Let’s say for the sake of argument you’re stuck at home for a long time watching too much of the stuff we euphemistically call “streaming content,” by which I mean movies and TV. Come up with your own reason — anything from being one of Japan’s pathologically introverted hikikomori to, say, hiding out from some sort of potentially lethal respiratory virus. In any case, you will at some point sour on all the available programming options and scroll glumly through all the familiar title selection menus until you give up. Tiger King is more of a punch line than a TV show at this point, and, sure, you could plumb the depths of history’s most creative auteurs over on the Criterion Channel, but that sounds hard, and if you are like me, you consider reading the morning news emotional labor.

L等对参数的缘故说你被困在家里很长一段时间看太多的STU 的F˚F我们委婉地称之为“流媒体内容，”我指的是电影和电视。 提出自己的理由-从成为日本病态内向型彦之一，到躲避某种可能致命的呼吸道病毒的任何事情。 无论如何，您有时会在所有可用的编程选项上感到厌烦，并在所有熟悉的标题选择菜单中苦恼地滚动直到放弃。 在这一点上， 老虎王更像是一部电视节目，而不是电视节目，当然，您可以在Criterion频道上浏览历史上最具创造力的导演的深度，但这听起来很难，如果您像我一样，可以考虑阅读早间新闻的情感劳动。

But what if there were a movie streaming service with no downsides? Call it “Black Box.” You would get exactly what you’re in the mood for every time, but unlike rewatching an old favorite, it wouldn’t be a retread, because no two movies on Black Box would ever be the same. On Black Box, rather than selecting a title, you would choose from a menu of options like genres, plots, types of characters, locations, and content keywords to include or exclude.

但是，如果有没有缺点的电影流媒体服务怎么办？ 称之为“黑匣子”。 您每次都会得到完全一样的心情，但是与重新观看旧的收藏夹不同，它不会被翻新，因为黑匣子上没有两部电影是相同的。 在黑匣子上，您可以从菜单中选择要包括或排除的类型，例如情节，情节，字符类型，位置和内容关键字，而不是选择标题。

Want a movie where a protagonist your age, race, sexuality, gender, and religion becomes an Olympic swimmer? You got it. Want a movie where someone demographically identical to your boss gets squeezed to death and devoured by a Burmese python? Your wish is its command. Want to leave out the specifics and let fate decide what never-before-imagined movie will be entertaining you this evening? Black Box has you covered.

想要一部影片，让您的年龄，种族，性别，性别和宗教信仰的主角成为奥运会游泳选手吗？ 你说对了。 想要一部电影，在人口统计学上与您的老板相同的人被缅甸Python挤死并吞噬吗？ 您的愿望就是它的命令。 想要忽略细节，让命运决定今晚将播放的一部前所未有的电影吗？ 黑匣子覆盖了您。

After you make your choices — and of course pay a nominal fee for the serious computational heavy lifting necessarily involved — your order is received at Black Box HQ, and an original movie will be on its way shortly.

在您做出选择之后-当然要为必不可少的计算量繁重的工作支付象征性的费用-您的订单将在Black Box HQ收到，并且很快将发行原创电影。

Black Box converts your specifications into data — or if you didn’t ask for anything specific, a blob of randomly generated numerical noise will do — and the creation process can begin. That first collection of ones and zeros will become a prompt, and will be fed into a type of A.I. called a transformer, which will spit out the text screenplay for your movie through a process a little like the autocomplete function on your smartphone.

黑匣子将您的规范转换为数据-或如果您不要求任何特定内容，则将产生一团随机生成的数字噪声-然后创建过程就可以开始。 一和零的那首集将成为一个提示，将被送入一个类型的AI称为变压器 ，它会吐出你的电影文本剧本经历一个过程有点像智能手机上的自动完成功能。

That screenplay will then be fed into a variation on today’s vector quantized variational autoencoders — neural nets that generate music, basically — producing chopped up little bits of sound that, when strung together, form an audio version of the spoken dialogue and sound effects in your custom movie, plus an orchestral score. Finally, in the most challenging part of the process, those 90 minutes of audio, along with the screenplay, get fed into the world’s most sophisticated GAN, or generative adversarial network. Working scene by scene, the Black Box GAN would generate a cast of live action characters — lifelike humans, or at least human-esque avatars — built from the ground up, along with all of the settings, monsters, car chases, dogs, cats, and little surprises that make it feel like a real movie.

