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What is an example of Generative AI?
Popular examples of generative AI include tools like ChatGPT for generating text, GitHub Copilot for writing code, and DALL·E for creating images. Across industries, GenAI is being used to support tasks like drafting content, designing visuals, composing music, and even generating video or voice narration.
What is generative AI in simple terms?
Generative AI is a type of artificial intelligence that can create new content—like writing text, generating images, composing music, or writing code—based on the patterns it has learned from existing data. You give it a prompt, and it produces something original in response. For language-based outputs, techniques like natural language processing (NLP) help models understand and generate human-like text.
When was generative AI created?
The foundations of generative AI began with early machine learning models in the 2010s, but it gained mainstream attention around 2014 with the introduction of Generative Adversarial Networks (GANs). The release of large language models (LLMs) like GPT-2 and GPT-3 between 2019 and 2020 pushed GenAI further into the spotlight.
» npm install @google/generative-ai
List models sample for AI studio Gemini. Did you forget the configure?
Copyfrom dotenv import load_dotenv
import google.generativeai as genai
import os
load_dotenv()
genai.configure(api_key=os.getenv("API_KEY"))
genai.configure(transport='grpc')
def list_models():
for i, m in zip(range(5), genai.list_models()):
print(f"Name: {m.name} Description: {m.description} support: {m.supported_generation_methods}")
if __name__ == "__main__":
list_models()
Not sure if it's still a problem, but one could do,
!python3 -m pip install -U -q "google-genai"
Copyfrom google import genai
import json
client = genai.Client(
vertexai=True, project='', location=''
)
# without using vertexai
# client = genai.Client(api_key=<API_KEY>)
client = genai.Client(
vertexai=True, project='', location=''
)
for model in client.models.list():
model_info = json.loads(model.model_dump_json())
pprint.pprint(model_info)