If you’re unfortunate enough to have got into a self-driving car conversation with me that involved wine, then I will have bored you with my Courchevel Test. It’s pretty simple: I’ll believe that we’ve cracked AI well enough to do general-purpose self-driving cars¹ when the CEO of the car company is able to complete these breezingly simple steps:

如果您很不幸与我进行了涉及葡萄酒的自动驾驶汽车交谈，那么我的高雪维尔测试会让您感到无聊。 很简单：我相信只要汽车公司的首席执行官能够轻松完成以下这些简单步骤，我们就可以很好地破解AI，以制造通用自动驾驶汽车¹：

• Fly into Lyon airport in France during the ski season,在滑雪季节飞往法国里昂机场，
• Get into the back seat of their self-driving car — alone — with a nice big book that they’ve not read before,带着一本他们从未读过的好书，独自进入自动驾驶汽车的后座，
• Have the car drive them to a hotel at the Courchevel 1850 ski resort whilst they read the book.在读书的同时，让汽车开车将他们带到高雪维尔1850滑雪胜地的酒店。

There must be no other human beings in the car. The CEO will be tested on the book on arrival. The car must drive at the appropriate speed for the conditions, so no creeping along at 10km/ph. Repeat at dawn, dusk, and in varying weather conditions.

车内不得有其他人。 首席执行官将在到达时在书上接受测试。 汽车必须以适合该条件的速度行驶，因此不能以10km / ph的速度爬行。 在黎明，黄昏和不同天气条件下重复上述步骤。

This is precisely the kind of problem that modern AI can’t — yet — solve safely, because the decision space is too broad, too unpredictable and requires context and understanding, not just pattern recognition. No number of radars, sensors and cameras can provide enough information to break the world around down accurately enough to make the real-time decisions necessary to avoid driving off a cliff, flattening a cyclist, or colliding with another vehicle.

这恰恰是现代AI无法安全解决的问题，因为决策空间太广，太不可预测并且需要上下文和理解，而不仅仅是模式识别。 没有任何雷达，传感器和摄像头能够提供足够的信息来足够准确地破坏周围的世界，从而做出必要的实时决策，从而避免从悬崖上驶下，压扁自行车手或与另一辆车发生碰撞。

Without extra magic, a safe solution requires general-purpose intelligence, and that is something that remains (no matter what anyone tells you) far, far away, like the galaxy in Star Wars and its cute baby Yoda.

如果没有额外的魔法，一个安全的解决方案，需要通用的情报，那就是一些遗体(不管别人告诉你)很远很远，像星球大战星系和它可爱的宝宝尤达 。

Passing the Courchevel Test² consistently would, to me, be the pinnacle of achievement for a self-driving car’s AI. It would indicate that there is a grand enough understanding of the surrounding environment to deal with the extraordinary range of conditions that will be found. Let’s take just a handful of variables: unpredictable reflections on partially iced or wet roads, weather that can change faster than you can describe it, car-sized holes in side-walls with perilous deadly drops beyond them, hairpin bends with large, sudden gains or drops in altitude. Then there’s small villages where pavements are missing, with narrow and wide bits around buildings that were engineered when horses were the premier tip-top way of getting around, constant animal or rock-fall hazards, insane speeding drivers³ that roar up these roads like they are invincible (and those car-sized gaps in the protective wall would indicate otherwise). It’s a puzzler, for sure.

对我来说，持续通过高雪维尔测试²将是无人驾驶汽车AI成就的顶峰。 这表明对周围环境有足够的了解，可以应对各种异常情况。 让我们考虑几个变量：在部分结冰或潮湿的道路上发生无法预测的反射，天气变化的速度比您所描述的更快，侧壁上的汽车大小的Kong具有致命的致命跌落，发夹弯弯时突然产生大的突然变化或海拔下降。 然后是一个小村庄，缺少人行道，建筑物周围狭窄而宽阔，当马是最主要的出行方式，不断的动物或跌落危险，疯狂的超速驾驶人³时，这些建筑物被设计成像它们一样轰鸣着是无敌的(防护墙中那些与汽车类似的缝隙可能会表明其他情况)。 当然，这是一个难题。

