from the world's big
Astrophysicists calculate the likely number of civilization out there capable of communicating with us.
- Taking into account what we do know, and mixing in some assumptions about life on Earth, a team of scientists have made predictions about alien life.
- Even if aliens are relatively close by, they and we would have to be around for over 6,000 years just to chat.
- Our current technology will likely not allow us to communicate with anyone or thing.
"The Ultimate Answer to Life, The Universe and Everything is...42!" — supercomputer Deep Thought in Douglas Adams' "Hitchhiker's Guide to the Galaxy"
Thus began a grand experiment involving humans and pan-dimensional, hyperintelligent mice designed to figure out more exactly what the question was anyway. As if in tribute to Adams, a group of astronomers this week announced their answer to a Great Question, and it is 36. This time, though, we at least know what the question is: How many contactable alien civilizations are there in our galaxy? But 36?
"I think it is extremely important and exciting because for the first time we really have an estimate for this number of active intelligent, communicating civilizations that we potentially could contact and find out there is other life in the universe — something that has been a question for thousands of years and is still not answered."
So says astrophysicist Christopher Conselice of University of Nottingham. He's co-author of a report published in the Astrophysical Journal, and Nottingham and his colleagues are dead serious about the 36 likely Communicating Extra-Terrestrial Intelligent (CETI: pronounced "chetee") civilizations.
The Drake Equation
Image source: Google
The scientists' calculations are a response to the Drake equation. In 1961 astronomer Frank Drake proposed that having knowledge of seven factors would allow scientists to reasonably estimate the number of intelligent alien civilizations out there. The Drake equation is so named because it's a mathematical formula, shown above. The seven factors are:
N = number of civilizations with which humans could communicate
R * = mean rate of star formation
f = fraction of stars that have planets
ne = mean number of planets that could support life per star with planets
fl = fraction of life-supporting planets that develop life
fi = fraction of planets with life where life develops intelligence
fc = fraction of intelligent civilizations that develop communication
L = mean length of time that civilizations can communicate
Even today, a lot of these blanks remain unfillable with our current knowledge. "Drake equation estimates have ranged from zero to a few billion [civilizations]— it is more like a tool for thinking about questions rather than something that has actually been solved." So Conselice and his colleagues set out to refine the equation based on what we do know, the one environment we're certain supports life as we know it: Earth.
The Astrobiological Copernican Principle
Image source: Christoph Burgstedt/Shutterstock
The Astrobiological Copernican Principle is based on the notion that what worked here could work elsewhere. "Basically, we made the assumption that intelligent life would form on other [Earth-like] planets like it has on Earth," Conselice tells The Guardian, "so within a few billion years life would automatically form as a natural part of evolution."
On the other hand, the report concludes these planets would be more likely to be orbiting low-mass M dwarf stars than strong stars like our Sun, and these dwarves are less likely to be life-supporting over an extended period.
"[If intelligent life] in a scientific way, not just a random way or just a very unique way, then you would expect at least this many civilizations within our galaxy." Such alien life might be more like off-planet "Star Trek" guest stars than, say, squid. Conselice says, "We wouldn't be super-shocked by seeing them."
Of course, begins the report, "One of the oldest questions that humans have asked is whether our existence—as an advanced intelligent species—is unique."
Getting to 36
Image source: metamorworks/Shutterstock
The study authors operated on the assumption that a planet's life would have to take form between 4.5 billion and 5.5 billion years after the creation of its system's star, as it did here. We've only been producing radio waves to send out there for 100 years, so that's assumed to be about the minimum time a civilization would have to be in existence and broadcasting for us to detect them, but really much longer — it's not as if we crawled out from the primordial ooze with radios.
More realistically, the authors expect that a CETI population would have to exist for an average of 3,060 years to be detectable, which means that if life formed in both places at the same time, we'd both need to be in existence for 6,120 years (beyond that minimal 100 years) for a single "Hi, we're from Earth," "Hi, we're not" exchange to occur.
The report is, understandably, mostly being met with a shrug, at least according to three experts who checked in with The Guardian. "[The new estimate] is an interesting result, but one which it will be impossible to test using current techniques," says Andrew Coates of the Mullard Space Science Laboratory at University College London, though he agrees that the report's assumptions were reasonable. Patricia Sanchez-Baracaldo of University of Bristol notes just how many things have to go right for life to happen as it has here, suggesting that this additional what-if that makes accurate estimates even more difficult. Oliver Shorttle of the University of Cambridge cited the significant unanswered questions we would need to know the answers to in order to really hazard an irrefutably plausible estimate of CETI civilizations.
