s DeepMind and the Human Brain Project are also working towards similar goals.
今天，数十亿美元用于AGI研究。 例如，由Elon-Musk支持的OpenAI的任务是实现或促进AGI的开发(并确保以负责任的方式使用它)。 同样，谷歌的DeepMind和人脑计划也在朝着类似的目标努力。
我们今天站在哪里？ (Where do we stand today?)
Today, we have the tools to create AI systems that display remarkable levels of understanding. Let’s take a look at some existing programs that display almost human level reasoning, performance or control — on specific tasks. We will look at this from progress in three different domains — Natural Language Processing, Reinforcement Learning and Autonomous Vehicles.
今天，我们拥有创建具有卓越理解水平的AI系统的工具。 让我们看一下一些现有程序，这些程序在特定任务上几乎可以显示人类的推理，性能或控制力。 我们将从三个不同领域( 自然语言处理， 强化学习和自动驾驶汽车)的进展中来研究这一问题。
NLP和聊天机器人 (NLP and Chatbots)
Natural Language Processing is the analysis and understanding of human languages for tasks like translation and sentiment classification. One application of Natural Language Processing is the creation of chatbots. In 2016, Microsoft revealed an AI Bot called Tay (Thinking About You). Although Tay made headlines for all the wrong reasons and had to be taken offline 16 hours after its release, its ability to tweet about a wide variety of topics appeared to be impressive.
自然语言处理是对人类语言的分析和理解，可用于翻译和情感分类等任务。 自然语言处理的一种应用是创建聊天机器人。 在2016年，微软揭露了一个名为Tay(Thinking About You)的AI Bot。 尽管Tay出于各种错误原因成为头条新闻，并且必须在发布后的16个小时内离线，但其发布各种主题的推文的能力似乎令人印象深刻。
Take the above tweet for example. Tay was trained to answer questions based on other examples of tweets discussing similar topics. In particular, to reply to this tweet it was able to identify the tweet as asking for an opinion, and then generated an aggregated answer based on other people’s replies to similar questions. Tay displays good textual analysis, paraphrasing and the ability to form meaningful sentences, but not exactly generalized intelligence. A real AGI system could reasonably come up with a reply all by itself, without paraphrasing other people’s opinions.
以上述推文为例。 Tay受过训练，可以根据讨论相似主题的其他推文示例回答问题。 特别是，要回复此推文，它可以将推文标识为征求意见，然后根据其他人对类似问题的回复生成汇总答案。 泰(Tay)表现出良好的文字分析，释义和形成有意义句子的能力，但并没有完全概括的智力。 一个真正的AGI系统可以合理地自己提出一个答复，而无需解释别人的意见。
To imagine a true AGI system, think about the kind of reasoning the bot would have to perform to come up with such a reply by itself (even assuming it was aware of the ongoing meme comparing Ted Cruz to the Zodiac killer). First, in addition to knowing that it’s being asked for an opinion, it would have to know about the two entities that are being compared — Ted Cruz and the Zodiac killer. Second, it would have to tie in its opinion of Ted Cruz with the actions of the Zodiac killer AND frame it in a humorous way by first disagreeing with the premise and then amplifying it. We’re not quite there yet.
要想像一个真正的AGI系统，请考虑该机器人自己做出这样的答复所必须执行的推理方式(即使假设它知道将Ted Cruz与生肖杀手进行比较的模因)。 首先，除了知道要征求意见外，还必须了解正在比较的两个实体-特德·克鲁兹和黄道十二宫杀手。 其次，它必须将其特德·克鲁兹(Ted Cruz)的观点与黄道十二宫杀手的行动联系起来，并以幽默的方式对其进行构架，方法是首先不同意前提，然后加以扩大。 我们还没到那儿。
Nevertheless, since 2016 NLP has made substantial progress with the release of large transformer-based models like BERT and GPT-2 in 2018 that were able to handily surpass the then state-of-the-art models. These models were created by feeding a program hundreds of thousands of Wikipedia and other publicly available text documents to “understand” the English language. This is akin to teaching a child to read by giving him knowledge of the meaning of words in their contexts. Once proficient at this, the child can then use this knowledge to reason about new questions and answer them appropriately.