然后，该剧本将被馈入当今矢量量化变分自动编码器的一种变体中 -神经网络基本上会产生音乐-产生切碎的一点声音，当串在一起时，会形成语音对话的音频版本和您的声音效果自定义电影，以及管弦乐乐谱。 最后，在过程中最具挑战性的部分中，这90分钟的音频以及剧本将被馈送到世界上最复杂的GAN或生成性对抗网络中 。 Black Box GAN会逐个场景地工作，它会从头开始构建一系列真人角色-栩栩如生的人类，或者至少是人类化身的化身，以及所有设置，怪物，汽车追逐，狗，猫，几乎没有什么惊奇之处，使它看起来像一部真正的电影。

Setting aside the sheer implausibility of this scenario for a moment, if someone managed to do all of this with present-day technology, the result would be deeply tweaked and possibly disturbing. No matter how much Black Box tried to make a normal movie, A.I. authorship would all but guarantee strangeness. For now.

暂时搁置这种情况的不现实性，如果有人设法用当今的技术来完成所有这些工作，那么结果将被深深地调整，并且可能会令人不安。 不管黑匣子试图制作一部普通电影有多少，人工智能的作者身份几乎都能保证其陌生性。 目前。

There was once a pretty well-worn joke about the output of supposedly creative robots being derivative and boring; it dates back at least a couple decades.

曾经有一个非常陈旧的笑话，关于所谓的创造性机器人的输出是衍生的和无聊的 ； 它至少可以追溯到几十年前。

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But generative A.I. systems actually exist now, and they’ve revealed themselves to have deeply warped creative instincts. In 2015, the A.I. nightmare known as DeepDream, one of the first popular examples of generative A.I., was released online. Google engineer Alexander Mordvintsev had created a system for generating outputs to match an image recognition system’s algorithmic understanding of the visual world, and when it went viral, the public got its first hellish taste of real computer “dreams” — nightmare landscapes dominated by wormlike swirls, eyes, and a ton of dogs for some reason. Since then, A.I.s have churned out disturbed cultural products like accidental horror stories and bizarre Frank Sinatra songs about spending Christmas in a hot tub.

但是生成型AI系统现在实际上已经存在，并且它们已经显示出自己深刻地扭曲了创新本能。 在2015年，被称为DeepDream的AI噩梦是在线生成AI的首批流行示例之一。 Google工程师Alexander Mordvintsev已经创建了一个生成输出的系统，以匹配图像识别系统对视觉世界的算法理解，并且在病毒式传播时，公众第一次感受到了真实计算机“梦”的地狱般的味道-梦般的风景以蠕虫状漩涡为主导，眼睛和大量狗出于某种原因 。 自那时以来，认可机构一直在搅扰不安的文化产品，例如意外的恐怖故事和关于在热水浴缸中度过圣诞节的怪异的弗兰克·辛纳屈(Frank Sinatra)歌曲 。

So given A.I.’s demonstrated creative capacities thus far, should we really expect someone to take on the demonic task of forcing an A.I. to make an entire movie?

因此，鉴于迄今为止AI表现出的创造力，我们真的应该期望有人承担起强迫AI制作整部电影的恶魔般的任务吗？

Yes, according to Duke University A.I. scientist David Carlson, PhD. “I think someone will eventually try to do this,” he told OneZero. And Carlson himself might be involved if they do, having helped engineer the A.I. systems that turned text into visual narrative media for papers published in 2018 and 2019. But, Carlson said, “That’s a long way off. You know, years at the minimum.”

是的，根据杜克大学AI科学家David Carlson博士说。 他告诉OneZero： “我认为有人最终会尝试这样做。” 卡尔森本人也可能会参与其中，帮助工程师设计了将文本转变为视觉叙事媒体的AI系统，以供2018年和2019年发表的论文使用。 但是，卡尔森说：“这还有很长的路要走。 你知道，最少要几年。”

It’ll take a lot of minds like Carlson’s to get an A.I. system to belch out a movie, because the task won’t be as simple as “making a robot watch every movie” and seeing what comes out. No matter how many comedians’ tweets you’ve read in that format, we’re actually decades or centuries from the technological milestone it implies — namely, general artificial intelligence, or something very close to it.

