IBM Journey to AI blog

APRIL 26, 2024

As large language models (LLMs) have entered the common vernacular, people have discovered how to use apps that access them. Modern AI tools can generate, create, summarize, translate, classify and even converse. Let’s examine these solutions from the perspective of a hybrid AI model.

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Flipboard

APRIL 23, 2023

The explosion of new generative AI products and capabilities over the last several months — from ChatGPT to Bard and the many variations from others based on large language models (LLMs) — has driven an overheated hype cycle. In turn, this situation has led to a similarly expansive and passionate …

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Unite.AI

JANUARY 23, 2024

Large language models (LLMs) have exploded in popularity over the last few years, revolutionizing natural language processing and AI. From chatbots to search engines to creative writing aids, LLMs are powering cutting-edge applications across industries.

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O'Reilly Media

NOVEMBER 19, 2024

One popular term encountered in generative AI practice is retrieval-augmented generation (RAG). Reasons for using RAG are clear: large language models (LLMs), which are effectively syntax engines, tend to “hallucinate” by inventing answers from pieces of their training data. at Facebook—both from 2020.

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ODSC - Open Data Science

MARCH 13, 2025

As AI continues to evolve, there is growing demand for lightweight large language models that balance efficiency and performance. Unlike their massive counterparts, lightweight LLMs offer a practical alternative for applications requiring lower computational overhead without sacrificing accuracy.

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Marktechpost

FEBRUARY 28, 2025

Large Language Models (LLMs) rely on reinforcement learning techniques to enhance response generation capabilities. One critical aspect of their development is reward modeling, which helps in training models to align better with human expectations.

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ODSC - Open Data Science

NOVEMBER 21, 2024

Understanding the Core Limitations of Large Language Models: Insights from Gary Marcus Gary Marcus, a leading voice and critic of AI, shared his thoughts in a recent podcast, where he explored LLMs’ limitations, the need for hybrid AI approaches, and more.

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