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A recent report published by Goldman Sachs has fueled a new debate around generativeAI’s business value. Titled “Gen AI: Too much spend, too little benefit,” the report presents a contrasting view on what the technology currently delivers, approximately two years after its initial boom. in the coming decade.
GenerativeAI applications driven by foundational models (FMs) are enabling organizations with significant business value in customer experience, productivity, process optimization, and innovations. In this post, we explore different approaches you can take when building applications that use generativeAI.
The questions are written by financial professionals using real-world data and financial knowledge. As such, they are closer to the kinds of questions that business and financial professionals would ask in a generativeAI application. In fact, access to Amazon Bedrock allowed the team to benchmark Anthropic Claude 3.5
GenerativeAI stretches our current copyright law in unforeseen and uncomfortable ways. In the US, the Copyright Office has issued guidance stating that the output of image-generatingAI isn’t copyrightable, unless human creativity has gone into the prompts that generated the output.
This hybrid model addresses the limitations of traditional generative systems. Introduction Retrieval Augmented Generation (RAG) represents a groundbreaking approach to artificial intelligence. Unlike standalone models, RAG enhances traditional generativeAI by leveraging external knowledge sources.
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