修改图灵测试 (Revising the Turing Test)

Let’s try something that’s surely easier. Steve Wozniak, co-founder of Apple, said he’d believe that AI had arrived when a robot could enter a strange house and make a decent cup of coffee. The get-dressed test was suggested to me by Steve Grand: get an AI powered robot to get dressed, appropriately, with what’s in the wardrobe and drawers. There are others, like assemble some IKEA flat-pack furniture, or pick strawberries and they sound deceptively simple until you start to break them down into their component parts. Simple becomes “uh oh, yeah, I see the problem”.

让我们尝试一些肯定更容易的事情。 苹果公司联合创始人史蒂夫·沃兹尼亚克(Steve Wozniak)说，他相信当机器人可以进入一个陌生的屋子并煮一杯像样的咖啡时，人工智能已经到来。 史蒂夫·格兰德(Steve Grand)向我建议进行穿衣测试：让一个配备AI的机器人根据衣橱和抽屉中的衣服进行适当穿衣。 还有其他一些东西，例如组装宜家的平装家具或摘草莓，在您将它们分解成组件之前，它们听起来很简单。 “简单”变成“呃，是的，我明白了”。

The Courchevel Test, the make-a-coffee test and the get-dressed test are possibly better indicators of how we are getting along with true AI than most other contrived ones. Sure, it’s impressive to be able to solve puzzles, play games and recognise and classify and transform images orders of magnitude better and faster than humans, but humans can solve problems that leave computers standing cold. We are absolutely, totally, brilliant and don’t let anyone tell you otherwise. Indeed, we’re not alone in being awesome: watch a bee navigate in 3D space and tell me you’ve seen AI do that better.

与大多数人为设计的人相比，高雪维尔测试，咖啡测试和穿衣测试可能是我们如何与真正的AI相处的更好指标。 当然，能够解决难题，玩游戏以及识别，分类和转换图像的幅度比人类更好，更快，这给人留下了深刻的印象 ，但是人类可以解决使计算机停滞不前的问题。 我们绝对，完全，出色，不要让任何人告诉你。 确实，我们并不单单表现出色：观看蜜蜂在3D空间中导航，并告诉我您已经看到AI可以做得更好。

The thing is, AI can’t see human level intelligence from where it is even if it is armed with the Hubble Telescope. I’ve written about this before in the context of the vast gap between where you might think we are and where we actually are. None of this, of course, takes away any of the life-changing benefits that we get every single day in our lives from the incredible advances that artificial intelligence, and in particular machine learning, are and will continue to deliver. We do, though, need to be both pragmatic and realistic about the chasm between AI and Artificial General Intelligence (AGI).

事实是，即使配备了哈勃望远镜，人工智能也无法从任何地方看到人类的智能。 在您可能认为我们与实际位置之间存在巨大差距的背景下，我已经写过有关此问题的文章。 当然，这一切都不会从人工智能(尤其是机器学习)正在并将继续提供的令人难以置信的进步中夺走我们每天获得的改变生活的任何收益。 但是，对于AI和人工智能(AGI)之间的鸿沟，我们确实需要务实和现实。

堆放猫 (Stacking Cats)

But, as always, there is more than one way to stack cats… and one of them is to make the world so easy to understand, that a drunken baboon could make sense of it.

但是，和往常一样，有不止一种堆放猫的方法……其中一种是使世界变得如此易于理解，以至于喝醉了的狒狒都可以理解。

Let’s touch on two approaches:

让我们来谈谈两种方法：

1. Take the options out of the scenario. When you see little robots trundle peacefully around a factory or warehouse, they are usually achieving it in one of two ways: either by detecting a metal ribbon in the floor, or following a yellow stripe on the ground. This is inflexible, and doesn’t respond well to changes (like a paint spill over the stripe, or a break/fault in the metal ribbon, or just a surprise like goods spilt in the way or a person not paying attention). It’s great for factories, and also for some city applications, but won’t pass our Courchevel test due to the cost to deploy. If you want this, take a train.

