Generative AI

What is Generative AI? 

Generative AI is a type of artificial intelligence that creates new content based on what it has learned from existing content. The process of learning from existing content is called training. And results in the creation of a statistical model when given a prompt, generative AI uses the model to predict what an expected response might be, and this generates new content. Essentially, it learns the underlying structure of the data.

Generative AI is a type of artificial intelligence (AI) that can create new content, such as text, images, audio, and video. It does this by learning from existing data and then using that knowledge to generate new and unique outputs.

Generative AI is used in a variety of applications, including 

1.Artificial Art: Generative AI can be used to create new pieces of art, such as paintings, sculptures, and music. 

2. Text generation: Generative AI can be used to generate new text, such as news articles, blog posts, and marketing copy. 

3. Image generation: Generative AI can be used to generate new images, such as product photos, stock photos, and marketing materials. 

4. Video generation: Generative AI can be used to generate new videos, such as product demos, training videos, and marketing videos.
5. Audio generation: Generative AI can be used to generate new audio, such as music, podcasts, and audiobooks.

A foundation model is a large AI model pretrained on a vast quantity of data that was "designed to be adapted” (or fine-tuned) to a wide range of downstream tasks, such as sentiment analysis, image captioning, and object recognition. 

Hallucinations are words or phrases that are generated by the model that are often nonsensical or grammatically incorrect.

  These are the factors that can cause hallucinations:
   The model is not given enough context. 

   The model is not trained on enough data. 

   The model is trained on noisy or dirty data. 

Prompt: A prompt is a short piece of text that is given to the large language model as input, and it can be used to control the output of the model in many ways. 

Example of both a generative AI model and a discriminative AI model: 

    A generative AI model could be trained on a dataset of images of cats and then used to generate new images of cats. 

   A discriminative AI model could be trained on a dataset of images of cats and dogs and then used to classify new images as either cats or dogs.