scientificamerican--2020-12-04--Will Artificial Intelligence Ever Live Up to Its Hype?

When I started writing about science decades ago , artificial intelligence seemed ascendant .
IEEE Spectrum , the technology magazine for which I worked , produced a special issue on how AI would transform the world .
I edited an article in which computer scientist Frederick Hayes-Roth predicted that AI would soon replace experts in law , medicine , finance and other professions .
That was in 1984 .
Not long afterward , the exuberance gave way to a slump known as an “ AI winter , ” when disillusionment set in and funding declined .
Years later , doing @ @ @ @ @ @ @ , I tracked Hayes-Roth down to ask how he thought his predictions had held up .
He laughed and replied , “ You ’ ve got a mean streak.
” AI had not lived up to expectations , he acknowledged .
Our minds are hard to replicate , because we are “ very , very complicated systems that are both evolved and adapted through learning to deal well and differentially with dozens of variables at one time.
” Algorithms that can perform a specialized task , like playing chess , can not be @ @ @ @ @ @ @ It is an example of what is called nonrecurrent engineering , ” Hayes-Roth explained .
That was 1998 .
Today , according to some measures , AI is booming once again .
Programs such as voice and face recognition are embedded in cell phones , televisions , cars and countless other consumer products .
Clever algorithms help me choose a Christmas present for my girlfriend , find my daughter ’ s building in Brooklyn and gather information for columns like this one .
Venture-capital investments in AI doubled between 2017 and 2018 to @ @ @ @ @ @ @ .
A Price Waterhouse study estimates that by 2030 AI will boost global economic output by more than $ 15 trillion , “ more than the current output of China and India combined.
” In fact , some observers fear that AI is moving too fast .
New York Times columnist Farhad Manjoo calls an AI-based reading and writing program , GPT-3 , “ amazing , spooky , humbling and more than a little terrifying.
” Someday , he frets , he might be “ put out to pasture by a machine.
” @ @ @ @ @ @ @ might need computer chips implanted in our brains to help us keep up with intelligent machines .
Elon Musk made headlines in 2018 when he warned that “ superintelligent ” AI , much smarter than we are , represents “ the single biggest existential crisis that we face.
” ( Really ?
Worse than climate change ?
Nuclear weapons ?
Psychopathic politicians ?
I suspect that Musk , who has invested in AI , is trying to promote the technology with his over-the-top fearmongering . )
Experts are pushing back against the hype @ @ @ @ @ @ @ in AI are based on flimsy evidence .
Last January , for example , a team from Google Health claimed in Nature that their AI program had outperformed humans in diagnosing breast cancer .
In October , a group led by Benjamin Haibe-Kains , a computational genomics researcher , criticized the Google health paper , arguing that the “ lack of details of the methods and algorithm code undermines its scientific value.
” Haibe-Kains complained to Technology Review that the Google Health report is “ more an advertisement for cool technology ” than @ @ @ @ @ @ @ The same is true of other reported advances , he said .
Indeed , artificial intelligence , like biomedicine and other fields , has become mired in a replication crisis .
Researchers make dramatic claims that can not be tested , because researchers—especially those in industry—do not disclose their algorithms .
found that only 15 percent of AI studies shared their code .
There are also signs that investments in AI are not paying off .
Technology analyst Jeffrey Funk recently examined 40 start-up companies developing AI for health care , manufacturing , @ @ @ @ @ @ @ and other industries .
Many of them were not “ nearly as valuable to society as all the hype would suggest , ” Funk reports in .
Advances in AI “ are unlikely to be nearly as disruptive—for companies , for workers , or for the economy as a whole—as many observers have been arguing.
” Science reports that “ core progress in AI has stalled in some fields , ” such as information retrieval and product recommendation .
A study of algorithms used to improve the performance of neural networks found “ @ @ @ @ @ @ @ a 10-year period.
” The longstanding goal of “ general ” artificial intelligence , possessing the broad knowledge and learning capacity to solve a variety of real-world problems , as humans do , remains elusive .
“ We have machines that learn in a very narrow way , ” Yoshua Bengio , a pioneer in the AI approach called deep learning , recently complained in WIRED .
“ They need much more data to learn a task than human examples of intelligence , and they still make stupid mistakes.
” Writing in The @ @ @ @ @ @ @ tech , AI entrepreneur and writer Gary Marcus accuses AI leaders as well as the media of exaggerating the field ’ s progress .
AI-based autonomous cars , fake news detectors , diagnostic programs and chatbots have all been oversold , Marcus contends .
He warns that “ if and when the public , governments , and investment community recognize that they have been sold an unrealistic picture of AI ’ s strengths and weaknesses that does n't match reality , a new AI winter may commence.
” Another AI veteran and writer @ @ @ @ @ @ @ myth ” that one day AI will inevitably equal or surpass human intelligence .
In The Myth of Artificial Intelligence : Why Computers Can ’ t Think the Way We Do , scheduled to be released by Harvard University Press in April , Larson argues that “ success with narrow applications gets us not one step closer to general intelligence.
” Larson says “ the actual science of AI ( as opposed to the pseudoscience of Hollywood and science fiction novelists ) has uncovered a very large mystery at the heart of intelligence @ @ @ @ @ @ @ clue how to solve .
Put bluntly : all evidence suggests that human and machine intelligence are radically different .
And yet the myth of inevitability persists.
” When I first started writing about science , I believed the myth of AI .
One day , surely , researchers would achieve the goal of a flexible , supersmart , all-purpose artificial intelligence , like HAL .
Given rapid advances in computer hardware and software , it was only a matter of time .
And who was I to doubt authorities like Marvin Minsky @ @ @ @ @ @ @ doubter , as I realized that our minds—in spite of enormous advances in neuroscience , genetics , cognitive science and , yes , artificial intelligence—remain as mysterious as ever .
Here ’ s the paradox : machines are becoming undeniably smarter—and humans , it seems lately , more stupid , and yet machines will never equal , let alone surpass , our intelligence .
They will always remain mere machines .
That ’ s my guess , and my hope .
How Would AI Cover an AI Conference ?
Do We Need Brain @ @ @ @ @ @ @ The Many Minds of Marvin Minsky ( R.I.P . )
The Singularity and the Neural Code Who Wants to Be a Cyborg ?

