Goto

Collaborating Authors

carbon footprint


Machine Learning changes the architecture

#artificialintelligence

The need for construction is more significant than ever. The projected 70% increase in urban population over the next 15 years will require many new buildings. Although the European Union anticipates that such a need will arise, builders still do not see this opportunity. So if you want to enter the construction industry or any other profession in this field, I have good news for you -- you are living in a hellish boom time! Unfortunately, this boom will lead to a climate catastrophe on a hitherto unknown scale.


Sustainable AI: Environmental Implications, Challenges and Opportunities

#artificialintelligence

This paper explores the environmental impact of the super-linear growth trends for AI from a holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize the carbon footprint of AI computing by examining the model development cycle across industry-scale machine learning use cases and, at the same time, considering the life cycle of system hardware. Taking a step further, we capture the operational and manufacturing carbon footprint of AI computing and present an end-to-end analysis for what and how hardware-software design and at-scale optimization can help reduce the overall carbon footprint of AI. Based on the industry experience and lessons learned, we share the key challenges and chart out important development directions across the many dimensions of AI. We hope the key messages and insights presented in this paper can inspire the community to advance the field of AI in an environmentally-responsible manner.


What would it take to make AI 'greener'?

#artificialintelligence

The carbon footprint of a model can be complicated to determine and compare across modelling approaches and data centre infrastructures. A reasonable place to start may be by assessing the number of floating-point operations – that is, a discrete count of how many simple mathematical operations (for example, multiplication, division, addition, subtraction, and variable assignment) – that need to be performed to train a model. This factor and others can impact energy consumption along with the architecture of the model and the training resources, such as hardware like GPU or CPUs. Additionally, the physical considerations of the storage and cooling of the servers comes into play. As a final complication, it also matters where the energy is sourced from.


Cognecto's AI-based equipment monitoring solution to be used at FURA's Sapphire mine - International Mining

#artificialintelligence

FURA Gems has announced a partnership with India-based Cognecto to improve operational efficiency, sustainability, productivity and decrease the carbon footprint of its Australian mining operation. Cognecto, which calls itself India's leading artificial intelligence-based heavy equipment monitoring company, has deployed an integrated custom-built hardware sensor and remote telemetry data protocol for FURA to share the data from its Sapphire mining operations in Queensland to company headquarters in Dubai. This collaborative effort forges a solution combining heavy equipment monitoring and analytics to empower operational visibility and control wherever and whenever, according to Cognecto. In addition, FURA employees can access real-time fleet updates via a "well-integrated, easy-to-implement, and zero-tech footprint AI platform created by Cognecto to improve operational conditions and enhances safety", it said. Operational insights for real-time tracking are delivered using a web interface, while the alerts can be relayed on any commonly used messaging platform.


Tech leaders can be the secret weapon for supercharging ESG goals – TechCrunch

#artificialintelligence

Environmental, social and governance (ESG) factors should be key considerations for CTOs and technology leaders scaling next generation companies from day one. Investors are increasingly prioritizing startups that focus on ESG, with the growth of sustainable investing skyrocketing. It's simple: Consumers are no longer willing to support companies that don't prioritize sustainability. According to a survey conducted by IBM, the COVID-19 pandemic has elevated consumers' focus on sustainability and their willingness to pay out of their own pockets for a sustainable future. In tandem, federal action on climate change is increasing, with the U.S. rejoining the Paris Climate Agreement and a recent executive order on climate commitments.


Let's Discuss The Undiscussed Of Artificial Intelligence

#artificialintelligence

Imagine you just woke up. Worried about being late for your big meeting, you decide it's best for you to muster up the strength to unravel your sheets and get ready. You don't have enough motivation to get up so you scream "HEY ALEXA, WHAT TIME IS MY MEETING?" in hopes of having a few more minutes of sleep. She responds, "Your meeting starts in 10 minutes!". All of a sudden, you feel a dose of adrenaline and jump right out of your bed and into the shower. There are countless ways in which Artificial Intelligence is improving our daily lives.


How companies can harness AI technologies for data-driven green reporting

#artificialintelligence

Despite emissions falling in 2020 at the fastest rate for nearly a century, scientists are worried they will rebound in 2021 as lockdown restrictions ease and'normal life' resumes. Across the business community, growing concern about climate change is driving greater action: with the majority of UK business leaders planning to increase long-term investment in sustainability initiatives. This is a shift in the right direction, but the tricky next step is making sure such green efforts actually drive change. While over half of company leaders recognize this calls for better green measurement, achieving that is difficult with limited frameworks for green reporting available both globally and locally. Additionally, business leaders are now responsible for the sustainability efforts of their entire network of suppliers, which means clear oversight is key in holding partners accountable for'greenwashing' – where organizations claim to be green through use of renewable energy, but they do not have the necessary processes in place to offset their overall carbon footprint.


The Green way of defining AI in Mortgages

#artificialintelligence

When one considers the top view of the mortgage industry, it is difficult to comprehend its associated carbon footprint. After all, it primarily involves a few transactions and some amount of paperwork, right? Dig deeper into the ecosystem, and you will discover the impact of offices, computers, data centers, paper mails, and travel – each of these elements contributing to the overall carbon footprint in one way or another. While some of these are invariable and bear some amount of environmental costs, others can be curbed in measurable ways. And in light of point #10 in our Green 2030 resolution, it is imperative that financial institutions take actionable measures towards the decarbonization of the economy.


AI Has An Emission Problem: Is It Fixable?

#artificialintelligence

According to Google Flights' estimate, a round trip of a fully loaded passenger jet between San Francisco and New York would release 180 tonnes of carbon dioxide equivalent (CO2e). Meanwhile, the training emissions of Google's 11 billion parameter T5 language model and OpenAI's GPT-3(175 billion parameters) stands 26%, 305% of the round trip, respectively. The "state-of-the-art" models require a substantial amount of computational resources and energy, leading to high environmental costs. Deep learning models are getting larger by the day. Such large models are routinely trained for thousands of hours on specialised hardware accelerators in data centers.


These Are The Startups Applying AI To Tackle Climate Change

#artificialintelligence

Climate change is the most pressing threat that the human species faces today. Artificial intelligence is the most powerful tool that humanity has at its disposal in the twenty-first century. Can we deploy the second to combat the first? A group of promising startups has emerged to do just that. Both climate change and artificial intelligence are sprawling, cross-disciplinary fields. Both will transform literally every sector of the economy in the years ahead. There is therefore no single "silver bullet" application of AI to climate change. Instead, a wide range of machine learning use cases can help in the race to decarbonize our world. Nearly every major activity that humanity engages in today contributes to our carbon footprint to some extent: building things, moving things, powering things, eating things, computing things.