Generative AI 란: 기본 개념과 실생활 활용 예시

Generative AI는 우리 일상에 큰 영향을 미치는 기술입니다.

Generative AI는 데이터를 기반으로 새로운 데이터를 생성하는能力를 가집니다.

이 기술은 다양한 분야에서 활용될 수 있습니다.

Generative AI는 이미지 생성, 음악 생성, 텍스트 생성 등 다양한 형태의 데이터를 생성할 수 있습니다.

이러한 기능은 게임, 교육, 의료 등 다양한 분야에서 활용될 수 있습니다.

Generative AI는 사람의 지도를 필요로 하지 않아도 새로운 데이터를 생성할 수 있습니다.

이러한 기술은 데이터 부족 문제를 해결하는 데 도움이 될 수 있습니다.

Generative AI is a type of AI that creates new content, such as text, images, music, and videos, based on the data it's been trained on.

It's a departure from traditional AI, which focused on data analysis and prediction. Generative AI aims to produce creative results, like completing a text or generating an image based on a description.

This technology is based on deep learning, which allows it to analyze and combine various data sources to produce new outputs. Generative AI can even mimic human creativity to some extent.

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For example, you can use it to write a text based on a prompt, or create an image that matches a given description. Some popular examples of generative AI include ChatGPT and DALL-E.

According to McKinsey, 75% of the potential value of generative AI is expected to come from just four areas: customer service, marketing and sales, software engineering, and research and development.

Generative AI uses large amounts of data to learn patterns and create new content. This is achieved through the use of models such as Generative Adversarial Networks (GANs) and Transformer models.

GANs are made up of two neural networks: a generator and a discriminator. The two networks compete with each other, with the generator creating an output based on some input and the discriminator trying to determine if the output is real or fake.

Transformer models, like ChatGPT, create outputs based on sequential data rather than individual data points. This approach helps the model efficiently process context and is why it's used to generate or translate text.

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There are several other techniques used in generative AI, including variational autoencoders (VAEs) and neural radiance fields (NeRFs). VAEs rely on two neural networks to generate new data based on sample data, while NeRFs are used to create 2D and 3D images.

Here are some of the most popular generative AI models:

Generative AI models come in various forms, each with its unique strengths and applications. There are three primary types of generative AI models: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Recurrent Neural Networks (RNNs).

VAEs are particularly useful for generating new data that is similar to existing data, such as images or text. They work by compressing the input data into a lower-dimensional space and then reconstructing it.

GANs, on the other hand, are ideal for generating new, realistic data that is indistinguishable from real data. They consist of two neural networks that work together to generate new data that is convincing and realistic.

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기존 AI 모델은 특정한 목적에 특화되어 설계되었다. 기존 방식의 AI 시스템은 일반적으로 신용 카드 사기 탐지, 운전 경로 결정, 또는 현재 개발 단계인 자동차 운전과 같은 특정한 작업을 사람보다 더 효과적으로 또는 저렴하게 수행하기 위해 설계된다.

기존 AI 모델은 대량의 데이터를 필요로 하며, 학습한 '지식'을 사용하여 예측을 수행하고 행동을 조정한다. 이들은 피드백 또는 신규 정보를 반영하여 각종 매개변수를 조정함으로써 시간이 지날수록 발전된 결과를 도출할 수 있다.

기존 AI 모델은 대부분 라벨화/범주화된 데이터만을 학습하는 지도 학습 기법을 사용한다.

Generative AI models come in various forms, each with its unique characteristics and applications.

The Variational Autoencoder (VAE) is a type of generative model that uses a neural network to learn a probabilistic representation of the input data.

VAEs are particularly useful for image generation and can be used for tasks such as image denoising and inpainting.

The Generative Adversarial Network (GAN) is another type of generative model that consists of two neural networks competing with each other.

GANs are commonly used for tasks such as image and video generation, as well as data augmentation.

The Transformer-based model is a type of generative model that uses self-attention mechanisms to process sequential data.

Transformer-based models are particularly useful for tasks such as language generation and text-to-image synthesis.

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Generative AI offers a lot of benefits, especially when it comes to automating routine tasks that take up a lot of time and energy. By doing so, it frees up people to focus on more high-level work.

One of the biggest advantages of generative AI is its ability to automate routine tasks, such as data analysis. For example, with a large database of social media posts about a specific product, you can use a generative AI model to extract the main topics, group them, and identify the most relevant ones.

You can also use generative AI to analyze text data and extract insights from it. For instance, you can use a language model to identify the most common topics in a dataset of social media posts and track their trends over time. This can help you understand what people are talking about and what they're interested in.

Here are some specific tasks you can perform with generative AI:

These tasks can be performed with just a few commands, making it much faster and more efficient than traditional methods. As Basim Baig, Senior Engineering Manager at Duolingo, puts it, "LLM's advantage is that it skips the expensive and laborious engineering step."

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Generative AI is already being used in various fields, making a significant impact on our daily lives.

ChatGPT and similar AI models can automatically generate text based on a user's input, making it a powerful tool for customer service, education, and marketing.

In fact, ChatGPT can generate text that is coherent and even creative, making it a valuable asset for businesses.

Here are some examples of how generative AI is being used in real-life scenarios:

These applications are not only limited to text generation, but also include image and music creation, making generative AI a versatile tool for various industries.

In fact, a Salesforce survey found that two-thirds of IT leaders are prioritizing generative AI for their business within the next 18 months, with one-third claiming it as a top priority.

This indicates that generative AI is becoming an essential tool for businesses, and its applications will only continue to grow in the future.

By leveraging generative AI, businesses can automate tasks, improve customer service, and even create new products and services.

For example, Salesforce's ProGen project has already shown promising results in creating new proteins that have not been found in nature, which can potentially lead to new medicines and treatments.

These examples demonstrate the vast potential of generative AI and its ability to transform various industries and aspects of our lives.

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