When I started writing about science decades ago, artificial intelligence seemed ascendant. IEEE Spectrum, the technology magazine for which I worked, produced a special issue on how AI would transform the world. I edited an article in which computer scientist Frederick Hayes-Roth predicted that AI would soon replace experts in law, medicine, finance and other professions.
That was in 1984. Not long afterward, the exuberance gave way to a slump known as an “AI winter,” when disillusionment set in and funding declined. Years later, doing research for my book The Undiscovered Mind, I tracked Hayes-Roth down to ask how he thought his predictions had held up. He laughed and replied, “You’ve got a mean streak.”
AI had not lived up to expectations, he acknowledged. Our minds are hard to replicate, because we are “very, very complicated systems that are both evolved and adapted through learning to deal well and differentially with dozens of variables at one time.” Algorithms that can perform a specialized task, like playing chess, cannot be easily adapted for other purposes. “It is an example of what is called nonrecurrent engineering,” Hayes-Roth explained.
That was 1998. Today, according to some measures, AI is booming once again. Programs such as voice and face recognition are embedded in cell phones, televisions, cars and countless other consumer products. Clever algorithms help me choose a Christmas present for my girlfriend, find my daughter’s building in Brooklyn and gather information for columns like this one. Venture-capital investments in AI doubled between 2017 and 2018 to $40 billion, according to WIRED. A Price Waterhouse study estimates that by 2030 AI will boost global economic output by more than $15 trillion, “more than the current output of China and India combined.”
In fact, some observers fear that AI is moving too fast. New York Times columnist Farhad Manjoo calls an AI-based reading and writing program, GPT-3, “amazing, spooky, humbling and more than a little terrifying.” Someday, he frets, he might be “put out to pasture by a machine.” Neuroscientist Christof Koch has suggested that we might need computer chips implanted in our brains to help us keep up with intelligent machines.
Elon Musk made headlines in 2018 when he warned that “superintelligent” AI, much smarter than we are, represents “the single biggest existential crisis that we face.” (Really? Worse than climate change? Nuclear weapons? Psychopathic politicians? I suspect that Musk, who has invested in AI, is trying to promote the technology with his over-the-top fearmongering.)
Experts are pushing back against the hype, pointing out that many alleged advances in AI are based on flimsy evidence. Last January, for example, a team from Google Health claimed in Nature that their AI program had outperformed humans in diagnosing breast cancer. In October, a group led by Benjamin Haibe-Kains, a computational genomics researcher, criticized the Google health paper, arguing that the “lack of details of the methods and algorithm code undermines its scientific value.”
Haibe-Kains complained to Technology Review that the Google Health report is “more an advertisement for cool technology” than a legitimate, reproducible scientific study. The same is true of other reported advances, he said. Indeed, artificial intelligence, like biomedicine and other fields, has become mired in a replication crisis. Researchers make dramatic claims that cannot be tested, because researchers—especially those in industry—do not disclose their algorithms. One recent review found that only 15 percent of AI studies shared their code.
There are also signs that investments in AI are not paying off. Technology analyst Jeffrey Funk recently examined 40 start-up companies developing AI for health care, manufacturing, energy, finance, cybersecurity, transportation and other industries. Many of them were not “nearly as valuable to society as all the hype would suggest,” Funk reports in IEEE Spectrum . Advances in AI “are unlikely to be nearly as disruptive—for companies, for workers, or for the economy as a whole—as many observers have been arguing.”
Science reports that “core progress in AI has stalled in some fields,” such as information retrieval and product recommendation. A study of algorithms used to improve the performance of neural networks found “no clear evidence of performance improvements over a 10-year period.”
The longstanding goal of “general” artificial intelligence, possessing the broad knowledge and learning capacity to solve a variety of real-world problems, as humans do, remains elusive. “We have machines that learn in a very narrow way,” Yoshua Bengio, a pioneer in the AI approach called deep learning, recently complained in WIRED. “They need much more data to learn a task than human examples of intelligence, and they still make stupid mistakes.”
Writing in The Gradient, an online magazine devoted to tech, AI entrepreneur and writer Gary Marcus accuses AI leaders as well as the media of exaggerating the field’s progress. AI-based autonomous cars, fake news detectors, diagnostic programs and chatbots have all been oversold, Marcus contends. He warns that “if and when the public, governments, and investment community recognize that they have been sold an unrealistic picture of AI’s strengths and weaknesses that doesn't match reality, a new AI winter may commence.”
Another AI veteran and writer, Erik Larson, questions the “myth” that one day AI will inevitably equal or surpass human intelligence. In The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do, scheduled to be released by Harvard University Press in April, Larson argues that “success with narrow applications gets us not one step closer to general intelligence.”
Larson says “the actual science of AI (as opposed to the pseudoscience of Hollywood and science fiction novelists) has uncovered a very large mystery at the heart of intelligence, which no one currently has a clue how to solve. Put bluntly: all evidence suggests that human and machine intelligence are radically different. And yet the myth of inevitability persists.”
When I first started writing about science, I believed the myth of AI. One day, surely, researchers would achieve the goal of a flexible, supersmart, all-purpose artificial intelligence, like HAL. Given rapid advances in computer hardware and software, it was only a matter of time. And who was I to doubt authorities like Marvin Minsky?
Gradually, I became an AI doubter, as I realized that our minds—in spite of enormous advances in neuroscience, genetics, cognitive science and, yes, artificial intelligence—remain as mysterious as ever. Here’s the paradox: machines are becoming undeniably smarter—and humans, it seems lately, more stupid, and yet machines will never equal, let alone surpass, our intelligence. They will always remain mere machines. That’s my guess, and my hope.
Further Reading:
How Would AI Cover an AI Conference?
Do We Need Brain Implants to Keep Up with Robots?
The Many Minds of Marvin Minsky (R.I.P.)
The Singularity and the Neural Code
Who Wants to Be a Cyborg?
Mind-Body Problems


scientificamerican--2020-12-04--Coronavirus News Roundup, November 28-December 4

The items below are highlights from the free newsletter , “ Smart , useful , science stuff about COVID-19.
” To receive newsletter issues daily in your inbox , sign up here .
Canada has pre-ordered more doses per person of vaccines against SARS-CoV-2 than have any other countries and purchasing groups , reports freelance science journalist Asher Mullard for Nature ( 11/30/20 ) .
The U.S. and UK come in 2nd and 3rd .
The story includes 2 graphics that illustrate the pre-ordering landscape .
“ Many people in low-income countries might have to wait until 2023 or 2024 @ @ @ @ @ @ @ the Duke Global Health Innovation Center , ” Mullard writes .
The makers of three vaccines that look like they will be distributed widely in 2021 ( AstraZeneca , Moderna , and Pfizer/BioNTech ) forecast making an estimated total of 5.3 billion doses next year .
That would cover between 2.6 billion and 3.1 billion people , the story states .
Most of those doses are already spoken for by individual , largely affluent or middle-income countries , the story states , leaving little for low- and middle-income nations , “ most of @ @ @ @ @ @ @ from ” a joint venture called COVAX .
The purchasing and distribution group was formed to ensure equitable global access to vaccines against SARS-CoV-2 .
Virologist and vaccine researcher Shane Crotty at the La Jolla Institute for Immunology tweeted a thread on 11/27/20 discussing the safety of RNA vaccines ( like those being tested by Moderna and Pfizer/BioNTech on large numbers of people and expected to receive emergency-use approval soon by the U.S. Food and Drug Administration ) .
Crotty writes that RNA is analogous in some ways to expiring messages on social @ @ @ @ @ @ @ are “ temporary messages instructing cells to make one viral protein temporarily.
” He adds , “ It takes 25 different coronavirus proteins [ complex molecules ] to make a coronavirus , so there is no worry about the RNA making a virus.
” Below is an estimated timeline for SARS-CoV-2 vaccine distribution in the U.S. , based on a thread tweeted by Dr. Ashish Jha , dean of the Brown University School of Public Health , on 11/22/20 .
Jha drew the details in part from a televised interview between CNN ’ @ @ @ @ @ @ @ , chief scientific advisor to Operation Warp Speed ( the U.S. ’ s federally funded vaccine development effort ) : https : //threadreaderapp.com/thread/1330519144178085899.html • First public immunizations ( health care workers and long-term care facility residents , if U.S. Centers for Disease Control ’ s 12/2/20 recommendations are followed ) would start Dec. 11 or 12 ; • perhaps 20 million people in U.S. vaccinated by end of December ; • about 30 million more people in U.S. vaccinated by end of January 2021 , and combined with total people who have recovered @ @ @ @ @ @ @ slow spread , ” Jha writes ; • about 40 percent immunity in the U.S. population by February , bringing “ meaningfully slower ” spread of SARS-CoV-2 ; • vaccines widely available in U.S. by April/May 2021 Moderna says it will soon start testing its messenger-RNA vaccine against SARS-CoV-2 in 3,000 teenagers , reports Denise Grady at The New York Times ( 12/2/20 ) .
AstraZeneca and Pfizer/BioNTech already made similar announcements for testing their vaccines in children , the story states .
All three vaccine groups each recently announced that their vaccines @ @ @ @ @ @ @ , but “ no vaccine can be widely given to children until it has been tested in them , ” Grady writes .
“ Vaccines meant for both adults and children are generally tested first in adults to help make sure they are safe for pediatric trials [ experiments ] .
” Vaccines that work well in adults tend to also work well in children , with some dosing adjustments , according to Dr. Paul Offit , a vaccine researcher at Children ’ s Hospital of Philadelphia , Grady writes : https : @ @ @ @ @ @ @ wearing even an N95 mask during vigorous exercise did not result in workouts that felt harder or actually were more physiologically draining , writes Gretchen Reynolds at The New York Times ( 11/18/20 ) .
One of the studies found more carbon dioxide in the breath of N95-masked exercisers , but none of the subjects complained of headaches or breathing issues , the story states .
The results came as a surprise to the study subjects , Reynolds writes .
The studies were conducted on healthy , active adults , so it is @ @ @ @ @ @ @ the same in people who are less healthy or less active or who have breathing problems , the story states .
“ The findings suggest that anyone who hesitates to wear a mask during exercise should try one — although not an N95 mask , ” the story describes one of the study authors , an internal medicine physician at Rambam Health Care Campus , as saying .
Those masks slightly increase exercisers ’ carbon dioxide levels and should be reserved for health care workers anyway , states the story , which also @ @ @ @ @ @ @ , the U.S. Centers for Disease Control ( CDC ) released recommendations ( on 12/2/20 ) that include shortening the quarantine period from 14 days to as few as 7 days , at least for people who think they ’ ve could ’ ve been exposed to SARS-CoV-2 but then test negative , reports Colin Dwyer at NPR .
“ People should still watch closely for symptoms — such as a fever , a cough or a loss of taste or smell — for a full 14 days after possible exposure , ” @ @ @ @ @ @ @ manager for its COVID-19 response , Dwyer writes .
The change is meant to increase the odds that people will comply with the agency ’ s quarantine recommendations , the story states .
It also “ allows health officials to focus their efforts on the period of time that people are most likely to become contagious , ” the story quotes epidemiologist Jennifer Nuzzo of the Johns Hopkins Center for Health Security as saying ( 12/2/20 ) .
And if you ’ re confused about the meaning of quarantine versus isolation , or @ @ @ @ @ @ @ , and the like , check out this helpful piece , “ Clarifying COVID-19 terminology , ” by Lindsay Smith Rogers for Johns Hopkins Bloomberg School of Public Health ( 11/23/20 ) .
An 11/16/20 Q & A at Scientific American with Andrew Huberman , a Stanford University neuroscientist who studies the visual system , details how our vision and our breathing can “ offer us easy and accessible releases from stress , ” particularly in the context of the pandemic challenges and overall intensity of 2020 .
For instance , emerging research @ @ @ @ @ @ @ brain regions responsible for arousal and panic , writes freelance science journalist Jessica Wapner in the introduction to the Q & A .
And when we see stressful or exciting news , it increases our heart rate and breathing , Huberman says .
You don ’ t want to miss this section : Our eyes are “ not connected to the brain ; they are the brain , ” Huberman says .
“ During development , the eyes are part of the embryonic forebrain .
Your eyes get extruded from the skull during @ @ @ @ @ @ @ reconnect to the rest of the brain.
” One of the tips Huberman mentions : one can turn off their brain 's stress response by changing the way we are viewing our environment , specifically by taking on a panoramic view that allows you to see “ far into the periphery.
” Also , “ every time you exhale , you ’ re slowing down the heart rate , ” he says .
Check out Betsy Ladyzhets 's weekly “ COVID-19 Data Dispatch , a newsletter written to help people make sense of @ @ @ @ @ @ @ pandemic data sources .
The publication provides news on public health agencies , resources for understanding and communicating about the pandemic , context on how COVID-19 data experts are thinking about major issues , and occasional interviews or original analysis .
Ladyzhets is a data journalist and science writer who ’ s been managing her publication ’ s pandemic coverage and volunteering at the COVID Tracking Project since the spring .
All data sources featured in the newsletter since its July inception are compiled in a public Google spreadsheet , and Ladyzhets is @ @ @ @ @ @ @ be useful for journalists , community leaders , and data nerds alike .
You might enjoy , “ Ten reasons why my New York bodega is better than your 7-Eleven , ” by Caroline Ulwick for McSweeney ’ s .

