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Climate change predictions: Anticipating and adapting to a warming world

IBM Journey to AI blog

The collaboration is currently focused on building an AI-powered foundation model to make climate and weather applications faster and more accurate. The collaboration is currently focused on building an AI-powered foundation model to make climate and weather applications faster and more accurate. And how will humans adapt to them then?

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A CEO’s 8 greatest fears (and how to face them)

Cambridge Intelligence

Interestingly, EY’s study found that 37% are “investing in data and technology to help them emerge from a potential recession in a stronger position than their competitors” --> Technology companies invest in our tools to enhance their current data analysis products and stay ahead of the competition.

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Separating debug symbols from executables

Hacker News

This article aims to introduce and explore the practice of splitting debug symbols away from C/C++ build artifacts to save space and time when building large codebases. Note that we want to retain access to the debug symbols if and when they are needed at a later date, hence we don’t want to merely remove (aka strip ) the debug symbols.

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Model Monitoring for Time Series

The MLOps Blog

Model monitoring process: defining a time series project To start off this article, we will define a simple project or a case study where we can dive much in detail about the nitty-gritty of the model monitoring process. For this project, we will design an ML model that can predict the unit sales for items sold at different stores.

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Explosion in 2019: Our Year in Review

Explosion

Jan 15: The year started out with us as guests on the NLP Highlights podcast , hosted by Matt Gardner and Waleed Ammar of Allen AI. Jan 16: Ines followed that up with an appearance on German documentary “Frag deinen Kühlschrank” (literally “ask your refrigerator”) for Bayerischer Rundfunk on German TV about AI technologies.

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Explosion in 2020: Our Year in Review

Explosion

Jan 28: 2020 started with a big release: the alpha of Thinc v8.0 , a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow or MXNet. We’ve been using it extensively in our projects ever since! ?

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