from the world's big
In 1983, Isaac Asimov predicted the world of 2019. Here's what he got right (and wrong).
Some of them were surprisingly astute.
- In 1983, the Toronto Star asked science fiction writer Isaac Asimov to predict what the world would be like in 2019.
- His predictions about computerization were mostly accurate, though some of his forecasts about education and space utilization were overly optimistic.
- Asimov's predictions highlight just how difficult it is to predict the future of technology.
Isaac Asimov was one the world's most celebrated and prolific science fiction writers, having written or edited more than 500 books over his four-decade career. The Russian-born writer was famous for penning hard science fiction in his books, such as that in I, Robot, Foundation and Nightfall. Naturally, his work contained many predictions about the future of society and technology.
Some of those predictions came true, such as our ability to use what he called sight-sound communication to contact anyone on Earth. But others — a machine that can convert yeast, algae and water into foods like "mock-turkey," for instance — never manifested.
In 1983, the Toronto Star invited Asimov to predict the answer to a specific question: "What will the world look like in 2019?" It was a fitting time to pose the question, the Star's editors figured, because 1983 was 35 years after George Orwell penned 1984.
Asimov wrote that it was pointless to imagine the future of society if the United States and the Soviet Union were to engage in nuclear war, so he assumed that wouldn't happen. He then broke down his predictions under two main themes: computerization and space utilization.
Asimov was more or less correct in many of his predictions on the future of computerization, even if some of his forecasts were a bit broad and obvious, including:
- "Computerization will undoubtedly continue onward inevitably."
- The "mobile computerized object" will "penetrate the home," and the increasing complexity of society will make it impossible to live without this technology.
- Computers will disrupt work habits and replace old jobs with ones that are radically different.
- Robotics will kill "routine clerical and assembly-line jobs."
- Society will need a "vast change in the nature of education must take place, and entire populations must be made "computer-literate" and must be taught to deal with a "high-tech" world."
- This education transition will be difficult for many, especially as world population grows at unprecedented rates.
Still, Asimov was wrong, or at least slightly off, on a few predictions about the future of computerization.
For instance, he predicted that technology will revolutionize education (correct), but that traditional schooling will become outdated as kids become able to learn everything they need to know from computers at home. That might technically be possible, but it also assumes that kids wouldn't spend all that time using technology to, say, play Fortnite.
"We will enter space to stay," Asimov claimed in his essay.
And he was mostly right: The International Space Station has been continuously occupied for more than 18 years.
But Asimov was a bit optimistic about future societies' space endeavors, predicting that humans would be "back on the moon in force" with mining operations, factories that "use of the special properties of space," observatories and even a solar power station that would beam microwaves back to Earth.
Asimov also thought we'd be on our way to establishing human settlements on the moon.
"By 2019, the first space settlement should be on the drawing boards; and may perhaps be under actual construction," he wrote. "It would be the first of many in which human beings could live by the tens of thousands, and in which they could build small societies of all kinds, lending humanity a further twist of variety."
NASA is indeed planning to send astronauts to the moon in about a decade, though it'll likely take longer than that for any nation to establish a permanent lunar settlement.
Why it's so difficult to predict the future of technology
It's possible to use observations such as Moore's law to predict the general kinds of technological advances we should see in one, two or even five years. But, as tech analyst Andy Oram of O'Reilly Media is quoted as saying in a Pew Research Center report on the future of the internet, "Beyond five years, everything is wide open."
That's mainly because it's impossible to predict the many innovative ways in which the next generation might make use of those major advances.
Ed Lyell, a former member of the Colorado State Board of Education and Telecommunication Advisory Commission, elaborated on this idea in a report from the Pew Research Center's Internet & American Life Project.
Peter Drucker wrote about the major transformations in history. The printing press, steam engine driven industrial revolution, and the then-emerging internet. His main point, that I share, is that it takes a generation, about 25 years, for the new 'thing' to real have its impact. At first society uses the new tool to better do what they have been doing. The generation raised with it finds totally new things and ways to do things. Thus we will be working in jobs that we cannot now see or define. Going through our work and play days doing things we cannot now envision or perhaps which only a few now envision, but have trouble getting others to see their vision.
Andy Samberg and Cristin Milioti get stuck in an infinite wedding time loop.
- Two wedding guests discover they're trapped in an infinite time loop, waking up in Palm Springs over and over and over.
- As the reality of their situation sets in, Nyles and Sarah decide to enjoy the repetitive awakenings.
- The film is perfectly timed for a world sheltering at home during a pandemic.
Richard Feynman once asked a silly question. Two MIT students just answered it.