The items below are highlights from the free newsletter, “Smart, useful, science stuff about COVID-19.” To receive newsletter issues daily in your inbox, sign up here.
Canada has pre-ordered more doses per person of vaccines against SARS-CoV-2 than have any other countries and purchasing groups, reports freelance science journalist Asher Mullard for Nature (11/30/20). The U.S. and UK come in 2nd and 3rd. The story includes 2 graphics that illustrate the pre-ordering landscape. “Many people in low-income countries might have to wait until 2023 or 2024 for vaccination, according to estimates from the Duke Global Health Innovation Center,” Mullard writes. The makers of three vaccines that look like they will be distributed widely in 2021 (AstraZeneca, Moderna, and Pfizer/BioNTech) forecast making an estimated total of 5.3 billion doses next year. That would cover between 2.6 billion and 3.1 billion people, the story states. Most of those doses are already spoken for by individual, largely affluent or middle-income countries, the story states, leaving little for low- and middle-income nations, “most of which seem to be relying on contributions from” a joint venture called COVAX. The purchasing and distribution group was formed to ensure equitable global access to vaccines against SARS-CoV-2.
Virologist and vaccine researcher Shane Crotty at the La Jolla Institute for Immunology tweeted a thread on 11/27/20 discussing the safety of RNA vaccines (like those being tested by Moderna and Pfizer/BioNTech on large numbers of people and expected to receive emergency-use approval soon by the U.S. Food and Drug Administration). Crotty writes that RNA is analogous in some ways to expiring messages on social media platforms, and that RNA vaccines are “temporary messages instructing cells to make one viral protein temporarily.” He adds, “It takes 25 different coronavirus proteins [complex molecules] to make a coronavirus, so there is no worry about the RNA making a virus.”
Below is an estimated timeline for SARS-CoV-2 vaccine distribution in the U.S., based on a thread tweeted by Dr. Ashish Jha, dean of the Brown University School of Public Health, on 11/22/20. Jha drew the details in part from a televised interview between CNN’s Jake Tapper and Dr. Moncef Slaoui, chief scientific advisor to Operation Warp Speed (the U.S.’s federally funded vaccine development effort): https://threadreaderapp.com/thread/1330519144178085899.html
First public immunizations (health care workers and long-term care facility residents, if U.S. Centers for Disease Control’s 12/2/20 recommendations are followed) would start Dec. 11 or 12;
perhaps 20 million people in U.S. vaccinated by end of December;
about 30 million more people in U.S. vaccinated by end of January 2021, and combined with total people who have recovered from infections, “that’ll slow spread,” Jha writes;
about 40 percent immunity in the U.S. population by February, bringing “meaningfully slower” spread of SARS-CoV-2;
vaccines widely available in U.S. by April/May 2021
Moderna says it will soon start testing its messenger-RNA vaccine against SARS-CoV-2 in 3,000 teenagers, reports Denise Grady at The New York Times (12/2/20). AstraZeneca and Pfizer/BioNTech already made similar announcements for testing their vaccines in children, the story states. All three vaccine groups each recently announced that their vaccines are effective in large-scale experiments with adults, but “no vaccine can be widely given to children until it has been tested in them,” Grady writes. “Vaccines meant for both adults and children are generally tested first in adults to help make sure they are safe for pediatric trials [experiments].” Vaccines that work well in adults tend to also work well in children, with some dosing adjustments, according to Dr. Paul Offit, a vaccine researcher at Children’s Hospital of Philadelphia, Grady writes: https://www.nytimes.com/2020/12/02/health/Covid-Moderna-vaccine-children.html.
Two recent studies suggest that wearing even an N95 mask during vigorous exercise did not result in workouts that felt harder or actually were more physiologically draining, writes Gretchen Reynolds at The New York Times (11/18/20). One of the studies found more carbon dioxide in the breath of N95-masked exercisers, but none of the subjects complained of headaches or breathing issues, the story states. The results came as a surprise to the study subjects, Reynolds writes. The studies were conducted on healthy, active adults, so it is not clear if the findings would be the same in people who are less healthy or less active or who have breathing problems, the story states. “The findings suggest that anyone who hesitates to wear a mask during exercise should try one — although not an N95 mask,” the story describes one of the study authors, an internal medicine physician at Rambam Health Care Campus, as saying. Those masks slightly increase exercisers’ carbon dioxide levels and should be reserved for health care workers anyway, states the story, which also was published in Spanish.
As anticipated, the U.S. Centers for Disease Control (CDC) released recommendations (on 12/2/20) that include shortening the quarantine period from 14 days to as few as 7 days, at least for people who think they’ve could’ve been exposed to SARS-CoV-2 but then test negative, reports Colin Dwyer at NPR. “People should still watch closely for symptoms — such as a fever, a cough or a loss of taste or smell — for a full 14 days after possible exposure,” according to the CDC’s incident manager for its COVID-19 response, Dwyer writes. The change is meant to increase the odds that people will comply with the agency’s quarantine recommendations, the story states. It also “allows health officials to focus their efforts on the period of time that people are most likely to become contagious,” the story quotes epidemiologist Jennifer Nuzzo of the Johns Hopkins Center for Health Security as saying (12/2/20).
And if you’re confused about the meaning of quarantine versus isolation, or about the distinction between asymptomatic and presymptomatic, and the like, check out this helpful piece, “Clarifying COVID-19 terminology,” by Lindsay Smith Rogers for Johns Hopkins Bloomberg School of Public Health (11/23/20).
An 11/16/20 Q&A at Scientific American with Andrew Huberman, a Stanford University neuroscientist who studies the visual system, details how our vision and our breathing can “offer us easy and accessible releases from stress,” particularly in the context of the pandemic challenges and overall intensity of 2020. For instance, emerging research suggests that altering our breathing can alter brain regions responsible for arousal and panic, writes freelance science journalist Jessica Wapner in the introduction to the Q&A. And when we see stressful or exciting news, it increases our heart rate and breathing, Huberman says. You don’t want to miss this section: Our eyes are “not connected to the brain; they are the brain,” Huberman says. “During development, the eyes are part of the embryonic forebrain. Your eyes get extruded from the skull during the first trimester, and then they reconnect to the rest of the brain.” One of the tips Huberman mentions: one can turn off their brain's stress response by changing the way we are viewing our environment, specifically by taking on a panoramic view that allows you to see “far into the periphery.” Also, “every time you exhale, you’re slowing down the heart rate,” he says.
Check out Betsy Ladyzhets's weekly “COVID-19 Data Dispatch, a newsletter written to help people make sense of myriad unstandardized (and often untrustworthy) pandemic data sources. The publication provides news on public health agencies, resources for understanding and communicating about the pandemic, context on how COVID-19 data experts are thinking about major issues, and occasional interviews or original analysis. Ladyzhets is a data journalist and science writer who’s been managing her publication’s pandemic coverage and volunteering at the COVID Tracking Project since the spring. All data sources featured in the newsletter since its July inception are compiled in a public Google spreadsheet, and Ladyzhets is working on other supplemental resources that may be useful for journalists, community leaders, and data nerds alike.
You might enjoy, “Ten reasons why my New York bodega is better than your 7-Eleven,” by Caroline Ulwick for McSweeney’s.
.


