Orcas have been known to slap around stingrays, punt seals into the air and otherwise play with their food. But orcas, or killer whales, don’t just toy with live prey. Some also seem to enjoy destroying the corpses of their kills like piñatas.

Scientists documented this brutal activity in the Gulf of California in 2024 and 2025. In both cases, one adult orca held a dead sharp-tail sunfish steady. A second then slammed into it with enough force to smash the sunfish to smithereens.

Killer whales might pulverize sunfish to break them into smaller bites for young orcas. Or they might just find it entertaining.

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Orcas are known for brutal hunting tactics. A researcher has filmed one using a new strategy. Kathryn Ayres/Getty Images/happywhale/Footage of the World/Waluyo 66/500px #Orca #killerwhale

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Researchers shared the findings July 23 in Frontiers in Ethology.

“I was absolutely gobsmacked,” says Marianne Nyegaard. “I thought it was the coolest thing I had ever seen.” Nyegaard is a marine biologist at Auckland Museum in New Zealand who did not take part in the work. She’d seen a couple videos of orcas battering sunfish on social media. The new paper now does a deep dive into this behavior.

Nyegaard is not surprised that orcas have come up with a creative way to deal with sharp-tail sunfish. Those humungous fish can get up to a few meters (10 feet) long.

“Orcas will do all sorts of things,” Nyegaard says. “They are so clever at figuring out how to hunt and deal with a ton of different prey types.” That hunting is often a team sport. Orcas band together to ambush blue whales and whale sharks. They also team up to make waves to knock penguins off ice.

Orcas most likely smash sunfish into small pieces to help feed their young, says Francesca Pancaldi. After one of the sunfish explosions described in the paper, a young orca fed on the small bits of carnage, she notes. Pancaldi is a marine biologist who also was not involved in the study. She works at the marine science center in La Paz associated with Mexico’s National Polytechnic Institute.

Feeding may not be the only reason orcas play sunfish piñata, Pancaldi adds. “Killer whales are highly social animals.” A single activity, she explains, can provide nutrition, help orcas bond and offer a way for young orcas to learn how to hunt.

Every time someone attempts to create an unbreakable password or send a private message, they must trust a number. That number is supposed to be one that a computer has generated completely at random. Unfortunately, no computer has been able to actually guarantee that this number is truly random. Until now.

To get truly random numbers, physicists at ETH Zurich in Switzerland turned to the quantum world. They can prove that the numbers their technique spits out really are random (with only a vanishingly small chance that they are wrong).

A number that’s proven to be random can help protect private data through encryption. Here’s why. Encryption works by turning private information into a scrambled code using a secret “key.” The key is built from a random number. If that number is truly random, then no one can unscramble the information without the key. However, if it’s not truly random, a hacker could figure out the pattern behind it and reconstruct the key. And that would expose the formerly masked information.

“When randomness is weak, the keys built from it become guessable. An attacker can shrink work that should take billions of years into something practical,” explains David Holtzman. In other words, a guessing game that could take longer than the age of the universe becomes a task that a computer might pull off in months or years.

Holtzman did not take part in the new work. Still, he knows about such things. He’s worked as a cryptographer for the U.S. National Security Agency and now runs a cybersecurity firm called Naoris Quantum Protocol.

The new study cites real cases where weak randomness let attackers steal private data — and offers a new solution.

Solving the bias problem

Regular computers haven’t been able to make truly random numbers because those machines are designed to be predictable. In a normal computer, the same starting conditions should always produce the same outcome. To create a “random” number, these devices use a formula and a starting number, called a “seed.” And while the outcome may look random, it’s really only sort of random.

Scientists call this pseudo-randomness. Why? If someone guesses the seed, they can rebuild the whole sequence of steps a computer takes to make a “random” number. This will break the code meant to securely mask data.

But a natural solution exists in the quantum world. The word “quantum” describes the very smallest things in our universe, such as tiny particles. In this realm of microscopic particles, physical processes are actually random. In fact, researchers have already used quantum devices to generate randomness. But so far, no device used to capture that randomness has been perfect. Small hardware flaws have kept the results from being perfectly random.

In the past, researchers have tried to build better, less-biased devices. The ETH Zurich team found a different way: a two-part system that works even if the devices are imperfect. The setup relies on running two experiments at once, in two different places, explains physicist Renato Renner. This kept the results of one from being able to directly affect the other.

Think of the split experiments like two rubber balls. The balls look the same. But each one has its own tiny surface imperfections. Those unique features give each ball its own slight bias, making it bounce one way more often than another. Because each ball was made separately, its imperfections have nothing to do with the other’s.