要让AI系统播出一部电影，就需要像卡尔森那样的很多头脑，因为这项任务不会像“让机器人观看每部电影”并看清结果一样简单。 无论您以这种格式阅读过多少喜剧演员的推文，我们实际上都离它所暗示的技术里程碑(即通用人工智能或与之非常接近)几十年或数百个世纪。

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Rather than dumping a Roomba on a couch with a Netflix subscription for 10,000 hours and then asking it nicely to generate a blockbuster, an A.I.-generated movie is something that probably has to be painstakingly engineered, step by step. That’s very different from a single GAN, trained on so much movie data that it can just spit out entire movies at the push of a button. There may not be enough silicon in the universe to create a system that can do that.

AI生成的电影可能不是必须将其制作成一部轰动一时的电影，而是将Roomba放到Netflix订阅的沙发上一万小时，然后好好地要求它，而是一步一步地精心设计。 这与单个GAN完全不同，GAN接受了大量的电影数据训练，只需按一下按钮就可以吐出整个电影。 宇宙中可能没有足够的硅来创建可以做到这一点的系统。

In fact, there’s a pretty daunting gap between what cutting-edge A.I. can do right now, and what seems feasible based on contemporary science fiction and deceptive headlines. As I write this, the latest headline on the Daily Mail about A.I. reads: “Chinese state news agency unveils ‘the world’s first 3D A.I. anchor’ after ‘cloning’ a human reporter.” As you might expect, the real story is not as exciting as it sounds — it’s about as convincing a human facsimile as Andy from Toy Story. Meanwhile, actual generative A.I. systems at the cutting edge of technology can still have a hard time recognizing basic objects like fire trucks and birds outside of lab conditions.

实际上，目前最先进的AI可以做的事情与基于当代科幻小说和欺骗性头条新闻似乎可行的事情之间存在巨大的差距。 在我撰写本文时，《 每日邮报》上有关AI的最新标题是 ：“中国国家新闻社在“克隆”一名人类记者后揭开了“世界上第一个3D AI锚”。 如您所料，真实的故事并没有听起来那么令人激动，而是像《 玩具总动员》中的安迪一样令人信服。 同时，处于技术前沿的实际的生成AI系统仍然很难识别实验室条件之外的基本物体，例如消防车和鸟类。

If we wanted to use what we have now to create a 100% A.I. movie — meaning no human input other than the initial prompt — Carlson proposed a “stepwise procedure,” that is, basically, the general framework of the Black Box system at the start of this article. “I think given the current technology, we could probably actually go from a screenplay to an audio recording that might be convincing in some way,” Carlson said. But how much harder is video than audio? In his own research, Carlson said, “the struggles in video are in things like scene changes and consistency.”

如果我们想用现在的方法制作一部100％的AI电影，这意味着除了最初的提示外没有人为输入，卡尔森提出了“逐步程序”，即基本上是黑匣子系统的通用框架。本文开头。 卡尔森说：“我认为，考虑到当前的技术，我们实际上可能会从剧本变成录音，这可能会令人信服。” 但是视频比音频难多少？ 卡尔森在自己的研究中说：“视频方面的挣扎在于场景变化和一致性方面。”

Consistency is one of the operative words in Carlson’s research. For our purposes, it means that a computer has a hard time with what we moviegoers call “continuity.” Objects that exit the frame may not be related to the ones that reenter the frame, or they may just disappear from reality altogether.

一致性是卡尔森研究中的重要用语之一 。 就我们的目的而言，这意味着一台计算机很难像我们电影观众所说的“连续性”那样。 退出框架的对象可能与重新进入框架的对象无关，或者它们可能完全消失了。

“Unless you specifically tell it that there has to be logical consistency between scenes, it’s very conceivable that you have your first scene where you have a set of people, and then you just switch angles and it’s a completely different set of people talking about the same thing,” Carlson said. The trick is to “represent the internal consistencies in math.”

“除非您明确地说场景之间必须有逻辑上的一致性，否则非常有可能您在第一个场景中拥有一组人，然后只是换个角度，而谈论场景的人则完全不同。同样的事情，”卡尔森说。 诀窍是“代表数学中的内部一致性”。

Or maybe inconsistencies and other such problems are the whole point. Talking to Oscar Sharp, director of Sunspring, the 2016 viral short film written by an A.I. and then conventionally made starring Silicon Valley’s Thomas Middleditch, one gets the impression that he’s more fascinated by what A.I. has to show him than what he can command an A.I. to produce. Sharp told OneZero that asking the question “what wouldn’t someone do?” is “a good shortcut to something that’s quite creative.” That way, he explained, “You’ve gone to somewhere we haven’t explored.”