scientificamerican--2020-12-04--How a Climate 'Stress Test' Can Foresee Collapsing Banks

Governments around the world are gearing up to use an old regulatory tool for a new purpose : protecting the economy from climate change .
Financial regulators for years have used “ stress tests ” to gauge whether major banks are prepared to stay afloat amid extreme , unanticipated—yet entirely plausible—economic shocks .
They were widely implemented in the United States and abroad following the 2007-08 global financial crisis to help prevent systemwide catastrophes down the line .
Economists , environmentalists and advisers to President-elect Joe Biden warn that global warming could spur that next catastrophe .
The climate finance @ @ @ @ @ @ @ should be required to undergo climate-related stress testing before it ’ s too late .
Standing in the way are a slew of challenges—including insufficient corporate climate disclosures , still-developing climate projections and the banking sector itself , which has already signaled opposition to the idea .
There ’ s a chance that banks could use their lobbying might to delay or water down future climate finance regulations , advocates say .
But global momentum around the issue—and the incoming Biden administration—could mean that mandated climate stress testing in the United States isn @ @ @ @ @ @ @ ’ s pretty tough to imagine that regulators wouldn ’ t do this , because I don ’ t see how they can act in an informed way if they don ’ t know what the problem is .
And the only way you know the problem is by looking under the hood of [ banks ’ ] balance sheets , ” said Justin Guay , director of global climate strategy at the Sunrise Project .
“ There is a very clear consensus agreement among regulators globally that climate change is a systemic @ @ @ @ @ @ @ So that ship has sailed.
” According to Monsur Hussain , who heads financial institution research at Fitch Ratings Inc. , the main point of stress testing is to help regulators assess how a financial system or firm would cope under immense pressure—climate-related or otherwise .
They do so by modeling the impacts that an economic shock would have on a bank ’ s profitability and balance sheet over a two- or three-year period .
The United States currently requires financial firms to conduct stress tests with a “ pass-or-fail threshold , ” @ @ @ @ @ @ @ not have enough capital on hand to stay in business during the simulated crisis , it fails .
The Federal Reserve then requires firms that fail the tests to set aside extra capital to prevent the hypothetical situation from taking place in real life .
Climate finance proponents say regulators in the United States need to take a similar approach to climate change .
They would do so by way of climate scenarios that consider how physical risks—like extreme weather events—and the transition to a low-carbon economy could affect banks ’ loans to @ @ @ @ @ @ @ and 2019 , for instance , companies in the fossil fuel industry received more than $ 2.7 trillion in financing from 35 major banks , according to a report compiled by a group of environmental organizations .
“ If we have two hurricanes , a wildfire and droughts in the farming country , will our banking system be able to withstand that ?
We don ’ t really know , ” said Sarah Dougherty , a former researcher at the Federal Reserve Bank of Atlanta who is now with the Natural Resources Defense @ @ @ @ @ @ @ of central banks—dubbed the Network for Greening the Financial System—published stress testing guidance for regulators around the world .
The document included several high-level climate stress testing scenarios .
Among them was an “ orderly transition ” scenario , which would help regulators gauge how much money banks would lose if governments set a price on carbon this year—and achieved net-zero emissions by 2050 .
But the NGFS also included a “ hot house world ” scenario , in which no additional emissions policies are adopted and the world blows past the warming @ @ @ @ @ @ @ severe climate impacts .
Central banks in countries including the United Kingdom , France and Japan have already unveiled their plans to roll out stress tests to gauge financial firms ’ exposure to global warming .
The U.S. central bank is much further behind its global peers on the issue and hasn ’ t yet indicated that it intends to do the same .
But there are signs of progress .
The Federal Reserve in November acknowledged for the first time in its Financial Stability Report that global warming “ is likely to @ @ @ @ @ @ @ ” ( Greenwire , Nov. 10 ) .
Also notable is the incoming Biden administration ’ s commitment to taking an all-of-government approach to climate change , including by tapping a number of climate hawks to fill high-level economic positions ( Climatewire , Nov. 25 ) .
“ For the first time , potentially ever , finance policy is being used as climate policy , which is a fundamental shift—it never has been in the past , ” Guay said .
The incoming administration and the Fed ’ s change in tone do @ @ @ @ @ @ @ how much climate change could cost banks in the long term .
Dougherty , Hussain and the Bank Policy Institute , a lobbying group , are among those who have emphasized that climate stress tests remain in their infancy—in part due to the reality that measuring banks ’ exposure to global warming is far more complicated than assessing other hypothetical shocks .
Climate stress tests would need to measure banks ’ risk over a longer period .
That ’ s because some climate impacts—such as sea-level rise and the transition to a low-carbon @ @ @ @ @ @ @ than several years .
Beyond that , developing climate-related scenarios would require banks to make a variety of difficult assumptions—including predicting how climate change would affect each portfolio company , how different sectors will adapt to rising temperatures , and what climate policies will be passed in the United States and elsewhere .
There ’ s widespread agreement that those factors make climate stress testing complex .
But there ’ s less consensus regarding what should be done with stress tests ’ results—and whether they should be required at all .
Greg Baer @ @ @ @ @ @ @ include Bank of America Corp. , Citibank and Goldman Sachs Group Inc.—said in a recent op-ed that “ banks have an important role to play in managing a transition away from carbon.
” But in an interview with E & E News , Baer said BPI ’ s stance is that climate stress tests are a “ bridge too far.
” “ Trying to capture climate change effects decades in advance—without considering the extraordinary adaptability of the financial system and economy—and incorporating those results into the regulatory capital framework is no easier than @ @ @ @ @ @ @ affect banks by 2050 , ” BPI argued in a recent research note .
For those reasons and more , the trade association concluded that it ’ s premature to incorporate climate change scenarios into existing macroeconomic stress tests— “ and even more so to link climate stress test [ s ] to capital requirements.
” JPMorgan Chase & Co. , Bank of America and the Financial Services Forum—another banking trade group—did not respond to questions about their organizations ’ stance on climate stress testing .
A Wells Fargo & Co. spokesperson declined @ @ @ @ @ @ @ American Bankers Association , another trade group , said in an email that quantifying climate impacts on financial firms “ is in its early stages ” and that “ any potential changes to the regulatory framework , no matter how well-intended , must be thoroughly understood to avoid any unintended consequences.
” Climate finance experts and advocates disagree with BPI ’ s position—or at least parts of it .
Financial regulatory expert Gregg Gelzinis , of the left-leaning Center for American Progress , acknowledged that measuring climate risk is riddled with challenges and @ @ @ @ @ @ @ necessarily be used to adjust banks ’ capital requirements right off the bat .
“ Yes , we are projecting out significantly into the future , and yes , a bank ’ s balance sheet as it stands in 2020 is going to be different than a bank ’ s balance sheet as it stands in 2030 , or 2040 , or 2050 , ” Gelzinis said .
But he disagreed that those challenges are insurmountable—or that they would render the tests ’ results useless .
“ If climate stress tests show that @ @ @ @ @ @ @ re expected to have ... severe losses on your carbon-sensitive assets—things like fossil fuel bonds and fossil fuel loans—and it encourages you to today shift away from those assets , that ’ s a positive outcome , ” Gelzinis added .
He also warned against regulators implementing stress tests that lack any consequences at all , amounting to what he called “ completely toothless exercises.
” In his view , banks think about climate stress tests as “ maybe an interesting informational exercise .
But they don ’ t want it in any @ @ @ @ @ @ @ have to fund themselves with , which is one of the most powerful financial regulatory tools in regulators ’ arsenal to bolster the resiliency of these banks.
” Even if not immediately , he said , climate stress tests should lay the groundwork for more aggressive regulatory measures that would actively safeguard the financial system to avoid an economic meltdown in the future .
“ Of course they don ’ t want to do something that might make them do more work and hold more capital in reserve , ” she said .
@ @ @ @ @ @ @ want them to fail and take the entire financial system down with them.
” Reprinted from Climatewire with permission from E & E News .
E & E provides daily coverage of essential energy and environmental news at www.eenews.net .

