It is the refrain of teachers everywhere preparing their students for standardised tests: “Make an educated guess.” An answer is always better than no answer, they say, and if you can apply what you know to the question to rule out some of the choices, even better. Of course, scientific research isn’t a multiple-choice exam, and its bedrock is the idea that an educated guess without solid evidence just isn’t good enough. But the concept is still a useful one, particularly when our questions outpace the technology we currently have to answer them.

The key part of the phrase “educated guess” is the first word. Put another way, a guess based on nothing may be worse than no guess at all. “But Leah,” I hear you asking, “Do you have a framework, or a favourite example, for educated guesses?” And just because you are clamouring for it, and I’m the kind of person who has favourite equations, I will tell you about it. The Drake equation, which turns 65 years old this month, is an incredibly useful tool for framing our thinking about life in the cosmos – even though, at its core, it is simply a method for making an educated guess.

Astrobiologist Frank Drake came up with his eponymous equation in 1961, before convening the first scientific conference on the search for extraterrestrial intelligence (SETI). According to written by his daughter, science journalist Nadia Drake, he sketched out the equation on a bit of a whim to get the few attendees of the meeting talking and is, to this day, astonished by how important it is now considered.

The equation has seven key terms, and when multiplied together, they provide an estimate of N: the number of intelligent civilisations in the Milky Way galaxy with whom we might someday be able to communicate. It looks like this:

N = R_* × f_p × n_e × f_l × f_i × f_c × L

The first few terms are relatively simple. R_* is the rate of star formation in our galaxy, which has been measured at about 1 to 3 stars per year. The fraction of those stars with planetary systems is f_p, and we now think that number is about 1 as well – most stars seem to have planets. The number of planets per star with an environment suitable for life is n_e, and that is where things start to get more complicated. “Suitable for life” is a broad idea, and one that’s difficult to define. Even the most basic requirement – that the planet be in its star’s habitable zone, where temperatures allow liquid water on the surface – is required for Earth-like life, as far as we know, but it’s certainly possible that other lifeforms have different needs. In our own solar system, there are either 2 or 3 planets in the habitable zone, depending how you slice it – but Venus and Mars don’t seem particularly suitable for life, and there are several moons in the outer solar system that may well be.

That is the first time the equation forces us to make an educated guess. The second comes hot on its heels with f_l, the fraction of the habitable planets that go on to develop life. On this, we have an even smaller sample size. We haven’t found life anywhere but Earth, so this number could be very small, or it could be 1 – remember, this includes life that arises at any point in the history of the cosmos. But until we do find life elsewhere, it will be more of a philosophical argument than anything else. The same is true of the next two terms of the Drake equation: f_i, which denotes the fraction of life that is intelligent – the definition of which is also wily – and f_c, the fraction of intelligent lifeforms that eventually broadcast their existence into space and are thus capable of communicating with us. The final term is L, the length of time in which a civilisation of intelligent lifeforms sends detectable signals out into space. As the astronomer Carl Sagan pointed out, this depends heavily on whether such civilisations can avoid self-destruction, which isn’t something we can know for sure – but we make our best guess based on the only evidence we have: human history.

If you combine all the lowest estimates, N could be one-trillionth or even lower, implying that we are totally alone in our galaxy. If you combine the highest estimates, there could be tens of millions of civilisations in the Milky Way that we could communicate with, which to me would say it’s only a matter of time before we find them. At Drake’s original SETI meeting, the researchers set N between 1000 and 100,000,000, and the lower end of that sounds about right to me – an estimate that, like those at the meeting, is based largely on my not-particularly-scientific opinions about how long our civilisation might last.

But when it comes down to it, the number itself isn’t the important part of the equation; the important part is the process of estimating each of its terms. In doing so, we force ourselves to consider some of the biggest questions in science: How similar are other places in the universe to our own cosmic neighbourhood? How likely are those most-similar places to develop life? What is life, anyway? What does life truly need? What is intelligence? How can we search for it? And how long will we be capable of searching for it? None of those questions are answered definitively, but pondering them can help us direct our scientific research – and more than that, it gives us an idea of our place in the grand scheme of the universe. That’s why teachers tell their students to make an educated guess, and it’s why the Drake equation is still considered so important. The value in an educated guess is in taking account of what you know and what is still out there, waiting to be known.

