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How a 27 Year-Old Poet Became the World's First Computer Programmer

Today's video is part of a series on female genius, in proud collaboration with 92Y's 7 Days of Genius Festival.

Maria Popova:  Augusta Ada King, Countess of Lovelace, was born Ada Byron on December 10th 1815 and is known today simply as Ada Lovelace. She is celebrated as the world's first computer programmer, the first person to marry the mathematical computational capabilities of machines with the poetic potentialities of symbolic logic. This novel combination was in no small part a function of Ada's unusual upbringing. She was the daughter of a reserved but mathematically gifted mother and the only legitimate child of the great romantic poet and notorious playboy Lord Byron. But Ada never actually met her father; her parents separated when she was only five years old and Lord Byron died in Greece when he was 36 and Ada was eight. Her mother decided to raise Ada all by herself and made a great effort to eradicate any trace of her father's ill influence, which meant removing all poetry from the little girl's life because she believed that poetry was the root of the Lord Byron's vice. So instead she immersed little Ada in math and science from the age of four. And by the time Ada was 12 she had grown fascinated with mechanical engineering. And at the age of 12 she wrote a book titled Flyology, in which she illustrated with her very own diagrams her plan to build a flying apparatus. But even so she felt that the poetic part of her was being repressed by her mother's insistence on science and one day famously quipped, and this is how teenage girls rebelled in the 1800s, she told her mother that she was going to pursue poetical science. 

Ada Lovelace struck up a friendship with the brilliant but eccentric Charles Babbage, who at the time was working on strange inventions that one day would have him celebrated as the father of the computer. Their collaboration was an extraordinary union of software and hardware. Lovelace brought the poetical science and Babbage the mechanical engineering for the machine. In 1843 she translated a scientific paper by an Italian military engineer adding to it seven footnotes. Together they measured 65 pages or two and a half times the length of the original paper. In one of those footnotes Lovelace wrote what is considered the first complete computer program, which made it the world's first paper on computer science and made Lovelace the world's first computer programmer. She was 27 years old.

 

This video is part of a series on female genius, in proud collaboration with 92Y's 7 Days of Genius Festival.


The story of Ada Lovelace, the world's first computer programmer, begins with a mathematically gifted mother and, as father, the Romantic poet Lord Byron. Notorious for his philandering, Byron contributed the strong poetical streak to his daughter's worldview. Lovelace's interest in poetry, however, was something her mother wanted stamp out, surrounding Lovelace with mathematics at the exclusion of the arts. But when Lovelace met Charles Babbage, the mechanical engineer behind the first computer, she found an outlet for her creativity, writing the first complete computer algorithm and becoming the world's first computer programmer, all at the age of twenty-seven.

Does conscious AI deserve rights?

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A new hydrogel might be strong enough for knee replacements

Duke University researchers might have solved a half-century old problem.

Photo by Alexander Hassenstein/Getty Images
Technology & Innovation
  • Duke University researchers created a hydrogel that appears to be as strong and flexible as human cartilage.
  • The blend of three polymers provides enough flexibility and durability to mimic the knee.
  • The next step is to test this hydrogel in sheep; human use can take at least three years.
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Hints of the 4th dimension have been detected by physicists

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Predicting PTSD symptoms becomes possible with a new test

An algorithm may allow doctors to assess PTSD candidates for early intervention after traumatic ER visits.

Image source: camillo jimenez/Unsplash
Technology & Innovation
  • 10-15% of people visiting emergency rooms eventually develop symptoms of long-lasting PTSD.
  • Early treatment is available but there's been no way to tell who needs it.
  • Using clinical data already being collected, machine learning can identify who's at risk.

The psychological scars a traumatic experience can leave behind may have a more profound effect on a person than the original traumatic experience. Long after an acute emergency is resolved, victims of post-traumatic stress disorder (PTSD) continue to suffer its consequences.

In the U.S. some 30 million patients are annually treated in emergency departments (EDs) for a range of traumatic injuries. Add to that urgent admissions to the ED with the onset of COVID-19 symptoms. Health experts predict that some 10 percent to 15 percent of these people will develop long-lasting PTSD within a year of the initial incident. While there are interventions that can help individuals avoid PTSD, there's been no reliable way to identify those most likely to need it.

That may now have changed. A multi-disciplinary team of researchers has developed a method for predicting who is most likely to develop PTSD after a traumatic emergency-room experience. Their study is published in the journal Nature Medicine.

70 data points and machine learning

nurse wrapping patient's arm

Image source: Creators Collective/Unsplash

Study lead author Katharina Schultebraucks of Columbia University's Department Vagelos College of Physicians and Surgeons says:

"For many trauma patients, the ED visit is often their sole contact with the health care system. The time immediately after a traumatic injury is a critical window for identifying people at risk for PTSD and arranging appropriate follow-up treatment. The earlier we can treat those at risk, the better the likely outcomes."

The new PTSD test uses machine learning and 70 clinical data points plus a clinical stress-level assessment to develop a PTSD score for an individual that identifies their risk of acquiring the condition.

Among the 70 data points are stress hormone levels, inflammatory signals, high blood pressure, and an anxiety-level assessment. Says Schultebraucks, "We selected measures that are routinely collected in the ED and logged in the electronic medical record, plus answers to a few short questions about the psychological stress response. The idea was to create a tool that would be universally available and would add little burden to ED personnel."

Researchers used data from adult trauma survivors in Atlanta, Georgia (377 individuals) and New York City (221 individuals) to test their system.

Of this cohort, 90 percent of those predicted to be at high risk developed long-lasting PTSD symptoms within a year of the initial traumatic event — just 5 percent of people who never developed PTSD symptoms had been erroneously identified as being at risk.

On the other side of the coin, 29 percent of individuals were 'false negatives," tagged by the algorithm as not being at risk of PTSD, but then developing symptoms.

Going forward

person leaning their head on another's shoulder

Image source: Külli Kittus/Unsplash

Schultebraucks looks forward to more testing as the researchers continue to refine their algorithm and to instill confidence in the approach among ED clinicians: "Because previous models for predicting PTSD risk have not been validated in independent samples like our model, they haven't been adopted in clinical practice." She expects that, "Testing and validation of our model in larger samples will be necessary for the algorithm to be ready-to-use in the general population."

"Currently only 7% of level-1 trauma centers routinely screen for PTSD," notes Schultebraucks. "We hope that the algorithm will provide ED clinicians with a rapid, automatic readout that they could use for discharge planning and the prevention of PTSD." She envisions the algorithm being implemented in the future as a feature of electronic medical records.

The researchers also plan to test their algorithm at predicting PTSD in people whose traumatic experiences come in the form of health events such as heart attacks and strokes, as opposed to visits to the emergency department.

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