尽管如此，自2016年以来，NLP在2018年发布了大型的基于变压器的模型(例如BERT和GPT-2)方面取得了重大进展，这些模型可以轻松超越当时的最新模型。 这些模型是通过向程序提供数十万个Wikipedia和其他可公开获取的文本文档来“理解”英语而创建的。 这类似于通过给孩子一个上下文中的单词含义的知识来教他阅读。 一旦精通这一点，孩子就可以利用这些知识来推理新问题并适当回答。
Famously, the GPT-2 model demonstrated an impressive ability to create near-flawless text based on any context that it was supplied with. For example, it was given the following prompt:
著名的是，GPT-2模型展示了令人印象深刻的能力，可以根据提供的任何上下文创建近乎完美的文本。 例如，出现以下提示：
In a shocking finding, scientist discovered a herd of unicorns living in a remote, previously unexplored valley, in the Andes Mountains. Even more surprising to the researchers was the fact that the unicorns spoke perfect English.
在一个令人震惊的发现中，科学家发现了一群独角兽，它们生活在安第斯山脉的一个偏远的，以前未被开发的山谷中。 对于研究人员而言，更令人惊讶的是，独角兽会说完美的英语。
and here is an extract of GPT-2’s output (the full text can be found here).
这是GPT-2输出的摘录(全文可在此处找到)。
The scientist named the population, after their distinctive horn, Ovid’s Unicorn. These four-horned, silver-white unicorns were previously unknown to science. Now, after almost two centuries, the mystery of what sparked this odd phenomenon is finally solved. Dr. Jorge Pérez, an evolutionary biologist from the University of La Paz, and several companions, were exploring the Andes Mountains when they found a small valley, with no other animals or humans. Pérez noticed that the valley had what appeared to be a natural fountain, surrounded by two peaks of rock and silver snow. Pérez and the others then ventured further into the valley. “By the time we reached the top of one peak, the water looked blue, with some crystals on top,” said Pérez.
这位科学家以其独特的号角Ovid的独角兽命名了这个种群。 这些四角银白色独角兽以前是科学界所不知道的。 现在，在将近两个世纪之后，终于解决了引发这种奇怪现象的谜团。 拉巴斯大学的进化生物学家豪尔赫·佩雷斯(JorgePérez)博士和几位同伴在安第斯山脉发现一个没有其他动物或人类的小山谷时正在探索。 佩雷斯注意到该山谷似乎是一个天然喷泉，周围环绕着两个岩石和银色雪峰。 佩雷斯和其他人然后冒险进了山谷。 佩雷斯说：“当我们到达一个山峰的顶部时，水看起来是蓝色的，上面有一些晶体。”
This model works by consuming the input prompt, and then generating output text one word at a time. Given this sequential generation process, the generated text shown above is surprisingly coherent, both syntactically (no grammatical or punctuation errors) and semantically (no non-sensical statements). It also contains multiple references to system-generated characters like Pérez and his companions.
该模型的工作方式是使用输入提示，然后一次生成一个单词的输出文本。 在这种顺序生成过程中，上面显示的生成文本在语法上(没有语法或标点错误)和语义上(没有无意义的陈述)令人惊讶地连贯。 它还包含对系统生成角色(例如Pérez和他的同伴)的多重引用。
NLP is still a rapidly evolving field, with new enhancements almost every day.
NLP仍然是一个快速发展的领域，几乎每天都有新的增强功能。
RL和游戏代理商 (RL and game-playing agents)
Reinforcement Learning is a sub-field of AI concerned with the design and analysis of agents that act in their environments to maximize a notion of reward (e.g. score in a game). In the field of Reinforcement Learning too, there has been substantial progress towards intelligent systems. Since the 2013 demonstration[1] for playing ATARI games using neural networks, reinforcement learning techniques have evolved rapidly.
