简易AI指南-AI对你有多了解及综合型ChatGPT4-慢速及文稿

What does AI know about me?
AI 对我了解多少？


Some AIs simply deal with numbers, collecting and combining them in volume to create a swarm of information, the products of which can be extremely valuable.
部分人工智能只是简单地处理数字，将它们大量收集和组合以创建大量信息，这样的成果能具有极高的价值。
There are likely already several profiles of your financial and social actions, particularly those online, which could be used to make predictions about your behaviour.
很可能已经有一些关于你的财务和社会行为的资料，尤其是在线的资料，可以用来预测你的行为。
Your supermarket loyalty card is tracking your habits and tastes through your weekly shop. The credit agencies track how much you have in the bank and owe on your credit cards.
超市会员卡会跟踪你每周购物的习惯和品味。 信贷机构会追踪个人的银行存款金额和信用卡欠款金额。
Netflix and Amazon are keeping track of how many hours of content you streamed last night. Your social media accounts know how many videos you commented on today.
网飞和亚马逊  会记录您昨晚浏览节目内容的时长。 你的社交媒体帐户知道你今天评论了多少个视频。
And it’s not just you, these numbers exist for everyone, enabling AI models to churn through them looking for social trends.
不仅仅是你，每个人都有这些数字档案，使人工智能模型能够通过这些数字来寻找社会趋势。
These AI models are already shaping your life, from helping decide if you can get a loan or mortgage, to influencing what you buy by choosing which ads you see online.
这些人工智能模型已经在塑造你的生活，从帮助你决定是否可以获得贷款或抵押贷款，到通过选择你在网上看到的广告来影响你的购买内容。

Will AI be able to do everything?

AI能做所有事情吗？

Would it be possible to combine some of these skills into a single, hybrid AI model?

是否有可能将其中一些技能组合到一个混合人工智能模型中？

That is exactly what one of the most recent advances in AI does.

这正是人工智能的最新进展之一。

It’s called multimodal AI and allows a model to look at different types of data - such as images, text, audio or video - and uncover new patterns between them.

它被称为多模式人工智能，允许模型查看不同类型的数据——例如图像、文本、音频或视频——并发现它们之间的新模式。

This multimodal approach was one of the reasons for the huge leap in ability between ChatGPT3, which was trained on text only, and ChatGPT4, which was trained with images as well.

这种多模态方法是 ChatGPT3（仅接受文本训练）和 ChatGPT4（也接受图像训练）之间能力巨大飞跃的原因之一。

The idea of a single AI model able to process any kind of data and therefore perform any task, from translating between languages to designing new drugs, is known as artificial general intelligence (AGI).

单一人工智能模型能够处理任何类型的数据，从而执行任何任务，从语言之间的翻译到设计新药物，这一想法被称为通用人工智能（AGI）。

For some it’s the ultimate aim of all artificial intelligence research; for others it’s a pathway to all those science fiction dystopias in which we unleash an intelligence so far beyond our understanding that we are no longer able to control it.

对一些人来说，这是所有人工智能研究的最终目标；对一些人来说，这是所有人工智能研究的最终目标。 对另一些人来说，这是通向科幻小说中的反乌托邦的一条途径，在这些反乌托邦中，我们开发出一种远远超出我们理解的智能，而我们对它已无法控制。

How do you train an AI?

如何训练人工智能？

Until recently the key process in training most AIs was known as "supervised learning".

直到最近，训练大多数人工智能的关键过程被称为“指导学习”。

Huge sets of training data were given labels by humans and the AI was asked to figure out patterns in the data.

人类给大量的训练数据贴上标签，并要求人工智能找出数据中的模式。

The AI was then asked to apply these patterns to some new data and give feedback on its accuracy.

然后，人工智能被要求将这些模式应用于一些新数据，并就其准确性提供反馈。

For example, imagine giving an AI a dozen photos - six are labelled "car" and six are labelled "van".

例如，想象一下给人工智能一张照片——六张照片被标记为“汽车”，六张照片被标记为“货车”。

Next tell the AI to work out a visual pattern that sorts the cars and the vans into two groups.

