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climate change

CES 2021: 'The run rate of the company going forward will be faster forever,' says Walmart CEO McMillon


Walmart is focused on things such as "what the future of AI will mean, or how robotics will change our business, and how 5G will change how people want to live and shop," says CEO Doug McMillon. Walmart CEO Doug McMillon was the big keynoter at the Consumer Electronics Show 2021, in a half an hour segment that covered a range of broad topics such as equality and leadership. He was hosted by Tiffany Moore, senior Vice President, political and industry affairs for the Consumer Technology Association. CTA president Gary Shapiro set the context: 11,000 stores in 26 countries, more than 2 million employees globally. McMillan started out a wage earner thirty years ago at the retail giant, loading trucks.

Start your smart home with a Google Home Mini for under $20

CNN Top Stories

The Google Home Mini, like the Amazon Echo Dot, really started the smart speaker revolution -- and while the Google Home Mini launched in 2016, it's still humming along with more smarts than ever before. Right now at StackSocial the Google Home Mini is just $19.99 -- nearly 60% off its original $49.95 price tag. The big appeal of the Home Mini is adding the Google Assistant to your room. You can ask for your favorite music, a trivia game show to entertain the children and even questions. The assistant knows how far Earth is from the sun and the weather in Cedar Rapids, Iowa, alike.

We saw the future in 2020 and the future sucks


Flying cars are starting to look like a crock of shit. I contend we're living in the future, and -- spoiler ahead -- flying cars aren't the future we got. Listen, I hate this gut feeling as much you probably do, but I can't quite shake it: 2020 looks a whole hell of a lot like the future. We lived through screens -- at least, you did if you were fortunate and caring -- and limited our human interaction to a bare minimum. Hours upon hours poured into television or immersive video game worlds. It all reminds me of a piece my friend Mike Murphy wrote for Quartz in 2016 titled, "The future is a place where we won't have to talk to or hear from anyone we don't want to."

How to ensure An 'AI for Good' Project is Actually Good


Artificial intelligence (AI) has been at the front and centre during the COVID-19 pandemic. The global pandemic has pushed governments and private organisations globally to propose AI solutions for everything from analysing cough sound to installing disinfecting robots in hospitals. Such efforts are part of a broader trend that has been picking up momentum- the deployment of projects by companies, governments, universities, and research institutes aiming to use AI for societal good. The goal of these programs is deploying cutting-edge AI technologies to solve crucial issues like poverty, hunger, crime, and climate change, under the'AI for good' umbrella. But the bigger question is what makes an AI project good? AI has the potential to address some of humanity's biggest challenges, such as poverty and climate change.

NeurIPS 2020


Climate change is one of the greatest threats humans have ever faced, with increasingly severe consequences feared as sea levels rise, ecosystems falter, and natural disasters multiply. Tackling climate change is a huge and complex challenge, where it's hoped that AI-powered efforts can play an equally huge and beneficial role. Organizers of NeurIPS 2020 (Conference on Neural Information Processing Systems) see machine learning (ML) as an invaluable tool in the fight against climate change. A wide array of applications and techniques are already being explored, from smart electric grid design to satellite-tracking of greenhouse gas emissions and countless others. Last Friday, NeurIPS 2020 partnered with Climate Change AI (CCAI) -- an organization of researchers, engineers, entrepreneurs, investors, policymakers, companies and NGOs aiming to catalyze impactful work at the intersection of climate change and machine learning -- to host the Tackling Climate Change with ML Workshop, which explored how the ML community could collaborate with other fields and practitioners in this fight. The all-virtual format of NeurIPS 2020, which ran December 6-12, provided a unique opportunity to foster cross-pollination between ML researchers and experts across diverse fields.

Modeling Climate Change With Python


Climate change is one of those critical issues that don't receive enough attention from the AI community. The main reason why machine learning developers and data scientists are building so few climate models is that climate change is painfully hard to forecast in the long run. While weather forecasts are increasing their accuracy every year, climate predictions and their socioeconomic impact are much harder to estimate, this is due to the huge amount of human variables that play a role in climate change. However, climatic models have experienced a boost in recent years thanks to Integrated Assessment Models. Integrated assessment models (IAMs) help us understand how human development and societal choices affect each other and the natural world, they are "integrated" because they combine different disciplines to model human society alongside parts of the Earth system.

Graph Neural Networks for Improved El Ni\~no Forecasting Machine Learning

Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Ni\~no-Southern Oscillation (ENSO). However, current deep learning models are based on convolutional neural networks which are difficult to interpret and can fail to model large-scale atmospheric patterns called teleconnections. Hence, we propose the application of spatiotemporal Graph Neural Networks (GNN) to forecast ENSO at long lead times, finer granularity and improved predictive skill than current state-of-the-art methods. The explicit modeling of information flow via edges may also allow for more interpretable forecasts. Preliminary results are promising and outperform state-of-the art systems for projections 1 and 3 months ahead.

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.

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.