强化学习是AI的一个子领域，它与设计和分析在其环境中起作用以最大化奖励概念(例如游戏中的得分)的代理有关。 在强化学习领域 同样，在智能系统方面也取得了实质性进展。 自2013年使用神经网络玩ATARI游戏的演示[1]以来，强化学习技术得到了Swift发展。
In 2019, OpenAI showcased a bot called OpenAI Five that controlled five agents in a game of Dota 2. This is a MOBA (Multiplayer Online Battle Arena) game that pits two teams of 5 people against each other in a contest to destroy the other team’s base. Over the course of one approximately 30-minute game the players make hundreds of short and long term decisions including skill and item choices that influence other players and may have delayed consequences. The bot was able to defeat Dota 2 world champions OG 2–0 in a publicly broadcast game. To test the robustness of the agent, it was also released publicly for a while and played about 7000 games with the community, achieving a 99.4% win rate.
在2019年，OpenAI展示了一个名为OpenAI Five的机器人，该机器人在Dota 2的游戏中控制了五个特工。这是一个MOBA(多人在线战斗竞技场)游戏，在两个竞赛中将5人的两支队伍互相对抗，以摧毁另一支队伍的基础。 在大约30分钟的游戏过程中，玩家做出了数百项短期和长期决策，包括影响其他玩家并可能造成延迟后果的技能和项目选择。 该机器人在一个公开播放的游戏中击败了Dota 2世界冠军OG 2-0。 为了测试该代理的健壮性，它还被公开发布了一段时间，并与社区玩了大约7000场游戏，赢率达到99.4％。
This agent was trained almost entirely through self-play. Crucially, the focus of this algorithm was not on mechanical advantages that an AI system typically has over humans like faster reaction time — the algorithm was even artificially penalized for highly mechanical objectives like last hitting and denying. Instead, the training rewards were geared towards a general understanding of the state of the game including things like when it should pick a fight and its current strength relative to its opponents.
这个特工几乎是完全通过自我训练来训练的。 至关重要的是，该算法的重点不在AI系统通常具有的对人类的机械优势(如更快的React时间)上，该算法甚至因最后击中和拒绝等高度机械目标而人为地受到了惩罚。 取而代之的是，训练奖励的目的是使人们对游戏的状态有一个大致的了解，包括应该何时打架以及相对于对手的当前实力。
In mid-2018, OpenAI showcased a robotic hand that was trained entirely in simulation to manipulate objects into various orientations.
在2018年中，OpenAI 展示了一只经过模拟训练的机器人手，可以将对象操纵到不同的方向。
Although the tasks look fairly simple, the key thing it achieved was the ability to perform well in novel situations despite never being trained specifically to behave in those situations. This was achieved through a training technique called Domain Randomization that allowed the system to identify key features of the environment, helping it generalize to new situations.
尽管任务看起来很简单，但是它实现的关键是在新颖的情况下表现出色的能力，尽管从未接受过专门训练以适应这种情况。 这是通过一种称为“ 域随机化”的训练技术来实现的，该技术使系统能够识别环境的关键特征，从而将其推广到新的情况。
One of the systems I personally found most impressive among these was multi-agent hide-and-seek, released in September 2019. This was a simulated series of games between two independently evolving teams of agents, one tasked to seek and the other to hide. They were trained over almost 500 million episodes (independent runs of hide-and-seek) and learned to use a variety of fixed and movable tools like walls, ramps and boxes. Here’s a video summarizing the evolution of the agents.
我个人认为其中最令人印象深刻的系统之一是于2019年9月发布的多代理人捉迷藏游戏。这是两个独立发展的代理人团队之间模拟的一系列游戏，一个任务是寻找而另一个则是隐藏。 他们接受了将近5亿集的训练(独立的捉迷藏游戏)，并学会了使用各种固定和可移动工具，例如墙壁，坡道和盒子。 这是一个视频，总结了代理的演变。
It is very fascinating to see the two sets of adversaries improve over time, continuously evolving new strategies to counteract their opponents’ progress. For example, when the hiders learned to use a room and sealed the entrance using boxes, the seekers learned to use a ramp to ‘jump’ over the wall to find them. In response, the hiders learned to take the ramp away by bringing it along with them to their room before blockading the entrance.