Here's a fun experiment to try. Go to your pantry and see if you have a box of spaghetti. If you do, take out a noodle. Grab both ends of it and bend it until it breaks in half. How many pieces did it break into? If you got two large pieces and at least one small piece you're not alone.
But science loves a good challenge<p>The mystery remained unsolved until 2005, when French scientists <a href="http://www.lmm.jussieu.fr/~audoly/" target="_blank">Basile Audoly</a> and <a href="http://www.lmm.jussieu.fr/~neukirch/" target="_blank">Sebastien Neukirch </a>won an <a href="https://www.improbable.com/ig/" target="_blank">Ig Nobel Prize</a>, an award given to scientists for real work which is of a less serious nature than the discoveries that win Nobel prizes, for finally determining why this happens. <a href="http://www.lmm.jussieu.fr/spaghetti/audoly_neukirch_fragmentation.pdf" target="_blank">Their paper describing the effect is wonderfully funny to read</a>, as it takes such a banal issue so seriously. </p><p>They demonstrated that when a rod is bent past a certain point, such as when spaghetti is snapped in half by bending it at the ends, a "snapback effect" is created. This causes energy to reverberate from the initial break to other parts of the rod, often leading to a second break elsewhere.</p><p>While this settled the issue of <em>why </em>spaghetti noodles break into three or more pieces, it didn't establish if they always had to break this way. The question of if the snapback could be regulated remained unsettled.</p>
Physicists, being themselves, immediately wanted to try and break pasta into two pieces using this info<p><a href="https://roheiss.wordpress.com/fun/" target="_blank">Ronald Heisser</a> and <a href="https://math.mit.edu/directory/profile.php?pid=1787" target="_blank">Vishal Patil</a>, two graduate students currently at Cornell and MIT respectively, read about Feynman's night of noodle snapping in class and were inspired to try and find what could be done to make sure the pasta always broke in two.</p><p><a href="http://news.mit.edu/2018/mit-mathematicians-solve-age-old-spaghetti-mystery-0813" target="_blank">By placing the noodles in a special machine</a> built for the task and recording the bending with a high-powered camera, the young scientists were able to observe in extreme detail exactly what each change in their snapping method did to the pasta. After breaking more than 500 noodles, they found the solution.</p>
The apparatus the MIT researchers built specifically for the task of snapping hundreds of spaghetti sticks.
(Courtesy of the researchers)
What possible application could this have?<p>The snapback effect is not limited to uncooked pasta noodles and can be applied to rods of all sorts. The discovery of how to cleanly break them in two could be applied to future engineering projects.</p><p>Likewise, knowing how things fragment and fail is always handy to know when you're trying to build things. Carbon Nanotubes, <a href="https://bigthink.com/ideafeed/carbon-nanotube-space-elevator" target="_self">super strong cylinders often hailed as the building material of the future</a>, are also rods which can be better understood thanks to this odd experiment.</p><p>Sometimes big discoveries can be inspired by silly questions. If it hadn't been for Richard Feynman bending noodles seventy years ago, we wouldn't know what we know now about how energy is dispersed through rods and how to control their fracturing. While not all silly questions will lead to such a significant discovery, they can all help us learn.</p>
The multifaceted cerebellum is large — it's just tightly folded.
- A powerful MRI combined with modeling software results in a totally new view of the human cerebellum.
- The so-called 'little brain' is nearly 80% the size of the cerebral cortex when it's unfolded.
- This part of the brain is associated with a lot of things, and a new virtual map is suitably chaotic and complex.
Just under our brain's cortex and close to our brain stem sits the cerebellum, also known as the "little brain." It's an organ many animals have, and we're still learning what it does in humans. It's long been thought to be involved in sensory input and motor control, but recent studies suggests it also plays a role in a lot of other things, including emotion, thought, and pain. After all, about half of the brain's neurons reside there. But it's so small. Except it's not, according to a new study from San Diego State University (SDSU) published in PNAS (Proceedings of the National Academy of Sciences).
A neural crêpe
A new imaging study led by psychology professor and cognitive neuroscientist Martin Sereno of the SDSU MRI Imaging Center reveals that the cerebellum is actually an intricately folded organ that has a surface area equal in size to 78 percent of the cerebral cortex. Sereno, a pioneer in MRI brain imaging, collaborated with other experts from the U.K., Canada, and the Netherlands.
So what does it look like? Unfolded, the cerebellum is reminiscent of a crêpe, according to Sereno, about four inches wide and three feet long.
The team didn't physically unfold a cerebellum in their research. Instead, they worked with brain scans from a 9.4 Tesla MRI machine, and virtually unfolded and mapped the organ. Custom software was developed for the project, based on the open-source FreeSurfer app developed by Sereno and others. Their model allowed the scientists to unpack the virtual cerebellum down to each individual fold, or "folia."
