人工智能技术并非已经完全成熟,而进入发展应用的阶段

但实际上,在这个之后,奇迹并没有发生准确一点说,今后或许会在个别领域取得进展,但是不会像之前预计的那样全面开花。特别是中国市场乐观的认为”中国市场大、数据多运用又不受限制,所以将来奇迹一定会发生在中国”。从目前的情况来看效果最好的事情还是这两件:图像识别、语音识别。我看了一下中国人工智能领域20个独角兽30个准独角兽企业近80%都跟图像识别或者语音识别有关系。
人工智能在围棋上战胜人类后产生了这种恐慌“大师才能做的事,人工智能居然能做我的工作这么平凡,肯定会被机器所替代”这里需要考虑一下它的局限性不要过于乐观人工智能能做的那三件事(语音识别、图像识别、围棋)是因为它满足五个条件,就是说只要满足了这五个条件,计算机就做好,只要有任何一个或者多个条件不满足,计算机做起来就困难了。
是必须具备充足的数,充足不仅仅是说数量大,还要多性,不能残缺等。
是确定性。
是最重要的需要完全的信息,围棋就是完全信息博,牌类是不完全信息博弈,围棋然复杂,但本质上只需要计算速度快,不要靠什么智能,可是在日常生活中,我们所有的决策都是在不完全信息下做的。
它是静态的,包括按照确定性规律进化,是可预测性问题,在复杂的路况下,自动驾驶不能满足这一问题;事实上,它既不满足确定性,也不满足完全信息。

[En]

It is static, including evolution according to the law of certainty, is the problem of predictability, which is not satisfied by autopilot in complex road conditions; in fact, it does not satisfy neither certainty nor complete information.

这是一个特定的领域,如果领域太宽,他就做不到。一个单一的任务,也就是下棋的人工智能软件,就是下棋,其他什么都做不了。

[En]

It’s a specific field, and if the field is too wide, he can’t do it. A single task, that is, artificial intelligence software for playing chess, is playing chess and can do nothing else.

如果你的工作符合这五个条件,它肯定会被电脑取代。符合这五个条件的工作特点非常明显,那就是收银员、收银员这四个字,就是“按规矩办事”,不需要灵活性。如果你的工作是灵活的和创造性的,电脑永远不能完全更换,当然,部分更换是可能的,因为必须有一些简单和重复的内容。如果你认识到这一原则,你就会意识到人工智能还处于发展的早期阶段。而不是像一些人估计的那样,“人工智能技术已经完全成熟,并进入了开发和应用阶段。”

[En]

If your work meets these five conditions, it will definitely be replaced by a computer. the work characteristics that meet these five conditions are very obvious, that is, four words “do things according to the rules” and do not need flexibility, such as cashiers and cashiers. If your work is flexible and creative, the computer can never be completely replaced, of course, partial replacement is possible, because there must be some simple and repetitive content. If you recognize this principle, you will realize that artificial intelligence is still in the early stages of development. Not as some people estimate that “artificial intelligence technology has been fully mature, and entered the stage of development and application.”

从应用角度来看,深度学习技术已经接近天花板。今天的深度学习本质上是基于概率和统计的。什么叫概率统计?这并不那么神秘。深度学习是寻找重复出现的模式,所以重复更多就被认为是定律(真理),所以重复一千次的谎言就被认为是真理,那么为什么大数据有时会得出非常荒谬的结果呢?因为不管对不对,只要再重复一遍,就会遵循这个规律,就是谁说得多就是谁说得多。

[En]

Deep learning technology is close to the ceiling from an application point of view. Today’s deep learning is essentially based on probability and statistics. What is called probability and statistics? It is not so mysterious. Deep learning is to look for patterns that occur repeatedly, so if you repeat more, it is considered a law (truth), so a lie repeated a thousand times is considered a truth, so why does big data sometimes make very ridiculous results? because no matter whether it is right or not, as long as it is repeated more, it will follow this law, that is, whoever says too much is the one who says too much.

Original: https://blog.csdn.net/weixin_45836589/article/details/122144454
Author: 科技全景
Title: 人工智能技术并非已经完全成熟,而进入发展应用的阶段

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