Human Relative Was Half-Man, Half-Ape

In a recent study, scientists compared the skull of Little Foot (shown here) with that of other hominins.
Credit: Photo courtesy of the University of the Witwatersrand

The brain of one of the oldest Australopithecus individuals ever found was a little bit ape-like and a little bit human.

In a new study, researchers scanned the interior of a very rare, nearly complete skull of this ancient hominin ancestor. Hominins include modern and extinct humans and all their direct ancestors, including Australopithecus, which lived between about 4 million and 2 million years ago in Africa, and early humans of the genus Homo would eventually evolve from Australopithecus ancestors.

The modern human brain owes a lot to these small, hairy human ancestors, but we know very little about their brains, said Amélie Beaudet, a paleontologist at the University of the Witwatersrand in South Africa.

Between ape and human

Beaudet and her colleagues used micro-computed tomography (micro-CT), a very sensitive version of the same sort of technology a surgeon might use to scan a bum knee. With this tool,the researchers reconstructed the interior of the skull of a very old Australopithecus.

The skull belongs to a fossil dubbed "Little Foot," first found two decades ago in Sterkfontein Caves near Johannesburg. At 3.67 million years old, Little Foot is among the oldest of any Australopithecus ever found, and its skull is nearly intact. The fossil's discoverers think it may belong to an entirely new Australopithecus species, Live Science reported.

With micro-CT, the research team could see very fine imprints of where the brain once lay against Little Foot's skull, including a record of the paths of veins and arteries, Beaudet told Live Science. Using the skull to infer brain shape in this way is called making an endocast.

Virtual rendering of the brain endocast of "Little Foot," possibly a new species of Australopithecus.
Credit: M. Lotter and R.J. Clarke/Wits University

I was expecting something quite similar to the other endocasts we knew from Australopithecus, but Little Foot turned out to be a bit different, in accordance with its great age," Beaudet said.

Today's chimpanzees and humans share an ancestor older than Little Foot: some long-lost ape that gave rise to both lineages. Little Foot's brain looks a lot like that predicted ancestor's should look, Beaudet said, more ape-like than human. Little Foot's visual cortex, in particular, took up a greater proportion of its brain than that area does in the human brain.

In humans, Beaudet said, the visual cortex has been pushed aside to accommodate the expansion of the parietal cortex, an area involved in complex activities like toolmaking.

Changing brains

Little Foot's brain was asymmetrical, with slightly differing protrusions on each side, the researchers found. This is a feature shared with both humans and apes, and it probably indicates that Australopithecus had brain lateralization, meaning that the two sides of its brain performed different functions. The finding means that brain lateralization evolved very early in the primate lineage.

Little Foot's brain was different from later Australopithecus specimens, Beaudet said. The visual cortex, in particular, was larger compared to later Australopithecus brains. These differences hint that brain evolution was a piecemeal process, occurring in fits and starts across the brain. .

The findings will appear in a special issue on Little Foot being published in the Journal of Human Evolution.

Mathematicians Discovered a Computer Problem that No One Can Ever Solve

Austrian-born mathematician Kurt Godel at the Institute of Advanced Study.
Credit: Alfred Eisenstaedt/The LIFE Picture Collection/Getty Images

Mathematicians have discovered a problem they cannot solve. It's not that they're not smart enough; there simply is no answer.

The problem has to do with machine learning — the type of artificial-intelligence models some computers use to "learn" how to do a specific task.

When Facebook or Google recognizes a photo of you and suggests that you tag yourself, it's using machine learning. When a self-driving car navigates a busy intersection, that's machine learning in action. Neuroscientists use machine learning to "read" someone’s thoughts. The thing about machine learning is that it's based on math. And as a result, mathematicians can study it and understand it on a theoretical level. They can write proofs about how machine learning works that are absolute and apply them in every case.

In this case, a team of mathematicians designed a machine-learning problem called "estimating the maximum" or "EMX."

To understand how EMX works, imagine this: You want to place ads on a website and maximize how many viewers will be targeted by these ads. You have ads pitching to sports fans, cat lovers, car fanatics and exercise buffs, etc. But you don't know in advance who is going to visit the site. How do you pick a selection of ads that will maximize how many viewers you target? EMX has to figure out the answer with just a small amount of data on who visits the site.

The researchers then asked a question: When can EMX solve a problem?

In other machine-learning problems, mathematicians can usually say if the learning problem can be solved in a given case based on the data set they have. Can the underlying method Google uses to recognize your face be applied to predicting stock market trends? I don't know, but someone might.

The trouble is, math is sort of broken. It's been broken since 1931, when the logician Kurt Gödel published his famous incompleteness theorems. They showed that in any mathematical system, there are certain questions that cannot be answered. They're not really difficult — they're unknowable. Mathematicians learned that their ability to understand the universe was fundamentally limited. Gödel and another mathematician named Paul Cohen found an example: the continuum hypothesis.

The continuum hypothesis goes like this: Mathematicians already know that there are infinities of different sizes. For instance, there are infinitely many integers (numbers like 1, 2, 3, 4, 5 and so on); and there are infinitely many real numbers (which include numbers like 1, 2, 3 and so on, but they also include numbers like 1.8 and 5,222.7 and pi). But even though there are infinitely many integers and infinitely many real numbers, there are clearly more real numbers than there are integers. Which raises the question, are there any infinities larger than the set of integers but smaller than the set of real numbers? The continuum hypothesis says, yes, there are.

Gödel and Cohen showed that it's impossible to prove that the continuum hypothesis is right, but also it's impossible to prove that it's wrong. "Is the continuum hypothesis true?" is a question without an answer.

In a paper published Monday, Jan. 7, in the journal Nature Machine Intelligence, the researchers showed that EMX is inextricably linked to the continuum hypothesis.

It turns out that EMX can solve a problem only if the continuum hypothesis is true. But if it's not true, EMX can't.. That means that the question, "Can EMX learn to solve this problem?"has an answer as unknowable as the continuum hypothesis itself.

The good news is that the solution to the continuum hypothesis isn't very important to most of mathematics. And, similarly, this permanent mystery might not create a major obstacle to machine learning.

"Because EMX is a new model in machine learning, we do not yet know its usefulness for developing real-world algorithms," Lev Reyzin, a professor of mathematics at the University of Illinois in Chicago, who did not work on the paper, wrote in an accompanying Nature News & Views article. "So these results might not turn out to have practical importance," Reyzin wrote.

Running up against an unsolvable problem, Reyzin wrote, is a sort of feather in the cap of machine-learning researchers.

It's evidence that machine learning has "matured as a mathematical discipline," Reyzin wrote.

Machine learning "now joins the many subfields of mathematics that deal with the burden of unprovability and the unease that comes with it," Reyzin wrote. Perhaps results such as this one will bring to the field of machine learning a healthy dose of humility, even as machine-learning algorithms continue to revolutionize the world around us. "

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