Showing items from Artificial Intelligence and Data Science

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How Open is Generative AI? Part 1

Welcome to this two-part series on Generative AI Openness, where we explore the history, current landscape, and potential future of open collaboration and proprietary control in the development of Language Language Models (LLMs). In this first part, we will delve into the importance of inspecting the openness of each component in the LLM training process, and how this can impact the potential limitations on the model use or reuse imposed by one or more of its components. In the second part of this series, we will explore the potential benefits and drawbacks of sharing Generative AIs openly for the collective advancement of society.

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Worldline Labs is already in green AI

Introduction

In 2021, the International Energy Agency (IEA) published a Net-Zero Emissions (NZE) Roadmap [1] by 2050 which outlines a scenario that the IEA has built to comply with the Paris climate agreement and, from this report, we know that we have an imperative to reduce urgently our energy consumption. For instance, according to the NZE, the total energy supply must fall by 7% between 2020 and 2030 and remain at around this level to 2050. The energy resource regenerates much more slowly than the needs and at the same time the world’s population will still grow. Using energy efficiently and reducing energy consumption becomes a key concern.

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Anomaly detection for predictive monitoring

Introduction

Monitoring, the practice of observing systems and determining if they are healthy, is hard and getting harder. There are many reasons for this: we are managing many more systems (servers and applications or services) and much more data than ever before, and we are monitoring them in higher resolution: it is not only possible but desirable to monitor practically everything we can, so we are also monitoring many more signals from these systems than we used to.

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