   从方案中删除选项 。 当您看到小型机器人在工厂或仓库周围和平地走动时，通常可以通过以下两种方式之一来实现：通过检测地板上的金属带或沿着地面上的黄色条纹。 这是不灵活的，并且不能很好地适应变化(例如，油漆溅到条纹上，或者金属色带断裂/断裂，或者只是意外的东西，例如，物品溢出或人不注意)。 这对工厂以及某些城市应用程序都非常有用，但是由于部署成本高昂而无法通过我们的Courchevel测试。 如果您要这样做，请坐火车。

2. Have the world describe itself. Rather than using cameras and other sensors to attempt to do a paint-by-numbers interpretation of the surrounding world, where lighting, weather and other things can destroy the reliability of the picture, turn the problem inside out. Cameras make mistakes that humans do not, because we’re paranoid, and we see and understand context. If all the objects in the world describe themselves, then you know what and where things are.

   让世界描述自己 。 与其使用照相机和其他传感器来尝试对周围的世界进行数字描绘，在光照，天气和其他事物可能破坏图片可靠性的情况下，将问题彻底解决了。 相机会犯人类不会犯的错误，因为我们很偏执，而且我们看到并理解了上下文。 如果世界上所有物体都描述了自己，那么您就知道事物在哪里，在哪里。

Option 2 could solve the Courchevel Test, but it requires a critical mass of things to announce themselves, an architecture to support such things, and a method of receiving the required information and building a picture of this augmented reality. If these issues were solved, you gain a load of interesting up-sides: you can see through fog. You can see in the dark. You can see through snow. You can see around corners. It’s the ultimate cheat mode for reality.

选项2可以解决高雪维尔测试，但它需要大量的东西来宣布自己，支持这些东西的体系结构以及接收所需信息并为增强现实构建图片的方法。 如果解决了这些问题，您将获得许多有趣的方面：​​可以看到雾气。 您可以在黑暗中看到。 你可以看透雪。 您可以看到角落。 这是现实的终极作弊模式 。

If everyone takes part, you have a visual on what’s going on that is exceptionally accurate, so much so that you can dedicate the cameras and sensors to just picking up the surprises and those that refuse to play. Your average fox, hedgehog, deer or drunken adult staggering back from a bar having dropped his phone in the toilet are unlikely to be represented in our digitally augmented world. But, in one fell swoop, we’ve hugely reduced the scope of the problem, and brought it within grasp of what AI can realistically do today.

如果每个人都参与其中，您将对发生的事情有一个非常准确的视觉印象，以至于您可以将摄像机和传感器专用于捡拾惊喜和拒绝玩耍的惊喜。 您的普通狐狸，刺猬，鹿或醉酒的成年人从将手机掉进厕所的酒吧里摇摇晃晃地返回，不太可能在我们的数字增强世界中得到体现。 但是，一口气，我们大大缩小了问题的范围，并将其掌握在AI今天可以实际完成的工作中。

使世界栩栩如生 (Bringing the world to life)

When I’m talking about AI and alternative approaches to understanding and navigating the world at conferences, I often show images like the one below. How many trees are in that picture? Humans can make a better guess than computers because we can see which ones are probably the same tree, and we can imagine the missing parts and speculate realistically as to what’s where we can’t see. But what about the road signs in that image? Only one, you say? Are you sure about that? What if one was knocked over and yet to be fixed? What if it said “Caution, thousands of hungry tigers 100 metres ahead at stop sign”, or “Bridge out: 500 metre vertical drop ahead”. Unless the sign itself talks to you, then you’re never going to know.

