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Woman with Rare Condition Couldn’t Hear Male Voices

Say what? Sudden hearing loss in a woman in China was unexpectedly selective.
Credit: Shutterstock
A woman in China suddenly developed an unusual condition that made her unable to hear male voices. And while that might seem enviable to some, the hearing loss could carry serious medical repercussions.

The woman, who is identified only by the surname Chen, visited a hospital after waking up one morning and being unable to hear her boyfriend's voice, Newsweek reported yesterday (Jan. 10). Chen also told doctors that the night before, she experienced ringing in her ears (a condition known as tinnitus) followed by vomiting.

At the hospital, Chen was treated by Dr. Lin Xiaoqing — a woman — who noted that while Chen was able to hear Xiaoqing's voice, she couldn't hear the voice of a nearby male patient "at all," according to Newsweek. Xiaoqing diagnosed Chen with reverse-slope hearing loss, a rare type of low-frequency hearing loss that likely impaired her ability to hear deep male voices.

Reverse-slope hearing loss (RSHL) gets its name from the shape it produces in visualizations of hearing tests — a slope that is a mirror image of the incline produced by high-frequency hearing loss, according to audiology clinic Audiology HEARS, P.C., in Cumming, Georgia. It affects an estimated 3,000 people in the U.S. and Canada — for every 12,000 people with hearing loss, only one individual has RSHL, the audiology clinic reported.

Humans detect sounds through the vibration of tiny hairs inside the ears, and over time (or because of genetics, injury or drug use) those hairs can become brittle and prone to breakage, said Dr. Michelle Kraskin, an audiologist and the assistant director of hearing and speech for Weill Cornell Medical Center at New York-Presbyterian Hospital in New York City. Kraskin wasn't involved in Chen's case.

The hairs that conduct high-frequency sounds are more delicate and because of this, they're the ones that tend to die first, Kraskin told Live Science. This explains why hearing loss more often affects our ability to hear higher-pitched sounds than lower-pitched ones, she said.

Loss of hearing of lower-pitched sounds (which is what Chen experienced) is also less common because the bass-processing portion of the cochlea — a snail-shaped structure deep in the inner ear — is very well protected, said Jackie Clark, a clinical professor with the School of Behavioral and Brain Sciences at the University of Texas at Dallas, who also wasn't involved with Chen's case.

Causes of the sudden onset of RSHL can include blood vessel problems or trauma, Clark told Live Science. Autoimmune disorders that affect the inner ear — which are thought to occur in about 1 percent of the U.S. population — may also be a cause of RSHL, Clark said. Indeed, autoimmune conditions in the inner ear can cause balance problems that may lead to vomiting — a symptom that Chen described to her doctor, Clark noted.

Though it might be amusing to imagine a world in which male voices are nonexistent, hearing loss is no laughing matter, Clark said. People who experience sudden and unexplained hearing loss should see a specialist as soon as possible.

The good news is that when RSHL is detected quickly, chances are good that the hearing loss can be reversed, Kraskin said.

"Most studies have shown that if you catch it within 48 hours, you have the best chance for recovery," she said. Treatment can involve high doses of steroids, but sometimes the condition goes away without any treatment whatsoever, she added.

In Chen's case, her doctor said that stress from working late and losing sleep caused Chen's low-frequency hearing decline, adding that rest would soon fully restore the woman's hearing, Newsweek reported.

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. "