Are we alone out there? The question of whether or not other advanced civilizations exist somewhere in the vast universe has long been one of the most perplexing uncertainties in science. But new research suggests that the odds of humans being the only intelligent species to have ever existed are next to zero.


In a recent study published in the journal Astrobiology, researchers Adam Frank and Woodruff Sullivan used newly obtained data to reformulate the famous Drake equation, which calculates the probability that intelligent life currently exists in the universe, to measure the probability that intelligent life has ever existed in the universe. Introduced by Dr. Frank Drake in 1961, the original Drake equation identified specific factors thought to play a role in the development of advanced civilizations in order to estimate the number of technological civilizations that may currently exist in our galaxy. Over the past half a century, it has been the most widely accepted tool in the scientific community to predict the current existence of other advanced civilizations.

Yet three large uncertainties plague the original Drake equation. “We’ve known for a long time approximately how many stars exist,” Frank explained in a statement. “We didn’t know how many of those stars had planets that could potentially harbor life, how often life might evolve and lead to intelligent beings, and how long any civilizations might last before becoming extinct… Thanks to NASA’s Kepler satellite and other searches, we now know that roughly one-fifth of stars have planets in ‘habitable zones,’ where temperatures could support life as we know it. So one of the three big uncertainties has now been constrained.”

Although it is still completely unknown how long other civilizations might survive, a simple change of semantics to the proposed question eliminates this issue altogether. By looking at the probability of how many times in the history of the universe life has evolved to an advanced state, Frank and Sullivan estimate a one in 10 billion trillion chance that humans are the only intelligent species to have ever existed. “One in 10 billion trillion is incredibly small,” Frank said. “To me, this implies that other intelligent, technology-producing species very likely have evolved before us.”

While the odds are extremely high that we aren’t the first intelligent civilization to have ever existed, the original question still remains: Are there any other advanced civilizations that currently exist? “The universe is more than 13 billion years old,” explains Sullivan. “That means that even if there have been a thousand civilizations in our own galaxy, if they live only as long as we have been around—roughly ten thousand years—then all of them are likely already extinct. And others won’t evolve until we are long gone. For us to have much chance of success in finding another “contemporary” active technological civilization, on average they must last much longer than our present lifetime.”

  • Probability underlies much of the modern world – an engineering professor explains how it actually works
    ​Probability can explain why a coin flip has a 50/50 chance of landing heads versus tails, but it also can be used for more powerful applications.Photo credit: Monty Rakusen/DigitalVision via Getty Images
    Zachary del Rosario

    Zachary del Rosario

    Probability underpins AI, cryptography and statistics. However, as the philosopher Bertrand Russell said, “Probability is the most important concept in modern science, especially as nobody has the slightest notion what it means.”

    I teach statistics to engineers, so I know that while probability is important, it is counterintuitive.

    Probability is a branch of mathematics that describes randomness. When scientists describe randomness, they’re describing chance events – like a coin flip – not strange occurrences, like a person dressed as a zebra. While scientists do not have a way to predict strange occurrences, probability does predict long-run behavior – that is, the trends that emerge from many repeated events.

    Mathematics, Education, Explainer, Statistics, Probability, Frequency, Doing science
    We may say ‘random’ to describe strange occurrences (person dressed as zebra), but probability describes chance events (a coin flip).Photo credit: Zebras in La Paz, Bolivia by EEJCC, Own Work CC A-SA 4.0; CC BY-SA

    Modeling with probability

    Since probability is about events, a scientist must choose which events to study. This choice defines the sample space. When flipping a coin, for example, you might define your event as the way it lands.

    Coins almost always land on heads or tails. However, it’s possible – if very unlikely – for a coin to land on its side. So to create a sample space, you’d have two choices: heads and tails, or heads, tails and side. For now, ignore the side landings and use heads and tails as our sample space.

    Next, you would assign probabilities to the events. Probability describes the rate of occurrence of an event and takes values between 0% and 100%. For example, a fair flip will tend to land 50% heads up and 50% tails up.