也许矛盾和其他此类问题才是重点。 与Sunspring总监Oscar Sharp交谈， 由AI编写的2016年病毒式短片，然后按惯例由硅谷的Thomas Middleditch主演，给人的印象是，他对AI展示给他的东西比对AI的命令更着迷。 夏普(Sharp)告诉OneZero ，问一个问题“某人不会做什么？” 是“非常有创意的捷径。” 这样，他解释说：“您去了一个我们没有探索过的地方。”

Indeed, Sunspring features some memorable moments that it’s hard to imagine any organic human being — other than maybe David Lynch or Alejandro Jodorowsky — thinking up. It’s also an exercise in torturing actors with truly baffling dialogue.

确实， Sunspring具有一些令人难忘的时刻，很难想象除了David Lynch或Alejandro Jodorowsky之外，任何有组织的人都在思考。 这也是通过令人困惑的对话折磨演员的一种方式。

Sharp is not a computer scientist — for that he relies on New York University A.I. researcher Ross Goodwin. “I only know somewhat about how the systems work,” he explained. “I prefer to blindfold myself — most of the time — from the exact processes that are being used.“ Nonetheless, he’s worked with enough A.I. members on his crew to know their limits. He said point blank that he would like to make the world’s first all-A.I. movie, but he, like Carlson, felt that no one will likely accomplish that by creating a single system trained on every movie ever that just generates whole movies with a single button push. “The processing would just be a bit much,” he said.

夏普不是计算机科学家，因为他依赖纽约大学AI研究员Ross Goodwin。 他解释说：“我只对系统的工作方式有所了解。” “在大多数情况下，我宁愿蒙蔽自己所使用的确切流程。”尽管如此，他还是与足够多的AI成员一起工作，以了解他们的局限性。 他说一点空白，他想制作世界上第一部全AI电影，但他像卡尔森一样，觉得没有人会通过创建一个受过每部电影训练的单一系统来实现这一目标，而该系统只能通过一部电影来制作整部电影。按钮按下。 他说：“处理会有点多。”

After Sunspring, Sharp attempted to have A.I. artists do as much of the creative work as possible on a project rather than just writing the screenplay, and the result was a short film called Zone Out. The film was created according to a step-by-step process not so unlike the one Carlson outlined — plus quite a bit more human intervention than Sharp originally intended. It wasn’t the work of a single GAN, but multiple A.I. systems handling different jobs. A convolutional neural network was meant to comb through public-domain movies and locate visuals that matched the places and objects in an A.I.-generated screenplay. Then another A.I. system was supposed to cast “actors” — people in those old movies who are similar to the people in the script, and “puppeteer their mouths” to match the screenplay dialogue. Yet another A.I. would then synthesize proper voices for the characters, and still another created music. “It fell down on most fronts,” Sharp said.

在Sunspring之后，Sharp尝试让AI艺术家在一个项目中完成尽可能多的创意工作，而不仅仅是写剧本，结果是一部名为Zone Out的短片。 这部电影是按照循序渐进的过程制作的，与卡尔森概述的过程没有什么不同，而且比夏普最初的意图要多得多的人工干预。 这不是单个GAN的工作，而是多个处理不同工作的AI系统。 卷积神经网络旨在梳理公共领域的电影，并找到与AI生成的剧本中的位置和对象匹配的视觉效果。 然后，另一个AI系统应该投下“演员”，即那些在老电影中与剧本中的人相似的人，并“伪造嘴巴”以匹配剧本对话。 然后，另一个AI将为角色合成适当的声音，还有另一个将创建音乐。 夏普说：“它在大多数方面都下降了。”

Consistency is certainly not one of Zone Out’s strong suits — the faces of the characters keep changing, to name just one problem. But judge for yourself:

一致性当然不是Zone Out的强项之一-角色的面Kong在不断变化，仅是一个问题。 但是要自己判断：

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The cobbled-together-from-parts feel of Zone Out was not a mistake. A.I.s as we know them today tend to gobble up information and spit out something derived from that information that’s different — and this is what seems to fascinate Sharp most of all about his A.I. projects. Sharp is interested in the similarities he sees between training data in an A.I. system, and the fragments of other people’s ideas live in the heads of creative human beings. He will wax philosophical at the drop of a hat if you ask him about how these similarities relate to A.I.-created art. “Machine learning is a very useful metaphor for human thinking,” he said. If you’re a creative person, that doesn’t mean you’re truly original. “We put a load of stuff that humans made in you, and now you make stuff.”