Governments around the world are gearing up to use an old regulatory tool for a new purpose: protecting the economy from climate change.
Financial regulators for years have used “stress tests” to gauge whether major banks are prepared to stay afloat amid extreme, unanticipated—yet entirely plausible—economic shocks. They were widely implemented in the United States and abroad following the 2007-08 global financial crisis to help prevent systemwide catastrophes down the line.
Economists, environmentalists and advisers to President-elect Joe Biden warn that global warming could spur that next catastrophe. The climate finance proponents among them argue that major lenders should be required to undergo climate-related stress testing before it’s too late.
Standing in the way are a slew of challenges—including insufficient corporate climate disclosures, still-developing climate projections and the banking sector itself, which has already signaled opposition to the idea.
There’s a chance that banks could use their lobbying might to delay or water down future climate finance regulations, advocates say. But global momentum around the issue—and the incoming Biden administration—could mean that mandated climate stress testing in the United States isn’t far off.
“It’s pretty tough to imagine that regulators wouldn’t do this, because I don’t see how they can act in an informed way if they don’t know what the problem is. And the only way you know the problem is by looking under the hood of [banks’] balance sheets,” said Justin Guay, director of global climate strategy at the Sunrise Project.
“There is a very clear consensus agreement among regulators globally that climate change is a systemic risk,” Guay added. “So that ship has sailed.”
According to Monsur Hussain, who heads financial institution research at Fitch Ratings Inc., the main point of stress testing is to help regulators assess how a financial system or firm would cope under immense pressure—climate-related or otherwise.
They do so by modeling the impacts that an economic shock would have on a bank’s profitability and balance sheet over a two- or three-year period.
The United States currently requires financial firms to conduct stress tests with a “pass-or-fail threshold,” Hussain said. If a bank does not have enough capital on hand to stay in business during the simulated crisis, it fails. The Federal Reserve then requires firms that fail the tests to set aside extra capital to prevent the hypothetical situation from taking place in real life.
Climate finance proponents say regulators in the United States need to take a similar approach to climate change. They would do so by way of climate scenarios that consider how physical risks—like extreme weather events—and the transition to a low-carbon economy could affect banks’ loans to vulnerable businesses and sectors.
Between 2016 and 2019, for instance, companies in the fossil fuel industry received more than $2.7 trillion in financing from 35 major banks, according to a report compiled by a group of environmental organizations.
“If we have two hurricanes, a wildfire and droughts in the farming country, will our banking system be able to withstand that? We don’t really know,” said Sarah Dougherty, a former researcher at the Federal Reserve Bank of Atlanta who is now with the Natural Resources Defense Council.
‘A fundamental shift’
In June, a coalition of central banks—dubbed the Network for Greening the Financial System—published stress testing guidance for regulators around the world.
The document included several high-level climate stress testing scenarios. Among them was an “orderly transition” scenario, which would help regulators gauge how much money banks would lose if governments set a price on carbon this year—and achieved net-zero emissions by 2050.
But the NGFS also included a “hot house world” scenario, in which no additional emissions policies are adopted and the world blows past the warming goals enshrined in the Paris Agreement—leading to severe climate impacts.
Central banks in countries including the United Kingdom, France and Japan have already unveiled their plans to roll out stress tests to gauge financial firms’ exposure to global warming.
The U.S. central bank is much further behind its global peers on the issue and hasn’t yet indicated that it intends to do the same.
But there are signs of progress. The Federal Reserve in November acknowledged for the first time in its Financial Stability Report that global warming “is likely to increase financial shocks and financial system vulnerabilities” (Greenwire, Nov. 10).
Also notable is the incoming Biden administration’s commitment to taking an all-of-government approach to climate change, including by tapping a number of climate hawks to fill high-level economic positions (Climatewire, Nov. 25).
“For the first time, potentially ever, finance policy is being used as climate policy, which is a fundamental shift—it never has been in the past,” Guay said.
The incoming administration and the Fed’s change in tone do not eliminate the challenges inherent to measuring how much climate change could cost banks in the long term.
Dougherty, Hussain and the Bank Policy Institute, a lobbying group, are among those who have emphasized that climate stress tests remain in their infancy—in part due to the reality that measuring banks’ exposure to global warming is far more complicated than assessing other hypothetical shocks.
That’s for a few reasons.
Climate stress tests would need to measure banks’ risk over a longer period. That’s because some climate impacts—such as sea-level rise and the transition to a low-carbon economy—will take place over several decades rather than several years.
Beyond that, developing climate-related scenarios would require banks to make a variety of difficult assumptions—including predicting how climate change would affect each portfolio company, how different sectors will adapt to rising temperatures, and what climate policies will be passed in the United States and elsewhere.
‘Bridge too far’
There’s widespread agreement that those factors make climate stress testing complex. But there’s less consensus regarding what should be done with stress tests’ results—and whether they should be required at all.
Greg Baer, the chief executive of BPI—whose members include Bank of America Corp., Citibank and Goldman Sachs Group Inc.—said in a recent op-ed that “banks have an important role to play in managing a transition away from carbon.”
But in an interview with E&E News, Baer said BPI’s stance is that climate stress tests are a “bridge too far.”
“Trying to capture climate change effects decades in advance—without considering the extraordinary adaptability of the financial system and economy—and incorporating those results into the regulatory capital framework is no easier than predicting how pandemics or machine learning will affect banks by 2050,” BPI argued in a recent research note.
For those reasons and more, the trade association concluded that it’s premature to incorporate climate change scenarios into existing macroeconomic stress tests—“and even more so to link climate stress test[s] to capital requirements.”
JPMorgan Chase & Co., Bank of America and the Financial Services Forum—another banking trade group—did not respond to questions about their organizations’ stance on climate stress testing. A Wells Fargo & Co. spokesperson declined to comment.
A spokesperson for the American Bankers Association, another trade group, said in an email that quantifying climate impacts on financial firms “is in its early stages” and that “any potential changes to the regulatory framework, no matter how well-intended, must be thoroughly understood to avoid any unintended consequences.”
Climate finance experts and advocates disagree with BPI’s position—or at least parts of it.
A global meltdown?
Financial regulatory expert Gregg Gelzinis, of the left-leaning Center for American Progress, acknowledged that measuring climate risk is riddled with challenges and that climate stress tests shouldn’t necessarily be used to adjust banks’ capital requirements right off the bat.
“Yes, we are projecting out significantly into the future, and yes, a bank’s balance sheet as it stands in 2020 is going to be different than a bank’s balance sheet as it stands in 2030, or 2040, or 2050,” Gelzinis said.
But he disagreed that those challenges are insurmountable—or that they would render the tests’ results useless.
“If climate stress tests show that 15 years from now, you’re expected to have ... severe losses on your carbon-sensitive assets—things like fossil fuel bonds and fossil fuel loans—and it encourages you to today shift away from those assets, that’s a positive outcome,” Gelzinis added.
He also warned against regulators implementing stress tests that lack any consequences at all, amounting to what he called “completely toothless exercises.”
In his view, banks think about climate stress tests as “maybe an interesting informational exercise. But they don’t want it in any way to impact how much capital they have to fund themselves with, which is one of the most powerful financial regulatory tools in regulators’ arsenal to bolster the resiliency of these banks.”
Even if not immediately, he said, climate stress tests should lay the groundwork for more aggressive regulatory measures that would actively safeguard the financial system to avoid an economic meltdown in the future.
Dougherty, the former Fed researcher, agreed.
“Of course they don’t want to do something that might make them do more work and hold more capital in reserve,” she said. “But we also don’t want them to fail and take the entire financial system down with them.”
Reprinted from Climatewire with permission from E&E News. E&E provides daily coverage of essential energy and environmental news at www.eenews.net.