Study's cross-sections of a folded cerebellum
Image source: Sereno, et al.
A complicated map
Sereno tells SDSU NewsCenter that "Until now we only had crude models of what it looked like. We now have a complete map or surface representation of the cerebellum, much like cities, counties, and states."
That map is a bit surprising, too, in that regions associated with different functions are scattered across the organ in peculiar ways, unlike the cortex where it's all pretty orderly. "You get a little chunk of the lip, next to a chunk of the shoulder or face, like jumbled puzzle pieces," says Sereno. This may have to do with the fact that when the cerebellum is folded, its elements line up differently than they do when the organ is unfolded.
It seems the folded structure of the cerebellum is a configuration that facilitates access to information coming from places all over the body. Sereno says, "Now that we have the first high resolution base map of the human cerebellum, there are many possibilities for researchers to start filling in what is certain to be a complex quilt of inputs, from many different parts of the cerebral cortex in more detail than ever before."
This makes sense if the cerebellum is involved in highly complex, advanced cognitive functions, such as handling language or performing abstract reasoning as scientists suspect. "When you think of the cognition required to write a scientific paper or explain a concept," says Sereno, "you have to pull in information from many different sources. And that's just how the cerebellum is set up."
Bigger and bigger
The study also suggests that the large size of their virtual human cerebellum is likely to be related to the sheer number of tasks with which the organ is involved in the complex human brain. The macaque cerebellum that the team analyzed, for example, amounts to just 30 percent the size of the animal's cortex.
"The fact that [the cerebellum] has such a large surface area speaks to the evolution of distinctively human behaviors and cognition," says Sereno. "It has expanded so much that the folding patterns are very complex."
As the study says, "Rather than coordinating sensory signals to execute expert physical movements, parts of the cerebellum may have been extended in humans to help coordinate fictive 'conceptual movements,' such as rapidly mentally rearranging a movement plan — or, in the fullness of time, perhaps even a mathematical equation."
Sereno concludes, "The 'little brain' is quite the jack of all trades. Mapping the cerebellum will be an interesting new frontier for the next decade."
What happens if we consider welfare programs as investments?
- A recently published study suggests that some welfare programs more than pay for themselves.
- It is one of the first major reviews of welfare programs to measure so many by a single metric.
- The findings will likely inform future welfare reform and encourage debate on how to grade success.
Welfare as an investment<p>The <a href="https://scholar.harvard.edu/files/hendren/files/welfare_vnber.pdf" target="_blank">study</a>, carried out by Nathaniel Hendren and Ben Sprung-Keyser of Harvard University, reviews 133 welfare programs through a single lens. The authors measured these programs' "Marginal Value of Public Funds" (MVPF), which is defined as the ratio of the recipients' willingness to pay for a program over its cost.</p><p>A program with an MVPF of one provides precisely as much in net benefits as it costs to deliver those benefits. For an illustration, imagine a program that hands someone a dollar. If getting that dollar doesn't alter their behavior, then the MVPF of that program is one. If it discourages them from working, then the program's cost goes up, as the program causes government tax revenues to fall in addition to costing money upfront. The MVPF goes below one in this case. <br> <br> Lastly, it is possible that getting the dollar causes the recipient to further their education and get a job that pays more taxes in the future, lowering the cost of the program in the long run and raising the MVPF. The value ratio can even hit infinity when a program fully "pays for itself."</p><p> While these are only a few examples, many others exist, and they do work to show you that a high MVPF means that a program "pays for itself," a value of one indicates a program "breaks even," and a value below one shows a program costs more money than the direct cost of the benefits would suggest.</p> After determining the programs' costs using existing literature and the willingness to pay through statistical analysis, 133 programs focusing on social insurance, education and job training, tax and cash transfers, and in-kind transfers were analyzed. The results show that some programs turn a "profit" for the government, mainly when they are focused on children:
This figure shows the MVPF for a variety of polices alongside the typical age of the beneficiaries. Clearly, programs targeted at children have a higher payoff.
Nathaniel Hendren and Ben Sprung-Keyser<p>Programs like child health services and K-12 education spending have infinite MVPF values. The authors argue this is because the programs allow children to live healthier, more productive lives and earn more money, which enables them to pay more taxes later. Programs like the preschool initiatives examined don't manage to do this as well and have a lower "profit" rate despite having decent MVPF ratios.</p><p>On the other hand, things like tuition deductions for older adults don't make back the money they cost. This is likely for several reasons, not the least of which is that there is less time for the benefactor to pay the government back in taxes. Disability insurance was likewise "unprofitable," as those collecting it have a reduced need to work and pay less back in taxes. </p>