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Stand by the launch: The world's 1st orbiting light sail
An elegant, 400-year-old means of navigating the stars takes flight.
- The Planetary Society is about to launch LightSail 2, a crowdfunded light sail craft.
- LightSail 2 uses photons from the sun as fuel.
- Space X's Falcom Heavy rocket will carry LightSail 2 aloft, 720 kilometers up.
In a 1608 letter to his friend Galileo Galilei, the German astronomer Johannes Kepler described his idea for space travel thusly:
"Provide ships or sails adapted to the heavenly breezes, and there will be some who will brave even that void."
Observing one of the 75-year transits of Earth by what would come to be known as Halley's Comet, he'd correctly intuited that the widening of that comet's tail, or coma, was produced by sunlight pushing material out and away from the main object.
Kepler seemed to immediately see the possibilities — i.e., a light sail.
Now — no later than June 24, 2019, as of this writing — the Planetary Society will be launching what they hope will be the first controlled light sail ever to enter and maintain Earth orbit. Their crowdfunded Lightsail 2 will ride aboard a Space X Falcon Heavy rocket departing from Launch Complex 39A at NASA's Kennedy Space Center in Florida for a year-long orbit.
"This is history in the making — LightSail 2 will fundamentally advance the technology of spaceflight," says Bill Nye, CEO of the Planetary Society.
The pieces of Kepler's dream have been falling into place bit by bit since that letter to Galileo. The discovery of photons in the late 1800s by James Clerk Maxwell revealed the energetic particles in light whose momentum could be transferred to other objects.
Friedrich Zander envisioned the "tremendous mirrors of very thin sheets" propelling craft through space, and then Carl Wiley foresaw a solar sail as a shiny, reflective, parachute-like material opening in the direction of sunlight.
By 1976, Carl Sagan went on TV to show off a demonstration model of a light sail craft, enthusing about the amazing technology and its potential.
Among Sagan's students some 40 years ago was Nye, a frequent Big Think contributor. The Society was founded by Sagan, Bruce Murray and Louis Friedman in 1980. In 2005, the Society launched the world's first light sail craft, the Cosmos 1, aboard a submarine-based ICBM. Unfortunately, it was lost when the ICBM failed before allowing Cosmos 1 a chance to fly on its own.
About the Planetary Society
The Planetary Society is the world's largest non-profit space organization, crowdfunded by over 50,000 members from over 100 countries, and supported by hundreds of volunteers. The Society was founded as outlet for the general public's interest in space, a level of interest not always reflected in governmental budgets. In addition to mounting projects such as the LightSail craft, the Society serves as an educational connection between the scientific community and the general public, advocates for robust governmental funding of space programs, and provides anyone an opportunity to get involved in some real space science.
The Society’s Lightsail craft
At the center of each frankly beautiful LightSail craft is a cubesat. While we tend to think of satellites as large, bus-sized objects, they can be much smaller for simpler missions. The cubesat for the upcoming LightSail 2, for example, is about the size of a loaf of bread.
At launch, the cubesat and sails are encased in four solar panels. Once in orbit, the panels swing up into operational position, exposing the cubesat and stored sails.
The sails themselves are four shiny Mylar sheets 4.5 microns thick — that's thinner than a human hair. They're next pulled outward by four cobalt-alloy booms that extend like tape measures. The process takes about three minutes. When deployed, the triangular sails together form a square that's just 32 square meters, about the size of a boxing ring.
The primary force to be overcome by LightSail craft is atmospheric drag, its collision with gas particles in the Earth's upper atmosphere. Think of it as friction that causes a satellite to slow and thus drop from orbit. In order for a craft to catch enough photon "propellant" — and to be high enough to get away from the upper atmosphere, its orbit needs to be above about 700 kilometers.
The Society has built two LightSail craft.
Image source: Planetary Society
Around 2014, NASA offered the Society a free ride aboard an Atlas V rocket as part of the agency's Educational Launch of Nanosatellites (ELaNa) program. Even though the Lightsail craft would be placed into orbit below the necessary 700-kilometer height, the Society decided to use one of their LightSails to test the mechanics of the sail deployment system.
Dubbed "LightSail 1," the sails successfully unfurled, as this selfie taken by LightSail 1 attests.
Image source: Planetary Society
And now LightSail 2
The second craft, now known and "LightSail 2," was slightly modified — particularly its software — according to insights gleaned during the first mission. It's scheduled as of this writing to go up from Kennedy Space Center in Florida later this month aboard a SpaceX Falcon Heavy as part of the U.S. Air Force's STP-2 mission from Kennedy Space Center in Florida.