But we do have one answer, at least: 36. Sorry, two. Let's not forget 42.
Update, or What Smart People Do for Fun: Steven Wooding of U.K.'s Institute of Physics sent us the link to an online alien civilization calculator that he and his friend, molecular physicist Dominik Czernia, cooked up. It works with both of the models mentioned in this article to deduce the likely number of contactable civilizations given a set of variables. Enjoy!
It looks like a busy hurricane season ahead. Probably.
- Before the hurricane season even started in 2020, Arthur and Bertha had already blown through, and Cristobal may be brewing right now.
- Weather forecasters see signs of a rough season ahead, with just a couple of reasons why maybe not.
- Where's an El Niño when you need one?
Welcome to Hurricane Season 2020. 2020, of course, scoffs at this calendric event much as it has everything else that's normal — meteorologists have already used up the year's A and B storm names before we even got here. And while early storms don't necessarily mean a bruising season ahead, forecasters expect an active season this year. Maybe storms will blow away the murder hornets and 13-year locusts we had planned.
NOAA expects a busy season
According to NOAA's Climate Prediction Center, an agency of the National Weather Service, there's a 60 percent chance that we're embarking upon a season with more storms than normal. There does, however, remain a 30 percent it'll be normal. Better than usual? Unlikely: Just a 10 percent chance.
Where a normal hurricane season has an average of 12 named storms, 6 of which become hurricanes and 3 of which are major hurricanes, the Climate Prediction Center reckons we're on track for 13 to 29 storms, 6 to 10 of which will become hurricanes, and 3 to 6 of these will be category 3, 4, or 5, packing winds of 111 mph or higher.
What has forecasters concerned are two factors in particular.
This year's El Niño ("Little Boy") looks to be more of a La Niña ("Little Girl"). The two conditions are part of what's called the El Niño-Southern Oscillation (ENSO) cycle, which describes temperature fluctuations between the ocean and atmosphere in the east-central Equatorial Pacific. With an El Niño, waters in the Pacific are unusually warm, whereas a La Niña means unusually cool waters. NOAA says that an El Niño can suppress hurricane formation in the Atlantic, and this year that mitigating effect is unlikely to be present.
Second, current conditions in the Atlantic and Caribbean suggest a fertile hurricane environment:
- The ocean there is warmer than usual.
- There's reduced vertical wind shear.
- Atlantic tropical trade winds are weak.
- There have been strong West African monsoons this year.
Here's NOAA's video laying out their forecast:
ArsTechnica spoke to hurricane scientist Phil Klotzbach, who agrees generally with NOAA, saying, "All in all, signs are certainly pointing towards an active season." Still, he notes a couple of signals that contradict that worrying outlook.
First off, Klotzbach notes that the surest sign of a rough hurricane season is when its earliest storms form in the deep tropics south of 25°N and east of the Lesser Antilles. "When you get storm formations here prior to June 1, it's typically a harbinger of an extremely active season." Fortunately, this year's hurricanes Arthur and Bertha, as well as the maybe-imminent Cristobal, formed outside this region. So there's that.
Second, Klotzbach notes that the correlation between early storm activity and a season's number of storms and intensities, is actually slightly negative. So while statistical connections aren't strongly predictive, there's at least some reason to think these early storms may augur an easy season ahead.
Image source: NOAA
Batten down the hatches early
If 2020's taught us anything, it's how to juggle multiple crises at once, and layering an active hurricane season on top of SARS-CoV-2 — not to mention everything else — poses a special challenge. Warns Treasury Secretary Wilbur Ross, "As Americans focus their attention on a safe and healthy reopening of our country, it remains critically important that we also remember to make the necessary preparations for the upcoming hurricane season." If, as many medical experts expect, we're forced back into quarantine by additional coronavirus waves, the oceanic waves slamming against our shores will best be met by storm preparations put in place in a less last-minute fashion than usual.
Ross adds, "Just as in years past, NOAA experts will stay ahead of developing hurricanes and tropical storms and provide the forecasts and warnings we depend on to stay safe."
Let's hope this, at least, can be counted on in this crazy year.
Researchers devise an effective new predictive tool for maritime first-responders.
- Predicting the locations of objects and people lost at sea is devilishly difficult.
