CIO
OCTOBER 24, 2023
In light of this, developer teams are beginning to turn to AI-enabled tools like large language models (LLMs) to simplify and automate tasks. Many developers are beginning to leverage LLMs to accelerate the application coding process, so they can meet deadlines more efficiently without the need for additional resources.
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.
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CIO
MAY 10, 2024
The bill does not limit AI’s definition to any specific area, such as generative AI, large language models (LLMs), or machine learning. Instead, any means of artificial intelligence, including using an optical character reader (OCR) to scan resumes, is covered.
Palo Alto Networks
APRIL 2, 2024
Artificial intelligence (AI) plays a crucial role in both defending against and perpetrating cyberattacks, influencing the effectiveness of security measures and the evolving nature of threats in the digital landscape. A large language model (LLM) is a state-of-the-art AI system, capable of understanding and generating human-like text.
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In our eBook, Building Trustworthy AI with MLOps, we look at how machine learning operations (MLOps) helps companies deliver machine learning applications in production at scale. AI operations, including compliance, security, and governance. AI ethics, including privacy, bias and fairness, and explainability.
CIO
DECEMBER 6, 2023
Many enterprises are accelerating their artificial intelligence (AI) plans, and in particular moving quickly to stand up a full generative AI (GenAI) organization, tech stacks, projects, and governance. We think this is a mistake, as the success of GenAI projects will depend in large part on smart choices around this layer.
AWS Machine Learning - AI
FEBRUARY 6, 2024
To support overarching pharmacovigilance activities, our pharmaceutical customers want to use the power of machine learning (ML) to automate the adverse event detection from various data sources, such as social media feeds, phone calls, emails, and handwritten notes, and trigger appropriate actions.
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