This time, LightSail 2 will be carried within another, slightly larger satellite, Prox-1, developed by students at Georgia Tech. The Prox-1 will be placed into orbit around 720 meters up, and a week later will launch LightSail 2.
After a few days of diagnostics, LightSail 2 will open up its solar arrays, and then a day later, unfurl its sails. Over the next month, it will continually re-position its sails relative to the sun to raise its orbit — this is the main part of the mission, the actual solar sailing.
Mission complete, the craft will orbit for about a year before drag takes its toll, and LightSail 2 burns up plummeting down through the atmosphere. During this year, its position will be tracked via ground-based laser ranging, and it may be visible to the naked eye. The Society will offers an online dashboard that can tell you where and when to look up to se this most elegant spacecraft.
Scientists discover what our human ancestors were making inside the Wonderwerk Cave in South Africa 1.8 million years ago.
- Researchers find evidence of early tool-making and fire use inside the Wonderwerk Cave in Africa.
- The scientists date the human activity in the cave to 1.8 million years ago.
- The evidence is the earliest found yet and advances our understanding of human evolution.
One of the oldest activities carried out by humans has been identified in a cave in South Africa. A team of geologists and archaeologists found evidence that our ancestors were making fire and tools in the Wonderwerk Cave in the country's Kalahari Desert some 1.8 million years ago.
A new study published in the journal Quaternary Science Reviews from researchers at the Hebrew University of Jerusalem and the University of Toronto proposes that Wonderwerk — which means "miracle" in Afrikaans — contains the oldest evidence of human activity discovered.
"We can now say with confidence that our human ancestors were making simple Oldowan stone tools inside the Wonderwerk Cave 1.8 million years ago," shared the study's lead author Professor Ron Shaar from Hebrew University.
Oldowan stone tools are the earliest type of tools that date as far back as 2.6 million years ago. An Oldowan tool, which was useful for chopping, was made by chipping flakes off of one stone by hitting it with another stone.
An Oldowan stone toolCredit: Wikimedia / Public domain
Professor Shaar explained that Wonderwerk is different from other ancient sites where tool shards have been found because it is a cave and not in the open air, where sample origins are harder to pinpoint and contamination is possible.
Studying the cave, the researchers were able to pinpoint the time over one million years ago when a shift from Oldowan tools to the earliest handaxes could be observed. Investigating deeper in the cave, the scientists also established that a purposeful use of fire could be dated to one million years back.
This is significant because examples of early fire use usually come from sites in the open air, where there is the possibility that they resulted from wildfires. The remnants of ancient fires in a cave — including burned bones, ash, and tools — contain clear clues as to their purpose.
To precisely date their discovery, the researchers relied on paleomagnetism and burial dating to measure magnetic signals from the remains hidden within a sedimentary rock layer that was 2.5 meters thick. Prehistoric clay particles that settled on the cave floor exhibit magnetization and can show the direction of the ancient earth's magnetic field. Knowing the dates of magnetic field reversals allowed the scientists to narrow down the date range of the cave layers.
The Kalahari desert Wonderwerk CaveCredit: Michael Chazan / Hebrew University of Jerusalem
Professor Ari Matmon of Hebrew University used another dating method to solidify their conclusions, focusing on isotopes within quartz particles in the sand that "have a built-in geological clock that starts ticking when they enter a cave." He elaborated that in their lab, the scientists were "able to measure the concentrations of specific isotopes in those particles and deduce how much time had passed since those grains of sand entered the cave."
Finding the exact dates of human activity in the Wonderwerk Cave could lead to a better understanding of human evolution in Africa as well as the way of life of our early ancestors.
If you ask your maps app to find "restaurants that aren't McDonald's," you won't like the result.
- The Chinese Room thought experiment is designed to show how understanding something cannot be reduced to an "input-process-output" model.
- Artificial intelligence today is becoming increasingly sophisticated thanks to learning algorithms but still fails to demonstrate true understanding.
- All humans demonstrate computational habits when we first learn a new skill, until this somehow becomes understanding.
It's your first day at work, and a new colleague, Kendall, catches you over coffee.
"You watch the game last night?" she says. You're desperate to make friends, but you hate football.
