Entertainment on Neural Networks, Artificial Intelligence and Machine Learning

The article will tell you the meaning of the terms Artificial Intelligence (AI), Machine Learning and Neural Networks in applications.

The article will tell you the meaning of the terms Artificial Intelligence (AI), Machine Learning and Neural Networks in applications.

Recently, both Google and Microsoft are very active in neural networks supplements for their language translation applications. Google also said that they are using machine learning to suggest music lists to users. Todoist claims that they are using AI to suggest when you need to complete a task. Any.do states that their bot with AI support can do some things for you.

All these statements have just been made recently. Some of them are advertising tricks but sometimes these changes are very useful. AI, machine learning and neural networks are terms that describe how computers can do more complex tasks and learn from their environment. In fact, these three terms have very different meanings.

Neural Networks analyzes complex data by simulating the human brain

Artificial neural networks (ANN or Neural Networks for short) refer to a special learning model that simulates how synapses work in the human brain. Traditional computing uses a variety of logical assertions to perform a task. Neural networks use a network of nodes (acting as nerve cells) and edges (acting as synapses) to process data. Input data will run through the entire system and a series of output results will be generated.

The output will then be compared to the data that the system was previously learned. For example, if you want to train your computer to recognize a dog's image, you will have to import millions of photos of dogs so that the image looks like a dog. After that, we will have to confirm which photos are really a dog. Next, the system will prioritize this result across all neural networks to find the right results. Over time and millions of iterations, the network eventually improves the accuracy of the results it offers.

For a better understanding of how neural networks work, you can try Quick, Draw! by Google. In this game, Google trains their network to recognize drawings. It compares your drawings with those drawn by others. The network will guess the drawings and then be reversed to identify future drawings based on what it looks like it has seen before. Even if your drawing skills are not good, Google's network still recognizes pretty basic figures like submarines, factories and ducks.

Neural networks are not the right solution for everything but it is especially outstanding when you need to handle complex data. Google and Microsoft are correct when using neural networks for translation applications because language translation is a very difficult job. Although there are still errors at present, with the ability to learn from accurate translations, neural networks can help the system produce better results over time.

The same thing happened with voice transcription technology. After Google applies neural networks to Google Voice, the error transcription rate has decreased by 49%. Although it is not possible to get immediate and imperfect results, neural networks can analyze complex data very well and thereby give you more natural features in applications.

You can see the video explaining neural networks of YouTube channel DeepLearning.TV above for more information, remember to turn on Vietnamese subtitles if you are not confident in your English ability.

Machine Learning teaches computers to improve ability

Machine Learning is a broad term that includes anything in which you teach computers to improve a task that it is performing. More specifically, machine learning refers to any system where the performance of a computer when performing a task will become better after completing that task multiple times. Neural networks are an example of machine learning but that is not the only way of learning computers.

For example, one of the other learning methods of computers is Reinforcement Learning. In this method, the computer will perform a task and then its results will be scored. The video explaining how the above-mentioned Android Authority computer learning chess is an example. The computer will play a chess game and finish it can win or lose. If it wins, its moves in that match will be labeled wins. After playing millions of games, the system can determine which moves are more likely to help it win based on the results of previous matches.

While neural networks are very good for tasks like pattern recognition in images, other types of machine learning are useful in other tasks such as identifying your favorite music. For example, Google said its music application will find you the music you want to listen to. It does this by selecting a music list based on your past listening habits. If you skip the music list it suggests, it will label the music list with failure. However, if you choose one of the suggestions, the system will successfully label that suggestion and reinforce the process of forming this suggestion so that more good suggestions can be made in the future.

In this case, you will not be able to benefit from ML if you do not use this feature regularly. The first time you open the Google music app, all the suggestions you see are unrelated to your musical taste. Theoretically, the more you use this application, the better the suggestions will be.

But anyway, machine learning is not perfect, you will still get inappropriate suggestions. However, surely you will receive inappropriate suggestions if you only use this application every 6 months. If you don't use the app regularly to help the system suggest learning songs, it won't be much better than the regular song suggestion system. As a common word, machine learning is more ambiguous than neural networks but it still implies that the software you are using will use your feedback to improve its performance.

Artificial Intelligence refers to anything intelligent

If neural networks are a form of machine learning, machine learning is a form of AI. However, the list of what is considered AI is difficult to identify. Currently we have achieved remarkable achievements in AI technology. For example, optical character recognition has been considered too complex for computers but now an application on the phone can scan documents and turn them into text. It seems a little overlooked if you use this term to describe the current basic features.

However, since AI actually has two different types, the basic features will also be considered AI. A low or narrow AI is used to describe any system designed for one or a series of small tasks. For example, the Google Assistant and Siri assistants are designed to do quite a few small tasks such as receiving voice commands and returning results or opening applications.

In contrast, high-level AI, or artificial intelligence in general or " Full AI ", refers to a system capable of doing whatever humans can do. Of course, this system does not or at least not appear. In the long run, we can build an AI system like Iron Man's assistant Jarvis.

Since almost any AI you use at present is considered a low-level AI, the AI phrase in application descriptions really just means it's a smart app. You can hear many statements and advertisements, but the current AI cannot compare with human intelligence.

Practical research in AI field is very useful and perhaps you have applied it to everyday life without knowing it. You can directly or indirectly benefit from AI studies every time your phone automatically remembers where you stop, identify the faces in your photos, suggest things you might be interested in. or automatically group all photos into travel folder .

At some point, real AI will help applications become smarter, as expected. However, machine learning and neural networks are the only ways to improve certain features.

However, machine learning and neural networks are not the same. It should not be assumed that an application with machine learning technology will be better because it depends on the user's use. For example, when a company develops strong neural networks that can solve some complex tasks that will help you, your life will be easier. But when other companies integrate machine learning into an application that already has smart suggestions, you don't care because you think these two proposed features are no different.

Machine learning and neural networks are extremely interesting technologies. However, you don't need to worry about doing anything when you see these terms in an application's description. Just do as you have, evaluate apps based on their usefulness to you.

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