Generative Data Intelligence

Tag: interpretability

What is DALL-E, and how does it work?

OpenAI created the ground-breaking generative artificial intelligence (AI) model known as DALL-E, which excels at creating distinctive, incredibly detailed visuals from textual descriptions. DALL-E,...

How Light & Wonder built a predictive maintenance solution for gaming machines on AWS | Amazon Web Services

This post is co-written with Aruna Abeyakoon and Denisse Colin from Light and Wonder (L&W). Headquartered in Las Vegas, Light & Wonder, Inc. is...

Why Implementing Generative AI Is Challenging For Banks

Generative artificial intelligence (AI) has the potential to revolutionize the banking industry, offering numerous benefits such as Enhanced customer experience Streamlined operations Fraud Detection...

Accelerate your learning towards AWS Certification exams with automated quiz generation using Amazon SageMaker foundations models | Amazon Web Services

Getting AWS Certified can help you propel your career, whether you’re looking to find a new role, showcase your skills to take on a...

Perturbation theory with quantum signal processing

Kosuke Mitarai1,2, Kiichiro Toyoizumi3, and Wataru Mizukami21Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan.2Center for Quantum Information and...

How Vericast optimized feature engineering using Amazon SageMaker Processing

This post is co-written by Jyoti Sharma and Sharmo Sarkar from Vericast. For any machine learning (ML) problem, the data scientist begins by working...

Question answering using Retrieval Augmented Generation with foundation models in Amazon SageMaker JumpStart

Today, we announce the availability of sample notebooks that demonstrate question answering tasks using a Retrieval Augmented Generation (RAG)-based approach with large language models...

The Computer Scientist Peering Inside AI’s Black Boxes

IntroductionMachine learning models are incredibly powerful tools. They extract deeply hidden patterns in large data sets that our limited human brains can’t parse. These...

Authoring custom transformations in Amazon SageMaker Data Wrangler using NLTK and SciPy

“Instead of focusing on the code, companies should focus on developing systematic engineering practices for improving data in ways that are reliable, efficient, and...

Zero-shot prompting for the Flan-T5 foundation model in Amazon SageMaker JumpStart

The size and complexity of large language models (LLMs) have exploded in the last few years. LLMs have demonstrated remarkable capabilities in learning the...

Using GAMs To Project Progression Of Diabetes

This article is based on the chapter from the Interpretable AI book by Ajay Thampi. Take 35% off Interpretable AI or any other product...

Predict football punt and kickoff return yards with fat-tailed distribution using GluonTS

Today, the NFL is continuing their journey to increase the number of statistics provided by the Next Gen Stats Platform to all 32 teams...

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