AWS Machine Learning - AI
APRIL 9, 2025
Organizations are increasingly using multiple large language models (LLMs) when building generative AI applications. Although an individual LLM can be highly capable, it might not optimally address a wide range of use cases or meet diverse performance requirements.
AWS Machine Learning - AI
OCTOBER 16, 2024
This post was co-written with Lucas Desard, Tom Lauwers, and Sam Landuydt from DPG Media. DPG Media is a leading media company in Benelux operating multiple online platforms and TV channels. DPG Media’s VTM GO platform alone offers over 500 days of non-stop content.
CIO
MARCH 20, 2025
Global competition is heating up among large language models (LLMs), with the major players vying for dominance in AI reasoning capabilities and cost efficiency. OpenAI is leading the pack with ChatGPT and DeepSeek, both of which pushed the boundaries of artificial intelligence.
CIO
APRIL 14, 2025
Artificial intelligence has great potential in predicting outcomes. Because of generative AI and large language models (LLMs), AI can do amazing human-like things such as pass a medical exam or an LSAT test. Calling AI artificial intelligence implies it has human-like intellect.
Crunchbase News
NOVEMBER 13, 2024
Bob Ma of Copec Wind Ventures AI’s eye-popping potential has given rise to numerous enterprise generative AI startups focused on applying large language model technology to the enterprise context. First, LLM technology is readily accessible via APIs from large AI research companies such as OpenAI.
CIO
MARCH 5, 2024
India’s Ministry of Electronics and Information Technology (MeitY) has caused consternation with its stern reminder to makers and users of large language models (LLMs) of their obligations under the country’s IT Act, after Google’s Gemini model was prompted to make derogatory remarks about Indian Prime Minister Narendra Modi.
O'Reilly Media - Ideas
NOVEMBER 19, 2024
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. Also, in place of expensive retraining or fine-tuning for an LLM, this approach allows for quick data updates at low cost.
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