scientificamerican--2020-12-04--Unprecedented 3-D view inside Animal Mummies

More than 2,000 years ago in Egypt , animals were often mummified as offerings to gods or buried along with human mummies .
But many details about the practice are unclear , in part because studying the creatures without damaging them is difficult .
Recent Micro CT scans of three animal mummies at Swansea University in Wales offered a more detailed view than ever before .
The three-dimensional x-ray images—with 100 times the resolution of a regular medical CT scan—allowed researchers to precisely measure the lengths of the animals ’ bones and other features .
They reported their findings in @ @ @ @ @ @ @ .
A cat mummy , for instance , turned out to be a kitten less than five months old , which might have been strangled to death .
Measurements of bones in a bird mummy suggest the animal was probably a Eurasian kestrel .
And a snake , identified via the scans as a juvenile Egyptian cobra , seems to have been killed by a whipping motion , and its kidneys showed signs that it had been deprived of water during its life .
The images also revealed that the snake ’ s @ @ @ @ @ @ @ was trauma in its teeth and jaw bones , suggesting the mouth might have been pried apart during the mummification process .
This is the first time such a complex ritualistic process has been documented in a snake , the researchers say .

More than 2,000 years ago in Egypt, animals were often mummified as offerings to gods or buried along with human mummies. But many details about the practice are unclear, in part because studying the creatures without damaging them is difficult. Recent Micro CT scans of three animal mummies at Swansea University in Wales offered a more detailed view than ever before. The three-dimensional x-ray images—with 100 times the resolution of a regular medical CT scan—allowed researchers to precisely measure the lengths of the animals’ bones and other features. They reported their findings in August 2020 in the journal Scientific Reports. A cat mummy, for instance, turned out to be a kitten less than five months old, which might have been strangled to death. Measurements of bones in a bird mummy suggest the animal was probably a Eurasian kestrel. And a snake, identified via the scans as a juvenile Egyptian cobra, seems to have been killed by a whipping motion, and its kidneys showed signs that it had been deprived of water during its life. The images also revealed that the snake’s jaw was wide open, and there was trauma in its teeth and jaw bones, suggesting the mouth might have been pried apart during the mummification process. This is the first time such a complex ritualistic process has been documented in a snake, the researchers say.
Science in Images


scientificamerican--2020-12-05--In Case You Missed It

In Case You Missed It

Western Joshua trees will get a year of temporary endangered species status in California while the state considers permanently listing the distinctive succulents as the first-ever plant species protected because of climate change–related threat .
A tropical forest ground survey revealed that one lightning strike often damages more than 20 trees , a quarter of which can die within a year .
Researchers combined this finding with satellite data to estimate that lightning kills 200 million tropical trees worldwide every year—a significant cause of their demise .
Climate researchers discovered records of an automatic weather station that measured −93.3 degrees @ @ @ @ @ @ @ colder than the average on Mars and the coldest ever recorded in the Northern Hemisphere .
Scientists have examined a shark found south of Sardinia that somehow survived to three years old without skin or teeth .
They concluded it was a genetic mutation and plan to check nearby sediment for potential pollutant causes .
Newly discovered and pristinely preserved fossils suggest two sleeping dinosaurs were buried alive in an underground burrow 125 million years ago .
The burrow may have collapsed under volcanic debris .
A new study shows how Australian grasslands @ @ @ @ @ @ @ by the grasses themselves .
Baking heat creates a hard clay crust over a patch of ground ; water runs off of it , forming a more welcoming zone at its edges that grasses bolster as they grow and cool the soil .

U.S.
Western Joshua trees will get a year of temporary endangered species status in California while the state considers permanently listing the distinctive succulents as the first-ever plant species protected because of climate change–related threat.
PANAMA
A tropical forest ground survey revealed that one lightning strike often damages more than 20 trees, a quarter of which can die within a year. Researchers combined this finding with satellite data to estimate that lightning kills 200 million tropical trees worldwide every year—a significant cause of their demise.
GREENLAND
Climate researchers discovered records of an automatic weather station that measured −93.3 degrees Fahrenheit one day in December 1991—a temperature colder than the average on Mars and the coldest ever recorded in the Northern Hemisphere.
ITALY
Scientists have examined a shark found south of Sardinia that somehow survived to three years old without skin or teeth. They concluded it was a genetic mutation and plan to check nearby sediment for potential pollutant causes.
CHINA
Newly discovered and pristinely preserved fossils suggest two sleeping dinosaurs were buried alive in an underground burrow 125 million years ago. The burrow may have collapsed under volcanic debris.
AUSTRALIA
A new study shows how Australian grasslands' strange barren patches—called fairy circles—are landscaped by the grasses themselves. Baking heat creates a hard clay crust over a patch of ground; water runs off of it, forming a more welcoming zone at its edges that grasses bolster as they grow and cool the soil.


scientificamerican--2020-12-05--Lack of Sleep Could Be a Problem for AIs

One of the distinguishing features of machines is that they don ’ t need to sleep , unlike humans and any other creature with a central nervous system .
Someday though , your toaster might need a nap from time to time , as may your car , fridge and anything else that is revolutionized with the advent of practical artificial intelligence technologies .
The change will come when ( and if ) AI systems that mimic living brains are incorporated into the wide range of devices that currently rely on conventional computers and microprocessors to help us through the @ @ @ @ @ @ @ the implication of new research that we are conducting in Los Alamos National Laboratory to understand systems that operate much like the neurons inside living brains .
Our realization came about as we worked to develop neural networks that closely approximate how humans and other biological systems learn to see .
We were investigating the way that these simulated networks respond to unsupervised dictionary training .
In this sort of activity , networks set about classifying objects without having prior examples with which to compare them .
Imagine handing many images of exotic @ @ @ @ @ @ @ them to group similar ones together .
The child might not know what an antelope is , but they would place them in a separate pile from the lions or penguins , for example .
It likely would come as no surprise to any teacher of young children that we found that our networks became unstable after continuous periods of learning .
However , when we exposed the networks to states that are analogous to the waves that living brains experience during sleep , stability was restored .
It was as though we @ @ @ @ @ @ @ of a good , long nap .
This sort of instability is not a characteristic of all AI networks .
The issue only arises when training biologically realistic processors , or when trying to understand biology itself .
The vast majority of researchers on machine learning , deep learning and AI never encounter this instability because , in the very artificial systems they study , they have the luxury of performing mathematical operations that have no equivalent in living neurons .
Our decision to expose our biologically realistic networks to an artificial analogue @ @ @ @ @ @ @ to stabilize them .
They were spontaneously generating images that were analogous to hallucinations .
We experimented with various types of numerical noise , roughly comparable to the static you might encounter between stations while tuning a radio .
The best results came when we used noise with a wide range of frequencies and amplitudes .
The noise mimics the input received by the neurons in your brain during slow-wave sleep , which is the deep sleep we can ’ t live without .
The results suggest that in both artificial and natural @ @ @ @ @ @ @ ensure that neurons maintain their stability and do not hallucinate .
Sleeplike states in neural networks are very different from the mode your PC enters after some set period of inactivity .
A conventional computer that has gone to “ sleep ” is effectively in suspended animation , with all computational activity frozen in time .
And the age-old advice from the IT department to try “ turning your computer off and then on again ” when a PC gets glitchy is tantamount to exposing your machine to a brief period of brain @ @ @ @ @ @ @ would do nothing to settle an unstable neural network .
And power cycling would simply reset the network and undo any prior training , effectively giving the network a severe case of amnesia .
In neural networks as well as living creatures , a sleeplike state is not inactivity , but a different kind of activity that is crucial to the proper functioning of neurons .
We are just starting to investigate an additional benefit of artificial sleep in our simulations .
Often , a few neurons in a simulated network fail to @ @ @ @ @ @ @ started .
We have found that applying artificial sleep states seems to reset idle neurons to ensure they become functioning components in the network .
As researchers build networks that increasingly resemble living nervous systems , it should probably come as little surprise that they seem to need sleep as much as we do .
Similarly , we expect that sophisticated AI systems will help us to more fully understand sleep and other characteristics in biological systems .
The napping toaster of the future may provide novel insights into the workings of our @ @ @ @ @ @ @ breakfast food .