    To assign probabilities, however, you need to think carefully about the scenario. What if the person flipping the coin is a cheater? There’s a sneaky technique to “wobble” the coin without flipping, controlling the outcome. Even if you can prevent cheating, real coin flips are slightly more probable to land on their starting face – so if you start the flip with the coin heads up, it’s very slightly more likely to land heads up.

    In both the cheating and real flip cases, you need an appropriate sample space: starting face and other face. To have a fair flip in the real world, you’d need an additional step where you randomly – with equal probability – choose the starting face, then flip the coin.

    Mathematics, Education, Explainer, Statistics, Probability, Frequency, Doing science
    The probabilities for different coin-flipping scenarios.Photo credit: Zachary del Rosario, CC BY-SA

    These assumptions add up quickly. To have a fair flip, you had to ignore side landings, assume no one is cheating, and assume the starting face is evenly random. Together, these assumptions constitute a model for the coin flip with random outcomes. Probability tells us about the long-run behavior of a random model. In the case of the coin model, probability describes how many coins land on heads out of many flips.

    But instead of using a random model, why not just solve the coin toss using physics? Actually, scientists have done just that, and the physics shows that slight changes in the speed of the flip determine whether it comes up heads or tails. This sensitivity makes a coin flip unpredictable, so a random model is a good one.

    Frequency vs. probability

    Probability differs from frequency, which is the rate of events in a sequence. For example, if you flip a coin eight times and get two heads, that’s a frequency of 25%. Even if the probability of flipping a coin and seeing heads is 50% over the long run, each short sequence of flips will come out different. Four heads and four tails is the most probable outcome from eight flips, but other events can – and will – happen.

    Frequency and probability are the same in one special setting: when the number of data points goes to infinity. In this sense, probability tells us about long-run behavior.

    Mathematics, Education, Explainer, Statistics, Probability, Frequency, Doing science
    Probabilities for all possible outcomes of eight ‘fair’ coin flips.Photo credit: Zachary del Rosario, CC BY-SA

    Applications to AI, cryptography and statistics

    Probability isn’t just useful for predicting coin flips. It underlies many modern technological systems.

    For example, AI systems such as large language models, or LLMs, are based on next-word prediction. Essentially, they compute a probability for the words that follow your prompt. For example, with the prompt “New York” you might get “City” or “State” as the predicted next word, because in the training data those are the words that most frequently follow.

    But since probability describes randomness, the outputs of a LLM are random. Just like a sequence of coin flips is not guaranteed to come out the same way every time, if you ask an LLM the same question again, you will tend to get a different response. Effectively, each next word is treated like a new coin flip.

    Randomness is also key to cryptography: the science of securing information. Cryptographic communication uses a shared secret, such as a password, to secure information. However, surprising randomness isn’t good enough for security, which is why picking a surprising word is a bad choice of password. A shared secret is only secure if it’s hard to guess. Even if a word is surprising, real words are easier to guess than flipping a “coin” for each letter.

    You can make a much stronger password by using probability to choose characters at random on your keyboard – or better yet, use a password manager.

    Finally, randomness is key in statistics. Statisticians are responsible for designing and analyzing studies to make use of limited data. This practice is especially important when studying medical treatments, because every data point represents a person’s life.

    The gold standard is a randomized controlled trial. Participants are assigned to receive the new treatment or the current standard of care based on a fair coin flip. It may seem strange to do this assignment randomly – using coin flips to make decisions about lives. However, the unpredictability serves an important role, as it ensures that nothing about the person affects their chance to get the treatment: not age, gender, race, income or any other factor. The unpredictability helps scientists ensure that only the treatment causes the observed result and not any other factor.

    So what does probability mean? Like any kind of math, it’s only a model, meaning it can’t perfectly describe the world. In the examples discussed, probability is useful for describing long-term behaviors and using unpredictability to solve practical problems.

    This article originally appeared on The Conversation. You can read it here.

