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Need a new idea? Start at the edge of what is known
We have all probably wondered how great minds achieved what they achieved, right? And the more astonishing their achievements are, the more we call them geniuses, perhaps aliens coming from a different planet, definitely not someone like us. But is that true?
So let me start with an example. You all know the story of Newton's apple, right? OK. Is that true? Probably not. Still, it's difficult to think that no apple at all was there. I mean some stepping stone, some specific conditions that made universal gravitation not impossible to conceive. And definitely this was not impossible, at least for Newton. It was possible, and for some reason, it was also there, available at some point, easy to pick as an apple. Here is the apple.
And what about Einstein? Was relativity theory another big leap in the history of ideas no one else could even conceive? Or rather, was it again something adjacent and possible, to Einstein of course, and he got there by small steps and his very peculiar scientific path? Of course we cannot conceive this path, but this doesn't mean that the path was not there.
So all of this seems very evocative, but I would say hardly concrete if we really want to grasp the origin of great ideas and more generally the way in which the new enters our lives. As a physicist, as a scientist, I have learned that posing the right questions is half of the solution. But I think now we start having a great conceptual framework to conceive and address the right questions. So let me drive you to the edge of what is known, or at least, what I know, and let me show you that what is known could be a powerful and fascinating starting point to grasp the deep meaning of words like novelty, innovation, creativity perhaps.
So we are discussing the "new," and of course, the science behind it. The new can enter our lives in many different ways, can be very personal, like I meet a new person, I read a new book, or I listen to a new song. Or it could be global, I mean, something we call innovation. It could be a new theory, a new technology, but it could also be a new book if you're the writer, or it could be a new song if you're the composer. In all of these global cases, the new is for everyone, but experiencing the new can be also frightening, so the new can also frighten us. But still, experiencing the new means exploring a very peculiar space, the space of what could be, the space of the possible, the space of possibilities. It's a very weird space, so I'll try to get you through this space. So it could be a physical space. So in this case, for instance, novelty could be climbing Machu Picchu for the first time, as I did in 2016. It could be a conceptual space, so acquiring new information, making sense of it, in a word, learning. It could be a biological space. I mean, think about the never-ending fight of viruses and bacteria with our immune system.
And now comes the bad news. We are very, very bad at grasping this space. Think of it. Let's make an experiment. Try to think about all the possible things you could do in the next, say, 24 hours. Here the key word is "all." Of course you can conceive a few options, like having a drink, writing a letter, also sleeping during this boring talk, if you can. But not all of them. So think about an alien invasion, now, here, in Milan, or me -- I stopped thinking for 15 minutes.
So it's very difficult to conceive this space, but actually we have an excuse. So it's not so easy to conceive this space because we are trying to conceive the occurrence of something brand new, so something that never occurred before, so we don't have clues. A typical solution could be looking at the future with the eyes of the past, so relying on all the time series of past events and hoping that this is enough to predict the future. But we know this is not working. For instance, this was the first attempt for weather forecasts, and it failed. And it failed because of the great complexity of the underlying phenomenon. So now we know that predictions had to be based on modeling, which means creating a synthetic model of the system, simulating this model and then projecting the system into the future through this model. And now we can do this in a lot of cases with the help of a lot of data.
Looking at the future with the eye of the past could be misleading also for machines. Think about it. Now picture yourself for a second in the middle of the Australian Outback. You stand there under the sun. So you see something weird happening. The car suddenly stops very, very far from a kangaroo crossing the street. You look closer and you realize that the car has no driver. It is not restarting, even after the kangaroo is not there anymore. So for some reasons, the algorithms driving the car cannot make sense of this strange beast jumping here and there on the street. So it just stops. Now, I should tell you, this is a true story. It happened a few months ago to Volvo's self-driving cars in the middle of the Australian Outback.
