How does artificial intelligence

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2019-08-13 14:40:18

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How does artificial intelligence

In recent years we . It is used almost everywhere: from the sphere of high technology and complicated mathematics to medicine, automotive industry and even with smartphones. The technology underlying the work of AI in the modern view, that we use every day and sometimes don't even have to think about it. But what is artificial intelligence? How does it work? And is it dangerous?

What is artificial intelligence

First, let's define the terminology. If you imagine artificial intelligence as something is able to think independently, make decisions, and generally show signs of consciousness, then hasten to disappoint you. Virtually all existing today, the system is not even close to «cost» definition of AI. And those systems that exhibit such activity, it is actually still operate within pre-defined algorithms.

Sometimes these algorithms , but they remain the «framework», within which the AI operates. No «Carta» and especially signs of consciousness have no cars. It's just a very productive program. But they «the best in the business». Moreover, AI systems continue to improve. Yes, and they are arranged quite unhackneyed. Even if you discard the fact that modern AI is far from perfect, it has us very much in common.

How does artificial intelligence

First of all AI can perform their tasks (about which later) and to acquire new skills through deep machine learning. This term we too often hear and use. But what does it mean? Unlike «classic» methods, when all necessary information is loaded into system in advance, the machine learning algorithms cause the system to evolve on their own, studying the available information. Which, moreover, the machine in some cases, also can look for yourself.

For Example, to create a program to detect fraud, the machine learning algorithm works with a list of Bank transactions and their end result (legal or illegal). The machine learning model considers the examples and develops statistical dependence between legitimate and fraudulent transactions. Then, when you provide algorithm details of a new credit transaction, he klassificeret it based on the patterns that he drew from examples in advance.

Generally, the more data you provide, the more accurate becomes the machine learning algorithm while performing their tasks. especially useful when solving problems where the rules are not predefined and can not be interpreted in binary. Returning to our example with the banking operations: in fact on leaving we was binary: 0 — a legitimate operation, 1 — illegal. But in order to reach this conclusion, the system requires to perform a whole bunch of parameters and if you make them manually, then it will take more than one year. And to predict all the options all the same will not work. A system based on deep machine learning will be able to recognize something, even if exactly such a case she never met before.

Deep learning and neural networks

At the time, as a classic machine learning algorithms solve many problems, in which there is a lot of information in databases, they do not cope with, so to speak, «visual, auditory» data like images, videos, audio files and so on.

For Example, creating predictive models of breast cancer using a classical machine learning approaches require programmers and mathematicians, says a researcher in the field of AI Jeremy Howard. Scientists would have to do a lot of smaller algorithms to machine learning to cope with the flow of information. A separate subsystem for studying x-ray images, private — for MRI, the other — for the interpretation of blood tests, and so on. For each analysis, we would need its own system. Then they all teamed up into one big system… This is a very difficult and resource-intensive process.

Deep learning Algorithms to solve the same problem using deep type of software architecture, inspired by human brain (although neural networks are different from biological neurons, the principle of operation are almost the same). Neural computer network — links «electronic neurons», which are able to process and classify information. They are how-to «layers» every «layer» is responsible for something, eventually forming an overall picture. For example, when you train a neural network on images of various objects she finds ways of extracting features from these images. Each layer of the neural network detects certain characteristics: the shape of objects, color of objects and so on.


a Superficial layers of neural networks discover common features. The deeper layers already reveals the actual objects. Figure a diagram of a simple neural network. Green represents input neurons (postupala information), blue — hidden neurons (data analysis), yellow — output neuron (decision)

Neural network — is an artificial humanbrain?

Despite the similar structure of machine and human neural networks, characteristics of our Central nervous system they do not possess. Computer neural networks are essentially all the same utilities. Just so happened that the most highly organized system for calculations turned out to be our brain. You've probably heard the expression «our brains — this computer»? Scientists simply «repeated» some aspects of its structure in the «digital form». It is only allowed to speed up calculations, but not to endow machines with consciousness.

This is interesting:

Neural networks have existed since the 1950-ies (at least in the form concepi). But until recently, they didn't get much development, because their creation required huge amounts of data and computing power. In the last few years it became available, so the neural network and came to the fore after his development. It is important to understand that for their full appearance lacked technology. As they do not have now in order to bring the technology to a new level.

why use deep learning and neural networks

There are several areas where these two technologies helped to achieve significant progress. Moreover, some of them we use every day in our lives and not even think about what's behind them.

the

    — is the ability of software to understand the content of images and videos. This is one of the areas where deep learning has made great progress. For example, the image processing algorithms deep learning can detect different types of cancer, lung diseases, heart and so on. And to do it faster and more effective doctors. But deep learning is also rooted in many applications that you use every day. Apple and Google Face ID Photos use deep learning to recognize faces and improve the quality of images. Facebook uses deep learning to automatically tag people in uploaded photos and so on. Computer vision also helps companies to automatically identify and block questionable content such as violence and nudity. And finally, deep learning plays a very important role in ensuring that self-driving cars, so they can understand their surroundings. the

  • voice Recognition and speech. When you say the command for your Google Assistant, algorithms, deep learning, transform your . Several online applications using deep learning to transcribe audio and video files. Even when you «catamite» song, in the case involving algorithms, neural networks, and deep machine learning.
  • the
  • search the web: even if you are looking for something in a search engine, in order for your request to be processed more accurately and the results were most correct, the company began to connect the algorithms of neural networks to their search engines. So, the performance of the search engine Google has increased several times after the system was switched to deep machine learning and neural networks.

the Limits of deep learning and neural networks

Despite all its advantages, deep learning and neural networks also have some disadvantages.

the
    an
  • Dependency on the data: in General, deep learning algorithms require a huge amount of training data to accurately complete their tasks. Unfortunately, for the solution of many problems of insufficient quality training data for creating working models.
  • the
  • Unpredictability: neural networks are in some strange way. Sometimes everything goes as planned. And sometimes (even if the neural network copes well with its task), even the creators are struggling to understand how the algorithms work. The lack of predictability makes it extremely difficult to troubleshoot and correct errors in algorithms of neural networks.
  • the
  • Algorithmic offset: algorithms deep learning is just as good as the data on which they are trained. The problem is that training data often contain hidden or obvious errors or omissions, and algorithms get their «inherited». For example, the face detection algorithm, trained mostly in photos of white people will work less accurately on people with different skin color.
  • the
  • Lack of aggregation: deep learning algorithms are good to perform purposeful tasks, but poorly generalize their knowledge. Unlike humans, the model of deep learning , will not be able to play another similar game: say, in WarCraft. In addition, deep learning does not cope with data that deviate from its training examples.

the Future of deep learning, neural networks, and AI

It's Clear that the work on deep learning and neural networks is still far from complete. Various efforts are being made to improve deep learning algorithms. Deep learning — it is a straightforward technique in creating artificial intelligence. It is becoming increasingly popular in the last few years, thanks to the abundance of data and increased computing power. This is the basic technology that underlies many of the applications we use every day.

But is born ever on the basis of this technology of consciousness? Real artificial life? One of the scientists believes that in a time when the number of connections between the components of artificial neural networks to approach the same figure that you have in the human brain between our neurons, something like this might happen. However, this zayavlenievery doubtful. In order for a real AI appeared, we need to rethink the approach to development of systems based on AI. All that is now — this is only an application program to a strictly limited range of tasks. As if we didn't want to believe that the future is now…

What do you think? Cause the people of AI? Please share your opinion in our

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