Collaborating Authors


How Artificial Intelligence Can Power Climate Change Strategy


Slowing down climate change is an urgent matter. If we fail, our world will face a more extensive crisis than we experienced because of the global COVID-19 pandemic. When artificial intelligence (AI) technology helps solve a problem, problem-solving can be done quicker, and the solution is often one that would have taken longer for humans to discover. There's no time to waste: atmospheric CO2 levels are the highest ever (even with significant drops from the stay-at-home orders for COVID-19), average sea levels are rising (3 inches in the last 25 years alone), and 2019 was the hottest year on record for the world's oceans. Artificial intelligence isn't a silver bullet, but it can certainly help us reduce greenhouse gas (GHG) emissions in various ways.

Imitating Interactive Intelligence Artificial Intelligence

A common vision from science fiction is that robots will one day inhabit our physical spaces, sense the world as we do, assist our physical labours, and communicate with us through natural language. Here we study how to design artificial agents that can interact naturally with humans using the simplification of a virtual environment. This setting nevertheless integrates a number of the central challenges of artificial intelligence (AI) research: complex visual perception and goal-directed physical control, grounded language comprehension and production, and multi-agent social interaction. To build agents that can robustly interact with humans, we would ideally train them while they interact with humans. However, this is presently impractical. Therefore, we approximate the role of the human with another learned agent, and use ideas from inverse reinforcement learning to reduce the disparities between human-human and agent-agent interactive behaviour. Rigorously evaluating our agents poses a great challenge, so we develop a variety of behavioural tests, including evaluation by humans who watch videos of agents or interact directly with them. These evaluations convincingly demonstrate that interactive training and auxiliary losses improve agent behaviour beyond what is achieved by supervised learning of actions alone. Further, we demonstrate that agent capabilities generalise beyond literal experiences in the dataset. Finally, we train evaluation models whose ratings of agents agree well with human judgement, thus permitting the evaluation of new agent models without additional effort. Taken together, our results in this virtual environment provide evidence that large-scale human behavioural imitation is a promising tool to create intelligent, interactive agents, and the challenge of reliably evaluating such agents is possible to surmount.

Using AI to tackle climate change


Artificial intelligence-powered use cases for climate action could help organisations meet up to 45% of the Economic Emission Intensity (EEI) targets of the Paris Agreement. New research from the Capgemini Research Institute has found that while AI offers many climate action use cases, only 13% of organisations are successfully combining climate vision with AI capabilities. AI use cases include improving energy efficiency, reducing dependence on fossil fuels and optimising processes to aid productivity. The research found that 67% of organisations have long-term business goals to tackle climate change. While many technologies address a specific outcome, such as carbon capture or renewable sources of energy, AI can accelerate organisations' climate action across sectors and value chains.

Greenhouse Gas Emission Prediction on Road Network using Deep Sequence Learning Machine Learning

Mitigating the substantial undesirable impact of transportation systems on the environment is paramount. Thus, predicting Greenhouse Gas (GHG) emissions is one of the profound topics, especially with the emergence of intelligent transportation systems (ITS). We develop a deep learning framework to predict link-level GHG emission rate (ER) (in CO2eq gram/second) based on the most representative predictors, such as speed, density, and the GHG ER of previous time steps. In particular, various specifications of the long-short term memory (LSTM) networks with exogenous variables are examined and compared with clustering and the autoregressive integrated moving average (ARIMA) model with exogenous variables. The downtown Toronto road network is used as the case study and highly detailed data are synthesized using a calibrated traffic microsimulation and MOVES. It is found that LSTM specification with speed, density, GHG ER, and in-links speed from three previous minutes performs the best while adopting 2 hidden layers and when the hyper-parameters are systematically tuned. Adopting a 30 second updating interval improves slightly the correlation between true and predicted GHG ERs, but contributes negatively to the prediction accuracy as reflected on the increased root mean square error (RMSE) value. Efficiently predicting GHG emissions at a higher frequency with lower data requirements will pave the way to non-myopic eco-routing on large-scale road networks {to alleviate the adverse impact on the global warming

Drone footage captures the moment cables supporting the 900-ton Arecibo Observatory SNAP

Daily Mail - Science & tech

New footage of Arecibo Observatory collapsing in the jungle of Puerto Rico shows the moment its main cables snapped and sent a massive platform hurling to the ground - triggering a reaction that led to its destruction. Drones were investigating cables around the telescope when the restraints failed and dropped the 900-ton platform onto to the reflector dish 400 feet below. In one of the videos, the platform begins swaying in the air before letting out a loud roar as it dislodged from the supporting cable and snapping into pieces as it dropped. The second part of the clip is a view of the cables at the top of a support tower, which shows a group of frayed wires and an empty spot where cables were that had previously failed this year. One of the cables begins to strain and then violently flies out of its support, creating a cloud of smoke and debris.

