CIO
NOVEMBER 16, 2023
A large language model (LLM) is a type of gen AI that focuses on text and code instead of images or audio, although some have begun to integrate different modalities. Vector databases and RAG For most companies looking to customize their LLMs, retrieval augmented generation (RAG) is the way to go.
CIO
FEBRUARY 12, 2024
In order to predict future developments for the good of all, we will need to absorb and analyze information on an unprecedented scale. Artificial intelligence (AI) has a pivotal role to play. Consider asynchronous distributed learning, in which AI systems in diverse domains collaborate and share knowledge. The answer?
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CIO
MAY 23, 2024
To keep pace with demand for insights that can drive quicker, better decision making, data scientists are looking to Artificial Intelligence (AI), Machine Learning (ML) and cognitive computing technologies to take analytics to the next level. No organization can afford to fall behind.
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.
CIO
APRIL 12, 2023
Data is a key component when it comes to making accurate and timely recommendations and decisions in real time, particularly when organizations try to implement real-time artificial intelligence. The underpinning architecture needs to include event-streaming technology, high-performing databases, and machine learning feature stores.
TechCrunch
AUGUST 25, 2021
Meet Taktile , a new startup that is working on a machine learning platform for financial services companies. This isn’t the first company that wants to leverage machine learning for financial products. They could use that data to train new models and roll out machine learning applications.
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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