ODSC - Open Data Science

MARCH 11, 2024

Google’s DeepMind is elbow-deep in some incredible pieces of research and AI innovation. EVENT — ODSC East 2024 In-Person and Virtual Conference April 23rd to 25th, 2024 Join us for a deep dive into the latest data science and AI trends, tools, and techniques, from LLMs to data analytics and from machine learning to responsible AI.

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AWS Machine Learning Blog

NOVEMBER 16, 2023

One of the key drivers of Philips’ innovation strategy is artificial intelligence (AI), which enables the creation of smart and personalized products and services that can improve health outcomes, enhance customer experience, and optimize operational efficiency. This is a joint blog with AWS and Philips.

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ODSC - Open Data Science

JUNE 26, 2023

After a successful book signing event for How to Become a Data Scientist at Open Data Science East in May, I wanted to check back in with ODSC folk by showcasing a short list of 9 important takeaways from the book. With this book’s guidance, you’ll discover you are more ready than you know. The purpose?

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Applied Data Science

DECEMBER 23, 2022

Linking to demos so that you can also review them yourself Have you been finding the leaps of AI in the last past years impressive? This was one of the first appearances of an AI model used for Text-to-Image generation. and the latter is a technique for generating data ( such as very realistic p hotographs).

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AWS Machine Learning Blog

MAY 17, 2024

Today, we are excited to announce the Mixtral-8x22B large language model (LLM), developed by Mistral AI , is available for customers through Amazon SageMaker JumpStart to deploy with one click for running inference. In this post, we walk through how to discover and deploy the Mixtral-8x22B model.

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Unite.AI

JUNE 13, 2023

Machado is a Fellow in Residence at the Alberta Machine Intelligence Institute (Amii), an adjunct professor at the University of Alberta, and an Amii fellow, where he also holds a Canada CIFAR AI Chair. Marlos’s work has been published in the leading conferences and journals in AI, including Nature, JMLR, JAIR, NeurIPS, ICML, ICLR, and AAAI.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

Now all you need is some guidance on generative AI and machine learning (ML) sessions to attend at this twelfth edition of re:Invent. And although generative AI has appeared in previous events, this year we’re taking it to the next level. And although our track focuses on generative AI, many other tracks have related sessions.

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Unite.AI

AUGUST 1, 2023

The spotlight is also on DALL-E, an AI model that crafts images from textual inputs. Such sophisticated and accessible AI models are poised to redefine the future of work, learning, and creativity. Generative models like GPT-4 can produce new data based on existing inputs. They are commonly used in image recognition tasks.

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DataRobot Blog

DECEMBER 15, 2022

AI-powered Time Series Forecasting may be the most powerful aspect of machine learning available today. By simplifying Time Series Forecasting models and accelerating the AI lifecycle, DataRobot can centralize collaboration across the business—especially data science and IT teams—and maximize ROI.

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DataRobot Blog

MARCH 23, 2023

AI adoption remains top-of-mind for organizations. Although companies are keen to gain competitive advantage by leveraging AI to more rapidly bring innovations to market, they are often unable to see end results as quickly as they’d like. In a challenging economy, agility, speed, and efficiency are vital.

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AWS Machine Learning Blog

SEPTEMBER 6, 2023

Generative AI foundation models have been the focus of most of the ML and artificial intelligence research and use cases for over a year now. Generative AI foundation models have been the focus of most of the ML and artificial intelligence research and use cases for over a year now.

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Google Research AI blog

JANUARY 24, 2023

Posted by Marian Croak, VP, Google Research, Responsible AI and Human-Centered Technology The last year showed tremendous breakthroughs in artificial intelligence (AI), particularly in large language models (LLMs) and text-to-image models. This poses a risk to ML system developers, and demands new model evaluation practices.

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