如何实践AI深度学习的十大惊艳案例 | 数盟

你可能已经听说过深度学习并认为它是骇人的数据科学里的一个领域。怎么可能让机器像人类一样学习呢?再者,对于某些人而言,更为骇人的是,我们为什么要让机器展现出类人的行为?这里,请看深度学习在实际应用中的十大案例,以便将其潜能视觉化。

深度学习是什么?

机器学习和深度学习都是人工智能的分支,但深度学习是机器学习的进一步深化。在机器学习中,由人类程序员设计的算法负责分析、研究数据,然后根据数据分析和研究作出决策。深度学习通过一个人造的神经网络来学习,这一人造神经网络运转起来与人类大脑非常相似,它可以让机器在一个框架内像人一样进行分析数据。深度学习的机器不需要人类程序员告诉他们要用数据做什么,这得赖于我们收集并消耗了大量的数据——数据是深入学习模型的燃料。

深度学习的十大应用案例

1. Customer experience 用户体验

机器学习已经被很多企业用来改善用户体验。部分案例诸如在线自助服务方案、定制靠谱的工作流程,部分聊天机器人等都已运用到深度学习模型。随着深度学习发展日趋成熟,我们可以预期,未来这一领域将被更多企业用来改善用户体验。

2、 Translations 翻译

尽管自动机器翻译并不新鲜,但深度学习正着力于使用神经网络的堆叠网络和图像翻译来增强文本的自动翻译。

3、 Adding color to black-and-white images and videos 为黑白图像、视频着色

过去,人们手动为黑白图像及视频着色的过程往往旷日持久,如今,这一工作可以完全由深度学习模型自动完成。

4、 Language recognition 语言识别

目前,深度学习机器开始致力于辨别不同的方言。机器确定某人说的是英语,然后利用AI学习辨别方言之间的差异。一旦确定是某种方言,另一个AI会继续专研这种方言,而这所有的过程均不需要人类参与。

5、 Autonomous vehicles 自动驾驶汽车

自动驾驶汽车在街上行驶时,并不只有一个AI模型在起作用。一些深度学习模型专门研究街道标识,而另一些则训练识别行人。当一辆自动驾驶的汽车在公路上行驶时,它将接收到成千上万条人工智能模型的信息来辅助其行驶。

6、 Computer vision 计算机视觉

在图片分类、目标检测、图片复原和分割方面,深度学习已经展现出超越人类的精确性——他们甚至能识别手写的数字。深度学习借助庞大的神经网络,利用机器自动化人类视觉系统所执行的任务。

7、 Text generation 创作文本

机器可以学习一段文本的标点、语法和风格,然后利用这个模式自动创作一篇全新的文章,这篇文章的拼写和语法都是正确的且风格与样本文章一致。从莎士比亚到维基百科,所有的文章都能由此创作。

8、 Image caption generation 生成图片标题

深度学习另一个能力也着实备受瞩目——识别图像,并创建一个符合语句结构的连贯标题,宛如人写的一样。

9、News aggregator based on sentiment 基于情感的新闻聚合器

如果你想要过滤掉消极新闻,不让它们进入你的世界,先进的自然语言处理程序和深度学习可以帮助你。使用这种新技术的新闻聚合器能够基于用户情感过滤新闻,因此你可以创建只报道正面消息的新闻流。

10、 Deep-learning robots 深度学习机器人

机器人的深度学习应用程序丰富而强大,它来自一个令人印象深刻的深度学习系统。通过观察人类完成任务的行为机器人就能学会家务,并通过几个其他人工智能的输入来进行操作。就像人类大脑如何处理来自过去的经验、当前的感官以及任何附加数据信息一样,深度学习模型将帮助机器人执行基于多个不同人工智能意见输入的任务。

深度学习模型的增长被寄予厚望:在未来几年里将加速发展,创造更具创新性的应用程序。

原文:10 Amazing Examples Of How Deep Learning AI Is Used In Practice?

Bernard Marr

You may have heard about deep learning and felt like it was an area of data science that is incredibly intimidating. How could you possibly get machines to learn like humans? And, an even scarier notion for some, why would we want machines to exhibit human-like behavior? Here, we look at 10 examples of how deep learning is used in practice that will help you visualize the potential.

What is deep learning?

Both machine and deep learning are subsets of artificial intelligence, but deep learning represents the next evolution of machine learning. In machine learning, algorithms created by human programmers are responsible for parsing and learning from the data. They make decisions based on what they learn from the data. Deep learning learns through an artificial neural network that acts very much like a human brain and allows the machine to analyze data in a structure very much as humans do. Deep learning machines don’t require a human programmer to tell them what to do with the data. This is made possible by the extraordinary amount of data we collect and consume—data is the fuel for deep-learning models. For more on what deep learning is please check out my previous post here.

10 ways deep learning is used in practice

Customer experience

Machine learning is already used by many businesses to enhance the customer experience. Just a couple of examples include online self-service solutions and to create reliable workflows. There are already deep-learning models being used for chatbots, and as deep learning continues to mature, we can expect this to be an area deep learning will be used for many businesses.

Translations

Although automatic machine translation isn’t new, deep learning is helping enhance automatic translation of text by using stacked networks of neural networks and allowing translations from images.

Adding color to black-and-white images and videos

What used to be a very time-consuming process where humans had to add color to black-and-white images and videos by hand can now be automatically done with deep-learning models.

Language recognition

Deep learning machines are beginning to differentiate dialects of a language. A machine decides that someone is speaking English and then engages an AI that is learning to tell the differences between dialects. Once the dialect is determined, another AI will step in that specializes in that particular dialect. All of this happens without involvement from a human.

Autonomous vehicles

There’s not just one AI model at work as an autonomous vehicle drives down the street. Some deep-learning models specialize in streets signs while others are trained to recognize pedestrians. As a car navigates down the road, it can be informed by up to millions of individual AI models that allow the car to act.

Computer vision

Deep learning has delivered super-human accuracy for image classification, object detection, image restoration and image segmentation—even handwritten digits can be recognized. Deep learning using enormous neural networks is teaching machines to automate the tasks performed by human visual systems.

Text generation

The machines learn the punctuation, grammar and style of a piece of text and can use the model it developed to automatically create entirely new text with the proper spelling, grammar and style of the example text. Everything from Shakespeare to Wikipedia entries have been created.

Image caption generation

Another impressive capability of deep learning is to identify an image and create a coherent caption with proper sentence structure for that image just like a human would write.

News aggregator based on sentiment

When you want to filter out the negative coming to your world, advanced natural language processing and deep learning can help. News aggregators using this new technology can filter news based on sentiment, so you can create news streams that only cover the good news happening.

Deep-learning robots

Deep-learning applications for robots are plentiful and powerful from an impressive deep-learning system that can teach a robot just by observing the actions of a human completing a task to a housekeeping robot that’s provided with input from several other AIs in order to take action. Just like how a human brain processes input from past experiences, current input from senses and any additional data that is provided, deep-learning models will help robots execute tasks based on the input of many different AI opinions.

The growth of deep-learning models is expected to accelerate and create even more innovative applications in the next few years.

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