We may be on the verge of solving the mystery of the Martian moons. On 20 October, the Martian Moons eXploration (MMX) spacecraft launches towards the larger of the two moons, Phobos, with the goal of bringing back a sample that may finally explain how Phobos and its sibling, Deimos, formed.

MMX will launch from Tanegashima Space Center in Japan. If all goes to plan, it should arrive at Mars in August 2027, entering an orbit that keeps it alongside Phobos so it can build a detailed map of the surface. Along the way, it will also observe the surface of Deimos.

Once the map is complete enough, the spacecraft’s operators at the Japan Aerospace Exploration Agency (JAXA) will use it to pick out a spot on Phobos for MMX to land. During one or two brief landings, it will take on two tasks: to drop off a small rover called IDEFIX and to pick up samples. The rover is the first anyone has ever placed on such a small cosmic body, so part of its purpose aside from scientific observations is to confirm that a wheeled rover even works on an object with such low gravity.

The samples, though, are the most important part of the mission. MMX has two different sampling devices: one that shoots a puff of gas onto the surface, blowing dust and pebbles back up into a sampling container, and another that blasts a hollow cylinder into the ground to take a sample a several centimetres deep. After the samples are taken, they will be stowed away inside the spacecraft, which will then head back towards Earth. It is expected to arrive home in 2031.

Once these samples get back to Earth and are analysed by scientists on the ground, they should finally solve the mystery of how Phobos and Deimos formed. For more than a century, researchers have debated whether the two are captured asteroids or formed from an impact on Mars, similar to how Earth’s moon formed. On first look, their shapes and surfaces resemble asteroids, but their circular orbits around Mars’s equator are more consistent with the impact hypothesis. Figuring out what they are made of should settle it once and for all.

Free newsletter

Sign up to Launchpad

Sign up to newsletter

This will also help illuminate what the solar system was like billions of years ago when these strange moons formed. “Having the answer will help illuminate that early era when Earth and the other inner planets may have been acquiring much of their water and organic compounds,” said , part of the NASA team that is collaborating on the MMX mission, in a .

If we are very lucky, the samples from Phobos may even contain bonus bits of dust and debris from Mars itself. Impacts on the planet could have launched material into space, which would have collected on the moon’s surface and been preserved there for billions of years. That means that these samples could illuminate not just the history of Phobos and Deimos, but also of Mars.

Gatekeeping is a dirty word. For decades, the internet has steadily eroded the power of gatekeepers and democratised access to information. Attempts to resist that are seen as censorship or elitism, neither of which anyone wants to be accused of.

But our new AI era may flip the script. Social media gives anyone a voice, breaking the old model of one-to-many broadcasting from institutions, but no one is forced to listen to this cacophony. Large language models that extrude text at speed are now changing this, with institutions under a many-to-one assault by information they must attend to.

This shows up in many forms – indeed, including in literal forms, as people use AI in their bureaucratic interactions with . These systems can’t deal with the volume of information and are clogging up. The implicit gatekeepers that kept such submissions under control – time, ability, access – have fallen to AI.

The same thing is happening in science. Mathematicians are calling for more mathematicians in the face of an AI onslaught. Meanwhile, the preprint server arXiv has seen submissions double in the past two years. Although preprints aren’t formally peer-reviewed, arXiv’s small team ensures minimum standards, and it is overwhelmed. In response, it recently announced it would limit submissions to two per month – still remarkably high, with few scientists producing 24 papers a year of any quality.

A kind of gatekeeping is the only way institutions can survive the AI era

This kind of gatekeeping is the only way institutions can survive the AI era, unless we want to fully hand over the reins to AI and let it handle inputs as well as outputs. But there are good reasons not to. As we report, tools for verifying mathematical conclusions are at risk from attack by AI, so keeping a human in the loop remains the only way for us to have a handle on the truth. That goes even more so for areas that are harder to verify than mathematics, which is essentially everything. So let’s cheer on the gatekeepers – they are going to have a lot of work to do.