Deep Learning
Deep learning is one of the biggest innovations is the complete history of computer science. This is the only concept which actually can allow man made machines to surpass human intelligence itself. In fact, 深度学习的应用已经成功地智取一些最伟大的人类在自己的游戏,,en,被称为深蓝计算机实际上设法击败回到了国际象棋冠军加里·卡斯帕罗维方式,,en,一个较新的例子是AlphaGo的围棋比赛VS,,en,李世石,,en,这是非常有趣,,en,但是，如何深度学习成为人工智能的一个巨大面,,en,设计本身,,en,究竟什么是深度学习,,en,有人看到了深刻的学习型AI系统所取得的成就时产生的一个大问题是，到底什么是深度学习,,en,深度学习,,en,简单的说,,en. For example, a computer known as Deep Blue actually managed to defeat the chess champion Gary Kasparov way back in 1997. A newer example is the Go match of AlphaGo vs. Lee Sedol, which is extremely interesting. But how is deep learning becoming a huge facet of artificial intelligent (AI) design itself?
What exactly is deep learning?
A big question that arises when someone sees the achievements of a deep learning-based AI system is what exactly is deep learning? Deep learning, simply put, 是一种特殊的机器学习方法在那里，而不是传统的算法,,en,神经网络使用,,en,这些人造基于神经网络的AI系统被证明是对人类非常有用,,en,他们在科学和商业领域找到广泛使用,,en,深度学习变得更深刻,,en,人工神经网络的概念已经存在了大约半个世纪,,en,深度学习的概念已经存在了，因为早期的90年代,,en,但在内心深处的学习已经比以往任何时候都最近变得越来越流行,,en,这背后的原因是，早期的深学习算法在本质上是原始,,en,机器学习需要，以正确了解，如图像和文本标记和未标记数据的巨大量,,en,互联网的前一天,,en, neural networks are used. These artificial neural network-based AI systems are proving to be extremely useful for humankind, and they are finding extensive usage in the field of both science and business.
Deep learning is becoming much deeper
The concept of artificial neural networks has been around for about half a century. Also, the concept of deep learning has been around since the early-90s. But deep learning has recently become more popular than ever before. The reason behind this is that early deep learning algorithms were primitive in nature. Machine learning requires an immense amount of labeled and unlabeled data such as images and texts in order to learn properly. Before the day of internet, data was extremely hard to get hold of as there were very less data producers. This problem was solved with the arrival of the World Wide Web. The internet proved to be a huge and easy source of data which could be used to drive the learning algorithms of the deep learning system. The biggest data producers in the internet are the various social networking sites like Facebook, Twitter and Instagram, which provides data scientists with both labeled and unlabeled instances of data.
Another major challenge which affected the development of deep learning systems in the 1990s was the processing power of the computers. At those times, the processing power of computers weren’t even close to current-generation’s processing power. New technology such as advanced CPUs (Central Processing Unit) 和内存都大大提升了现代计算机的速度,,en,即影响如此深的学习系统的速度的另一个非常重要的技术是云计算,,en,云计算允许极快的速度计算与分布式系统的帮助下，大量的存储,,en,如何深度学习作品,,en,深度学习使用非常的概念，动态算法从一个数据学到东西,,en,当一个人的面部图像，提供给深学习系统学习识别,,en,深学习系统认真分析面对的最突出的特点形状,,en,这可能包括像眼睛的功能,,en,鼻子,,en,眉毛和嘴唇,,en,这些功能不仅是最突出的特点,,en,但也是最常用的功能,,en. Another very important technology that is influencing the speed of such deep learning systems is cloud computing. Cloud computing allows extremely fast computing speeds and a large amount of storage with the help of distributed systems.
How does deep learning works?