Climate change: Why it could be time to cut back on new gadgets and HD streams


We need to cut global emissions, and fast – and in doing so, tech businesses are both part of the the problem - and the solution. A new report from the UK's Royal Society finds that as technologies keep growing at pace, the onus is on the digital sector not only to reduce its own carbon footprint, but also to come up with innovative ways to reverse climate change globally. While there is no exact figure that sums up the impact of digital technologies on the environment, the report estimates that the sector currently represents between 1.4% and 5.9% of global greenhouse gas emissions. At the same time, the industry is projected to make huge strides in the coming years: for example, the total number of internet users is expected to reach 5.3 billion by 2023, up from less than four billion in 2018. All this extra connectivity comes at an environmental cost.

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.

Characterization of Industrial Smoke Plumes from Remote Sensing Data Artificial Intelligence

The major driver of global warming has been identified as the anthropogenic release of greenhouse gas (GHG) emissions from industrial activities. The quantitative monitoring of these emissions is mandatory to fully understand their effect on the Earth's climate and to enforce emission regulations on a large scale. In this work, we investigate the possibility to detect and quantify industrial smoke plumes from globally and freely available multi-band image data from ESA's Sentinel-2 satellites. Using a modified ResNet-50, we can detect smoke plumes of different sizes with an accuracy of 94.3%. The model correctly ignores natural clouds and focuses on those imaging channels that are related to the spectral absorption from aerosols and water vapor, enabling the localization of smoke. We exploit this localization ability and train a U-Net segmentation model on a labeled sub-sample of our data, resulting in an Intersection-over-Union (IoU) metric of 0.608 and an overall accuracy for the detection of any smoke plume of 94.0%; on average, our model can reproduce the area covered by smoke in an image to within 5.6%. The performance of our model is mostly limited by occasional confusion with surface objects, the inability to identify semi-transparent smoke, and human limitations to properly identify smoke based on RGB-only images. Nevertheless, our results enable us to reliably detect and qualitatively estimate the level of smoke activity in order to monitor activity in industrial plants across the globe. Our data set and code base are publicly available.

This AI-powered parking garage rewards you for not driving


The trial project is being led by U.K.-based and Munich-based blockchain company Datarella and was just launched at one of the central Munich offices owned by Connex Buildings. The goal is to control the pricing and use of the building's parking spaces dynamically, and to disincentivize people from driving to work by rewarding them with public transit passes for all the time they aren't using the parking garage. "It could say okay if you park closer, you're going to be charged more; if you park farther away, you'll be charged less," says Humayun Sheikh, CEO of "We reward you for doing certain actions and we discourage you from doing certain actions." Sheikh says that if the trial program is expanded to parking garages citywide, it could cut car usage by 10% annually, resulting in a reduction of more than 37,000 tons of CO2 emissions, which is equivalent to the emissions from the annual energy use of nearly 4,000 homes.

AI to help organizations cut greenhouse gas emissions by 16%


The potential positive impact of Artificial Intelligence (AI) is significant and organizations can expect to cut GHG emissions by 16% in the next three to five years through AI-driven climate action projects according to a research report by Capgemini Research Institute. Despite the considerable potential of AI for climate action, adoption remains low. More than eight in ten organizations spend less than 5% of climate change investment on AI and data tracking; 54% have fewer than 5% of employees with the skills to take up data and AI-driven roles; and more than a third (37%) of sustainability executives have decelerated their climate goals in light of COVID-19, with the highest deceleration in the energy and utilities industry. Only 13% of organizations have aligned their climate vision and strategy with their AI capabilities – these are who Capgemini defines as climate AI champions. Two-fifths of these come from Europe, followed by the Americas and APAC.