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Artificial intelligence and neural network: what is the difference and the difference

Artificial intelligence and neural network: what is the difference and the difference

It is difficult for many to navigate the flow of information that is now "pouring" from everywhere about artificial intelligence. In order to immerse yourself in this area at least a little, it is necessary first of all to understand the basic concepts. For example, it is important to understand why artificial intelligence is not synonymous with neural networks and how they differ from each other.

Let's try to clarify this with a simple explanation and an example.

Artificial Intelligence (AI)

Artificial intelligence is a smart system that can do various tasks that usually require human intelligence. For example, AI can recognize speech, translate texts, play chess, or recommend movies.

Example: Imagine a voice assistant like Siri or Google Assistant. He can understand your commands, answer questions, and complete tasks - all examples of artificial intelligence.

Neural network

A neural network is one of the technologies used to create artificial intelligence. This is something like a "smart brain" that studies information and learns from examples. Neural networks consist of "neurons" (similar to neurons in the human brain) that process data. Example: Let's look at how a neural network can recognize images of cats and dogs. We show the neural network thousands of photos of cats and dogs, and she learns to distinguish between them. After that, when we show her a new image, she will be able to tell if it is a cat or a dog based on what she has learned.

In general:

Artificial intelligence is a broad concept that encompasses many technologies and methods, including neural networks.

Neural network is a specific method or tool that helps to create AI.

Thus, AI is like a general term for "smart" machines, and the neural network is one of the ways in which we can create such "smart" machines.

Artificial intelligence is a general term that encompasses a variety of technologies, including neural networks. Neural networks, in turn, are one of the tools used to develop AI systems. It's like the difference between a tool and its field of application: AI is an area, and neural networks are one of the tools in this area.

In other words, a neural network can be considered as a kind of program or algorithm that solves specific tasks, and, depending on how it is used, the result can be part of artificial intelligence (AI).

Example:

If we take a neural network trained to recognize faces in photographs, this neural network will solve a specific task. If its results allow us to determine with high accuracy who is depicted in the photo, then we can say that this is part of an artificial intelligence system, since it performs a complex task that traditionally requires human intelligence.

Neural networks themselves are represented by a large number of models and their subspecies.

Here is the main list of the most popular in our time.

Artificial Intelligence models

Natural Language Processing (NLP) models

  • GPT (Generative Pre-trained Transformer):
    • Versions: GPT-2, GPT-3, GPT-4, etc.
    • Scope of application: Text generation, chatbots, summarization and translation.
  • BERT (Bidirectional Encoder Representations from Transformers):
    • Developed by Google.
    • Scope of application: Understanding the language, classifying the text, answering questions.
  • RoBERTa:
    • An improved version of BERT from Facebook.
    • Scope of application: Similar tasks, but with a more powerful learning architecture.
  • T5 (Text-to-Text Transfer Transformer):
    • Developed by Google.
    • Scope of application: A universal platform for the application of various NLP tasks.
  • XLNet:
    • Combines auto-narrative models and BERT.
    • Scope of application: Understanding and generation of language.
  • LLaMA (Large Language Model Meta AI):
    • Meta (Facebook) was developed.
    • Scope of application: Text generation and understanding.
  • Claude:
    • The model is from Anthropic.
    • Scope of application: Chatbots and text generation with an emphasis on security.

Computer vision models

  • CNN (Convolutional Neural Networks):
    • Scope of application: Image classification, object recognition.
  • ResNet (Residual Networks):
    • Scope of application: Deep image classification and identification.
  • YOLO (You Only Look Once):
    • Scope of application: Real-time object detection.
  • EfficientNet:
    • Optimized for efficiency and productivity.
    • Scope of application: Classification of images with high quality.

Models for image generation

  • DALL-E:
    • Developed by OpenAI.
    • Image generation based on text descriptions.
  • Midjourney:
    • Generation of artistic images based on text queries.
    • Creates unique visual styles.
  • Stable Diffusion:
    • An open source model for generating images.
    • Scope of application: Converting text descriptions into visual works.
  • DeepArt:
    • Uses neural networks to style images.
    • Scope of application: Turning photos into works of art in various styles.

Models for audio and music

  • WaveNet:
    • Developed by DeepMind.
    • Scope of application: Generation of realistic speech and music.
  • MuseGAN:
    • The generation of musical compositions using generative competitive networks.
    • Scope of application: Creation of harmonious music tracks.
  • Jukedeck:
    • Music generation based on emotions and themes.
    • Scope of application: Creating background music for videos and projects.
автор - Михаленко Р.
M R. Автор - kaktotak.by Specialization: financial and economic design - modeling of business, investment projects of the real sector, analysis and evaluation of efficiency, optimization of the management decision system.

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