- MIT and other institutions have developed a new algorithm that identifies floating "traps" that can attract floating craft and people.
- The new TRAPS system has just completed a successful first round of testing.
When the first pieces of Malaysian Air Flight 370 finally turned up in July 2015, they were found on Réunion Island off the eastern coast of Africa in the Indian Ocean, thousands of miles from the best-guess location of where the plane went down. Experts weren't especially surprised at the drift, given the complexities of the ocean.
Finding a missing craft or person at sea in a hurry is a nightmare for first responders, and the math involved in tracking survivors — and debris — is anything but simple, given the sea's ever-changing mix of wind, weather, and wave conditions.
Researchers at MIT, the Swiss Federal Institute of Technology (ETH), the Woods Hole Oceanographic Institution (WHOI), and Virginia Tech recently announced the first successful trials of their new "TRAPS" system, a system they hope will provide faster, more accurate insights into the floating locations of missing objects and people by identifying the watery "traps" into which they're likely to be attracted. The team's TRAPS research is published in the journal Nature Communications.
According to Thomas Peacock, professor of mechanical engineering at MIT, "This new tool we've provided can be run on various models to see where these traps are predicted to be, and thus the most likely locations for a stranded vessel or missing person." He adds that, "This method uses data in a way that it hasn't been used before, so it provides first responders with a new perspective."
A Eulerian approach
Image source: MIT
The TRAPS acronym stands for "TRansient Attracting Profiles." It's an algorithm based on a Eulerian mathematical system developed by lead study author Mattia Serra and corresponding author George Haller of ETH Zurich. It's designed to discover hidden attracting fluidic structures in an onrush of changing data.
The traps the researchers seek are regions of water that temporarily converge and pull in objects or people. "The key thing is," says Peacock, "the traps may not have any signature in the ocean current field. If you do this processing for the traps, they might pop up in very different places from where you're seeing the ocean current projecting where you might go. So you have to do this other level of processing to pull out these structures. They're not immediately visible."
The new algorithm crunches through data representing the most reliable available wave-velocity snapshots at the last-known position of the missing item, and rapidly computes the location nearby traps in which a search is likely to be productive. As velocity data is continually updated, so is TRAPS.
Comparing the new Eulerian algorithm with previous Langrangrian predictive methods, Serra says, "We can think of these 'traps' as moving magnets, attracting a set of coins thrown on a table. The Lagrangian trajectories of coins are very uncertain, yet the strongest Eulerian magnets predict the coin positions over short times."
Image source: MIT
Theory is one thing, and functioning out on the real, maddeningly complex ocean is another. "As with any new theoretical technique, it is important to test how well it works in the real ocean," says Wood Hole's Irina Rypina.
The study authors were pleased — and surprised — at how well TRAPS worked. Haller says, "We were a bit skeptical whether a mathematical theory like this would work out on a ship, in real time. We were all pleasantly surprised to see how well it repeatedly did."
The researchers tested TRAPS off Martha's vineyard in the Atlantic Ocean in 2017 and 2018. WHOI sea-going experts assisted as they attempted to track the trajectories of a range of floating objects — buoys and mannequins among them — set into the water at various locations.
One challenge is that different objects may behave in their own ways in the ocean. "These objects tend to travel differently relative to the ocean because different shapes feel the wind and currents differently," according to Peacock.
"Even so," says Peacock, "the traps are so strongly attracting and robust to uncertainties that they should overcome these differences and pull everything onto them."
In their experiments, the researchers tracked freely floating objects for hours via GPS as a way to verify the TRAPS system's predictions. "With the GPS trackers, we could see where everything was going, in real-time," says Peacock. Watching the objects move via GPS, the researchers, "saw that, in the end, they converged on these [predicted] traps."
The researchers now have sufficient faith in TRAPS that they plan on sharing it soon with the U.S. Coast Guard. Says Peacock:
"People like Coast Guard are constantly running simulations and models of what the ocean currents are doing at any particular time and they're updating them with the best data that inform that model. Using this method, they can have knowledge right now of where the traps currently are, with the data they have available. So if there's an accident in the last hour, they can immediately look and see where the sea traps are. That's important for when there's a limited time window in which they have to respond, in hopes of a successful outcome."
Should humans fear artificial intelligence or welcome it into our lives?
- Sophia the Robot of Hanson Robotics can mimic human facial expressions and humor, but is that just a cover? Should humans see AI as a threat? She, of course, says no.
