/AI News/Issue 61

Issue #61 17 stories

Global AI Weekly

Can the climate survive AI’s thirst for energy?

Published Tuesday, July 16, 2024

In this issue

Highlights

2 stories
Can the climate survive AI’s thirst for energy? – podcast
theguardian.com

Can the climate survive AI’s thirst for energy? – podcast

Artificial intelligence companies have lofty ambitions for what the technology could achieve, from curing diseases to eliminating poverty. But the energy required to power these innovations is threatening critical environmental targets. Madeleine Finlay hears from the Guardian’s energy correspondent, Jillian Ambrose, and UK technology editor, Alex Hern, to find out how big AI’s energy problem is, and whether it can be solved before it is too late.

In this issue

Research

1 story
arxiv.org

Better relation and entity extraction with LLMs

We've talked about building knowledge graphs before. It's still a very new topic, and a lot of new research is coming out every week. In this week's paper, for example, we explore how to get better results extracting relationships from documents by mixing large language models and traditional entity and relationship recognition methods. Again, one of those papers is niche but very useful.

In this issue

Video

2 stories
Imitation Intelligence, my keynote for PyCon US 2024
simonwillison.net

Imitation Intelligence, my keynote for PyCon US 2024

I gave an invited keynote at PyCon US 2024 in Pittsburgh this year. My goal was to say some interesting things about AI - specifically about Large Language Models - both to help catch people up who may not have been paying close attention, but also to give people who were paying close attention some new things to think about.

In this issue

Articles

11 stories
Diffusion Model from Scratch in Pytorch
towardsdatascience.com

Diffusion Model from Scratch in Pytorch

In our implementation of the model, we will start by defining our imports (possible pip install commands commented for reference) and coding our sinusoidal time step embeddings. The authors of the DDPM paper used the UNET architecture originally designed for medical image segmentation to build a model to predict the noise for the diffusion reverse process.

In this issue

Code

1 story
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