接下来，告诉人工智能制定一种视觉模式，将汽车和货车分为两组。

Now what do you think happens when you ask it to categorise this photo?

现在，当您要求它对这张照片进行分类时，您认为会发生什么？

Unfortunately, it seems the AI thinks this is a van - not so intelligent.

不幸的是，人工智能似乎认为这是一辆货车——它似乎不那么聪明。

Now you show it this. And it tells you this is a car.

现在你向它展示这个。它告诉你这是一辆汽车。

It’s pretty clear what’s gone wrong.

问题出在哪里很明显。

From the limited number of images it was trained with, the AI has decided colour is the strongest way to separate cars and vans.

从训练时使用的有限图像来看，人工智能认为颜色是区分汽车和货车的最强方法。

But the amazing thing about the AI program is that it came to this decision on its own - and we can help it refine its decision-making.

但人工智能程序的惊人之处在于，它自己做出了这个决定——而我们可以帮助它完善决策。

We can tell it that it has wrongly identified the two new objects - this will force it to find a new pattern in the images.

我们可以告诉它，它错误地识别了两个新对象——这将迫使它在图像中找到新的模式。

But more importantly, we can correct the bias in our training data by giving it more varied images.

但更重要的是，我们可以通过提供更多样的图像来纠正训练数据中的偏差。

These two simple actions taken together - and on a vast scale - are how most AI systems have been trained to make incredibly complex decisions.

大多数人工智能系统都是通过大规模这两个简单行为同时训练，来做出极其复杂的决策的。

 What is deep learning?

什么是深度学习？

Many of the most recent breakthroughs in AI have been made possible by deep learning.

人工智能领域的许多最新突破都是通过深度学习实现的。

In the simplest terms, this is where the use of complex algorithms and huge datasets means the AI can learn without any human guidance.

简而言之，复杂算法和庞大数据集的使用意味着人工智能可以在没有任何人类指导的情况下学习。

ChatGPT is the most well-known example.

ChatGPT 是最著名的例子。

The amount of text on the internet and in digitised books is so vast that over many months ChatGPT was able to learn how to combine words in a meaningful way by itself.

互联网和数字化书籍中的文本之海量，让 ChatGPT 能够在几个月内学会如何以有意义的方式自行组合单词。

Imagine you had a big pile of books in a foreign language, maybe some of them with images.

想象一下，您有一大堆外语书籍，其中可能有一些带有图像。

Eventually you might work out that the same word appeared on a page whenever there was drawing or photo of a tree, and another word when there was a photo of a house.

最终你可能会发现，每当有树的图画或照片时，页面上就会出现同一个单词，而当有房子的照片时，页面上就会出现另一个单词。

And you would see that there was often a word near those words that might mean “a” or maybe “the” - and so on.

你会发现这些词附近经常有一个词可能表示“a”或“the”，等等。

This is the deep learning model, also known as unsupervised learning.

这就是深度学习模型，也称为无监督学习。

It relies on enormous amounts of computing power which allows the AI to memorise vast amounts of words - alone, in groups, in sentences and across pages - and then read and compare how they are used over and over and over again in a fraction of a second.

它依赖于巨大的计算能力，使人工智能能够记住大量的单词——单独的、成组的、句子中的和跨页的——然后在几乎一瞬间的时间内一遍又一遍地阅读和比较它们的使用方式.

The rapid advances made by deep learning models in the last year have driven the new wave of enthusiasm and concern over the potential of artificial intelligence, and there is no sign of it slowing down.

去年深度学习模型取得的快速进步引发了人们对人工智能潜力的新一波热情和担忧，但这种发展没有任何放缓的迹象。

The promises and warnings of science fiction seem to have suddenly crept up on us and we find we are already living in a world where AI is beginning to reveal its strange inhuman abilities.

科幻小说中的承诺和警告似乎突然出现在我们身边，我们突然发现已经生活在一个人工智能开始展现其奇怪的非人能力的世界。