Andy Clark Jan 5th, 2024 EST丨1433words丨★★★☆☆

What Generative AI Reveals About the Human Mind

天生式人工智能揭示了人类思维哪些东西？

Generative AI—think DALL·E, ChatGPT-4, and many more—is all the rage. It’s remarkable successes, and occasional catastrophic failures, have kick-started important debates about both the scope and dangers of advanced forms of artificial intelligence. But what, if anything, does this work reveal about natural intelligences such as our own?

天生式AI（比如DALL·E、ChatGPT-4 等等）正风靡环球。
它所取得的非凡成功和偶尔涌现的灾害性失落败，引发了关于前辈AI的范围和危险性的激烈谈论。
但是，如果说这项事情揭示了自然智能（比如我们自己的智能）什么的话，那又揭示了什么呢？

I’m a philosopher and cognitive scientist who has spent my entire career trying to understand how the human mind works. Drawing on research spanning psychology, neuroscience, and artificial intelligence, my search has drawn me towards a picture of how natural minds work that is both interestingly similar to, yet also deeply different from, the core operating principles of the generative AIs. Examining this contrast may help us better understand them both.

我是一名哲学家和认知科学家，全体职业生涯都在试图理解人类思维是如何运作的。
通过对生理学、神经科学和AI的研究，我创造自然思维的运作办法与天生式AI的核心运行事理既有有趣的相似之处，又有很大的不同。
研究这种比拟可能有助于我们更好地理解它们。

The AIs learn a generative model (hence their name) that enables them to predict patterns in various kinds of data or signal. What generative there means is that they learn enough about the deep regularities in some data-set to enable them to create plausible new versions of that kind of data for themselves. In the case of ChatGPT the data is text. Knowing about all the many faint and strong patterns in a huge library of texts allows ChatGPT, when prompted, to produce plausible versions of that kind of data in interesting ways, when sculpted by user prompts—for example, a user might request a story about a black cat written in the style of Ernest Hemingway. But there are also AIs specializing in other kinds of data, such as images, enabling them to create new paintings in the style of, say, Picasso.

AI通过学习天生模型（因此得名），能够预测各种数据或旗子暗记中的模式。
所谓的天生，指的是AI通过学习某些数据集中的深层规律性，能够为自己创建该类数据的合理新版本。
以ChatGPT为例，它的数据是文本。
理解了弘大文本库中所有微弱模式和强烈的模式后，ChatGPT 就能根据用户的提示，以有趣的办法天生该数据的合理版本。
例如，用户可以哀求用海明威的风格写一篇关于黑猫的故事。
不过，也有一些AI专门研究其他类型的数据，例如图像，使它们能够以毕加索的画风创作新的绘画作品。

"Faint patterns"和"strong patterns"是相对的观点，用来描述在数据集中涌现的模式的可见程度或显著性。

"Faint patterns"指的是在数据集中涌现频率较低或者对数据集整体特色影响较小的模式。
这些模式可能是一些不太常见或者不太明显的构造或规律，须要更深入的剖析和挖掘才能创造它们。

比较之下，"strong patterns"指的是在数据集中涌现频率较高或者对付数据集的总体特色具有更显著影响的模式。
这些模式可能是一些常见、明显且易于被捕捉到的构造或规律。

What does this have to do with the human mind? According to much contemporary theorizing, the human brain has learnt a model to predict certain kinds of data, too. But in this case the data to be predicted are the various barrages of sensory information registered by sensors in our eyes, ears, and other perceptual organs.

这与人类思维有什么关系呢？根据许多当代理论，人类大脑也学会了预测某些数据的模型。
但在这种情形下，须要预测的数据是由我们眼睛、耳朵和其他感知器官中的传感器所记录的大量感官信息。

Now comes the crucial difference. Natural brains must learn to predict those sensory flows in a very special kind of context—the context of using the sensory information to select actions that help us survive and thrive in our worlds. This means that among the many things our brains learn to predict, a core subset concerns the ways our own actions on the world will alter what we subsequently sense. For example, my brain has learnt that if I accidentally tread on the tail of my cat, the next sensory stimulations I get will often include sightings of wailing, squirming, and occasionally feelings of pain from a well-deserved retaliatory scratch.

现在，关键的差异来了。
自然人的大脑必须学会在一种非常分外的环境中预测这些感官流，即利用感官信息来选择行动，以帮助我们在自己的天下中生存和发展。
这意味着，在我们的大脑学会预测的许多事情中，有一个核心子集涉及我们自己对天下的行为将如何改变我们随后的觉得。
例如，我的大脑已经知道，如果我欠妥心踩到我家猫的尾巴，接下来的感官刺激每每会包括看到猫的嚎叫、蠕动，偶尔还会感到活该被报复的抓挠而产生的疼痛感。

This kind of learning has special virtues. It helps us separate cause and simple correlation. Seeing my cat is strongly correlated with seeing the furniture in my apartment. But neither one of these causes the other to occur. Treading on my cat’s tail, by contrast, causes the subsequent wailing and scratching. Knowing the difference is crucial if you are a creature that needs to act on its world to bring about desired (or to avoid undesired) effects. In other words, the generative model that issues natural predictions is constrained by a familiar and biologically critical goal—the selection of the right actions to perform at the right times. That means knowing how things currently are and (crucially) how things will change and alter if we act and intervene on the world in certain ways.

