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#machinelearning

5 posts5 participants0 posts today

This article in the FT today about the astronomical valuations of a lot of #AI start-ups - most of which seem to have little idea of how they will make revenue let alone profits made me wonder: Google Graphcast , WeatherNext and other NWP applications of #MachineLearning have presumably cost hundreds of millions of dollars too, what could we have done with that money in the #weather and #climate space? Never mind the publicly funded weather services, who provided most of the training data for these services?

Who needs revenue when you’re a multibillion-dollar AI start-up?

on.ft.com/3FGvFUA

People seem to really like one of my earlier projects.

It was even translated into 7 other languages !

"Make Your Own Neural Network"

* no previous expertise needed
* introduces basic python and Jupyter notebooks
* explains learning from examples
* builds a simple network to classify handwritten numbers

www.amazon.com/dp/B01EER4Z4G/

all the code is on GitHub
github.com/makeyourownneuralne

Hello #fediverse #introduction

I'm Michael, professor in the institutes of #mathematics and #materials science and head of the @MatMat group at #EPFL.

I work on the #atomistic simulations of materials, mainly density-functional theory (DFT) methods, understanding #simulation errors and #uncertainties in predicted materials properties.

I use techniques from
#physics #computerscience #machinelearning and
develop related #julialang packages such as the density-functional toolkit (#dftk).

TinyML (the ML stands for machine learning) is a low-cost, low-power implementation of AI that is being increasingly adopted in resource-poor regions, especially in the Global South. In contrast to the large language models (LLMs) that have dominated the news with their versatility and uncanny knack for humanlike expression, tinyML devices currently have modest, specialized capabilities. Yet they can be transformative. Murugan’s tinyML-equipped drones, for example, have been able to identify cashew leaves with the fungal disease Anthracnose with 95% to 99% accuracy. They should save farmers time they would otherwise spend looking for signs of disease themselves. And their ability to target treatments to diseased plants removes the need to indiscriminately spray pesticides on all the plants, which is both expensive and damaging to health and the environment.

TinyML

$2–$60: Cost per device (including sensors)
≤1–100 milliwatts: Average power consumption per device

LLMs

$25K–$70K: Average cost per AI chip, requires tens of thousands of chips
700–1200 Watts: Average power consumption per AI chip

#MachineLearning science.org/content/article/wh

Join us on Tuesday for an evening of #MachineLearning Learning in #Astronomy! Ashod Khederlarian of the University of #Pittsburgh will share how state-of-the-art machine learning techniques are used to analyze vast astronomical datasets.

We have a special venue for this event: the Allegheny Observatory has graciously agreed to host the talk! After the talk, observatory staff will offer a free private tour of this fascinating scientific landmark.

news.pypgh.org/p/machine-learn

PyData Pittsburgh · Machine Learning in AstronomyBy Patrick Harrison