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Tackling climate change with machine learning [part 1] – Electricity systems


On 10th of June, 2019, twenty-two AI researchers, including Andrew Ng, David Rolnick and Yoshua Bengio, published a paper on how climate change can be tackled with machine learning. I really enjoyed reading it and I am convinced that the paper as well as the For that reason i created a series of blog posts and videos which provide a dense summary, listing many of the proposed solutions and linking research work as well as ongoing projects. In the big picture, all solutions aim to reduce greenhouse gas emissions. As my contribution to the global #ClimateStrike week from September 20th to 27th, I will post one chapter (video and blog post) on every working day.

Data Science: The Key Tool Cities Need To Reduce Carbon Emissions


A cyclist passes electric automobiles charging at Ubeeqo SAS electric vehicle charge stations in ... [ ] Paris, France, on Wednesday, May 27, 2020. President Emmanuel Macron's plan includes incentives for the purchase of electric cars, cash-for-clunkers to encourage consumers to trade in older, more polluting cars and subsidies for struggling car-parts makers. In November 2019, the first case of Covid-19 was reported in Wuhan, China. During the early days of the outbreak, local authorities attempted to clamp down on sharing information about the virus, but as the transmission strengthened in the region, the government imposed lockdown measures across China's Hubei province to control the spread of Covid-19. On January 22, Wuhan became the first major city under quarantine, and in the months that followed, many cities followed suit that caused a shock to the global economy.

Bitcoin mining emissions in China will hit 130 million tonnes by 2024

New Scientist

The carbon emissions associated with mining bitcoin have accelerated rapidly in China, and they will soon outstrip the total annual emissions of mid-sized European countries. Analysis by Guan Dabo at Tsinghua University in Beijing, China, and his colleagues suggests that the total carbon footprint of bitcoin mining in China will peak in 2024, releasing around 130 million metric tonnes of carbon. This figure exceeds the annual carbon emissions of countries including Italy and the Czech Republic. By 2024, bitcoin mining in China will require 297 terawatt-hours of energy and account for approximately 5.4 per cent of the carbon emissions from generating electricity in the country. Mining bitcoin relies on computers racing to solve mathematical puzzles, with miners receiving bitcoin for being the first to process a batch of verified transactions.

Scaling the Wall Between Data Scientist and Data Engineer - KDnuggets


One of the most exciting things in machine learning (ML) today, for me at least, is not at the bleeding-edge of deep learning or reinforcement learning. Rather it has more to do with how models are managed and how data scientists and data engineers effectively collaborate as teams. Navigating those waters will lead organisations towards a more effective and sustainable application of ML. Sadly, there is a divide between "scientist" and "engineer." "Building production machine learning applications is challenging because there is no standard way to record experiments, ensure reproducible runs, and manage and deploy models," says Databricks.

Facebook and Google announce plans to become carbon neutral

The Guardian

Facebook and Google are becoming carbon neutral businesses, joining competitors Apple and Microsoft in committing to put no excess carbon into the atmosphere, both companies have independently announced. But the details of the two companies' ambitions differs greatly. At Google, which first committed to going carbon neutral in 2007, the announcement sees the company declaring success in retroactively offsetting all carbon it has ever emitted, since its foundation in 1998. It has also committed to being powered exclusively by renewable energy by 2030. If that sounds familiar, it's because it is: in 2017, Google became a "net-zero" company, buying renewable energy to match its energy usage, but was unable to fully commit to eliminating carbon-emitting generation entirely.