这种学习办法具有分外的优点。
它能帮助我们区分因果关系和大略的干系性。
看到我的猫与看到我公寓里的家具之间存在着很强的干系性。
但这两者之间不存在因果关系。
与此相反，踩到猫的尾巴会导致随后的嚎叫和抓挠。
如果你是一个须要通过行动来实现期望效果（或避免不肯望的结果）的生物，那么理解两者之间的差异就至关主要。
换句话说，发出自然预测的天生模型受到一个熟习且在生物学上至关主要的目标的限定——即在精确的韶光选择精确的行动。
这意味着要理解当前事物的状态，以及（至关主要的是）如果我们以某种办法采纳行动和干预天下，事物将会如何变革和改变。

How do ChatGPT and the other contemporary AIs look when compared with this understanding of human brains and human minds? Most obviously, current AIs tend to specialize in predicting rather specific kinds of data—sequences of words, in the case of ChatGPT. At first sight, this suggest that ChatGPT might more properly be seen as a model of our textual outputs rather than (like biological brains) models of the world we live in.

与对人类大脑和人类思维的这种理解比较，ChatGPT 和其他当代AI又是若何的呢？最明显的是，当前的AI方向于专门预测特定类型的数据--就 ChatGPT 而言，预测的是文本序列。
乍一看，这彷佛表明 ChatGPT 可能更适宜被视为我们文本输出的模型，而不是（像生物大脑一样）我们所处天下的模型。

That would be a very significant difference indeed. But that move is arguably a little too swift. Words, as the wealth of great and not-so-great literature attests, already depict patterns of every kind—patterns among looks and tastes and sounds for example. This gives the generative AIs a real window onto our world. Still missing, however, is that crucial ingredient—action.

这的确是一个非常主要的差异。
但是，这种意见可能过于草率。
正如大量伟大的和那些不那么伟大的文学作品所证明的那样，笔墨已经描述出了各种模式——例如外不雅观、味道和声音等方面的模式。
这为天生式AI供应了一个理解我们天下的真正窗口。
然而，我们还短缺一个关键要素——行动。

At best, text-predictive AIs get a kind of verbal fossil trail of the effects of our actions upon the world. That trail is made up of verbal descriptions of actions ("Andy trod on his cat’s tail") along with verbally couched information about their typical effects and consequences. Despite this the AIs have no practical abilities to intervene on the world—so no way to test, evaluate, and improve their own world-model, the one making the predictions.

在最好的情形下，文本预测型AI充其量只能通过记录我们在世界上产生的影响来得到一种口头化的记录。
这个记录由行动的口头描述（“安迪踩了他的猫的尾巴”）以及有关其范例效果和后果的信息组成。
这个过程就像将我们的行动在世界上留下的痕迹变成了口头化的“化石”一样，这些“化石”可以帮助AI预测未来可能发生的事情。
但是，这种记录只是表面的，AI并不具备干预天下的实际能力，因此也就无法测试、评估和改进它们自己的天下模型，即预测模型。

This is an important practical limitation. It is rather as if someone had access to a huge library of data concerning the shape and outcomes of all previous experiments, but were unable to conduct any of their own. But it may have deeper significance too. For plausibly, it is only by poking, prodding, and generally intervening upon our worlds that biological minds anchor their knowledge to the very world it is meant to describe.

这是一个主要的实际限定。
这就好比有人可以访问一个弘大的数据图书馆，个中包含了以往所有实验的形式和结果，但却无法进行任何自己的实验。
但这也可能有更深层次的意义。
由于很明显，只有通过对我们的天下进行探究和干预，生物思维才能将其知识与它所要描述的天下紧密联系起来。

By learning what causes what, and how different actions will affect our future worlds in different ways, we build a firm basis for our own later understandings. It is that grounding in actions and their effects that later enables us to truly understand encountered sentences such as "The cat scratched the person who trod on its tail." Our generative models—unlike those of the generative AIs—are forged in the fires of action.

通过学习什么会导致什么，以及不同的行为会如何以不同的办法影响我们未来的天下，我们为自己日后的理解奠定了坚实的根本。
正是这种行动及其影响的根本，使我们后来才能够真正理解，比如 "猫抓了踩到它尾巴的人 "这样的句子。
与天生AI的天生模型不同，我们的天生模型是在实践过程中中形成的。

Might future AIs build anchored models in this way too? Might they start to run experiments in which they launch responses into the world to see what effects those responses have? Something a bit like this already occurs in the context of online advertising, political campaigning, and social media manipulating, where algorithms can launch ads, posts and reports and adjust their future behavior according to specific effects on buyers, voters, and others. If more powerful AIs closed the action loop in these ways, they would be starting to turn their currently passive and "second-hand" window onto the human world into something closer to the kind of grip that active beings like us have on our worlds.

