First, let's define GenAI: according to AWS, it is a new type of AI that will create new content based on Foundation Models. These Models are pretrained on massive amounts of data.
As opposed to Traditional ML methods, which are siloed to perform specific tasks ( by following the approach: gather labeled data - train - deploy), Foundation models are meant to perform multiple tasks. In other words, they can be customized to perform domain specific functions such as Text Generation, Summarization, Information Extraction, Q&A or Chatobot tasks.
This approach requires Prompt Engineers to refine the output of the Model (Designing and refining prompts). Gen AI relies on this approach to initiate an action from a Foundation Model, it receives the prompt or text given by the user, the model generates a response to the Prompt (inference) and the result of this is displayed as an output.
One of Amazon Products "Amazon Q Developer" uses this approach to deliver a robust AI assistant for code development. It can generate real-time suggestions to improve productivity or act as an agent to help implement features or write documentation.
It is important to understand that there are 3 Foundation model types:
- Text-to-Text: which extends user input with newly generated text
- Text-to-Embedding: compares input with indexed data (Amazon search Bar)
- Multimodal: Can generate different formats (text-to-image)
Some of the use cases can be applied to Enhance customer support, increase employee productivity, optimize proceses and enhance creativity. Of course, this set of application can be broadened to several fields, such as:
- Healthcare (Medical image interpreter, Intelligent health assist, Personalized medicine based on patient genetics)
- Life Sciences (Enhanced medical trials, Automated research reporting)
- Financial Services (AI-managed portfolios, Increase the business value from unstructured data - such as emails)
- Manufacturing (Supply chain traceability, equipment diagnostics)
- Retail (Virtual Try-On, personalized recommendations)
- Entertainment (Produce high-quality content, Automated highlight generation)
This technology excells at being flexible, secure, cost-effective and builder friendly. And it can be accessed through AWS top list of products:
- Amazon Bedrock for Foundation Model access
- Q Developer as a Code assistant
- AWS Inferentia for high performance inference predictions (Via Neuron AWS SDK)
- AWS Trainium for DL training acceleration (Via EC2 Tr1 instances)
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