What Is Generative AI

Generative AI refers to a category of artificial intelligence techniques that are designed to generate new, original content. It involves using algorithms to create something new, such as images, text, music, or even videos, that resembles or mimics human creativity.

Generative AI models are trained on large datasets and learn patterns and structures within the data. They can then generate new examples that are similar to the training data. These models are capable of generating content without explicit human instructions or pre-programmed rules.

One popular approach to generative AI is the use of generative adversarial networks (GANs). GANs consist of two neural networks: a generator and a discriminator. The generator generates new samples, while the discriminator tries to distinguish between real and generated samples. Through an iterative process, both networks improve their performance, resulting in more realistic and convincing outputs.

Generative AI has been used in various fields. For example, in computer vision, it can be used to generate realistic images or enhance low-resolution images. In natural language processing, it can generate coherent text or assist in language translation. It also has applications in art, music, and design, enabling the creation of novel and unique pieces.

However, it's worth noting that generative AI models are not perfect and may sometimes produce outputs that are unrealistic or nonsensical. The technology is still advancing, and researchers are constantly working to improve the quality and reliability of generative AI systems.
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[ektowfu]

Generative AI, short for Generative Artificial Intelligence, is a branch of artificial intelligence that focuses on creating models capable of generating new content that resembles human-generated data. Unlike traditional AI models that are designed for specific tasks, such as classification or prediction, generative AI models are intended to produce original and creative outputs.

Generative AI models work by learning patterns and structures from large datasets and then generating new data that shares similar characteristics. These models can be applied to various types of data, such as text, images, audio, and even video.

There are several types of generative AI models, including:

Generative Adversarial Networks (GANs): GANs consist of two neural networks: a generator and a discriminator. The generator's role is to create synthetic data, while the discriminator's task is to differentiate between real and generated data. Through a competitive process, the generator gets better at producing realistic data as it learns from the feedback given by the discriminator.

Variational Autoencoders (VAEs): VAEs are a type of neural network that aims to learn the underlying distribution of the input data. They work by encoding the input data into a lower-dimensional latent space and then decoding it back to generate new samples.

Transformer Models: Transformers are a type of neural network architecture that has been highly successful in natural language processing tasks. They can generate human-like text by predicting the probability of the next word in a sequence given the preceding context.

Applications of generative AI are diverse and include:

Image Generation: Creating realistic images of objects, scenes, or people that do not exist in reality.

Text Generation: Generating human-like text for creative writing, chatbots, or language translation.

Music Composition: Generating original music compositions in various styles.

Video Synthesis: Creating synthetic videos based on given inputs or scenarios.

Generative AI has shown impressive results and potential, but it also comes with challenges, such as ensuring the generated content is accurate, safe, and free from biases. Ethical considerations are crucial when deploying generative AI models, as they could potentially be misused or create harmful content if not properly controlled. Nonetheless, generative AI holds promise in pushing the boundaries of creativity and innovation in various fields.

[vivekraj]

We all are waiting for the playlist covering Gen AI, LLM 🙏🏼

[RamaChandran-fchp]

Thank you sir . Now I know the differences between generative and discriminative Ai in Deep learning. Next level explanation. Love you 😍😍😍

[geekyprogrammer]

Finally! Was waiting for it from long time!

[pythonforfree]

Outstanding explanation never seen such great teacher
Thank you very much dear Krish.
Your innocent student from Pakistan

[maharajahponnaiah]

When Krish said: 'Consider you are a Human being' .. that really touched me :D

[BoSong]

this is the master's course, 100x more clear and effective than the same generative AI introduction course by google.

[jatinpriyadarshi]

Thanks for sharing, eagerly waiting this video, easy to understand from your channel.

[lshanker]

Like the energy and the way he has explained the various details in very nice way

[deepthik]

Wow super Krish, wonderful explanation. Amazing.Completely stick in ur presentation. Magical

[Santhosh_]

These are the most awaited😍 videos from you

[asheeshmathur]

Brilliant ...a transformer. ! Transforms complex concepts in simple easy to Digest pieces. God Bless

[sreshthachakraborty]

Very informative thank you so much for this kind of video....very helpful 🎉

[Limauser]

Llm models video please . Can u share few finance domain use cases. Great video and new subscriber. Can u elaborate on how we guage which ML model to use. Feature engineering and hyperparanwter tuning as well

[jvrsatish]

Hello Krish, Wonderfull Content. I want to learn so much from you. Your teaching is excellent and Amazing. Thanks a lot for your time and patience.

[reallegends]

I grateful that you explained gen AI so good...there is not much videos with this much information

[sankethujare]

Great work sir ...keep uploading such vedioes thank you🙏

[HRandAI]

Thank you for your time and I feel these sessions are very easy to understand based on your teaching style

[mailmessage]

please come up with the prompt engineering course at ineuron

[MrRobo]

Egarly waiting for this. Thank you so much sir