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原创译文 | 为什么真正的人工智能不从深度学习中获得?

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尽管深度学习前景光明也取得一些初步的成功,但它实际上可能会阻碍真正的人工智能的发展。文末更多往期译文推荐


人工智能是一个包含许多概念和开发方法的强有力的保护伞。这些不同但不排他的方法能够让机器拥有接近人类的推理能力和智能。

深度学习是人工智能平台采用的一种推理方式。深度学习,作为人工智能这一概念的代表,正备受瞩目,大量的研究工作,投资,甚至媒体都关注其动向和发展。

深度学习是通向人工智能的真正的道路吗?


自从计算机技术诞生以来,开发人员通过编写代码让机器译成精确指令来创建程序和算法。计算机,无论其功能多么强大,多么万能,却常常无法执行人类毫不费力可以完成的任务。将实时体验考虑在内的复杂问题还不能简化为一行行的代码,因此需要更新的解决方法。

深度学习提供了一种不同的解决问题的方法。

与其采用代码编写程序去解决问题,不如让程序自己学会解决问题。

这是深度学习背后的一个广泛概念,依赖于多层神经网络,每一层都从上一层停止的位置开始,去解决问题。

需要注意的是,神经网络是独立的,但连接的节点是同时运行计算,但它们只与我们自己的神经系统相似。

一些专家认为,深度学习并不是赋予机器近似人类智能的真正途径,它可能阻碍了真正的人工智能的发展。

目前正在使用的所有深度学习系统都是受监督的,这意味着数据都是预先确定好的,深度学习系统会对占用大量资源的数据进行分类,要不然这些资源将会被用于开发人工智能,并被称为无监督人工智能。

与人脑不同的是,无人监管的人工智能将可以识别出新的模式,自己进行标记,并在没有人工输入的情况下对其进行分类。这就是麻省理工学院的科学家们所说的无人监管。

大公司和受监督的深度学习

自20世纪60年代以来,深层神经网络就一直存在,但近年来,随着大数据和计算能力的结合,才发展起来。

深度学习系统必须拥有大量的数据,并且有足够的计算能力来不断更新自己,从他们的经验中学习并不断改进。

深度学习让一些数据处理应用程序成为可能,比如语音识别、图像识别和映射。

这些应用程序和其他许多应用程序代表谷歌、Facebook、苹果、微软、亚马逊等公司,它们都押宝深度学习。

大公司已经开始利用大量的数据流,将庞大的资源投入到受监督的深度学习中。

除了一些谨慎的初期发展(比如谷歌的人工大脑),没有一个无人监督的人工智能大项目取得进展了。

在麻省理工学院的技术评论中,Quoc(谷歌的一名大脑研究科学家)认为,无人监督的学习是开发真正的人工智能的最大挑战,它可以在不需要标记数据的情况下学习。

在什么情况下你会允许无监督AI进入你的生活?

英文原文

Here’s why True AI Won’t Come From Deep Learning

As promising as Deep Learning is, and despite its initial success stories, it might actually slow the development of true AI.

If you’ve been keeping tabs on our recent AI coverage, you are probably aware that AI is the umbrella under which exists many concepts and methods of development. In the end, these different but not exclusive approaches seek to bestow machines with more human-like reasoning and intelligence.

Deep Learning is one type of reasoning that AI platforms adopt. Deep Learning, as a representation of that concept, is receiving significant research efforts, investment, and even media buzz.

Deep Learning, the “True” Path to Human-Like Intelligence?

Since the dawn of computing technology, developers created programs and algorithms by writing code that machines translate into precise instructions.

Computers, however powerful and versatile they might be, are often times incapable of carrying out tasks that humans perform effortlessly. Complex problems that take real-time experience into account can’t yet be reduced into code lines, hence the need for more novel approaches.

Deep Learning proposes a different way of solving problems.

Instead of code-writing the way a program solves a problem, the program “learns” to solve it on its own.

That’s the broad concept behind Deep Learning, which relies on multi-layered neural networks, where each layer starts where the last layer has left off to solve the problem.

It should be noted that neural networks are separate but linked nodes that simultaneously run calculations, yet they only resemble our own neural system.

Some experts argue that Deep Learning isn’t the true path to create human-like intelligence for machines, and that it might be hindering “true AI” progress.

All Deep Learning systems currently in use are “supervised”–meaning they need pre-determined data that they will, basically, classify–which take up huge resources that otherwise would be directed to the development of AI with real potential, referred to as “unsupervised AI”.

In a not too dissimilar way than the human brain, unsupervised AI would recognize new patterns, label them on its own and classify them without human prior input. This is the “true AI” MIT scientists were referring to as “unsupervised”.

Big Corporations Make do With “Supervised” Deep Learning

Deep neural networks have been around since the 1960s, but their development has really only taken off in recent years when two conditions have come together: Big Data and computing power.

Deep Learning systems must have at their disposal large amounts of data, and sufficient computing power to continuously update themselves, learn from their experience and keep improving.

Thanks to Deep Learning, several data crunching applications are made possible, such as voice recognition, image recognition, and mapping.

These applications and many others represent for Google, Facebook, Apple, Microsoft, Amazon and the likes the stakes of deep learning.

Big corporations are already cashing on the constant flow of data, dedicating their colossal resources to supervised Deep Learning.

Besides some initiatives with timid progress (like Google’s Artificial Brain), no “unsupervised AI” big project has gotten off the ground.

Per MIT Technology Review, Quoc Le (one of Google’s Brain research scientists) has identified “unsupervised learning” as the biggest challenge to developing true AI that can learn without the need for labeled data.

Under what circumstances would you unleash unsupervised AI into your life?

文章翻译:灯塔大数据

文章编辑:柯一


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