Collaborating Authors

global warming

ClimaText: A Dataset for Climate Change Topic Detection Artificial Intelligence

Climate change communication in the mass media and other textual sources may affect and shape public perception. Extracting climate change information from these sources is an important task, e.g., for filtering content and e-discovery, sentiment analysis, automatic summarization, question-answering, and fact-checking. However, automating this process is a challenge, as climate change is a complex, fast-moving, and often ambiguous topic with scarce resources for popular text-based AI tasks. In this paper, we introduce \textsc{ClimaText}, a dataset for sentence-based climate change topic detection, which we make publicly available. We explore different approaches to identify the climate change topic in various text sources. We find that popular keyword-based models are not adequate for such a complex and evolving task. Context-based algorithms like BERT \cite{devlin2018bert} can detect, in addition to many trivial cases, a variety of complex and implicit topic patterns. Nevertheless, our analysis reveals a great potential for improvement in several directions, such as, e.g., capturing the discussion on indirect effects of climate change. Hence, we hope this work can serve as a good starting point for further research on this topic.

CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims Artificial Intelligence

We introduce CLIMATE-FEVER, a new publicly available dataset for verification of climate change-related claims. By providing a dataset for the research community, we aim to facilitate and encourage work on improving algorithms for retrieving evidential support for climate-specific claims, addressing the underlying language understanding challenges, and ultimately help alleviate the impact of misinformation on climate change. We adapt the methodology of FEVER [1], the largest dataset of artificially designed claims, to real-life claims collected from the Internet. While during this process, we could rely on the expertise of renowned climate scientists, it turned out to be no easy task. We discuss the surprising, subtle complexity of modeling real-world climate-related claims within the \textsc{fever} framework, which we believe provides a valuable challenge for general natural language understanding. We hope that our work will mark the beginning of a new exciting long-term joint effort by the climate science and AI community.

DeSMOG: Detecting Stance in Media On Global Warming Artificial Intelligence

Citing opinions is a powerful yet understudied strategy in argumentation. For example, an environmental activist might say, "Leading scientists agree that global warming is a serious concern," framing a clause which affirms their own stance ("that global warming is serious") as an opinion endorsed ("[scientists] agree") by a reputable source ("leading"). In contrast, a global warming denier might frame the same clause as the opinion of an untrustworthy source with a predicate connoting doubt: "Mistaken scientists claim [...]." Our work studies opinion-framing in the global warming (GW) debate, an increasingly partisan issue that has received little attention in NLP. We introduce DeSMOG, a dataset of stance-labeled GW sentences, and train a BERT classifier to study novel aspects of argumentation in how different sides of a debate represent their own and each other's opinions. From 56K news articles, we find that similar linguistic devices for self-affirming and opponent-doubting discourse are used across GW-accepting and skeptic media, though GW-skeptical media shows more opponent-doubt. We also find that authors often characterize sources as hypocritical, by ascribing opinions expressing the author's own view to source entities known to publicly endorse the opposing view. We release our stance dataset, model, and lexicons of framing devices for future work on opinion-framing and the automatic detection of GW stance.

Differentiable Open-Ended Commonsense Reasoning Artificial Intelligence

Current commonsense reasoning research mainly focuses on developing models that use commonsense knowledge to answer multiple-choice questions. However, systems designed to answer multiple-choice questions may not be useful in applications that do not provide a small list of possible candidate answers to choose from. As a step towards making commonsense reasoning research more realistic, we propose to study open-ended commonsense reasoning (OpenCSR) -- the task of answering a commonsense question without any pre-defined choices, using as a resource only a corpus of commonsense facts written in natural language. The task is challenging due to a much larger decision space, and because many commonsense questions require multi-hop reasoning. We propose an efficient differentiable model for multi-hop reasoning over knowledge facts, named DrFact. We evaluate our approach on a collection of re-formatted, open-ended versions of popular tests targeting commonsense reasoning, and show that our approach outperforms strong baseline methods by a large margin.

Is the carbon footprint of AI too big?


It's no surprise that AI has a carbon footprint, which refers to the amount of greenhouse gases (carbon dioxide and methane, primarily) that producing and consuming AI releases into the atmosphere. In fact, training AI models requires so much computing power, some researchers have argued that the environmental costs outweigh the benefits. However, I believe they've not only underestimated the benefits of AI, but also overlooked the many ways that model training is becoming more efficient. Greenhouse gases are what economists refer to as an "externality" -- a cost borne inadvertently by society at large, such as through the adverse impact of global warming, but inflicted on us all by private participants who have little incentive to refrain from the offending activity. Typically, public utilities emit these gases when they burn fossil fuels in order to generate electricity that powers the data centers, server farms, and other computing platforms upon which AI runs. During the past few years, AI has been unfairly stigmatized as a major contributor to global warming, owing to what some observers regard as its inordinate consumption of energy in the process of model training.

Artificial Intelligence and Satellite Technology to Enhance Carbon Tracking Measures


New carbon emission tracking technology will quantify emissions of greenhouse gas, holding the energy industry accountable for its CO2 output. Backed by Google, this cutting-edge initiative will be known as Climate TRACE (Tracking Real-Time Atmospheric Carbon Emissions). Advanced AI and machine learning now make it possible to trace greenhouse gas (GHG) emissions from factories, power plants and more. By using image processing algorithms to detect carbon emissions from power plants, AI technology makes use of the growing global satellite network to develop a more comprehensive global database of power plant activity. Because most countries self-report emissions and manually compile results, scientists often rely on data that is several years out of date.



Can artificial intelligence be deployed to slow down global warming, or is AI one of the greatest climate sinners ever? That is the interesting debate that finds (not surprisingly) representatives from the AI industry and academia on opposite sides of the issue. While PwC and Microsoft published a report concluding that using AI could reduce world-wide greenhouse gas emissions by 4% in 2030, researchers from the University of Amherst Massachusetts have calculated that training a single AI model can emit more than 626,000 pounds of carbon dioxide equivalent--nearly five times the lifetime emissions of the average American car. The big players have clearly understood that the public sensibility towards climate change offers a wonderful marketing opportunity. IBM has launched its Green Horizons project to analyze environmental data and predict pollution.

Agriculture Industry Moves Forward Using Artificial Intelligence (AI) To Improve Crop Management


It is always fun to look at the widening expansion of sectors that are being helped by artificial intelligence (AI). Farming has regularly used technology to improve yields. In recent years, global warming has made it more important to manage water resources through improved irrigation. Now the agriculture industry is looking at adopting AI in many ways. One of those methods is to analyze crops to better manage yield.

Climate change: What do all the terms mean?

BBC News

Climate change is seen as the biggest challenge to the future of human life on Earth, and understanding the scientific language used to describe it can sometimes feel just as difficult. But help is at hand. Use our translator tool to find out what some of the words and phrases relating to climate change mean. Keeping the rise in global average temperature below 1.5 degrees Celsius will avoid the worst impacts of climate change, scientists say.

Fighting climate change with AI


More and more, across the globe, the effects of global warming are being felt. Global movements like Extinction Rebellion have repeatedly caused disruption by protesting in cities around the world and are symbolic of the growing attention being paid to this important issue. Despite the boom in public awareness, methods to combat the issue have been slow to develop – separating rubbish into recycling and general waste is as far as the majority of households go. More advanced technology, such as solar panels and wind turbines remain out of reach to many due to their high cost and space required for installation. However, another technology may have a far bigger impact in the fight against climate change.