In the same way, ETH Zurich’s two devices each produced random numbers with its own bias. But because the devices were separated, the biases in their outputs were completely unrelated. Renner’s team collected results from its two experiments, then combined their findings. To do this, they used something called a randomness extractor.

That extractor doesn’t fix either experiment’s output to be perfectly random on its own. Instead, it looks at the two outputs together. Then it combines them using a mathematical formula. Because the flaws in each device’s random number are unrelated, combining them cancels those flaws out. The result is truly unpredictable.

For the ETH Zurich experiment, the team used two quantum devices 30 meters (about 100 feet) apart. This separation made sure no signal — even if it were traveling at the speed of light — could pass between them before each outcome was generated. It’s one guarantee that the output from one could not influence the other.

Each device measured the outcome of a quantum event, such as a photon passing through a light-splitting device. Running the results from both through the randomness extractor increased the randomness already present in each.

Renner’s group described how it did this on May 27 in Nature.

Passing the ‘Bell test’

Proving randomness is very hard, says Nicole Yunger Halpern, who did not take part in the new work. “We can certify a random-number generator only by proving that no possible attack can have corrupted it,” explains this quantum physicist. She works at the U.S. National Institute of Standards and Technology in Gaithersburg, Md.

To confirm that the output from its extractor was genuine, the ETH Zurich team used something known as a Bell test. This physics test proves a quantum result wasn’t faked or planned in advance.

Passing this test meant something important. The team didn’t have to trust that their devices were perfect. A device might have a hidden flaw or secretly been tampered with. Either way, it couldn’t fake the exact pattern found. This pattern could only be due to genuine, unpredictable quantum behavior.

Scientists call this type of proof “device-independent.” The certainty comes from the test itself. There is no need for blind faith in the equipment or the test setup.

In 2025, a team led by Minzhao Liu at JPMorgan Chase in New York City claimed it had generated numbers with “certified randomness.” However, its claim relied on one big premise. It assumed that no classical — that is, non-quantum — computer would ever be powerful enough to copy its results. (Otherwise, a hacker using an ordinary computer might eventually guess or predict that team’s random numbers.)

“Our result,” says Renner, “does not require any [limits] on the capabilities of someone trying to predict the randomness.” As such, he claims this is the first real-world showcase that natural quantum randomness can be turned into something perfectly random. Renner and a co-worker first proposed this idea in 2012. Now, he’s helped show it’s true.

There are some skills that people think set us apart from other animals. These usually include language, tool use or the ability to do some types of math. But at least some cows can use tools and cockatoos can make their own. Other animals appear to have language. And now, it seems, we’re not even the only ones with a head for math.

A new study compared preschool kids, monkeys and groups of adults who hadn’t studied geometry (the math of shapes). All appeared to understand shapes in similar ways.

Researchers shared what they learned July 20. The work appeared in Proceedings of the National Academy of Sciences.

People spend a lot of time asking what it is that makes us different from other, similar animals, such as monkeys. “We look around, we notice how weird we are in the world and we want to know how something like this happens,” says Jessica Cantlon. A cognitive neuroscientist at Carnegie Mellon University in Pittsburgh, Pa., she studies how humans and animals think.

One thing scientists thought might make us different from other primates is how we think about geometry, notes Jialin Li. She, too, is a cognitive neuroscientist at Carnegie Mellon. Tens of thousands of years ago, she notes, people were drawing “right angles, parallel lines, equal size, equal lines.” These are basic ideas behind geometry.

That led some to suspect that the human mind must process shapes in a different way than a monkey’s — a way that has nothing to do with whether we’ve been to a geometry class.

Monkeying around with shapes

To test this, Cantlon and Li needed some shapes, clever games and a bunch of people. Oh, and monkeys, too. Don’t forget the monkeys.

The scientists studied how well different groups matched shapes in a game. They compared two groups of people: Tsimane’ people from Bolivia and Himba people from Namibia. Neither group goes to schools where they would study shapes, as most U.S. students do. The scientists also compared U.S. preschoolers and two groups of monkeys — rhesus monkeys in a lab and olive baboons in a zoo.

All had to complete tests on a screen, matching shapes and angles. The monkeys worked for treats, Li notes, and some really enjoyed the games. Preschoolers worked for stickers and were a little harder to convince. “Sometimes kids reject you,” she says. “You have to bargain with them.”

