Why is generative AI sought after by various industries?

Generative AI is a type of human artificial intelligence technology that can generate various types of content, including text, images, audio and synthetic data. So what is artificial intelligence? What is the difference between artificial intelligence and machine learning?

Artificial intelligence is the discipline, a branch of computer science, that studies the creation of intelligent agents that can reason, learn, and execute autonomously Action system.

At its core, artificial intelligence is concerned with the theories and methods of building machines that think and act like humans. Within this discipline, machine learning ML is a field of artificial intelligence. It is a program or system that trains a model based on input data. The trained model can make useful predictions from new or unseen data derived from the unified data on which the model was trained.

Machine learning gives computers the ability to learn without explicit programming. The two most common types of machine learning models are unsupervised and supervised ML models. The main difference between the two is that for supervised models we have labels, labeled data is data with labels like name, type or number and unsupervised data is data without labels.

#This figure is an example of a problem that a supervised model might try to solve.

For example, let’s say you are a restaurant owner and you have historical data on bill amounts, how much tips different people gave based on the order type, whether it was pickup or delivery based on the order type To so many different people. In supervised learning, a model learns from past examples to predict future values. So the model here uses the total bill amount to predict future spending based on whether the order is for pickup or delivery.

This example shows a problem situation that an unsupervised model might solve, where you want to look at tenure and income, and then group employees to get clusters and see if anyone On the fast track. The unsupervised problem is all about looking at the raw data and seeing if it groups naturally. Let's go a little deeper and show this graphically.

The above concepts are the basis for understanding generative AI.

In supervised learning, test data values ​​are fed into a model, which outputs a prediction, and that prediction is compared with the training data used to train the model Compare.

If the predicted test data value and the actual training data value are far apart, it is called an error, and the model will try to reduce this error until the predicted and actual values ​​are closer.

We have explored the differences between artificial intelligence and machine learning, supervised learning and unsupervised learning. So, let’s briefly explore deep learning.

While machine learning is a broad field that encompasses many different techniques, deep learning is a type of machine learning that uses artificial neural networks, allowing them to process more complex patterns than machine learning.

Artificial neural networks are inspired by the human brain. They are composed of many interconnected nodes or neurons that can be Process data and make predictions to learn to perform tasks.

Deep learning models usually have multiple layers of neurons. This allows them to learn more complex patterns than traditional machine learning models. Neural networks can work with both labeled and unlabeled data, which is called semi-supervised learning. In semi-supervised learning, a neural network is trained on a small amount of labeled data and a large amount of unlabeled data. Labeled data helps the neural network learn the basic concepts of the task. And unlabeled data helps neural networks generalize to new examples.

position in this artificial intelligence discipline, which means using artificial neural networks, labeled and unlabeled data can be processed in supervised, unsupervised and semi-supervised methods. Large language models are also a subset of deep learning, deep learning models or machine learning models in general.

Deep learning can be divided into two types: discriminative and generative. A discriminative model is a model used to classify or predict the labels of data points. Discriminative models are typically trained on datasets of labeled data points. They learn the relationship between the features and labels of data points, and once the discriminative model is trained, it can be used to predict the labels of new data points. The generative model generates new data instances based on the learned probability distribution of existing data, so the generative model produces new content.

The generative model can output new data instances, while the discriminative model can distinguish different types of data instances.

This figure shows a traditional machine learning model, the difference is the relationship between the data and the labels, or what you want to predict. The bottom image shows a generative AI model trying to learn content patterns in order to generate and output new content.

When the output outer label is a number or probability, it is non-generative AI, such as spam and non-spam. When the output is natural language, it is generative AI, such as speech, text, images and videos.

The model output is a function of all inputs, and if Y is a number, such as predicted sales, then it is not GenAI. If Y is a sentence, it's like defining sales. It is generative in that questions elicit textual responses. His responses will be based on all the massive amounts of big data the model has been trained on.

In summary, the traditional, classic supervised and unsupervised learning process uses training code and labeled data to build the model. Depending on the use case or problem, the model can give you predictions, it can classify or cluster something, use this force to show how robust the process that generated it is.

The GenAI process can obtain training code, labeled data and unlabeled data of all data types, build a basic model, and then the basic model can generate new content . Such as text, code, images, audio, video, etc.

From traditional programming to neural networks to generative models, we have come a long way. In traditional programming, we used to have to code rules for distinguishing cats. The type is an animal, with 4 legs, 2 ears, fur, etc.

In the wave of neural networks, we can feed the network pictures of cats and dogs. and asked if it was a cat. He will predict a cat. In the generative AI wave, we, as users, can generate our own content.

Whether it is text, image, audio, video, etc., models such as Python

language model or conversational application language model. Get very large data from multiple sources on the internet. Build basic language models that can be used simply by asking questions. So, when you ask him what a cat is, he can tell you everything he knows about cats.

