Sat.Jan 18, 2020

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Cracking the complexity code

Paul Hobcraft

There was a good article within the McKinsey Quarterly published way back in 2007 entitled “Cracking the complexity code,” written by three authors Suzanne Heywood, Jessica Spungin, and David Turnbull. It still has a lot of relevancy in my mind today. They lead this article with “one view of complexity that holds that it is largely a bad thing- that simplification generally creates value by removing unnecessary costs.

System 227
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Hold the Front Page! The Rise of Robot Journalists

IdeaConnection

The growth of robot or automated journalism - news stories written by artificial intelligence software.

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Responsible Artificial Intelligence

Norbert Bol

Artificial Intelligence (AI) is growing rapidly as it succeeds to improve productivity and customer experience in every domain of our society such as education, industry, agriculture, healthcare, finance, transportation, entertainment, security, energy, communication, etc. AI can be described as machines capable of imitating certain functionalities of human intelligence, including such features as perception, learning, reasoning, problem-solving, language interaction, and even producing creative

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Responsible Artificial Intelligence

Norbert Bol

Artificial Intelligence (AI) is growing rapidly as it succeeds to improve productivity and customer experience in every domain of our society such as education, industry, agriculture, healthcare, finance, transportation, entertainment, security, energy, communication, etc. AI can be described as machines capable of imitating certain functionalities of human intelligence, including such features as perception, learning, reasoning, problem-solving, language interaction, and even producing creative

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.