but aside from that, The most interesting thing to me was that I must admit it but the level of humbleness within Sharla is quite something of another level.
Like she had PhD from UCLA in mathematics and she was teaching maths at high school, joining RAND in the side and taking the side and in some sense meaningfully contributing to the making of internet, yet still being humble all throughout.
I really enjoyed this statement: Friends who knew Sharla say she should absolutely be celebrated for her technological achievements, but not reduced to them because she did so much more with her life.
In a world so focused on achievements and advancements, money/fame. I really liked this line. Another one that I really liked: By the time Sharla Perrine Boehm died in 2023, at the age of 93, nearly two-thirds of the world’s population was using the internet that she had unknowingly helped usher in as a young computer programmer.
But throughout her decades as a teacher, mother, and community leader, she also touched many lives much more intimately. She was the woman supporting so many girls, focused not on her own legacy, but on theirs, on how much they could accomplish, now that the world was theirs.
I don't know but there is just something so profoundly fascinating when someone does good deeds when they are not visible. Just being a good person for the sake of it. I am sure her life had ups and downs but from reading this article, she did the things that she enjoyed which were programming and she was an interesting person. I do feel a bit greatful that I got to read about her and know about her.
My point is, reading the article, it seems that she did these things just because she could and that it was fun to her in some sense to her all at the same time being humble and being a person just so much more than just these achievements overall too. I do hope to take some aspects of that spirit from her hopefully too.
I sometimes get the impression that these people like Sharla only exist in a different century altogether but I sometimes feel like we might already be surrounded by people like Sharla's even in the present but just as Sharla's life, they were hidden and that is precisely why we mighn't know about them. This does give a bit of hope in humanity to me, lets hope that we are able to overcome our flaws and just get for a better future indeed.
I'm also reminded about how the guy from Project Hail Mary, despite getting the opportunity to return home to a hero's welcome, decided to stay and teach the children of Rocky's race about science. (Oops, spoiler...)
Here you go. Hope you enjoy the article, I am gonna go read it too now.
(PS: I have created htmlpipe and I have written enough about it in submissions/comments etc. so I will hopefully let the project speak for itself now but feel free to ask me any questions as I love to talk and also a minor wish but I hope that more people could use my software but no biggies if they don't as I am happy using it for myself because I built it for myself and to help others! Have a nice day)
It would be called "machine learning" because that's the buzzword du jour.
> She was teaching the network to learn how to respond to nodes dropping out.
That's just called "writing software" not "teaching the network."
> Machine learning was definitely nonexistent at that point.
Are you sure about that?
> And yet, if you look at this 1964 paper, it's kind of unquestionably what it is.
The document: https://www.rand.org/pubs/research_memoranda/RM3103.html
The claim: highly questionable.
The paper is interesting in it's own right, but, to hype it up in this way is gross.
> Are you sure about that?
Incredible statement to make, not only did machine learning exist, but neural networks existed!
The first perceptrons were built in the 50s: https://en.wikipedia.org/wiki/Perceptron
If you take a machine learning class, what is the most basic network you will probably build/learn about as an introduction? The MLP - multi-layer perceptron.
It's not even remotely obscure to know ML existed in the 50s and 60s.
I would have expected better from Scientific American. The transcript read as very repetitive.
Do we know which?