  • GLP‑1 drugs may fight addiction across every major substance, according to a study of 600,000 people
    With GLP-1 drugs becoming more accessible and affordable, they could also be within reach for substance use treatment.Photo credit: Michael Siluk/Universal Images Group via Getty Images
    ,

    GLP‑1 drugs may fight addiction across every major substance, according to a study of 600,000 people

    A massive study of veterans suggests these medications may quiet cravings far beyond food.

    A patient of mine, a veteran who had tried to quit smoking for over a decade, told me that after he started a GLP-1 drug for his diabetes, he lost interest in cigarettes. He didn’t use a patch. He didn’t set a quit date. He simply lost interest. It happened without effort.

    Another patient on one of these drugs for weight loss told me that alcohol had lost its pull – after years of failed attempts to quit.

    People struggling with many addictions, ranging from opioids to gambling, are reporting similar experiences in clinics, on social media and around dinner tables. None of them started these drugs to quit. This pattern of people losing their cravings across a broad range of addictive substances has no precedent in medicine.

    But my patients were giving me an important clue. People taking GLP-1 drugs often talk about “food noise” vanishing: the constant mental chatter about food that dominated their days simply goes quiet. But my patients were reporting that it wasn’t just food: They were noticing that the preoccupation with smoking, drinking and using drugs that drives people back despite their best intentions to stop was going quiet too.

    As a physician whose patients are often on GLP-1 drugs, and as a scientist who works on answering pressing public health questions – from long COVID to medication safety – I saw a problem hiding in plain sight: Many addictions have no approved treatment. The few medications that exist are massively underutilized, and none works across all substances. The idea that a drug already taken by millions might do what no addiction treatment has done before was too important to ignore.

    My team and I set out to test whether GLP-1 drugs – medications like semaglutide (Ozempic and Wegovy) and tirzepatide (Mounjaro and Zepbound), originally developed for diabetes and then approved for obesity – could do what no existing addiction treatment does: curb craving itself.

    Our evidence strongly suggests they can.

    Biological basis of cravings

    The hormone that these drugs mimic – GLP-1 – is not only produced in the gut. It is also active in the brain, where the receptors it binds to cluster in regions governing reward, motivation and stress – the same circuitry that gets hijacked by addiction. At therapeutic doses, GLP-1 drugs cross the blood-brain barrier and dampen dopamine signaling in the brain’s core reward center, making addictive substances less rewarding.

    GLP-1 drugs seem to inhibit cravings for several different substances in multiple animal models. For instance, rodents given GLP-1 drugs drink less alcoholself-administer less cocaine and show less interest in nicotine. When researchers gave semaglutide to green vervet monkeys – primates that voluntarily drink alcohol much like humans do – the animals drank less without showing signs of nausea or changes in water intake. This suggests the drug lowered the reward value of alcohol rather than making the animals feel sick.

    From animals to people

    To find out whether these drugs have a similar effect on people, we turned to the electronic health records of more than 600,000 patients with Type 2 diabetes at the U.S. Department of Veterans Affairs – one of the largest health care databases in the world.

    We designed a study that applied the rigor of randomized controlled trials – the gold standard in medicine – to real-world data. We compared people who started GLP-1 drugs to people who did not, adjusting for differences in health history, demographics and other factors, and followed both groups for three years.

    My team and I asked two questions: For people already struggling with addiction, did the drugs reduce overdoses, drug-related hospitalizations and deaths? And for people with no prior substance use disorder, did GLP-1 drugs reduce their risk of developing one across all major addictive substances: alcohol, opioids, cocaine, cannabis and nicotine?

    What we found was striking. In the group already struggling with addiction, there were 50% fewer deaths due to substance use among those taking GLP-1 drugs compared with those who were not. We also found 39% fewer overdoses, 26% fewer drug-related hospitalizations and 25% fewer suicide attempts. Over three years, this translated to roughly 12 fewer serious events in total per 1,000 people using GLP-1 drugs – including two fewer deaths.