看到两组对手随着时间的推移而不断改进，不断发展新的策略来抵消对手的进步，这非常令人着迷。 例如，当藏匿者学会使用房间并用盒子将入口密封时，寻找者学会了使用坡道“跳过”墙壁找到他们。 作为回应，藏匿者学会了通过将斜坡与斜坡一起带入自己的房间，然后封锁入口，从而将斜坡带走。
自动驾驶汽车 (Autonomous Vehicles)
The creation of self-driving cars is arguably the most mainstream application of Artificial Intelligence. Autonomous driving requires the agent to have a range of diverse capabilities, such as computer vision and strong object classification, along with continuous assessment of its situation and interaction with its environment (similar to reinforcement learning).
无人驾驶汽车的创建可以说是人工智能的最主流应用。 自主驾驶要求代理具有多种多样的能力，例如计算机视觉和强大的对象分类，以及对情况的持续评估以及与环境的交互作用(类似于强化学习)。
Big industry players like Uber, Tesla and Google-backed Waymo are actively working towards building self-driving cars, and some of these companies have begun testing almost-fully automated vehicles in controlled and extensively-mapped environments. The front runners in this race have already achieved what is called Level 4 automation (the car can operate without human input or oversight but only under select conditions defined by factors such as road type or geographic area) in such environments. But the jump to the final Level 5 — which is intuitively the stage where the car is able to operate in any condition or geography, fully autonomously — is some way off still.
优步(Uber)，特斯拉(Tesla)和Google支持的Waymo等大型行业参与者正在积极致力于制造自动驾驶汽车，其中一些公司已开始在受控且映射广泛的环境中测试几乎完全自动化的汽车。 在这种情况下，这场比赛的领先者已经实现了所谓的4级自动化 (汽车可以在没有人工输入或监督的情况下运行，但只能在由道路类型或地理区域等因素定义的特定条件下运行)。 但是，跃升到最终的5级水平(直觉上是汽车可以在任何条件或地理条件下完全自主运行的阶段)距离还有一段距离。
I suspect that unless all cars on the road are replaced with driverless cars (and so are always aware of and know what other cars are doing), it will be difficult to achieve Level 5 automation. It may even be the case that level 5 automation is only achieved with AGI, because of the requirement to be able to deal with so many different scenarios and failure modes.
我怀疑除非将道路上的所有汽车都替换为无人驾驶汽车(这样才能始终意识到并知道其他汽车在做什么)，否则将很难实现5级自动化。 甚至可能只有通过AGI才能实现5级自动化，因为需要能够处理这么多不同的场景和故障模式。
通往AGI之路 (The Road to AGI)
The systems described above, although individually powerful in the tasks that they have been trained to perform, do not contain a generalized understanding of the world. As an example, an agent that is trained to play Dota 2 will not be able to read about changes to the game in a patch note and adapt its playing style to incorporate the changes in the patch. On the other hand, a human that is able to play the game proficiently can reasonably adapt his playing style after reading the changes a new update brings.
上述系统尽管在训练有素的任务中具有强大的功能，但并不包含对世界的一般理解。 例如，经过培训可以玩Dota 2的座席将无法阅读补丁说明中的游戏更改，也无法调整其玩法以将更改合并到补丁中。 另一方面，能够熟练玩游戏的人在阅读新的更新带来的变化后可以合理地调整其玩法。
For tasks like these, we need to build an agent and endow it with abilities that span all the domains of Artificial Intelligence research, from Natural Language Processing to computer vision, to problem solving and game playing.
