MIT moves toward greener, more sustainable artificial intelligence

May 15, 2020 by  
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While current  artificial intelligence  (AI) technology holds strategic and transformative potential, it isn’t always environmentally-friendly due to high energy consumption. To the rescue are researchers from Massachusetts Institute of Technology (MIT) , who have devised a solution that not only lowers costs but, more importantly, reduces the AI model training’s carbon footprint. Back in June 2019, the  University of Massachusetts at Amherst revealed  that the amount of  energy  utilized in AI model training equaled 626,000 pounds of carbon dioxide. How so? Contemporary AI isn’t just run on a personal laptop or simple server. Rather, deep neural networks are deployed on diverse arrays of specialized hardware platforms. The level of energy consumption required to power such AI technologies is approximately five times the lifetime  carbon emissions  from an average American car, including its manufacturing.  Related:  This AI food truck could bring fresh produce directly to you Moreover, both  Analytics Insight  and  Kepler Lounge  warned that Google’s AlphaGo Zero — the  AI  that plays the game of Go against itself to self-learn — generated a massive 96 tons of  carbon dioxide  over 40 days of research training. That amount of carbon dioxide equals 1,000 hours of air travel as well as the annual  carbon footprint  of 23 American homes! The takeaway then? Numbers like these would make AI model deployment both unfeasible and unsustainable over time. MIT’s research team has devised a groundbreaking automated AI system, termed a once-for-all (OFA) network, described in  their paper here . This AI system — the OFA network — minimizes  energy consumption  by “decoupling training and search, to reduce the cost.” The OFA network was constructed based on automatic machine learning (AutoML) advancements.  Essentially, the OFA network functions as a ‘mother’ network to numerous subnetworks. As the ‘mother’ network, it feeds its knowledge and past experiences to all the subnetworks, training them to operate independently without the need for further retraining. This is unlike previous AI technology  that had to “repeat the network design process and retrain the designed network from scratch for each case. Their total cost gr[ew] linearly … as the number of deployment scenarios increase[d], which … result[ed] in excessive energy consumption and  CO2  emission.” In other words, with the OFA network in use, there is little need for additional retraining of subnetworks. This efficiency decreases costs, curtails carbon emissions and improves  sustainability . Assistant Professor Song Han, of MIT’s Department of Electrical Engineering and Computer Science, was the project’s lead researcher. He shared that, “Searching efficient neural network architectures has until now had a huge carbon footprint. But we reduced that footprint by orders of magnitude with these new methods.” Also of particular interest was Chuang Gan, co-author of the MIT research paper, who added, “The model is really compact. I am very excited to see OFA can keep pushing the boundary of efficient deep learning on edge devices.” Being compact means AI can progress towards miniaturization. That could spell next-generation advantages in green operations that improve environmental impact. + MIT News Images via Pexels and Pixabay

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MIT moves toward greener, more sustainable artificial intelligence

Trend: The bots are coming (to ratings and reporting)

March 23, 2020 by  
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Corporate reporting on sustainability has grown more than fivefold in the past 10 years. Roughly 20 percent of S&P 500 companies published a sustainability report in 2011. In 2018, that number rose to 86 percent.

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Trend: The bots are coming (to ratings and reporting)

How Google.org accelerates social good with artificial intelligence

March 11, 2020 by  
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A conversation with Brigitte Hoyer Gosselink, who is helping the tech giant support emerging technologies that could make a positive impact in health, education, economic opportunity and empowerment, environmental protection and conservation.

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How Google.org accelerates social good with artificial intelligence

Startup accelerator spotlights bright ideas amid dark days

March 11, 2020 by  
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More and more entrepreneurs and investors are awakening to climate tech and its emerging opportunity.

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Startup accelerator spotlights bright ideas amid dark days

These gorgeous designs guard against flooding

March 11, 2020 by  
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Letting the water in poses no threat to these communities in these ingenious designs.

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These gorgeous designs guard against flooding

Welcome to the roaring 2020s, the artificial intelligence decade

January 2, 2020 by  
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The applications being made possible by breakthroughs in machine learning, image recognition, analytics and sensors are profoundly practical.

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Welcome to the roaring 2020s, the artificial intelligence decade

Welcome to the roaring 2020s, the artificial intelligence decade

January 2, 2020 by  
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The applications being made possible by breakthroughs in machine learning, image recognition, analytics and sensors are profoundly practical.

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Welcome to the roaring 2020s, the artificial intelligence decade

Entrepreneurs and government are teaming up to boost food security in the United Arab Emirates — and beyond

December 3, 2019 by  
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From vertical farms to fish caves, new technologies aim to boost food production and vanquish hunger.

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Entrepreneurs and government are teaming up to boost food security in the United Arab Emirates — and beyond

Why a measured shift to electric vehicles would benefit the US

December 3, 2019 by  
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There is no point in putting forward an EV plan that is so aggressive that it cannot be implemented.

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Why a measured shift to electric vehicles would benefit the US

ThredUP’s Chris Homer on how the company uses machine learning in its operations

November 14, 2019 by  
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CTO and Cofounder Chris Homer says the resale market has doubled in the last five years and expects it to double again over the next five to 10 years.

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ThredUP’s Chris Homer on how the company uses machine learning in its operations

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