Text-to-image generation is a task in which a machine learning (ML) model generates an image from a textual description. The goal is to generate an image that closely matches the description, capturing the details and nuances of the text. This task is challenging because it requires the model to understand the semantics and syntax of the text and to generate photorealistic images. There are many practical applications of text-to-image generation in AI photography, concept art, building architecture, fashion, video games, graphic design, and much more. Stable Diffusion is a text-to-image model ..read more

One of the most common applications of generative AI and large language models (LLMs) in an enterprise environment is answering questions based on the enterprise’s knowledge corpus. Amazon Lex provides the framework for building AI based chatbots. Pre-trained foundation models (FMs) perform well at natural language understanding (NLU) tasks such summarization, text generation and question answering on a broad variety of topics but either struggle to provide accurate (without hallucinations) answers or completely fail at answering questions about content that they haven’t seen as part of their ..read more

Amazon Kendra is a highly accurate and intelligent search service that enables users to search unstructured and structured data using natural language processing (NLP) and advanced search algorithms. With Amazon Kendra, you can find relevant answers to your questions quickly, without sifting through documents. However, just enabling end-users to get the answers to their queries is not enough in today’s world. We need to constantly understand the end-user’s search behavior, such as what are the top queries for the month, have any new query that queries appeared recently, what percentage of quer ..read more

Intelligent document processing (IDP) is a technology that automates the processing of high volumes of unstructured data, including text, images, and videos. IDP offers a significant improvement over manual methods and legacy optical character recognition (OCR) systems by addressing challenges such as cost, errors, low accuracy, and limited scalability, ultimately leading to better outcomes for organizations and stakeholders. Natural language processing (NLP) is one of the recent developments in IDP that has improved accuracy and user experience. However, despite these advances, there are stil ..read more

In this three-part series, we present a solution that demonstrates how you can automate detecting document tampering and fraud at scale using AWS AI and machine learning (ML) services for a mortgage underwriting use case. This solution rides on a more significant global wave of increasing mortgage fraud, which is worsening as more people present fraudulent proofs to qualify for loans. Data suggests high-risk and suspected fraudulent mortgage activity is on the rise, noting a 52% increase in suspected fraudulent mortgage applications since 2013. (Source: Equifax) Part 1 of this series discusses ..read more

Today we are excited to announce that you can now perform batch transforms with Amazon SageMaker JumpStart large language models (LLMs) for Text2Text Generation. Batch transforms are useful in situations where the responses don’t need to be real time and therefore you can do inference in batch for large datasets in bulk. For batch transform, a batch job is run that takes batch input as a dataset and a pre-trained model, and outputs predictions for each data point in the dataset. Batch transform is cost-effective because unlike real-time hosted endpoints that have persistent hardware, batch tra ..read more

Amazon Kendra is a highly accurate and simple-to-use intelligent search service powered by machine learning (ML). Amazon Kendra offers a suite of data source connectors to simplify the process of ingesting and indexing your content, wherever it resides. Valuable data in organizations is stored in both structured and unstructured repositories. An enterprise search solution should be able to pull together data across several structured and unstructured repositories to index and search on. One such unstructured data repository is Confluence. Confluence is a team workspace that gives knowledge wor ..read more

This is a guest blog post co-written with Vik Pant and Kyle Bassett from PwC. With organizations increasingly investing in machine learning (ML), ML adoption has become an integral part of business transformation strategies. A recent PwC CEO survey unveiled that 84% of Canadian CEOs agree that artificial intelligence (AI) will significantly change their business within the next 5 years, making this technology more critical than ever. However, implementing ML into production comes with various considerations, notably being able to navigate the world of AI safely, strategically, and responsibly ..read more

The seeds of a machine learning (ML) paradigm shift have existed for decades, but with the ready availability of virtually infinite compute capacity, a massive proliferation of data, and the rapid advancement of ML technologies, customers across industries are rapidly adopting and using ML technologies to transform their businesses. Just recently, generative AI applications have captured everyone’s attention and imagination. We are truly at an exciting inflection point in the widespread adoption of ML, and we believe every customer experience and application will be reinvented with generative ..read more

Generative AI is in the midst of a period of stunning growth. Increasingly capable foundation models are being released continuously, with large language models (LLMs) being one of the most visible model classes. LLMs are models composed of billions of parameters trained on extensive corpora of text, up to hundreds of billions or even a trillion tokens. These models have proven extremely effective for a wide range of text-based tasks, from question answering to sentiment analysis. The power of LLMs comes from their capacity to learn and generalize from extensive and diverse training data. The ..read more