Permafrost is melting 70 years earlier than expected in Arctic Canada
It's a "canary in the coalmine," said one climate scientist.
- A team of researchers discovered that permafrost in Northern Canada is melting at unusually fast rates.
- This can cause dangerous and costly erosion, and it's likely speeding up climate change because thawing permafrost releases heat-trapping gasses into the atmosphere.
- This week, Canada's House of Commons declared a national climate emergency.
A new study shows that permafrost in the Canadian Arctic is melting 70 years earlier than predicted. The melting was triggered by a series of unusually hot summers, said researchers from the University of Alaska Fairbanks, who measured the thawing while visiting remote outposts in Northern Canada. "What we saw was amazing," Prof. Vladimir E. Romanovsky told Reuters. "It's an indication that the climate is now warmer than at any time in the last 5,000 or more years."
Permafrost is ground that's been frozen for two or more consecutive years. This frozen soil helps to structurally support mountain ranges and slopes. "Think of permafrost as sort of the glue that holds the northern landscape together," permafrost scientist Steve Kokelj told CBC.
When permafrost thaws quickly, it not only causes landscapes to erode, but also releases tons of heat-trapping gasses into the atmosphere. This can start a dangerous feedback loop that speeds up climate change and threatens the ability to maintain and build new infrastructure.
For example, there were 87 landslides in one night in Canada's Northwest Territories. Nobody was injured in those remote areas, but Canadian climate scientists have a saying: "What happens in the North doesn't stay in the North."
"It's a canary in the coalmine," Louise Farquharson, a post-doctoral researcher and co-author of the study, told Reuters. "It's very likely that this phenomenon is affecting a much more extensive region and that's what we're going to look at next."
Thawing permafrost might already be limiting where new buildings and infrastructure can be built.
"We have to figure out what we're going to do in the future," Aurora Research Institute professor Chris Burn told CBC. "Because otherwise, when we make an investment in a building [or road] which is meant to last 50 years, if in 15 years it's no good we've wasted a huge amount of resources."
A 'climate emergency' in Canada
Canada is especially vulnerable to climate change. A report issued in April from the Environment and Climate Change Canada said that Canada is warming twice as quickly as the rest of the world, but that the warming is "effectively irreversible." This week, Canada's House of Commons voted to declare a national climate emergency.
"This is a national security issue, it is time we started treating it as one," wrote Green Party Leader Elizabeth May on Twitter.
Jennifer Morgan, Executive Director of Greenpeace International, echoed a similar sense of urgency to Reuters. "Thawing permafrost is one of the tipping points for climate breakdown and it's happening before our very eyes," she said. "This premature thawing is another clear signal that we must decarbonize our economies, and immediately."
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He says his company would survive even if Instagram disappeared tomorrow.
- Los Angeles truck, CVT Soft Serve, charges people asking for free ice cream $8 for per cone instead of $4.
- The truck's founder, Joe Nicchi, highlights the message in the hashtag, #WeLoveMostOfOurCustomers.
- Nicchi does not appreciate "influencers" asking for free food when he has a business to run.
A consortium of scientists and engineers have proposed that the U.S. and Mexico build a series of guarded solar, wind, natural gas and desalination facilities along the entirety of the border.
- The proposal was recently presented to several U.S. members of Congress.
- The plan still calls for border security, considering all of the facilities along the border would be guarded and connected by physical barriers.
- It's undoubtedly an expensive and complicated proposal, but the team argues that border regions are ideal spots for wind and solar energy, and that they could use the jobs and fresh water the energy park would create.
MIT and Google researchers use deep learning to decipher ancient languages.
- Researchers from MIT and Google Brain discover how to use deep learning to decipher ancient languages.
- The technique can be used to read languages that died long ago.
- The method builds on the ability of machines to quickly complete monotonous tasks.
There are about 6,500-7,000 languages currently spoken in the world. But that's less than a quarter of all the languages people spoke over the course of human history. That total number is around 31,000 languages, according to some linguistic estimates. Every time a language is lost, so goes that way of thinking, of relating to the world. The relationships, the poetry of life uniquely described through that language are lost too. But what if you could figure out how to read the dead languages? Researchers from MIT and Google Brain created an AI-based system that can accomplish just that.
While languages change, many of the symbols and how the words and characters are distributed stay relatively constant over time. Because of that, you could attempt to decode a long-lost language if you understood its relationship to a known progenitor language. This insight is what allowed the team which included Jiaming Luo and Regina Barzilay from MIT and Yuan Cao from Google's AI lab to use machine learning to decipher the early Greek language Linear B (from 1400 BC) and a cuneiform Ugaritic (early Hebrew) language that's also over 3,000 years old.
Linear B was previously cracked by a human – in 1953, it was deciphered by Michael Ventris. But this was the first time the language was figured out by a machine.
The approach by the researchers focused on 4 key properties related to the context and alignment of the characters to be deciphered – distributional similarity, monotonic character mapping, structural sparsity and significant cognate overlap.
They trained the AI network to look for these traits, achieving the correct translation of 67.3% of Linear B cognates (word of common origin) into their Greek equivalents.
What AI can potentially do better in such tasks, according to MIT Technology Review, is that it can simply take a brute force approach that would be too exhausting for humans. They can attempt to translate symbols of an unknown alphabet by quickly testing it against symbols from one language after another, running them through everything that is already known.
Next for the scientists? Perhaps the translation of Linear A - the Ancient Greek language that no one has succeeded in deciphering so far.
You can check out their paper "Neural Decipherment via Minimum-Cost Flow: from Ugaritic to Linear B" here.