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Applications of Artificial Intelligence (AI) in business

Hacker Earth Developers Blog

Businesses that use Artificial Intelligence (AI) and related technology to reveal new insights “will steal $1.2 Recent advances in AI have been helped by three factors: Access to big data generated from e-commerce, businesses, governments, science, wearables, and social media. predicts Forrester Research.

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Revolutionizing clinical trials with the power of voice and AI

AWS Machine Learning - AI

This is where the integration of cutting-edge technologies, such as audio-to-text translation and large language models (LLMs), holds the potential to revolutionize the way patients receive, process, and act on vital medical information.

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How DPG Media uses Amazon Bedrock and Amazon Transcribe to enhance video metadata with AI-powered pipelines

AWS Machine Learning - AI

This post shows how DPG Media introduced AI-powered processes using Amazon Bedrock and Amazon Transcribe into its video publication pipelines in just 4 weeks, as an evolution towards more automated annotation systems. The following were some initial challenges in automation: Language diversity – The services host both Dutch and English shows.

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10 Key Trends of Digital Transformation in Healthcare in 2022

OTS Solutions

Clinics that use cutting-edge technology will continue to thrive as intelligent systems evolve. At the heart of this shift are AI (Artificial Intelligence), ML (Machine Learning), IoT, and other cloud-based technologies. The intelligence generated via Machine Learning.

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Lessons learned turning machine learning models into real products and services

O'Reilly Media - Data

Why model development does not equal software development. Artificial intelligence is still in its infancy. Today, just 15% of enterprises are using machine learning, but double that number already have it on their roadmaps for the upcoming year. Models degrade in accuracy as soon as they are put in production.

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Ground truth generation and review best practices for evaluating generative AI question-answering with FMEval

AWS Machine Learning - AI

These assistants can be powered by various backend architectures including Retrieval Augmented Generation (RAG), agentic workflows, fine-tuned large language models (LLMs), or a combination of these techniques. To learn more about FMEval, see Evaluate large language models for quality and responsibility of LLMs.

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The future of data: A 5-pillar approach to modern data management

CIO

It was not alive because the business knowledge required to turn data into value was confined to individuals minds, Excel sheets or lost in analog signals. We are now deciphering rules from patterns in data, embedding business knowledge into ML models, and soon, AI agents will leverage this data to make decisions on behalf of companies.

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