未来的AI是否也会以这种办法建立锚定模型？它们会不会开始进行实验，通过向天下发起相应来不雅观察这些相应产生的影响？在网络广告、政治竞选和社交媒体操作中，已经涌现了一些类似的情形，算法可以发布广告、帖子和报告，并根据对买家、选民和其他人的详细影响来调度它们未来的行为。
如果更强大的AI能以这些办法完成行动循环，那么它们将开始把目前被动的、"间接"的人类天下窗口转变为一种更靠近于像我们这样的主动体对我们的天下所拥有的节制能力。

"锚定模型"（anchored model）是指在某个特定领域或任务中建立起来的模型，通过节制关键的基准信息或样本，来建立一个可靠的参考点。
这些基准信息可以是已知的事实、履历或者标准，用来比拟和评估其他干系数据或模型。
锚定模型可以帮助我们更好地理解和解释繁芜的征象，并供应一个稳定的根本来进行推理和决策。

But even then, there’d be other things missing. Many of the predictions that structure human experience concern our own internal physiological states. For example, we experience thirst and hunger in ways that are deeply anticipatory, allowing us to remedy looming shortfalls in advance, so as to stay within the correct zone for bodily integrity and survival. This means that we exist in a world where some of our brain’s predictions matter in a very special way. They matter because they enable us to continue to exist as the embodied, energy metabolizing, beings that we are. We humans also benefit hugely from collective practices of culture, science, and art, allowing us to share our knowledge and to probe and test our own best models of ourselves and our worlds.

但即便如此，还是会有其他东西缺失落。
形成人类履历的许多预测都与我们自身的内部生理状态有关。
例如，我们体验口渴和饥饿的办法具有很强的预见性，使我们能够提前办理潜在的不敷，以保持身体完全和生存的精确状态。
这意味着，在我们生存的天下里，大脑的某些预测以一种非常分外的办法发挥着重要浸染。
它们之以是主要，是由于它们使我们能够连续作为有形的、能量代谢的生命体而存在。
我们人类也从文化、科学和艺术的集体实践中获益匪浅，让我们能够分享知识，探索和测试我们自己和天下的最佳模型。

In addition, we humans are what might be called "knowing knowers"—we depict ourselves to ourselves as having knowledge and beliefs, and we have slowly designed the complex worlds of art, science, and technology to test and improve our own knowledge and beliefs. For example, we can write papers that make claims that are swiftly challenged by others, and then run experiments to try to resolve the differences of opinion. In all these ways (even bracketing obvious but currently intractable questions about ‘true conscious awareness’) there seems to be a very large gulf separating our special kinds of knowing and understanding from anything so far achieved by the AIs.

此外，我们人类可以被称为 "知识的理解者"——我们向自己描述自己拥有知识和信念的样子，我们逐步设计出繁芜的艺术、科学和技能天下来考验和改进我们自己的知识和信念。
例如，我们可以撰写论文，提出的不雅观点很快就会受到他人的质疑，然后进行实验来试图办理见地不合。
在所有这些方面（纵然我们暂时搁置关于“真正的自我意识”这样明显但目前又难以办理的问题），彷佛存在着一个非常大的鸿沟，将我们分外的知识和理解与迄今为止AI所取得的任何造诣区分开来。

Could AIs one day become prediction machines with a survival instinct, running baseline predictions that pro-actively seek to create and maintain the conditions for their own existence? Could they thereby become increasingly autonomous, protecting their own hardware and manufacturing and drawing power as needed? Could they form a community, and invent a kind of culture? Could they start to model themselves as beings with beliefs and opinions? There is nothing in their current situation to drive them in these familiar directions. But none of these dimensions is obviously off-limits either. If changes were to occur along all or some of those key missing dimensions, we might yet be glimpsing the soul of a new machine.

AI是否有一天会成为具有生存本能的预测机器，运行根本预测以主动寻求创建和掩护其自身存在条件？它们是否会变得越来越自主，保护自己的硬件，并根据须要制造和汲取能量？它们是否会形成一个社区，发明一种文化？它们是否会开始把自己塑造成有崇奉和不雅观点的生命？目前没有任何情形能够驱动它们朝着这些熟习的方向发展。
但这些方面显然也都不是禁区。
如果在所有或部分关键缺失落的维度上发生变革，我们或许就能瞥见新机器的灵魂。

"missing dimensions"是指人类独特的知识、理解和特性，与目前的AI比较存在缺失落的方面。
这包括我们人类的生理状态、文化传承、艺术创造力、崇奉和不雅观点等。

原文取自《时期周刊》，更多内容请前往官网查看，翻译&解析归本号所有，转载请备注来源。

Clark is Professor of Cognitive Philosophy at the University of Sussex. For more on the picture of human minds as action-based prediction machines, see Clark, A. The Experience Machine: How Our Minds Predict and Shape Reality (Penguin Random House, 2023).