对于此类任务，我们需要构建一个代理并赋予其跨人工智能研究各个领域的能力，从自然语言处理到计算机视觉，再到解决问题和玩游戏。
How can we build such a system? Broadly, there are two schools of thought on how such an AGI system will be built.
我们如何建立这样的系统？ 大致而言，关于如何构建这样的AGI系统有两种思路。
Ilya Sutskever, chief scientist at OpenAI, openly claims that we already have the most crucial tool we need to build AGI — good ol’ Deep Learning. If you don’t know what that is, it is a foundational technology that is used in all the domains of AI described above — NLP, Reinforcement Learning and Computer Vision and Control. Deep learning essentially allows a system to build hierarchical representations of the problem it is trying to solve. On a recent podcast, Ilya claimed that AGI will be achieved through a combination of deep learning “plus some other ideas”. Crucially, these other ideas might already be with us. They could be existing tools like self-play and domain randomization, or a different reformulation of the same problem.
OpenAI的首席科学家Ilya Sutskever公开宣称，我们已经拥有构建AGI所需的最关键工具-优秀的深度学习 。 如果您不知道这是什么，那么它是上述AI所有领域(NLP，强化学习以及计算机视觉和控制)中使用的一项基础技术。 深度学习本质上允许系统构建它试图解决的问题的分层表示。 在最近的播客中 ，Ilya声称将通过结合深度学习“加上一些其他想法”来实现AGI。 至关重要的是，这些其他想法可能已经与我们同在。 它们可能是现有的工具，例如自我扮演和领域随机化，或者是对同一问题的不同重新表述。
A step that OpenAI in collaboration with an MIT PhD student (Joseph Suarez) have taken towards this is the Neural MMO — a simulation environment based on Massively Multiplayer Online Role-Playing Games (MMORPGs) like World of Warcraft and Runescape.
OpenAI与MIT博士生( 约瑟夫·苏亚雷斯 )( Joseph Suarez )合作迈出的一步是神经MMO ，这是一种基于大型多人在线角色扮演游戏(MMORPG)的模拟环境，例如魔兽世界和Runescape。
This is a competitive online environment that requires agents to forage for resources like food and water, and engage in melee and ranged combat with other agents in order to stay alive. This environment creates a basic framework similar to the real world, where organisms compete for resources to stay alive and procreate. The rationale[2] behind building this MMO environment is that the problem of creating “agents that scale to the real world” can be split into two subproblems:
这是一个竞争激烈的在线环境，要求特工必须搜寻食物和水等资源，并与其他特工进行近战和远程战斗才能生存。 这种环境创建了一个类似于现实世界的基本框架，在该框架中，生物竞争资源以维持生存和繁殖。 构建此MMO环境的基本原理[2]是，创建“ 可扩展到现实世界的代理 ”的问题可以分为两个子问题：
Agents that scale to their environment, i.e. learn to perform well in whatever environment they are placed.
可以根据自己的环境进行扩展的代理 ，即学会在所处的任何环境中都表现良好。
Environments that scale to the real world.
可扩展到现实世界的环境 。
“Agents that scale to their environment” is the current focus of most machine learning research. It requires the development of better algorithms that allow agents to maximize their potential in their environment. This requires better understanding of and solutions to the core tasks that an agent must perform in its environment, such as exploration and handling memory.
“根据环境扩展的代理商”是大多数机器学习研究的当前重点。 它需要开发更好的算法，以允许代理最大化其在环境中的潜力。 这需要更好地理解和解决代理必须在其环境中执行的核心任务，例如探索和处理内存。
“Environments that scale to the real world” is a crucial part, because as agents get better, there comes a point where they’re limited by their environment. Preventing this requires developing simulations that are better approximations to the real world. Neural MMO is an attempt to create such an environment, and it is more complex than existing environments that typically present very specific challenges. Basing the environment on MMORPG’s, the author argues, also allows it to be extremely scalable.
