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The Green Promise of Vertical Farms

Indoor farms run by AI and lit by LEDs can be more efficient than field agriculture, but can they significantly reduce greenhouse gas emissions?

11 min read
Staff looking at a wall of basil.

Wall of Plenty: A wall of basil is bathed in light from LED tubes, which are optimized for this particular crop by Plenty's proprietary machine-learning algorithms.

Photo: Plenty Unlimited

I emerge from the Tokyo Monorail station on Shōwajima, a small island in Tokyo Bay that's nestled between downtown Tokyo and Haneda Airport. Disoriented and dodging cargo trucks exiting a busy overpass, I duck under a bridge and consult the map on my phone, which leads me deeper into a warren of warehouses. I eventually find Espec Mic Corp.'s VegetaFarm, in a dilapidated 1960s office building tucked between a printing plant and a beer distributor. Stepping inside the glass-walled lobby on the second floor, I see racks upon racks of leafy green lettuce and kale growing in hydroponic solutions of water and a precisely calibrated mix of nutrients. Energy-efficient LEDs emit a pinkish light within a spectral range of 400 to 700 nanometers, the sweet spot for photosynthesis.

I'm here to find out how plant factories, called vertical or indoor farms in Western countries, can help reduce the greenhouse gas emissions associated with conventional field agriculture. According to the World Bank, 48.6 million square kilometers of land were farmed worldwide in 2015. Collectively, agriculture, forestry, and other land uses contributed 21 percent of global greenhouse gas emissions, per a 2017 report from the Food and Agriculture Organization of the United Nations, mostly through releases of carbon dioxide, methane, and nitrous oxide.

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GPT Protein Models Speak Fluent Biology

Deep learning language models design artificial proteins for tricky chemical reactions

3 min read
Two protein structures labelled ProGen Generated and 25% Mutation.

By learning the "language" of functional proteins, the AI learned to prioritize its most structurally important segments.

SalesForce

Artificial intelligence has already shaved years off research into protein engineering. Now, for the first time, scientists have synthesized proteins predicted by an AI model in the lab, and found them to work just as well as their natural counterparts.

The research used a deep learning language model for protein engineering called ProGen, which was developed by the company Salesforce AI Research in 2020. ProGen was trained, on 280 million raw protein sequences from publicly available databases of sequenced natural proteins, to generate artificial protein sequences from scratch.

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Forecasting the Ice Loss of Greenland’s Glaciers With Viscoelastic Modeling

Researchers at the Alfred Wegener Institute in Germany are developing new models to simulate how glaciers behave

8 min read
Aerial view of Nioghalvfjerdsbræ showing the extensive patterns of the crevasses

This sponsored article is brought to you by COMSOL.

To someone standing near a glacier, it may seem as stable and permanent as anything on Earth can be. However, Earth’s great ice sheets are always moving and evolving. In recent decades, this ceaseless motion has accelerated. In fact, ice in polar regions is proving to be not just mobile, but alarmingly mortal.

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