AWS Announced “Amazon Bed Rock” a new service that makes Foundation Model from AI21 Labs, Anthropic, Stability AI, and Amazon accessible via an API.
Bedrock helps us to access all the Foundation Models which are pre-trained with a large amount of dataset and Billions of Parameters.
Amazon announced the Amazon Titan Foundation Models.
- Generate AI - Large Language Model LLM
Titan Text is a powerful large language model (LLM) that can automate natural language tasks such as summarization and text generation. It can also be used for classification, open-ended Q&A, and information extraction, making it a versatile tool for various language-related applications.
- Embedding.
Embeddings are used to represent queries and documents in a vector space, where the similarity between vectors can be used to rank search results. By using embeddings, search engines can better understand the meaning of queries and documents, and return more relevant results.
For example, suppose a user searches for "best Pizza restaurant in New York City." By using embeddings, a search engine can represent this query as a vector in a high-dimensional space, where words like "Pizza," "restaurant," "best," and "New York City" are mapped to specific coordinates in the vector space. Similarly, each document in the search index can also be represented as a vector.
The search engine can then rank search results based on the similarity between the query vector and the document vectors. Documents that are more similar to the query vector will be ranked higher, and returned as the top search results. By using embeddings, search engines can better understand the meaning of queries and documents, and return more relevant results.
- Responsible AI
AWS is Committed to advance the design and deployment of safe and trustworthy artificial intelligence which benefits all of humanity by becoming a member of https://www.responsible.ai/
Titan FMs are designed to promote responsible use of AI by identifying and mitigating inappropriate or harmful content. They are capable of detecting and eliminating harmful content in the data, preventing inappropriate content in user input, and filtering out model outputs that contain inappropriate content such as hate speech, profanity, or violent language.
For More details
https://aws.amazon.com/blogs/machine-learning/announcing-new-tools-for-building-with-generative-ai-on-aws/
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