One of the distinguishing features of machines is that they don’t need to sleep, unlike humans and any other creature with a central nervous system. Someday though, your toaster might need a nap from time to time, as may your car, fridge and anything else that is revolutionized with the advent of practical artificial intelligence technologies.
The change will come when (and if) AI systems that mimic living brains are incorporated into the wide range of devices that currently rely on conventional computers and microprocessors to help us through the day. At least that’s the implication of new research that we are conducting in Los Alamos National Laboratory to understand systems that operate much like the neurons inside living brains.
Our realization came about as we worked to develop neural networks that closely approximate how humans and other biological systems learn to see. We were investigating the way that these simulated networks respond to unsupervised dictionary training. In this sort of activity, networks set about classifying objects without having prior examples with which to compare them. Imagine handing many images of exotic animals to a child, and asking them to group similar ones together. The child might not know what an antelope is, but they would place them in a separate pile from the lions or penguins, for example.
It likely would come as no surprise to any teacher of young children that we found that our networks became unstable after continuous periods of learning. However, when we exposed the networks to states that are analogous to the waves that living brains experience during sleep, stability was restored. It was as though we were giving the neural networks the equivalent of a good, long nap.
This sort of instability is not a characteristic of all AI networks. The issue only arises when training biologically realistic processors, or when trying to understand biology itself. The vast majority of researchers on machine learning, deep learning and AI never encounter this instability because, in the very artificial systems they study, they have the luxury of performing mathematical operations that have no equivalent in living neurons.
Our decision to expose our biologically realistic networks to an artificial analogue of sleep was nearly a last-ditch effort to stabilize them. They were spontaneously generating images that were analogous to hallucinations. We experimented with various types of numerical noise, roughly comparable to the static you might encounter between stations while tuning a radio. The best results came when we used noise with a wide range of frequencies and amplitudes. The noise mimics the input received by the neurons in your brain during slow-wave sleep, which is the deep sleep we can’t live without.
The results suggest that in both artificial and natural intelligence systems slow-wave sleep may act to ensure that neurons maintain their stability and do not hallucinate.
Sleeplike states in neural networks are very different from the mode your PC enters after some set period of inactivity. A conventional computer that has gone to “sleep” is effectively in suspended animation, with all computational activity frozen in time. And the age-old advice from the IT department to try “turning your computer off and then on again” when a PC gets glitchy is tantamount to exposing your machine to a brief period of brain death.
That kind of sleep mode would do nothing to settle an unstable neural network. And power cycling would simply reset the network and undo any prior training, effectively giving the network a severe case of amnesia. In neural networks as well as living creatures, a sleeplike state is not inactivity, but a different kind of activity that is crucial to the proper functioning of neurons.
We are just starting to investigate an additional benefit of artificial sleep in our simulations. Often, a few neurons in a simulated network fail to function at all when a simulation is started. We have found that applying artificial sleep states seems to reset idle neurons to ensure they become functioning components in the network.
As researchers build networks that increasingly resemble living nervous systems, it should probably come as little surprise that they seem to need sleep as much as we do. Similarly, we expect that sophisticated AI systems will help us to more fully understand sleep and other characteristics in biological systems. The napping toaster of the future may provide novel insights into the workings of our brains—in addition to a warm and crispy breakfast food.


Staring at Nothing

What are you staring at ?
said the mother , said the cousin , said the teacher to the child— Nothing , he said .
Then his wife asked .
Nothing .
Nothing and more nothing and nothing more .
What a waste of time , said his colleagues , valuable time .
People would kill for that .
One December for ten nights and a hundred hours , he stared at nothing .
He looked at where there was n't anything but nothing , more nothing , and nothing more .
Nothing but death and birth merging into light—collisions @ @ @ @ @ @ @ white .
Spirals , ellipticals , nothing but the universe quintupling in size .
What wasn't is teeming with galaxies , gleaming innumerably .
It 's nothing , said he .
Look at nothing to see .
AUTHOR 'S NOTE : In December 1995 astronomer Robert Williams took a risk that was mocked by his colleagues at the Space Telescope Science Institute .
As director , Williams used his discretionary time with the Hubble Space Telescope to point at nothing—an apparently empty spot of sky—over a 10-day period .
The astounding revelation of @ @ @ @ @ @ @ the legendary Hubble Deep Field .

—for Dr. Robert Williams, astronomer
Edited by Dava Sobel
What are you staring at? said the mother,
said the cousin, said the teacher to the child—
Nothing, he said. Then his wife asked. Nothing.
Nothing and more nothing and nothing more.
What a waste of time, said his colleagues,
valuable time. People would kill for that.
One December for ten nights and a hundred
hours, he stared at nothing. He looked at where
there wasn't anything but nothing, more nothing,
and nothing more. Nothing but death and birth
merging into light—collisions of blue,
red, yellow, white. Spirals, ellipticals, nothing
but the universe quintupling in size. What wasn't
is teeming with galaxies, gleaming innumerably.
It's nothing, said he. Look at nothing to see.
AUTHOR'S NOTE: In December 1995 astronomer Robert Williams took a risk that was mocked by his colleagues at the Space Telescope Science Institute. As director, Williams used his discretionary time with the Hubble Space Telescope to point at nothing—an apparently empty spot of sky—over a 10-day period. The astounding revelation of thousands of galaxies is now known as the legendary Hubble Deep Field.


scientificamerican--2020-12-06--The Secret Sauce in Opinion Polling Can Also Be a Source of Spoilage