Now let’s define, what is generative artificial intelligence?

GenAI is a type of artificial intelligence that creates new content based on knowledge learned from existing content. The process of learning from existing content is called training. And create a statistical model when a prompt is given, use that model to predict what the expected response might be, and generate new content.

Essentially, it learns the underlying structural content of the data and can then generate new samples that are similar to the training data. As mentioned before, a generative language model can take what it has learned from the examples it was shown and create something entirely new based on that information.

Large language models are a type of generative artificial intelligence because they generate novel text combinations in the form of naturally-sounding language, generate image models, take images as input, and can output text , another image or video. For example, under Output Text you can get visual Q&A, while under Output Image generate image completion, and under Output Video generate animation.

# Generates a language model that takes text as input and can output more text, images, audio or decisions. For example, generate a question and answer under the output text and a video under the output image.

We have said that generative language models learn about patterns and language through training data, and then given some text, they predict what will happen next.

Generative language models are pattern matching systems, they learn patterns based on the data you provide them. Based on what he learned from the training data, he provides a prediction of how to complete the sentence. It was trained on large amounts of text data and was able to communicate in response to a variety of prompts and questions and generate human-like text.

In the transformer, Hallucin is a word or phrase generated by the model, which is usually nonsense or grammatically incorrect. Hallucinations can be caused by a variety of factors, including the model not being trained on enough data, or the model being trained on noisy or dirty data, or not giving the model enough context, or not giving the model enough constraints. .

They can also make the model more likely to generate incorrect or misleading information, such as miscellaneous TPT3.5 which may sometimes generate information that is not necessarily correct. Prompt words are small pieces of text given as input to large language models. And it can be used to control the output of a model in a variety of ways.

Hint design is the process of creating hints that will produce the desired output content from a large language model. As mentioned before, LLM depends heavily on the training data you input. It learns by analyzing the patterns and structure of the input data. But by accessing browser-based prompts, users can generate their own content.

We have shown a roadmap for data-based input types, and here are the relevant model types.

Text-to-text model. Takes natural language input and generates text output. These models are trained to learn mappings between texts. For example, translation from one language to another.

Text to image model. Because text-to-image models are trained on a large number of images. Each image comes with a short text description. Diffusion is one method used to achieve this.

Text to video and text to 3D. Text-to-video models only generate video content from text input, which can be anything from a single sentence to a complete script. The output is video-like text corresponding to the input text to a 3D model that generates three-dimensional objects corresponding to the user's textual description. This could be used for games or other 3D worlds, for example.

Text to task model. Once trained, it can perform defined tasks or actions based on text input. This task can be extensive. For example, answer a question, perform a search, make a prediction, or take some action. Text-to-task models can also be trained to guide queries or make changes to documents.

#The basic model is a large AI model pre-trained on a large amount of data. The aim is to adapt or fine-tune a variety of downstream tasks such as sentiment analysis, image, captioning and object recognition.

Fundamental models have the potential to revolutionize many industries, including healthcare, finance, and customer service, where they can be used to detect predictions and provide personalized customer support. OpenAI provides a basic model source language, including those for chat and text.

The basic vision model includes stable diffusion, which can effectively generate package quality images from text descriptions. Let's say you have a case where you need to gather information about how customers feel about your product or service.

Generative AI Studio, from a developer's perspective, allows you to easily design and build applications without writing any code. It has a visual editor that makes it easy to create and edit application content. There is also a built-in search engine that allows users to search for information within the app.

There is also a conversational artificial intelligence engine that helps users interact with applications using natural language. You can create your own digital assistant, custom search engine, knowledge base, training app, and more.

Model deployment tools help developers deploy models into production environments using a number of different deployment options. And model monitoring tools help developers monitor the performance of ML models in production using dashboards and many different metrics.

If generative AI application development is viewed as the assembly of a complex puzzle, each of the technical capabilities it requires, such as data science, machine learning, and programming, is equivalent to each piece of the puzzle.

It is already difficult for enterprises without technical accumulation to understand these puzzle pieces, and putting them together becomes an even more difficult task. But if there are services that can provide these traditional enterprises with weak technical capabilities with some pre-assembled puzzle pieces, these traditional enterprises can complete the entire puzzle more easily and quickly.

Judging from the actual situation in the domestic market, the development of generative AI is neither as optimistic as expected by practitioners chasing the trend, nor as pessimistic as described by naysayers.

Enterprise users pursue the robustness, economy, security and usability of applications. This is in line with the fact that generative AI such as large language models do not hesitate to spend high computing power costs in the training process to achieve higher capabilities. Completely different path.

A core issue behind this is that in the field of enterprise-level generative AI with greater imagination, the most important thing is not how powerful the large model is, but how it can evolve from a basic model to Specific applications in various fields, thereby empowering the development of the entire economy and society.

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