CIO
JANUARY 9, 2025
Generative artificial intelligence ( genAI ) and in particular large language models ( LLMs ) are changing the way companies develop and deliver software. These autoregressive models can ultimately process anything that can be easily broken down into tokens: image, video, sound and even proteins.
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.
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CIO
FEBRUARY 5, 2025
As insurance companies embrace generative AI (genAI) to address longstanding operational inefficiencies, theyre discovering that general-purpose large language models (LLMs) often fall short in solving their unique challenges. Claims adjudication, for example, is an intensive manual process that bogs down insurers.
CIO
NOVEMBER 14, 2024
Data scientists and AI engineers have so many variables to consider across the machine learning (ML) lifecycle to prevent models from degrading over time. Fine-Tuning Studio Lastly, the Fine-tuning Studio AMP simplifies the process of developing specialized LLMs for certain use cases.
Advertiser: Data Robot
The game-changing potential of artificial intelligence (AI) and machine learning is well-documented. Any organization that is considering adopting AI at their organization must first be willing to trust in AI technology.
CIO
NOVEMBER 19, 2024
From obscurity to ubiquity, the rise of large language models (LLMs) is a testament to rapid technological advancement. Just a few short years ago, models like GPT-1 (2018) and GPT-2 (2019) barely registered a blip on anyone’s tech radar. If the LLM didn’t create enough output, the agent would need to run again.
Openxcell
JANUARY 20, 2025
LLM or large language models are deep learning models trained on vast amounts of linguistic data so they understand and respond in natural language (human-like texts). These encoders and decoders help the LLM model contextualize the input data and, based on that, generate appropriate responses.
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