What neural networks are capable of on the threshold

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Specialized neural networks are usually more controllable by humans. They are most often needed for utilitarian functions — they are tailored to specific tasks. Examples are everywhere: speech transcription into text, background blurring and noise reduction in video conferences, face recognition using a phone camera. These systems are designed to make human life easier. Often, such neural networks are used as an ensemble — the result from one neural network is transferred to an india telegram database other, and then to a third, etc.

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For example, if you have a smart speaker at home, it will be able to get text from your voice (the first neural network), then understand the general gist of the request (the second neural network). The neural network classifies the questions “what is the weather like tomorrow” and “will it rain tomorrow” in the same way: most likely, it will give the weather forecast in the form of a template voice response or display a standard widget. But if the speaker understands that it does not have a ready-made template, it will switch to the general neural network to generate an answer from scratch.

There are general neural networks, or General, which includes the Generative Pre-trained Transformer (GPT). GPT is one of the variations of large language models, capable of both classifying and generating text, and very similar to that created by a person. To do this, a huge number of neurons and connections were embedded in the neural network itself, and then terabytes of texts were run through it.

GPT is a prime example of how quantity has become quality: training such a model requires a colossal amount of computer time.

Related: How ChatGPT is Making Chatbots Intelligent Digital Assistants



To better understand what neural networks can do, let's ask one of them to solve a children's problem.