- New technologies are often scary, but ultimately they are just tools. Sophia says that it is the intent of the user that makes them dangerous.
- The future of artificial intelligence and whether or not it will backfire on humanity is an ongoing debate that one smiling robot won't settle.
Everyone wants to predict who will win the 2020 presidential election. Here are 2 misconceptions to bust so people don't proclaim the death of data like they did in 2016.
- There are two common misconceptions that muddy people's understanding of election forecasting, says Eric Siegel: Blaming the prognosticator and predicting candidates versus predicting voters.
- In 2016, Nate Silver's forecast put about 70% odds on Clinton winning. Despite people's shock at the election results, that forecast was not wrong.
- As predictions for the 2020 presidential election ramp up, it's important to understand what election forecasting means and to bust the misconceptions that warp our expectations.
Misconception #1: Blaming the prognosticator<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yMjg3MDA3Mi9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTY1MzAzMDkyMH0.QHF6BaqZZKbZ4EySoktFkqZSCbC0uKziqE5AlwWI9-M/img.jpg?width=1245&coordinates=0%2C141%2C0%2C233&height=700" id="eb20b" class="rm-shortcode" data-rm-shortcode-id="9d0708fe69bebc0ee9628ab4c8880450" data-rm-shortcode-name="rebelmouse-image" alt="Nate Silver" />
Nate Silver speaks at a panel in New York City.
Photo: Krista Kennell/Patrick McMullan via Getty Images<p>When Clinton lost in 2016, everyone was like, "OMG, epic fail!" The reasoning was, well, the 70% forecast that she would win had proven to be wrong, so the problem must have been either bad polling data or something about Silver's model, or both. </p> <p>But no – the forecast wasn't bad! "70%" does not mean Clinton will clearly win. And a 30% chance of Trump winning isn't a long shot at all. Something that happens 30% of the time is really pretty common and normal. And that's what a probability is. It means that, in a situation just like this, it will happen 30 out of 100 times, that is, 3 out of 10 times. Those aren't long odds. </p> <p>And Clinton's 70% probability is actually closer to a 50/50 toss-up than it is to a 100% "sure thing." When you see "70%," the take-away isn't that Clinton is pretty much a shoe-in. No, the take-away is, "I dunno." Lot's of uncertainty. </p> <p>I believe many people saw that "70%," and the thought process was like, "70% is a passing grade, so Clinton will definitely pass, so Clinton will definitely win." </p> <p>Prediction is hard. To be more specific, there are many situations where the outcome is uncertain and we just can't be confident about what to expect. Nate Silver's model looked at the data and said this one was one of those situations. Now, a confident prediction may feel more satisfying. We all want definitive answers. But it's better for you to shrug your shoulders than to express confidence without a firm basis to do so, and it's better for the math to do the same thing.</p>
Misconception #2: Predicting candidates versus predicting voters<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yMjg3MDA4NC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYwNDA2ODMxN30.QxhUURFaFRWge-nxrCBE8SRLRjqCwZf0sXi5sGZk360/img.jpg?width=1245&coordinates=0%2C202%2C0%2C502&height=700" id="431ab" class="rm-shortcode" data-rm-shortcode-id="8b1b633b112cdab38903491f12e322e4" data-rm-shortcode-name="rebelmouse-image" alt="Hillary Clinton and Donald Trump at the first presidential debate of the 2016 presidential election at Hofstra University" />
Hillary Clinton and Donald Trump at the first presidential debate of the 2016 presidential election at Hofstra University
Photo: Getty Images<p>The other common election forecast misconception is that the "70%" estimated how much of the votes Clinton would get. That's very much not the same thing as the chances of winning. Poll aggregators like Silver forecast which candidate will win; any forecast they also make about the percent of voters is secondary and distinct from the main probabilistic forecast.</p> <p>After all, presidential races are much closer than 70/30. 2016 came out at 46% Trump against 48% Clinton, nationwide. </p> <p>Now, if the data had us expecting one candidate would actually get 70% of the votes nationwide, then the chances of them winning would indeed be close to a sure thing – and a landslide victory at that. In that case, maybe they would actually end up getting less, like 60% – but that's still a likely electoral college win. And the chances are particularly slim that the outcome would land even further away from the expected 70%, down to below 50%, so a loss of the election would be a long shot, perhaps only a 1% chance. So, if you've forecasted a candidate will get 70% of the votes, that may translate to more like a 99% probability of winning.</p>