我们可能都曾好奇过， 聪明人是怎样有所成就的，对吗？ 并且他们的所作所为越令人惊叹， 我们越习惯于叫他们天才， 或者是“外星人”， 来自另外的星球， 反正绝对不像我们这样普通。 但是，真的是这样吗？
让我来举个例子说明。 我们都知道牛顿的苹果。 那个故事真的发生过吗? 可能没有。 当然，我们很难想象传说中的 苹果其实并不存在。 我的意思是，宇宙万有引力定律的发现 是基于特定环境或媒介的铺垫。 这种说法有一定道理， 至少对于牛顿来说是这样。 这是可能的， 因为一些原因，它就在那里， 像够到那个苹果一样容易， 触手可及。
那么对于爱因斯坦呢？ 相对论是历史上又一大 新思想的飞跃。 除了爱因斯坦， 就没人可以提出了吗？ 或者说，相对论当时 就在我们身边， 对于爱因斯坦也是一样， 他一步步走在自己的科学发现之路上， 最终发现了相对论。 当然我们无从知道 这是一条怎样的路， 但这不能否认那条路的存在。 这两个例子好像暗示了
一些什么，却又不具象， 尤其当我们真的希望 找到变得更优秀的源头， 或通俗一点说，我们怎样 在生活中发现新鲜事物的时候。 作为一个物理学家，科学家， 我知道，提出正确的问题， 问题就解决了一半。 而我想，我们现在已经拥有了 很棒的概念性的框架 来发现和解决问题。 那么现在，让我带大家 进入身边所熟悉的领域， 或至少，是我熟悉的。 让我来说明一下，从熟悉的领域开始 去感知新奇，创新，或者创造 这类词语更深层的含义， 是一个多么好的起点。
我们在讨论“新”， 同时还有它背后的科学。 “新”可以由不同的方式 进入我们的生活， 可以是很私人的， 比如，我认识了一个新朋友， 读了一本新书或者听了一首新歌； 也可以是普遍化的， 比如，我们所说的创新， 可以是新理论，新技术， 同样也可以是一本新书， 前提是你是个作家， 也可以是一首新歌， 如果你是个作曲家。 这所有的例子里的“新”， 是每个人都有机会接触发现的。 但体验“新”却也常常令人担忧， 因为我们面对“新”，会有畏惧感。 同时，体验“新”意味着 我们在探索一段奇特的领域， 它具有任意性， 还有可能性。 这是个很神奇的领域， 不过我会尝试带大家领略一下。 它可以是某个物理空间。 比如， 我在2016年第一次爬上 马丘比丘（古代印加城遗址， 在今秘鲁中南部)。 也可以是理论上的空间， 如获取新的信息， 简而言之，就是学习。 它还可以是生物层次的。 想想我们的免疫系统 与病毒及细菌之间 永不停歇的对抗。
但是先别忙着乐观， 我们非常不擅于察觉到“新”的存在。 想一想是不是这样， 我们来做个实验。 尝试思考在未来的24小时内， 你可以做的所有可能的事情。 记住，关键词是“所有”。 下意识地，你会有几个选择， 比如喝一杯饮料，写封信， 或者在我无聊的陈述中打个小盹， 如果你们想的话。 但这不是所有我们要做的事情。 想一想外星人入侵， 对，就是现在，在米兰， 或者是我，在接下来的 15分钟内停下来不去思考。
所以，要察觉到 所有可能发生的事情并不容易。 但这可以理解。 不容易实现的原因是 我们都尝试着 去发现一些绝对的“新”， 一些以前从未发生的事情， 所以我们找不到任何线索。 那么有什么解决办法吗？ 用目睹了过去的眼睛看未来， 就是凭借着在过去发生的事， 这些经历能支持我们预测未来。 但实际上，这种方法的效果差强人意。 就跟首次播报天气失败了一样。 因为事情多发生在表面， 而内部的复杂性却被忽略了。 所以，我们会通过建模来帮助预测， 就是建立一个系统的综合模型， 通过模型模拟，预测系统的 未来发展。 在很多情况下，基于大量数据， 我们都可以建模。
但用过去的眼睛（数据） 预测未来（系统）， 也可能会出错， 对计算机来说也是一样。 设想一个画面， 你在澳大利亚内陆地区， 站在太阳底下， 看到了一些奇怪的事情。 远远地，一辆车突然停住了， 在它前面很远处 有一只袋鼠在过马路。 你仔细一看， 发现车里竟没有司机。 袋鼠过完马路后， 汽车也没有重新启动。 因为一些原因， 这辆无人驾驶汽车内置的算法 并不能理解这种现象， 一只奇怪的庞然大物 在街上蹦来蹦去。 于是它就停下了。 这是个真实的故事。 几个月前，沃尔沃的 无人驾驶汽车就这样 停在了澳洲内陆中部地区。
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