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Carbon Footprints of AI & How to Manage Them

AI tools never cease to amaze whether they help us identify our favourite songs by recognizing and matching humming or whistling sounds or whether they help in devising better methodologies of vaccine distribution, or even in making mental healthcare more accessible. It is an oxymoron then that AI which helps humankind in making accurate climate change predictions and smart grid design etc., itself has a huge carbon footprint problem which needs to be addressed on war footing.

University of Massachusetts, Amherst, researchers have found that training an AI model leads to CO2 emissions of up to 284 tonnes which is equal to five times the lifetimes emissions of an average car in America, including the car’s manufacturing process as well! Even as natural-language processing (NLP) models are making continuous strides and impressing with their sentence completion, dialogue and conversation, machine translation etc, its model training process requires training on big data sets. Not only is this expensive but the method is substantially energy intensive. In their research, it was also found that NAS OR neural architecture search, including the automation of a neural network’ design through trial and error – was highly energy intensive.

Shrinking the carbon footprint | Steps to tackle the situation at hand

Agreeing on paper to resolve the issue

Tech giants such as Amazon and Google are investing in renewable energy to reduce AI training carbon emissions. In fact, September 2019 saw employees of Amazon, Microsoft, Google, Facebook, and Twitter, join a worldwide march against climate change whereby they demanded that their employers should issue an assurance towards reducing emissions to zero by 2030. This can be made possible when the tech giants enter into deals with fuel companies coupled with putting an end to the exploitation of climate refugees. Tech giants can also lead the way for others to follow in reducing emissions.

Hardware for better efficiency of deep net algorithms| Efficient deep nets

Researchers are looking for alternatives such as optical computers that use photons in place of electrons and quantum computers which are capable of increasing the computing power etc. To decrease the carbon footprint, researchers are looking at finding ways of storing and reusing data locally instead of shuttling data from a dedicated memory site. This speeds up model training and makes deep learning apps to run even more efficiently.

Marching towards Green AI

Allen Institute of Artificial Intelligence is credited with starting the Green AI movement through their research work. Their paper roots for AI research yielding the desired results without making the computational cost any higher, or even reducing it in some processes.

Besides these measures, AI companies could also look at carbon transparency, accounting for the full-stack supply chain, saying no to fossil-fuel consumption, tech regulations and green deal policy making should be integrated, and more importantly highlighting and knowing what and where AI harms and its impact on climate refugees.

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Video editing and film-making

If you have ever been in the business of video editing, especially when a famous celebrity was involved, and no matter how much you try to salvage the video, you cannot; deepfakes will help with that clip and your video will be made uninterrupted. Earlier, movie directors would spend millions on creating the perfect location for their movies. This is now achievable with a far lesser sum using deepfake and AI. Deepfakes can be real game-changers in film-making. Imagine new movies starring Charlie Chaplin opening to full houses. This, ofcourse, after all the copyright issues are taken into consideration..

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wasim basir

marketing, board member

It’s most obvious in the digital media space, from click buys to personalized web experiences. For marketing, the AI journey has just kick-started, while in the tech sector it has been applied for a while now. We are still at an early stage where inroads are being made into AI content via chatbots and even some explanatory content creation but what will make anyone jump up and embrace it is when we will start seeing a lot of mainstream content being created by AI.

rich arnold

board member

Prior to joining Infinite Analytics, Richard served as the CFO of CrowdFlower, COO and CFO of Phoenix Technologies, as a member of the board of directors and chairman of the Audit Committee at Intellisync, and previously as CFO and executive vice president strategy and corporate development at Charles Schwab.

pravin gandhi

board member

Pravin Gandhi has over 50 years of entrepreneurial operational and investing experience in the IT industry in India. He was a founding partner of the first early stage fund India - INFINITY. Subsequently a founding partner in Seedfund I & II. With over 18 years of investing experience, he is extensively well networked in investment and entrepreneurial scene and is an active early stage angel investor in tech & impact space. Pravin holds a BS in Industrial Engineering from Cornell University, and serves on the board of several private corporations in India. He is on the board of SINE, IIT Mumbai Incubator.

Purushotham Botla

co-founder & cto

Puru has his Masters in Engineering and Management from MIT. Prior to MIT, he worked with Fidelity Investments building electronic trading products and high volume market data processing applications. He has completed his BE from VJTI, Mumbai.


Deb Roy

Executive Director, MIT Media Lab

Deb Roy is Professor of Media Arts and Sciences at MIT where he directs the MIT Center for Constructive Communication, and a Visiting Professor at Harvard Law School. He leads research in applied machine learning and human-machine interaction with applications in designing systems for learning and constructive dialogue, and for mapping and analyzing large scale media ecosystems. Deb is also co-founder and Chair of Cortico, a nonprofit social technology company that develops and operates the Local Voices Network to surface underheard voices and bridge divides.

Roy served as Executive Director of the MIT Media Lab from 2019-2021. He was co-founder and CEO of Bluefin Labs, a media analytics company that analyzed the interactions between television and social media at scale. Bluefin was acquired by Twitter in 2013, Twitter’s largest acquisition of the time. From 2013-2017 Roy served as Twitter’s Chief Media Scientist.

Erik Brynjolfsson

Board Member

Erik Brynjolfsson is the Jerry Yang and Akiko Yamazaki Professor and Senior Fellow at the Stanford Institute for Human-Centered AI (HAI), and Director of the Stanford Digital Economy Lab. He also is the Ralph Landau Senior Fellow at the Stanford Institute for Economic Policy Research (SIEPR), Professor by Courtesy at the Stanford Graduate School of Business and Stanford Department of Economics, and a Research Associate at the National Bureau of Economic Research (NBER).

Akash Bhatia

Co-Founder and CEO

Akash co-founded IA while studying for his MBA from MIT. Prior to MIT Sloan, he co-founded Zoonga. Before this, Akash was an engineer with Oracle in Silicon Valley. He has completed his M.S from University of Cincinnati and B.E from the College of Engineering, Pune.