    Reductions of this magnitude are rare in addiction medicine – and what’s remarkable is that the finding came from drugs initially designed for diabetes, later repurposed for obesity and never intended to treat addiction.

    The drugs also appeared to prevent addiction from developing in the first place. Among people with no prior substance use disorder, those taking GLP-1 drugs had an 18% lower risk of developing alcohol use disorder, a 25% lower risk of opioid use disorder and an approximately 20% lower risk of cocaine and nicotine dependence. Over three years, this translated to roughly six to seven fewer new diagnoses per 1,000 GLP-1 users.

    With tens of millions of people already using GLP-1 drugs, the reductions in deaths, overdoses, hospitalizations and new diagnoses could translate into thousands of prevented serious events each year.

    Converging evidence

    Our findings align with a growing body of evidence.

    A Swedish nationwide study of 227,000 people with alcohol use disorder found that those taking GLP-1 drugs had 36% lower risk of alcohol-related hospitalizations. This is more than double the 14% reduction that the same study found with naltrexone, which was the best-performing medication approved for treatment of alcohol use disorder in that analysis. Other observational studies have linked GLP-1 drugs to lower rates of new and recurring alcohol use disorderreduced diagnoses and relapse in cannabis use disorderfewer health care visits for nicotine dependence and lower risk of opioid overdose.

    Meanwhile, randomized controlled trials that directly test whether these drugs help people with addiction also show promise. In one trial, semaglutide reduced both craving and alcohol consumption in people with alcohol use disorder. In another, dulaglutide reduced drinking. More than a dozen additional trials are already underway or actively enrolling, and several more are planned.

    The future of addiction treatment

    GLP-1 drugs are the first type of medication to show potential benefit across multiple substance types simultaneously. And unlike existing addiction medications, which are prescribed by specialists and remain vastly underused, GLP-1 drugs are already prescribed at enormous scale by primary care doctors. The delivery system to reach millions of patients already exists.

    The consistency of GLP-1 effectiveness across alcohol, opioids, cocaine, nicotine and cannabis suggests these drugs may act on a shared vulnerability underlying addiction – not on any single substance pathway. If confirmed, that would represent a fundamental shift in how society understands addiction and how doctors treat it.

    Some unanswered questions remain, though, about how these drugs would affect addiction. Many people who take GLP-1 drugs to treat obesity or diabetes discontinue them; afterward, their appetite typically returns and they regain the weight they lost. Whether the same rebound would occur with addiction, and what it would mean for someone in recovery to face the roar of craving again, is unknown. Nor is it clear whether the benefits persist over years of continuous use, or whether the brain adapts in ways that dampen those effects.

    Also, because GLP-1 drugs engage the brain’s reward circuitry – the same system that governs not just craving but everyday motivation – prolonged use could, in theory, dampen motivational drive in some people. Whether that might affect real-world outcomes, such as initiative, competitive drive or performance at work, remains an open question.

    What comes next

    GLP-1 drugs have not been approved for addiction, and there is not yet enough evidence to prescribe them solely for that purpose. But for millions of people already weighing whether to start a GLP-1 drug for diabetes, obesity or another approved indication, it is one more factor worth considering.

    A patient living with diabetes who is also trying to quit smoking might reasonably choose a GLP-1 drug over another glucose-lowering medication, not because it is approved for smoking cessation, but because it may help them quit, a benefit that other diabetes drugs do not offer. Similarly, for people living with obesity who also struggle with alcohol, the potential for benefit beyond weight loss could be one more reason to consider a GLP-1 drug.

    If additional trials confirm that they effectively curb cravings across addictive substances, these drugs could begin to close one of the most consequential treatment gaps in medicine. And the most promising lead in addiction in decades will have come not from a deliberate search but from patients reporting a benefit no one anticipated. Like my patient who quit smoking after a lifetime of trying, it happened without effort.

    This article originally appeared on The Conversation. You can read it here.

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