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, 깊은 학습의 응용 프로그램은 이미 자신의 게임에서 가장 큰 인간의 일부를 현명 관리해야,,en,딥 블루 (Deep Blue)로 알려진 컴퓨터가 실제로 다시 체스 챔피언 게리 카스파롭 길을 물리 치기 위해 관리,,en,더 새로운 예는 AlphaGo의 이동 일치 대입니다,,en,이세돌,,en,이는 매우 흥미 롭다,,en,그러나 얼마나 깊은 학습은 인공 지능의 거대한면되고있다,,en,자체 설계,,en,정확히 깊은 학습은 무엇입니까,,en,사람이 깊은 학습 기반의 AI 시스템의 성과를 볼 때 발생하는 큰 문제는 정확히 깊은 학습이 무엇이며,,en,깊은 학습,,en,간단히 말하면,,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, is a special kind of machine learning procedure where instead of traditional algorithms, 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, 매우 적은 데이터 생산자가 있었다 등의 데이터를 잡아 매우 힘들었다,,en,이 문제는 월드 와이드 웹의 도래와 함께 해결되었다,,en,인터넷은 깊은 학습 시스템의 학습 알고리즘을 구동하는 데 사용 될 수있는 데이터의 거대하고 쉬운 소스 것으로 판명,,en,인터넷에서 가장 큰 데이터 생산자는 페이스 북과 같은 다양한 소셜 네트워킹 사이트입니다,,en,트위터와 인스 타 그램,,en,이는 데이터를 모두 표시 및 레이블이없는 경우와 데이터 과학자를 제공합니다,,en,1990 년대에 깊은 학습 시스템의 개발에 영향을 또 다른 주요 과제는 컴퓨터의 처리 능력이었다,,en,그 시간에서,,en,컴퓨터의 처리 능력은 현재 세대의 처리 능력에 근처에도 아니었다,,en,이러한 고급의 CPU와 같은 새로운 기술,,en,중앙 처리 유닛,,en. 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) 메모리는 크게 현대 컴퓨터의 속도를 향상 시켰습니다,,en,같은 깊은 학습 시스템의 속도에 영향을 미치는 또 다른 매우 중요한 기술은 클라우드 컴퓨팅이다,,en,클라우드 컴퓨팅은 매우 빠른 컴퓨팅 속도와 분산 시스템의 도움으로 저장 많은 양의 수,,en,얼마나 깊은 학습 작업을 수행,,en,깊은 학습 데이터의 조각에서 뭔가를 배울 매우 개념 및 동적 알고리즘을 사용,,en,사람의 얼굴의 이미지를 식별 학습을위한 깊은 학습 시스템에 제공 될 때,,en,깊은 학습 시스템은 조심스럽게 얼굴의 가장 눈에 띄는 기능의 모양을 분석,,en,이것은 눈과 같은 기능을 포함 할 수있다,,en,코,,en,눈썹과 입술,,en,이러한 기능뿐만 아니라 가장 눈에 띄는 기능입니다,,en,뿐만 아니라 가장 일반적인 기능입니다,,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. 깊은 학습은 이미지 분석을위한 여러 가지 네트워크를 가지고,,en,이러한 인공 신경 네트워크의 각 데이터의 특정 부분에 초점을 맞추고 그것을 분석,,en,이미지는 신경 네트워크에 제공하고 미래의 용도를 검사한다,,en,깊은 학습 기계의 성공 사례,,en,깊은 학습 기계는 점차적으로 한 번에 여러 기관에 매우 유용되고있다,,en,예 언어로 번역되어,,en,일반 번역은 단순히 대상 언어의 문법에 따라 순서를 변경 한 후 각 단어 번역에 의해 다른 언어로 텍스트의 일부를 번역으로 작업하는 동안,,en,깊은 학습 기반의 번역은 신중하게 모든 텍스트를 스캔,,en,다음, 각 단어의 모든 가능한 번역 함께 네트워크를 형성,,en,이 네트워크는 해당 분야에서 문구를 배치하도록하는 데 도움이,,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. This network helps them to place the phrases in appropriate areas, 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?
The most exciting field where deep learning can be applied is the field of machine-human interaction. 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, the current technology is still not good enough in order to allow deep learning systems reach this level. Both visual and speech technologies are still ages apart from that of real humans. Also, 현재 처리 능력은 여전히 인간의 뇌의 복잡성에 도달하는 것을 처리하지 않은,,en,신경망 오늘은 간단한 개구리의 뇌의 처리 능력을 가질 수있다,,en,가장 큰 문제는,,en,인간의 정신 그 자체,,en,깊은 학습 컴퓨터 개발,,en,그들은 인간 스스로 그들에게 배운 것을 배우고있다,,en,사람이 자신의 고정 관념과 편견은 시스템에 들어온다,,en,이것은 매우 불만족 할 수있다 이러한 시스템의 최종 결과로 매우 해로울 수 있습니다,,en,깊은 학습 시스템은 인공 지능 프로그래밍 분야의 다음 단계입니다,,en,그들은 인간에게 매우 도움이 될 증명할 수와 존재의 거의 모든 분야에서 그들을 도울 수,,en,하지만 인간들은 먼저주의 깊게 시스템을 개발해야 할 것이다,,en. 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.