"Sure, I can't believe that result," you say, vaguely, and it works. She nods happily and talks at you for a while. Every day after that, you live a lie. You listen to a football podcast on the weekend and then regurgitate whatever it is you hear. You have no idea what you're saying, but it seems to impress Kendall. You somehow manage to come across as an expert, and soon she won't stop talking football with you.
The question is: do you actually know about football, or are you imitating knowledge? And what's the difference? Welcome to philosopher John Searle's "Chinese Room."
The Chinese Room
Searle's argument was designed as a critique of what's called a "functionalist" view of mind. This is the philosophy that argues that our mind can be explained fully by what role it plays, or in other words, what it does or what "function" it has.
One form of functionalism sees the human mind as following an "input-process-output" model. We have the input of our senses, the process of our brains, and a behavioral output. Searle thought this was at best an oversimplification, and his Chinese Room thought experiment goes to show how human minds are not simply biological computers. It goes like this:
Imagine a room, and inside is John, who can't speak a word of Chinese. Outside the room, a Chinese person sends a message into the room in Chinese. Luckily, John has an "if-then" book for Chinese characters. For instance, if he gets <你好吗>, the proper reply is <我还好>. All John has to do is follow his instruction book.
The Chinese speaker outside of the room thinks they're talking to someone inside who knows Chinese. But in reality, it's just John with his fancy book.
What is understanding?
Does John understand Chinese? The Chinese Room is, by all accounts, a computational view of the mind, yet it seems that something is missing. Truly understanding something is not an "if-then" automated response. John is missing that sinking in feeling, the absorption, the bit of understanding that's so hard to express. Understanding a language doesn't work like this. Humans are not Google Translate.
And yet, this is how AIs are programmed. A computer system is programmed to provide a certain output based on a finite list of certain inputs. If I double click the mouse, I open a file. If you type a letter, your monitor displays tiny black squiggles. If we press the right buttons in order, we win at Mario Kart. Input — Process — Output.
Can imitation become so fluid or competent that it is understanding.
But AIs don't know what they're doing, and Google Translate doesn't really understand what it's saying, does it? They're just following a programmer's orders. If I say, "Will it rain tomorrow?" Siri can look up the weather. But if I ask, "Will water fall from the clouds tomorrow?" it'll be stumped. A human would not (although they might look at you oddly).
A fun way to test just how little an AI understands us is to ask your maps app to find "restaurants that aren't McDonald's." Unsurprisingly, you won't get what you want.
The Future of AI
To be fair, the field of artificial intelligence is just getting started. Yes, it's easy right now to trick our voice assistant apps, and search engines can be frustratingly unhelpful at times. But that doesn't mean AI will always be like that. It might be that the problem is only one of complexity and sophistication, rather than anything else. It might be that the "if-then" rule book just needs work. Things like "the McDonald's test" or AI's inability to respond to original questions reveal only a limitation in programming. Given that language and the list of possible questions is finite, it's quite possible that AI will be able to (at the very least) perfectly mimic a human response in the not too distant future.
What's more, AIs today have increasingly advanced learning capabilities. Algorithms are no longer simply input-process-output but rather allow systems to search for information and adapt anew to what they receive.
A notorious example of this occurred when a Microsoft chat bot started spouting bigotry and racism after "learning" from what it read on Twitter. (Although, this might just say more about Twitter than AI.) Or, more sinister perhaps, two Facebook chat bots were shut down after it was discovered that they were not only talking to each other but were doing so in an invented language. Did they understand what they were doing? Who's to say that, with enough learning and enough practice, an AI "Chinese Room" might not reach understanding?
Can imitation become understanding?
We've all been a "Chinese Room" at times — be it talking about sports at work, cramming for an exam, using a word we didn't entirely know the meaning of, or calculating math problems. We can all mimic understanding, but it also begs the question: can imitation become so fluid or competent that it is understanding.
The old adage "fake it, 'till you make it" has been proven true over and over. If you repeat an action enough times, it becomes easy and habitual. For instance, when you practice a language, musical instrument, or a math calculation, then after a while, it becomes second nature. Our brain changes with repetition.
So, it might just be that we all start off as Chinese Rooms when we learn something new, but this still leaves us with a pertinent question: when, how, and at what point does John actually understand Chinese? More importantly, will Siri or Alexa ever understand you?
With the rise of Big Data, methods used to study the movement of stars or atoms can now reveal the movement of people. This could have important implications for cities.
- A treasure trove of mobility data from devices like smartphones has allowed the field of "city science" to blossom.
- I recently was part of team that compared mobility patterns in Brazilian and American cities.
