CIO
MARCH 18, 2024
For successful AI deployments, IT leaders not only need the latest GPU/CPU silicon, they also need artificial intelligence (AI) servers that establish a foundation. By enabling greater computational density for servers, these options reduce operational costs while sustaining maximum AI system performance.
CIO
JANUARY 31, 2024
Credit rating agencies and customers are paying closer attention to environmental, social, and governance (ESG) issues such as carbon emissions, says Faith Taylor, VP of global sustainability and ESG officer at global infrastructure services provider Kyndryl. Is it necessary for a model that can also write a sonnet to write code for us?”
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CIO
MAY 29, 2024
On the surface and as it exists today, AI and sustainability take you in opposite directions,” says Srini Koushik, president of AI, technology and sustainability at Rackspace Technology. “AI AI consumes a lot of power, whether it’s training large language models or running inference. They share them here.
CIO
APRIL 30, 2024
Saudi Aramco is spearheading the innovations by embracing cutting-edge technologies like artificial intelligence, both within its core operations and beyond, which places the company ahead of the curve. These images are then analyzed using machine learning algorithms to detect oil spills and pollutants.
Xebia
FEBRUARY 1, 2024
LLM Fine-tuning NeurIPS 2023 featured a challenge to efficiently fine-tune open-source LLM models on public datasets. If you are considering fine-tuning LLMs, there are some things to consider: Infra, Data, Base Model, Training, Inference and Evaluation. Training should be reproducible.
Invid Group
MARCH 19, 2024
What Is Machine Learning Used For? By INVID With the rise of AI, the term “machine learning” has grown increasingly common in today’s digitally driven world, where it is frequently credited with being the impetus behind many technical breakthroughs. Let’s break it down. Take retail, for instance.
AWS Machine Learning - AI
APRIL 24, 2024
In this post, we share how we analyzed the feedback data and identified limitations of accuracy and hallucinations RAG provided, and used the human evaluation score to train the model through reinforcement learning. To increase training samples for better learning, we also used another LLM to generate feedback scores.
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