当我在会议上谈论人工智能和理解和导航世界的替代方法时，我经常会显示以下图像。 那张照片里有几棵树？ 与计算机相比，人类可以做出更好的猜测，因为我们可以看到哪些可能是同一棵树，并且我们可以想象缺失的部分并就我们看不到的地方进行实际推测。 但是该图像中的路标呢？ 你只说一个？ 你确定吗？ 如果一个人被撞倒而又无法解决怎么办？ 如果显示“警告，停车标志前方100米处有成千上万的饥饿老虎”或“跳出桥梁：前方500米垂直下落”该怎么办？ 除非标牌本身与您对话，否则您永远不会知道。

And this is where Autonomous Economic Agents come in. Clearly, you are never going to put computing devices on all the dumb, static street furniture out there, but thanks to comprehensive databases of such items, it is now possible to spawn digital representatives of everything, and for those representatives to be viable economic units. For small fractions of a cent, they are able to sell the information about themselves, plus additional intelligence based on what they can glean from the type and frequency of other requests. This incentive to operate such things coupled with using decentralised incentives to play-well, be up-to-date and accurate is a powerful combination of technologies. We’re armed with some incredible parts of this jigsaw puzzle that were not true as recently as half a decade ago:

这就是自治经济代理人进入的地方。显然，您永远不会在所有笨拙的静态街道家具上放置计算设备，但是由于有了此类物品的全面数据库，现在可以生成所有物品的数字代表，并使这些代表成为可行的经济单位。 他们只需花费一分钱，就可以出售自己的信息，并根据其他请求的类型和频率收集到的信息，从而获得更多情报。 操作此类事物的这种激励作用，再加上使用分散激励措施来发挥作用，保持最新和准确，是技术的强大组合。 我们配备了这个拼图游戏中令人难以置信的部分，这些部分在50年前是不正确的：

• Mobile devices with GPS and permanent data connections are ubiquitous. We all have them. They can run a representative of themselves, and this provides intelligence information on where they are, how fast they are going, in what direction, and at what altitude. This brings almost every person, vehicle and building to life: an unprecedented source and quantity of real-time knowledge.

  具有GPS和永久数据连接的移动设备无处不在 。 我们都有他们。 他们可以运行自己的代表，这可以提供有关其位置，行进速度，方向和高度的情报信息。 这几乎使每个人，车辆和建筑物都栩栩如生：前所未有的实时知识源和数量。

• Digital representatives can operate without any centralised entity being involved and can control their own value generation. Thanks to, wait for it, because you knew it was coming, blockchain, individual entities can take part in the digital economy autonomously. They can create their own “account”, have it populated with tokens/cryptocurrency and can take part in the economy around them immediately. If we can figure out self-bootstrapping, token-powered e-SIMs, then this becomes even more powerful.

  数字代表可以在不涉及任何集中实体的情况下进行操作，并且可以控制自己的价值创造 。 多亏了，等待它，因为您知道它即将来临， 区块链 ，单个实体可以自主地参与数字经济。 他们可以创建自己的“帐户”，并使用令牌/加密货币填充该帐户，并可以立即参与其周围的经济活动。 如果我们能够弄清楚以令牌为动力的自引导式e-SIM，那么它将变得更加强大。

• Blockchain, coupled with verifiable credentials, digital identity and other cryptographic technologies can provide decentralised, self-service trust. You need not take anyone’s word for it, you can establish enough about reputation yourself to decide if any given interaction fits your risk profile.

  区块链加上可验证的凭证，数字身份和其他密码技术可以提供去中心化的自助服务信任 。 您无需为此而信服，您可以自己建立足够的声誉声誉，以决定任何给定的互动是否符合您的风险状况。

• Again, blockchain (it keeps on giving) enables scale through decentralisation, and incentives for taking part in providing the computing power. Now, the dream of having, say, every single street sign represented by a digital entity with its own identity, account, reputation and knowledge is no longer laughable science fiction.

  再次，区块链 (一直在奉献) 通过去中心化实现规模扩展，并通过激励机制来参与提供计算能力 。 现在，拥有一个拥有自己的身份，帐户，声誉和知识的数字实体代表的每个路牌的梦想不再是可笑的科幻小说。

The issue over discovery and organisation of such a population of autonomous digital entities remains, or at least it did, until we at Fetch.ai created our Agent Framework and decentralised agent search and discovery mechanism. We’ve demonstrated several examples of how the real and the digital worlds can be connected, with both decentralised ride-sharing and autonomous agents representing each and every train and station in the UK, and we’re many slices in to the street-furniture-as-agents cake, too. Depending on the time of day you check, there has been a large population of agents representing the space around Cambridge, UK, in our Digital World for some time now and it’s growing all the time.

