Nearly 20 years ago, the British mathematician Clive Humby coined a snappy phrase that has turned into a platitude: "data is the new oil". We have an insatiable appetite for data, we can't stop generating it, and, just like oil, it's turning out to be bad news for the environment. So the Guardian set me a challenge: to try to give a sense of how much data an average person uses in a day, and what the carbon footprint of normal online activity might be. To do that, I tried to tot up the sorts of things I and millions of others do every day, and how that tracks back through the melange of messaging services, social networks, applications and tools, to the datacentres that keep our digital lives going. My own carbon tally gets off to a bad start, and it is not even my fault.

The discovery of new catalysts is essential for the design of new and more efficient chemical processes in order to transition to a sustainable future. We introduce an AI-guided computational screening framework unifying linguistic reasoning with quantum-chemistry based feedback from 3D atomistic representations. Our approach formulates catalyst discovery as an uncertain environment where an agent actively searches for highly effective catalysts via the iterative combination of large language model (LLM)-derived hypotheses and atomistic graph neural network (GNN)-derived feedback. Identified catalysts in intermediate search steps undergo structural evaluation based on spatial orientation, reaction pathways, and stability. Scoring functions based on adsorption energies and reaction energy barriers steer the exploration in the LLM's knowledge space toward energetically favorable, high-efficiency catalysts. We introduce planning methods that automatically guide the exploration without human input, providing competitive performance against expert-enumerated chemical descriptor-based implementations. By integrating language-guided reasoning with computational chemistry feedback, our work pioneers AI-accelerated, trustworthy catalyst discovery.

Around the world, ambitious projects are testing everything from seeding clouds with chemicals to pouring artificial whale excrement into the sea. The goal is to remove CO2 from the atmosphere via so called'geoengineering' and'carbon capture' processes - and help to mitigate climate change. Geoengineering sees heat from the sun reflected back into space to limit climate change, while'carbon capture' captures CO2 from the air, either directly or by capturing it in rain among other techniques. The White House cautiously supported further research into an idea straight out of science fiction - 'blocking the sun' to cool the atmosphere - in a report last year. The federally mandated report said that there is'a compelling case for research to better understand both the potential benefits and risks'.

The fundamental success of large language models hinges upon the efficacious implementation of large-scale distributed training techniques. Nevertheless, building a vast, high-performance cluster featuring high-speed communication interconnectivity is prohibitively costly, and accessible only to prominent entities. In this work, we aim to lower this barrier and democratize large-scale training with limited bandwidth clusters. We propose a new approach called CO2 that introduces localupdating and asynchronous communication to the distributed data-parallel training, thereby facilitating the full overlap of COmunication with COmputation. CO2 is able to attain a high scalability even on extensive multi-node clusters constrained by very limited communication bandwidth. We further propose the staleness gap penalty and outer momentum clipping techniques together with CO2 to bolster its convergence and training stability. Besides, CO2 exhibits seamless integration with well-established ZeRO-series optimizers which mitigate memory consumption of model states with large model training. We also provide a mathematical proof of convergence, accompanied by the establishment of a stringent upper bound. These experiments serve to demonstrate the capabilities of CO2 in terms of convergence, generalization, and scalability when deployed across configurations comprising up to 128 A100 GPUs. The outcomes emphasize the outstanding capacity of CO2 to hugely improve scalability, no matter on clusters with 800Gbps RDMA or 80Gbps TCP/IP inter-node connections. Distributed optimization is crucial for the efficient training of large-scale deep neural networks. Mini-batch parallel optimization methods (Goyal et al., 2017; Li et al., 2014) like stochastic gradient decent (SGD) with distributed data parallel (DDP) paradigm are commonly used, but communication overhead can pose significant challenges when scaling out to larger GPU clusters. Existing techniques leverage gradient bucketing to partially overlap communication with backward computation to enhance training efficiency, but residual overhead remains a challenge in scenarios with large model sizes and limited inter-node communication bandwidth. Various strategies have been proposed to address the communication-related issues.

The past three months alone has seen the UK announce three major milestones – covering carbon storage, offshore wind and hybrid energy projects – to propel it further down the road towards net zero. But that journey is no longer only about creating a sustainable, green future. World events have brought security of supply sharply into focus, placing new impetus on governments to accelerate alternative energy projects. While moving at pace is critical for the planet, the old proverb of more haste, less speed – warning against making errors by acting too quickly and without due diligence – should be weighing on the minds of developers. Nicola Blanshard, CEO of Geoteric, a world-leading AI-driven seismic interpretation software provider, believes the balance of speed and success can be achieved through appropriate application of technology.

A way of directly measuring the carbon dioxide released by burning fossil fuels could help cities and countries monitor their efforts to reduce emissions in near real time. "We are in a shrinking window of time to do this, so I think we really need to know what the situation is as quickly and as accurately as possible," says Penelope Pickers at the University of East Anglia, UK. At present, governments and research organisations estimate countries' overall emissions based on data such as how much oil or gas has been sold. While initial estimates are often made fairly quickly, it can take years to fully compile this information and estimates can vary substantially. Measuring fossil fuel emissions directly would help confirm the accuracy of these inventory-based estimates and reveal more quickly if emission-reduction policies are working or not.

Climate change mitigation is about reducing greenhouse gas (GHG) emissions. The worldwide goal is to reach net zero, which means balancing the amount of GHG emissions produced and the amount removed from the atmosphere. On the one hand, this implies reducing emissions by using low-carbon technologies and energy efficiency. On the other hand, it implies deploying negative emission technologies such as carbon storage, which is the subject of this post. Carbon capture and storage (CCS) refers to a group of technologies that contribute to directly reducing emissions at their source in key power sectors such as coal and gas power plants and industrial plants.

Artificial intelligence helps solve the most complex problems beneath our feet By Hari Viswanathan For The New Mexican May 9, 2021 Save Few technological developments have captured the minds -- and fear -- of humanity like artificial intelligence. Whether it's robots rising up to subdue their makers like in the Westworld series, or the malevolent computer Hal 9000 from the classic movie 2001: A Space Odyssey, machines that can learn are depicted as threats to the world as we know it. Obviously, these futures are the work of imaginative screenwriters. In fact, artificial intelligence, or AI, is at work in the field of geological science right now helping to preserve the world and save lives. That topic is the focus of a virtual seminar series throughout the summer called "Machine Learning in Solid Earth Geoscience," a series that has been hosted in Santa Fe in pre-COVID-19 years.

The Earth is losing forests at an alarming rate. The United Nations Food and Agriculture Organization estimates that 420 million hectares of forest have been lost to agricultural use (largely cattle ranching, soya bean and oil palm farming) since 1990. Between 2015 and 2020, some 10 million hectares were destroyed each year. The Amazon rainforest, for example, lost an area the size of Yellowstone (3,769 square miles) in 2019, and saw deforestation rates spike 30 percent to their highest point in a decade. What's more, Climate change-induced wildfires, as we've seen recently in Australia and in California, have been especially destructive.

AI has become the buzzword of the world, and an integral part of almost every company's digital transformation agenda. AI users have become producers of AI tools and services. Corporate leaders and even the White House have come with forward with a directive on promotion, promulgation, and advancement of artificial intelligence. On February 11, 2019, President Trump signed Executive Order 13859 announcing the American AI Initiative. Executive Order 13859 is the United States' national strategy on artificial intelligence.