- We found that, in many cities, low-income and high-income residents rarely travel to the same geographic locations. Such segregation has major implications for urban design.
Almost 55 percent of the world's seven billion people live in cities. And unless the COVID-19 pandemic puts a serious — and I do mean serious — dent in long-term trends, the urban fraction will climb almost to 70 percent by midcentury. Given that our project of civilization is staring down a climate crisis, the massive population shift to urban areas is something that could really use some "sciencing."
Is urbanization going to make things worse? Will it make things better? Will it lead to more human thriving or more grinding poverty and inequality? These questions need answers, and a science of cities, if there was such a thing, could provide answers.
Good news. There already is one!
The science of cities
With the rise of Big Data (for better or worse), scientists from a range of disciplines are getting an unprecedented view into the beating heart of cities and their dynamics. Of course, really smart people have been studying cities scientifically for a long time. But Big Data methods have accelerated what's possible to warp speed. As "exhibit A" for the rise of a new era of city science, let me introduce you to the field of "human mobility" and a new study just published by a team I was on.
Credit: nonnie192 / 405009778 via Adobe Stock
Human mobility is a field that's been amped up by all those location-enabled devices we carry around and the large-scale datasets of our activities, such as credit card purchases, taxi rides, and mobile phone usage. These days, all of us are leaving digital breadcrumbs of our everyday activities, particularly our movements around towns and cities. Using anonymized versions of these datasets (no names please), scientists can look for patterns in how large collections of people engage in daily travel and how these movements correlate with key social factors like income, health, and education.
There have been many studies like this in the recent past. For example, researchers looking at mobility patterns in Louisville, Kentucky found that low-income residents tended to travel further on average than affluent ones. Another study found that mobility patterns across different socioeconomic classes exhibit very similar characteristics in Boston and Singapore. And an analysis of mobility in Bogota, Colombia found that the most mobile population was neither the poorest nor the wealthiest citizens but the upper-middle class.
These were all excellent studies, but it was hard to make general conclusions from them. They seemed to point in different directions. The team I was part of wanted to get a broader, comparative view of human mobility and income. Through a partnership with Google, we were able to compare data from two countries — Brazil and the United States — of relatively equal populations but at different points on the "development spectrum." By comparing mobility patterns both within and between the two countries, we hoped to gain a better understanding of how people at different income levels moved around each day.
Mobility in Brazil vs. United States
Socioeconomic mobility "heatmaps" for selected cities in the U.S. and Brazil. The colors represent destination based on income level. Red depicts destinations traveled by low-income residents, while blue depicts destinations traveled by high-income residents. Overlapping areas are colored purple.Credit: Hugo Barbosa et al., Scientific Reports, 2021.
The results were remarkable. In a figure from our paper (shown above), it's clear that we found two distinct kinds of relationship between income and mobility in cities.
The first was a relatively sharp distinction between where people in lower and higher income brackets traveled each day. For example, in my hometown of Rochester, New York or Detroit, the places visited by the two income groups (e.g., job sites, shopping centers, doctors' offices) were relatively partitioned. In other words, people from low-income and high-income neighborhoods were not mixing very much, meaning they weren't spending time in the same geographical locations. In addition, lower income groups traveled to the city center more often, while upper income groups traveled around the outer suburbs.
The second kind of relationship was exemplified by cities like Boston and Atlanta, which didn't show this kind of partitioning. There was a much higher degree of mixing in terms of travel each day, indicating that income was less of a factor for determining where people lived or traveled.
In Brazil, however, all the cities showed the kind of income-based segregation seen in U.S. cities like Rochester and Detroit. There was a clear separation of regions visited with practically no overlap. And unlike the U.S., visits by the wealthy were strongly concentrated in the city centers, while the poor largely traversed the periphery.
Data-driven urban design
Our results have straightforward implications for city design. As we wrote in the paper, "To the extent that it is undesirable to have cities with residents whose ability to navigate and access resources is dependent on their socioeconomic status, public policy measures to mitigate this phenomenon are the need of the hour." That means we need better housing and public transportation policies.
But while our study shows there are clear links between income disparity and mobility patterns, it also shows something else important. As an astrophysicist who spent decades applying quantitative methods to stars and planets, I am amazed at how deep we can now dive into understanding cities using similar methods. We have truly entered a new era in the study of cities and all human systems. Hopefully, we'll use this new power for good.
A small percentage of people who consume psychedelics experience strange lingering effects, sometimes years after they took the drug.