在我们这样的自治数字实体的发现和组织方面，这个问题仍然存在，或者至少一直存在 ，直到我们在Fetch.ai建立了我们的Agent框架和分散的Agent 搜索和发现机制 。 我们已经展示了几个示例，说明了如何实现现实世界与数字世界之间的连接，分散的乘车共享和自治代理代表了英国的每一个火车站和车站 ，而我们在街车中也涉及很多领域-代理蛋糕也是如此。 根据您检查一天的时间，在我们的“数字世界”中，有大量的代理商代表英国剑桥周围的空间已有一段时间，并且一直在增长。

These networks-of-agents are the building blocks for decentralising the delivery business, optimising congestion out of travel, increasing capacity and efficiency of supply chains, providing an augmented, smart reality for self-driving cars and more.

这些代理商网络是分散配送业务，优化出行拥堵，提高供应链容量和效率，为自动驾驶汽车提供增强的智能现实等的基础。

It’s possible for anyone to take part in this digital economy: bringing the static dumb world to life, and building a collective intelligence about what’s going on that’s owned by and for the benefit of everyone. As I am fond of saying, life need not be a zero-sum-game, and the incentive mechanisms blockchain provides makes it more profitable to be good than bad, limits damage, and tends away from the tragedy of commons one can often see with shared resources.

任何人都有可能参与这种数字经济：将静态的愚蠢世界带入生活，并就所有人所拥有的利益和所有人的利益建立集体情报。 就像我喜欢说的那样，生活不必是零和游戏，区块链提供的激励机制使其变得好于弊，限制损害并远离人们经常看到的公地悲剧。共享资源。

I’m still not sure if I’d take part in the Courchevel Test myself, at least not first, or sober, but I do look forward to a day where it is possible. And by augmenting the environment with autonomous agents, we close the gap substantially. AGI, though? True human-level intelligence in a machine? Well, there’s a way to go, but here’s a little clue: pattern generation is as important, if not more so, than pattern recognition when it comes to intelligence, because it enables imagination, effective goal selection and execution.

我仍然不确定我是否会参加高雪维尔测试，至少不是第一次或清醒，但我确实希望有一天可以参加。 通过使用自治代理来扩大环境，我们可以大大缩小差距。 但是，AGI？ 机器中真正的人类智能？ 好的，还有一条路要走，但是这里有一些线索：在智能方面，模式生成与模式识别同等重要(如果不是那么重要)，因为它可以实现想象力，有效的目标选择和执行。

It won’t surprise you to hear, we’re on the way.

听到您的消息就不会感到惊讶，我们正在路上。

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Twitter: @pretzelsnake

推特：@pretzelsnake

[1] — that’s actual SAE level 5 with knobs on, not “Elon Musk tweet level 5”.

[1]-这是带旋钮的实际SAE 5级，而不是“ Elon Musk tweet 5级”。

[2] — then there’s the advanced version of the Courchevel Test: same scenario, but the steering wheel and pedals are removed from the vehicle to stop the CEO from leaping between the seats to save him or herself when things go… well, you know.

[2]-然后是高雪维尔测试的高级版本 ：相同的情况，但是方向盘和踏板已从车辆上卸下，以防止CEO在事情发生时在座椅之间跳跃以救他或她自己……好吧， 你知道。

[3] — mostly French, of course, who demonstrate a miraculous knack for surviving overtaking on a blind hairpin corner, at speed, on ice, with the sun in their eyes, whilst smoking. Then you eventually find them at the hotel five beers ahead of you looking astonishingly, and unreasonably, cool.

[3]-当然，大多数是法国人，他们表现出了奇迹般的诀窍，能够在盲发pin的拐角处，在冰上，在太阳下，在吸烟的同时，在高速，冰上幸存下来。 然后，您最终在酒店前发现了五种啤酒，它们看上去惊人而无理地酷。