Zone Out的鹅卵石拼凑而成的感觉并不是一个错误。 众所周知，当今的AI往往会吞噬信息，并从这些信息中吐出一些与众不同的东西，这似乎使Sharp对他的AI项目最着迷。 夏普对他看到的在AI系统中训练数据之间的相似性以及其他人的思想碎片生活在富有创造力的人的脑海中感兴趣。 如果您问他这些相似性与AI创作的艺术之间的关系，他会在哲学上大跌眼镜。 他说：“机器学习是人类思维的非常有用的隐喻。” 如果您是一个有创造力的人，那并不意味着您是真正的原创。 “我们放置了人类制造的大量物品，现在您可以制造物品。”

This is where the comparison to dreams comes in for Sharp. “When you shut your eyes, the model is still there and it’s still predicting,” he said. “That’s what a dream is. It’s also what the screenplays that I’ve been directing are like.”

这是夏普与梦想进行比较的地方。 他说：“当您闭上眼睛时，模型仍然存在并且仍在预测。” “这就是梦想。 这也是我一直在执导的剧本的样子。”

Yet Aaron Hertzmann, PhD, the principal scientist at Adobe Research and a frequent commentator on whether or not computers can create art, was eager to burst the whole “computers can dream” bubble. “The analogy between human dreaming and random sampling from a generative model is an imperfect one,” he told OneZero, “because generative models do not have consciousness or subconscious. Sometimes the random samples have a dreamlike quality; that’s a stylistic description, not a cognitive one.”

然而，Adobe Research的首席科学家亚伦·赫兹曼(Aaron Hertzmann)博士并经常评论计算机是否可以创造艺术，他渴望打破整个“计算机可以梦想”的泡沫。 他告诉OneZero ：“人类做梦与从生成模型中随机抽样进行的类比是不完善的，因为生成模型没有意识或潜意识。 有时随机样本具有梦幻般的品质； 那是一种风格上的描述，而不是一种认知上的描述。”

Hertzmann told me an A.I.-generated feature film — if it can be made — will neither be art nor worthwhile pop culture. “There is no reason to believe that any kind of technology currently exists that can create a cultural product without a human author guiding the process,” he said. “Even as pure speculation, there is no meaningful way to even imagine what this would be like; it’s like asking Jules Verne in 1850 what books will be like in the year 2020. It’s fine to discuss as science fiction, but you have to make some assumptions that don’t match what we currently know.”

赫兹曼告诉我，如果能够制作AI生成的故事片，它将既不是艺术，也不是有价值的流行文化。 他说：“没有理由相信，目前存在任何可以在没有人工指导的情况下创建文化产品的技术。” “即使是纯粹的猜测，也没有有意义的方式甚至可以想象这会是什么样子； 这就像在1850年问儒勒·凡尔纳(Jules Verne)一样，到2020年会是什么样。作为科幻小说进行讨论是可以的，但是您必须做出一些与我们目前所知不符的假设。”

Even Carlson admits that the possibility of reaching the milestone of an “A.I.-created movie” varies depending on where the goalposts are.

甚至卡尔森也承认，达到“人工智能创造的电影”里程碑的可能性取决于球门柱的位置。

“Maybe I’m being overly optimistic,” he said. “It depends on what you consider success.” He said if anyone is champing at the bit for the first robo-movie, it might shorten the wait time to settle for something animated, or a short rather than a feature, or a feature with a lot of human input along the way.

他说：“也许我过于乐观了。” “这取决于您对成功的看法。” 他说，如果有人急于要制作第一部机器人电影，这可能会缩短等待时间来解决动画或短片而不是功能，或在此过程中需要人工输入的功能。

“Do you need it to be photorealistic on everything, with lots of consistency, etcetera?” he said. “We won’t have that this year.”

“您是否需要使它在所有内容上都具有真实感，并且具有很多一致性，等等？” 他说。 “今年我们不会有那个。”