“可扩展到现实世界的环境”是至关重要的部分，因为随着代理变得越来越好，有时会受到环境的限制。 要避免这种情况，就需要开发更接近真实世界的模拟。 神经MMO试图创建这样的环境，它比通常会带来非常具体挑战的现有环境要复杂得多。 作者认为 ，基于MMORPG的环境也允许它具有极高的可伸缩性。
The other school of thought on how to build AGI systems believes that AGI will be radically different from existing systems. Microsoft Co-founder Paul Allen famously claimed in his 2014 post in MIT Technology review that we need radical new technologies to achieve AGI.
关于如何构建AGI系统的另一种观点认为，AGI将与现有系统有根本的不同。 微软联合创始人保罗·艾伦(Paul Allen)在其2014年的《麻省理工学院技术评论》中著名地宣称，我们需要彻底的新技术来实现AGI。
“But if the singularity is to arrive by 2045, it will take unforeseeable and fundamentally unpredictable breakthroughs, and not because the Law of Accelerating Returns made it the inevitable result of a specific exponential rate of progress.” — Paul Allen
“但是，如果要在2045年之前实现单一性，它将取得不可预见且根本上无法预测的突破，而不是因为加速收益定律使它成为特定指数增长率的必然结果。” 保罗·艾伦
时间线 (Timelines)
When will AGI be realized? This is where we turn from facts and hard research to conjecture and hope!
AGI何时实现？ 这就是我们从事实和艰辛的研究转向猜想和希望的地方！
The answer, in short, is that nobody knows for certain. Mainstream AI researchers express any optimism cautiously, partly owing to the long history of disappointments in AI. Indeed, AI research saw a huge first wave in the 1960s and ’70s with some people predicting that they’d be able to build fully conversational and human-like AI within a generation. When the research community failed to produce the results it had promised, the field saw a significant slowdown in the ’80s and ‘90s.
简而言之，答案是没有人知道。 主流AI研究人员谨慎地表示乐观，部分原因是AI长期以来令人失望。 确实，人工智能研究在1960年代和70年代掀起了巨大的第一波， 有人预测他们将能够在一代人的时间内建立完全对话式和类人的AI。 当研究界未能实现承诺的结果时，该领域在80年代和90年代出现了显着放缓。
The dawn of the 21st century, however, saw a resurgence in the field, this time with a focus on AI that was applicable to the real world. Research advanced steadily throughout the first decade. And then, in 2012 Deep Learning came into the mainstream. The years following the advent of Deep Learning were something like the Cambrian explosion of modern AI research.
然而，二十一世纪的曙光使该领域复兴了，这次的重点是适用于现实世界的AI。 在最初的十年中，研究稳步发展。 然后，2012年，深度学习成为主流。 深度学习问世后的几年就像是现代AI研究的寒武纪爆发一样。
A survey of experts[3] in June 2016 showed optimism in the belief that AGI will be accomplished within the 21st century. According to the survey, experts on average predict a 50% chance that high-level machine intelligence will be developed around 2040–2050, rising to a 90% chance by 2075.
2016年6月的专家调查[3]显示出乐观的信念，即AGI将在21世纪内完成。 根据调查，专家平均预计2040年至2050年左右开发高级机器智能的可能性为50％，到2075年将上升到90％。
Hopefully, this gets you thinking about some of the problems with building Artificial General Intelligence and potential ways to solve them! What is your opinion on how AGI will be achieved, and how far off are we?
希望这能使您思考构建人工智能的一些问题以及解决这些问题的潜在方法！ 您对如何实现AGI有什么看法，我们还有多远？
引文和进一步阅读 (Citations and further reading)
Playing Atari with Deep Reinforcement Learning [Mnih et al. 2013]
通过深度强化学习玩Atari [Mnih等。 2013]
Artifical Life: Objective and Approach [Joseph Suarez].
人工生活：目标和方法 [Joseph Suarez]。
Future Progress in Artificial Intelligence: A Survey of Expert Opinion
翻译自: https://towardsdatascience.com/the-quest-for-artificial-general-intelligence-34ecf9e7e88e
ai人工智能