Deep learning uses very concept and dynamic algorithms to learn something from a piece of data. For example, when an image of a person’s face is provided to the deep learning system for learning identification, the deep learning system carefully analyze the shape of the most prominent features of the face. This may include features like the eyes, nose, eyebrows and lips. These features are not only the most prominent features, but are also the most common features. 深度学习有分析图像几个网络,,en,每个人工神经网络的重点是数据的某一部分并对其进行分析,,en,图像提供给神经网络，他们对其进行扫描以供将来使用,,en,深学习机的成功案例,,en,深学习机正逐渐成为许多机构的一次非常有益的,,en,一个例子是语言翻译,,en,而正常的翻译对一段文字翻译成另一种语言的工作，只需根据目标语言的语法翻译每一个单词，然后更改订单,,en,深度学习基于翻译仔细扫描所有文字,,en,然后形成与每个字的所有可能的翻译的网络,,en. Each of these artificial neural networks focuses on a certain part of the data and analyses it. In this way, an image is provided to the neural networks and they scan it for future uses.
Success stories of deep learning machines
Deep learning machines are gradually becoming very useful for many institutions at once. An example is language translation. While normal translators work towards translating a piece of text to another language by simply translating each word and then changing the order according to the target language’s grammar, deep learning based translators carefully scan all the text, and then form a network with all the possible translations of each word. 该网络可以帮助他们将在适当的地区短语,,en,不仅仅是语法正确地区,,en,其中深学习系统表现出卓越的另一个重要领域是数据挖掘领域,,en,数据挖掘有助于科学家数据获取非常多的数据用于制作机器学习更容易,,en,谷歌使用了特殊的深度学习系统，以增强其基于语音的服务质量,,en,该系统更进一步了解一个人的语音模式通过分析各种语音样本是找上门来,,en,这样的用途的另一实例是由Twitter,,en,它采用深度学习方法删除某些内容有害于社会,,en,能深入学习真正做到人机交互更好,,en, not just grammatically correct areas.
Another major area where deep learning systems have shown excellence is the field of data mining. Data mining helps data scientists to acquire lots and lots of data for making machine learning much easier. For example, Google uses a special deep learning system in order to enhance the quality of its voice-based services. This system learns more about the speech patterns of a human being by analyzing the various voice samples that come its way. Another example of such a use is by Twitter, which uses deep learning methods for removing certain content harmful for the community.
Can deep learning really make human-computer interaction better?
最令人兴奋的领域可以应用深度学习是机器人机交互领域,,en,深度学习系统正逐渐成为非常先进,,en,这意味着它们正慢慢开始理解情绪,,en,语言，甚至道德,,en,这将允许该机器与人类沟通的非常类似人类的方式,,en,新技术，如面部识别和语言处理算法将是这些相互作用的关键技术,,en,会不会有什么并发症,,en,并发症实际上几乎存在于那个曾经被创建的每个新的雄心勃勃的技术,,en,首先是,,en,同样是深学习系统的自然沟通的情况下,,en,对于初学者,,en. Deep learning systems are gradually becoming very advanced, which means that they are slowly starting to understand emotions, languages and even morality. This will allow an extremely human-like way for the machines to communicate with human beings. New technology such as facial recognition and language processing algorithms will be the key technologies for these interactions.
Will there be any complications?
Complications are actually present in nearly every new and ambitious technology that has ever been created, at first that is. Same is the case with natural communications with deep learning systems. For starters, 目前的技术尚不够好，以使深度学习系统达到这一水平,,en,视觉和语音技术仍年龄距真正的人类,,en,目前的处理能力仍然没有勉强达到人脑的复杂性,,en,神经网络今天只能有一个简单的青蛙大脑的处理能力,,en,最大的问题是,,en,人的心态本身,,en,由于深学习计算机开发,,en,他们正在学习由人类自己教给他们的东西,,en,人自身的成见和偏见都匍匐进入系统,,en,这可以是作为这样的系统的最终结果可能是非常不理想非常有害,,en,深度学习系统是AI编程领域的下一步,,en. Both visual and speech technologies are still ages apart from that of real humans. Also, the current processing power has still not managed to reach the complexity of a human brain. In fact, a neural network today can only have the processing power of a simple frog’s brain.
The biggest problem is, however, human mentality itself. As deep learning computers are developing, they are learning things that are taught to them by human beings themselves. Slowly, man’s own stereotypes and prejudices are creeping into the system. This can be very harmful as the end result of such a system may be very unsatisfactory.
Deep learning systems are the next step in the field of AI programming. They can prove to be very beneficial for human beings and can assist them in nearly every field in existence, but for that humans will have to develop these systems carefully first.