On November 6 , 2020 , I woke up to a flood ( for a statistician ) of tweets about my 2018 article “ Statistical Paradises and Paradoxes in Big Data ( I ) : Law of Large Populations , Big Data Paradox , and the 2016 US Presidential Election.
” A kind soul had offered it as an answer to the question : “ What ’ s wrong with polls ?
” which led to the article going viral .
As much as I was flattered by the attention , I was disappointed that no one had asked “ @ @ @ @ @ @ @ right in the first place ?
” A poll typically samples a few hundreds or thousands of people , but it aims to learn about a population many times larger .
For predicting a U.S. presidential election , conducting a poll of size n=5,000 to learn about the opinions of N=230 million ( eligible ) voters is the same as asking just about two people out of every 100,000 voters on average .
Isn ’ t it absurd to expect to learn anything reliably about so many from the opinions of so few @ @ @ @ @ @ @ founder of Statistics Norway , proposed the idea to replace a national census by “ representative samples ” during the 1895 World Congress of the International Statistics Institute ( ISI ) , the reactions “ were violent and Kiær ’ s proposals were refused almost unanimously !
” as noted by former ISI President Jean-Louis Bodin .
It took nearly half a century for the idea to gain general acceptance .
The statistical theory for polling might be hard to digest for many , but the general idea of representative sampling is much @ @ @ @ @ @ @ about Gallop Poll going to Canada ( Ottawa Citizen , Nov 27 , 1941 ) , Gregory Clark wrote : “ When a cook wants to taste the soup to see how it is coming , he doesn ’ t have to drink the whole boilerful .
Nor does he take a spoonful off the top , then a bit from the middle , and some from the bottom .
He stirs the whole cauldron thoroughly .
Then stirs it some more .
And then he tastes it .
That is how the @ @ @ @ @ @ @ for polling therefore is thorough stirring .
Once a soup is stirred thoroughly , any part of it becomes representative of the entire soup .
And that makes it possible to sample a spoonful or two to assess reliably the flavor and texture of the soup , regardless of the size of its container .
Polling achieves this “ thorough stirring ” via random sampling , which creates , statistically speaking , a miniature that mimics the population .
But this secret sauce is also the source of spoilage .
My 2018 article @ @ @ @ @ @ @ of thorough stirring , and demonstrates how a seemingly minor violation of thorough stirring can cause astonishingly large damage because of the “ Law of Large Populations ” ( LLP ) .
It also reveals that the polling error is the product of three indexes : data quality , data quantity and problem difficulty .
To understand these terms intuitively , let ’ s continue to enjoy soup .
The flavoring of a soup containing only salt would be much easier to discern than a Chinese soup with five spices .
Problem difficulty @ @ @ @ @ @ @ regardless of how we stir it or the spoon size .
Data quantity captures the spoon size , relative to the size of the cooking container .
This shift of emphasis from only the sample size n to the sample fraction n/N , which depends critically on the population size N , is the key to LLP .
The most critical index and also the hardest one to assess is data quality , a measure of the lack of thorough stirring .
Imagine some spice clumps did not dissolve completely in the cooking @ @ @ @ @ @ @ of getting caught by the cook ’ s spoon , then what the cook tastes is likely to be spicier than the soup actually is .
For polling , if people who prefer candidate B over A are more ( or less ) likely to provide their opinions , than the polling will overpredict ( or underpredict ) the vote shares for B than for A .
This tendency can be measured by the so-called Pearson correlation—let ’ s denote it by r—between preferring B and responding ( honestly ) to the poll @ @ @ @ @ @ @ ( the magnitude of r ) , the larger the polling error .
A positive r indicates overestimation , and a negative r underestimation .
The whole idea of stirring thoroughly or random sampling is to ensure r is negligible , or technically to ensure it is on the order of the reciprocal of the square root of N. Statistically , this is as small as it can be since we have to allow some sampling randomness .
For example , for N=230 million , |r| should be less than one out of @ @ @ @ @ @ @ election polling , r was –0.005 , or about one out of 200 in magnitude for predicting Trump ’ s vote shares , as estimated in my article ( based on polls carried out by YouGov ) .
Whereas a half a percent correlation seems tiny , its impact is magnified greatly when multiplied by the square-root of N. As an illustration of this impact , my article calculated how much statistical accuracy was reduced by |r|=0.005 .
Opinions from 2.3 million responses ( about 1 percent of the eligible voting population in @ @ @ @ @ @ @ expected polling error as that resulting from 400 responses in a genuinely random sample .
This is a 99.98 percent reduction of the actual sample sizes , an astonishing loss by any standard .
A quality poll of size 400 still can deliver reliable predictions , but no ( qualified ) campaign manager would stop campaigning because a poll of size 400 predicts winning .
But they may ( and indeed some did ) stop when the winning prediction is from 2.3 million responses , which amount to 2,300 polls that each have @ @ @ @ @ @ @ in 2016 , and unfortunately again in 2020 ( but see this article in Harvard Data Science Review ) , is the devastating impact of LLP .
Statistical sampling errors tend to balance out when we increase the sample size , but systematic selection bias only solidifies when sample size increases .
Worse , the selection bias is magnified by the population size : the larger the population , the larger the magnification .
That is the essence of LLP .
When a bit of soup finds itself on a cook ’ s @ @ @ @ @ @ @ that “ Well , I ’ m a bit too salty , so let me jump out !
” But in an opinion poll , there is nothing to stop someone from opting out because of the fear of the ( perceived ) consequences of revealing a particular answer .
Until our society knows how to remove such fear , or the pollsters can routinely and reliably adjust for such selective responses , we can all be wiser citizens of the digital age by always taking polling results with a healthy dose of @ @

On November 6, 2020, I woke up to a flood (for a statistician) of tweets about my 2018 article “Statistical Paradises and Paradoxes in Big Data (I): Law of Large Populations, Big Data Paradox, and the 2016 US Presidential Election.” A kind soul had offered it as an answer to the question: “What’s wrong with polls?” which led to the article going viral.
As much as I was flattered by the attention, I was disappointed that no one had asked “Why would anyone expect polls to be right in the first place?” A poll typically samples a few hundreds or thousands of people, but it aims to learn about a population many times larger. For predicting a U.S. presidential election, conducting a poll of size n=5,000 to learn about the opinions of N=230 million (eligible) voters is the same as asking just about two people out of every 100,000 voters on average. Isn’t it absurd to expect to learn anything reliably about so many from the opinions of so few?
Indeed when Anders Kiær, the founder of Statistics Norway, proposed the idea to replace a national census by “representative samples” during the 1895 World Congress of the International Statistics Institute (ISI), the reactions “were violent and Kiær’s proposals were refused almost unanimously!” as noted by former ISI President Jean-Louis Bodin. It took nearly half a century for the idea to gain general acceptance.
The statistical theory for polling might be hard to digest for many, but the general idea of representative sampling is much more palatable. In a newspaper story about Gallop Poll going to Canada (Ottawa Citizen, Nov 27, 1941), Gregory Clark wrote:
“When a cook wants to taste the soup to see how it is coming, he doesn’t have to drink the whole boilerful. Nor does he take a spoonful off the top, then a bit from the middle, and some from the bottom. He stirs the whole cauldron thoroughly. Then stirs it some more. And then he tastes it. That is how the Gallup Poll works.”
The secret sauce for polling therefore is thorough stirring. Once a soup is stirred thoroughly, any part of it becomes representative of the entire soup. And that makes it possible to sample a spoonful or two to assess reliably the flavor and texture of the soup, regardless of the size of its container. Polling achieves this “thorough stirring” via random sampling, which creates, statistically speaking, a miniature that mimics the population.
But this secret sauce is also the source of spoilage. My 2018 article shows how to mathematically quantify the lack of thorough stirring, and demonstrates how a seemingly minor violation of thorough stirring can cause astonishingly large damage because of the “Law of Large Populations” (LLP). It also reveals that the polling error is the product of three indexes: data quality, data quantity and problem difficulty.
To understand these terms intuitively, let’s continue to enjoy soup. The flavoring of a soup containing only salt would be much easier to discern than a Chinese soup with five spices. Problem difficulty measures the complexity of the soup, regardless of how we stir it or the spoon size. Data quantity captures the spoon size, relative to the size of the cooking container. This shift of emphasis from only the sample size n to the sample fraction n/N, which depends critically on the population size N, is the key to LLP.
The most critical index and also the hardest one to assess is data quality, a measure of the lack of thorough stirring. Imagine some spice clumps did not dissolve completely in the cooking, and if they have more chance of getting caught by the cook’s spoon, then what the cook tastes is likely to be spicier than the soup actually is. For polling, if people who prefer candidate B over A are more (or less) likely to provide their opinions, than the polling will overpredict (or underpredict) the vote shares for B than for A. This tendency can be measured by the so-called Pearson correlation—let’s denote it by r—between preferring B and responding (honestly) to the poll. The higher the value of |r| (the magnitude of r), the larger the polling error. A positive r indicates overestimation, and a negative r underestimation.
The whole idea of stirring thoroughly or random sampling is to ensure r is negligible, or technically to ensure it is on the order of the reciprocal of the square root of N. Statistically, this is as small as it can be since we have to allow some sampling randomness. For example, for N=230 million, |r| should be less than one out of 15,000. However, for the 2016 election polling, r was –0.005, or about one out of 200 in magnitude for predicting Trump’s vote shares, as estimated in my article (based on polls carried out by YouGov). Whereas a half a percent correlation seems tiny, its impact is magnified greatly when multiplied by the square-root of N.
As an illustration of this impact, my article calculated how much statistical accuracy was reduced by |r|=0.005. Opinions from 2.3 million responses (about 1 percent of the eligible voting population in 2016) with |r|=0.005 have the same expected polling error as that resulting from 400 responses in a genuinely random sample. This is a 99.98 percent reduction of the actual sample sizes, an astonishing loss by any standard. A quality poll of size 400 still can deliver reliable predictions, but no (qualified) campaign manager would stop campaigning because a poll of size 400 predicts winning. But they may (and indeed some did) stop when the winning prediction is from 2.3 million responses, which amount to 2,300 polls that each have 1,000 responses.
What was generally overlooked in 2016, and unfortunately again in 2020 (but see this article in Harvard Data Science Review), is the devastating impact of LLP. Statistical sampling errors tend to balance out when we increase the sample size, but systematic selection bias only solidifies when sample size increases. Worse, the selection bias is magnified by the population size: the larger the population, the larger the magnification. That is the essence of LLP.
When a bit of soup finds itself on a cook’s spoon, it cannot tell itself that “Well, I’m a bit too salty, so let me jump out!” But in an opinion poll, there is nothing to stop someone from opting out because of the fear of the (perceived) consequences of revealing a particular answer. Until our society knows how to remove such fear, or the pollsters can routinely and reliably adjust for such selective responses, we can all be wiser citizens of the digital age by always taking polling results with a healthy dose of salt.