For some tests with monkeys, Cantlon and Li’s team worked with Caroline DeLong. She studies animal minds at the Rochester Institute of Technology in New York. Together, the researchers developed a Primate Portal. It let a group of olive baboons at the Seneca Park Zoo in Rochester play math games.

The portal is a cabin inside the olive baboon enclosure. Outside the hut, a screen displays games for the monkeys to play. Inside the hut sits a scientist putting games on the screen and giving out treats. One big male monkey, Kalamata, is an avid gamer. “He will play for hours a day,” DeLong says. The problem is trying to give other monkeys a chance!

How everyone shaped up

The researchers looked at how well each group did — and what mistakes they made. Then, they compared those mistakes to two computer models. The models were meant to mimic different ways of thinking about shapes.

Both monkey species and all of the humans made similar types of mistakes. This hinted that their minds were thinking about shapes in similar ways.

“Humans are not unique in this,” Li concludes, “but we are better.”

It’s exciting to see scientists looking at how geometric thinking came to be, says Moira Dillon, who did not take part in the study. She works at New York University in New York City, where she studies how minds develop. A model can offer one way to explain something, she says. But computer models aren’t minds. And, she notes, scientists can’t get into minds to follow how a monkey or kid thinks while playing a math game. So, she says, “I think there still remains the question of whether the mind is doing it that way.”

The new work used only two computer models, points out Véronique Izard. “Could they model it even better?” she wonders. Perhaps monkeys and humans seem very similar now, but a different model might find differences that these two missed. Izard works at the University of Paris Cité and the National Centre for Scientific Research in France. There, she studies how minds develop.

A primate portal

Preschoolers weren’t the only kids involved in this research. Elementary school students are now programming games for the baboons.

To test shape knowledge, for instance, monkeys played a game called match-to-sample. A shape pops up, like a square. Then two shapes pop up, a square and a triangle. The monkey needs to pick which shape matches the first. If the monkey gets it right, it receives a piece of fruit cereal.

“It’s like a weird Froot Loop, and it does not taste good,” says Michael Sufra. Michael, 11, is a student at QUEST Elementary School in Hilton, N.Y. He coded his own match-to-sample games for monkeys. “It’s just really fun to make your own games,” he says.

He was part of a program where every kid got to code their own match-to-sample game. Michael used rocket shapes and a cartoon cat. Not only do students have to think about code, but also about the pictures, explains Cora Dimino, 12. If images are too much alike, “monkeys can’t really do that,” she says. (Her game had monkeys match animal images in different colors.)

Cora and Michael are two out of several hundred kids who have been able to make monkey games. They also watched monkeys play them. “There’s real joy in their faces when they see that,” DeLong says. And she’s shown that coding games for monkeys helps kids get better at problem-solving. DeLong hopes to even use some of the kids’ games in her monkey research.

Both Cora and Michael enjoy coding and were interested to learn that games like theirs showed that monkeys think about math much as we do. “I feel like monkeys are pretty smart, based off of what we saw,” Cora says. “It’s justice for the monkeys.”

Mammal (noun, “MAM-mal”)

Mammals are a diverse group of animals. They include about 6,600 known, living species. Many of them, including humans, cats and dogs, live on land. Others, such as dolphins and whales, live underwater. And some — bats — even fly.

Mammals tend to share a few distinguishing traits. All are vertebrates. That means they have a backbone. Almost all have fur or hair, even if it is just a tiny bit. Most mammals give birth to live young. And mother mammals produce milk to feed their babies.

Some exceptions to these rules do exist. Whales, for instance, are mostly hairless. This helps them swim faster. Even so, many whales have whiskerlike hairs on their chins or heads to detect their surroundings.

Biologists sort mammals into three groups based on how they reproduce. Those groups are placentals, marsupials and monotremes.

Most mammals are placentals. These mammals give birth to live, somewhat developed, young. Humans are placental mammals.

Marsupials come with built-in pouches and give birth to live, less-developed young. Kangaroos, for example, give birth to babies about the size of jellybeans. Infant kangaroos then clamber into their mother’s pouch as a safe harbor to grow. Most marsupials live in Australia. North America’s only native marsupial is the Virginia opossum.

Monotremes are a weird bunch that break some rules of being a mammal. They do not give birth to live young. Instead, they lay eggs. Only five species of these mammals exist today: the duck-billed platypus and four kinds of echidnas.

In a sentence

The urine from marine mammals can help disperse nitrogen and other nutrients throughout the ocean.

Check out the full list of Scientists Say.