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, applications of deep learning have already managed to outsmart some of the greatest humans in their own game. 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, eta erabilera zabala aurkitzeko ari dira biak, zientzia eta enpresa eremuan,,en,Deep ikaskuntza askoz sakonagoa bihurtzen ari da,,en,neurona-sareen kontzeptua da inguruan, mende erdi inguru batentzat,,en,ikaskuntza sakona kontzeptua da inguruan, goiz-90eko geroztik,,en,Baina sakona ikaskuntza duela gutxi bihurtu da inoiz baino gehiago popular,,en,Honen arrazoia zera da, goiz sakona ikasketa-algoritmoak naturan primitiboak ziren,,en,Makina ikaskuntza adibidez, irudiak eta testuak gisa etiketatu eta etiketarik gabeko datu kopuru izugarria eskatzen du behar bezala ikasteko,,en,internet eguna baino lehen,,en,datuak oso gogorra izan zen sotoan bezala, ez ziren oso gutxiago datuak ekoizleek,,en,Arazo hau, World Wide Web iristearekin batera konpondu zen,,en.
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. internet frogatu datuak iturri handi eta erraz bat, ikaskuntza sakona ikaskuntza sistemaren algoritmoak gidatzeko erabili behar izan izango da,,en,Handiena datuak interneten ere ekoizleen hainbat sare sozial Facebook bezalako guneak dira,,en,Twitter eta Instagram,,en,horrek bai etiketatu eta etiketarik gabeko datuak instantzia datuak zientzialari eskaintzen,,en,Beste erronka nagusietako bertan ikasketa-sistemak sakon garatzen kaltetutako 1990eko hamarkadan prozesatzeko ordenagailuak boterea izan zen,,en,Garai hartan,,en,prozesatzeko ordenagailuak boterea ez ziren, nahiz eta egungo belaunaldiko en prozesatzeko power hurbil,,en,Teknologia berriak, besteak beste, PUZ aurreratu bezala,,en,Central Processing Unit,,en,eta memoria dute asko hobetu du ordenagailuak modernoaren abiadura,,en. 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) and memory have greatly enhanced the speed of the modern computers. 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. Deep learning has several networks for analyzing the image. 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.
Beste inguruan nagusien non sakona ikaskuntza sistemetan bikaintasuna erakutsi dute datu meatzaritza eremuan dago,,en,Datu meatzaritza laguntzen datuak zientzialari asko eta asko datuen eskuratzeko makina askoz errazago ikasten egiteko,,en,Google sakona ikaskuntza sistema berezi bat erabiltzen honen ahotsa oinarritutako zerbitzuen kalitatea hobetzeko,,en,Sistema honek ikasten hizkera gizaki baten ereduak buruz gehiago hainbat ahotsa laginak bere bidea etortzen diren aztertuz,,en,erabilera horren adibide bat Twitter da,,en,bertan ikasketa metodoak sakon erabiltzen du zenbait eduki kaltegarriak kendu komunitatearentzat for,,en,Ezin sakon ikasteko benetan giza-ordenagailu elkarrekintza hobeto,,en,Gehien zirraragarria arlo non sakona ikaskuntza aplikatu daiteke makina-giza elkarrekintza eremuan dago,,en,Deep ikasketa-sistemak pixkanaka oso aurreratuak bilakatu dira,,en. 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, 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.
