• 18 Posts
  • 21 Comments
Joined 3 years ago
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Cake day: February 19th, 2024

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  • And rightly so. The use of the bullshit term “AI” is a red flag for pseudo-science, clickbait, grifting, etc.

    In this example, it’s extremely telling how the article retreats from the clickbait “AI” to the similarly meaningless “AI-guided” and “AI tool”, but it never explains where the “intelligence” is. The actual explanation is completely mundane data processing that they would never put in a headline.

    To design the bird flu vaccine, the team collected the genetic sequence data of all the bird flu viruses logged by surveillance programmes around the world, which take virus samples from wildlife, livestock and humans infected with bird flu.

    They built an AI tool Actually intelligent humans developed a data processing program to integrate the huge amount of genetic data and use it to design a range of ‘super-antigens’, which contain the antigen features common to this whole group of viruses. These were screened in cells to select the best one for the vaccine, which was then tested in animal trials.

    Show me a headline that talks about data processing and I’ll happily upvote.








  • This post is false. The result is unverified. Many of these so-called results have turned out to be fake. Nobody should ever trust Sam Altman and fellow grifters. They’re just trying to keep the hype train rolling until it explodes off the tracks.

    On October 26, 2026, OpenAI claimed to have disproved ETH.[10][11][12][13][14] The company is asking academics to check the work before anyone treats it as settled.[12] As of October 26, 2026, the claim has not been peer reviewed. OpenAI stated future review of the proof would be done in collaboration with complexity researchers Russell Impagliazzo, Ramamohan Paturi, and Ryan Williams.[10][3] Anthropic then also announced an internal Claude model on October 16th had also disproved ETH and published the details on GitHub. - https://en.wikipedia.org/wiki/Exponential_time_hypothesis

    It’s also worth noting that this result doesn’t change the unknown status of P/NP.

    this is closer to curing cancer than painting a picture.

    No, this is definitely not curing cancer.




  • I’m a fan of “computer science” but not the term itself. I generally divide the field into three more legit fields: math, science, and programming (aka computer enginnering?).

    Math is math. Computational math precedes actual computers. Turing, Godel, and pals were overly doing math when they invented computers.

    Science is science. It involves research, theories, experiments, evidence, etc. People do science when they research new materials and methods for making computers.

    Programming is programming. It’s when someone sits down at a computer and programs it.

    I don’t believe in “AI”, but the purported technologies are a mixture of all three. The math is very basic. The science has allowed the increases in storage and computation. And then people have written programs.

    I doesn’t seem like a mystery to me. This is how almost every field works. People in math do theory. People in science do research. People in engineering/programming use these results to create applications - whether it’s a computer program, a physical bridge, chemical engineering, etc.

    TL;DR: There’s computer math, computer science, and computer engineering. (or equivalent terms)…

    (I’m tempted to further divide the engineering between engineering programs and engineering physical computer parts. The line between science and engineering is somewhat gray for me.)








  • Condescending video title and feeding the “AI” grift.

    OP could’ve posted the video summary at least…

    AI is solving mathematical problems that have resisted some of the world’s greatest mathematicians for decades. Why are mathematicians concerned instead of celebrating? The answer reveals a fundamental misunderstanding of what makes mathematics valuable. From Euler’s seven bridges of Königsberg to the race to the Moon, I explain why solving a problem is not necessarily the same as advancing science, and why the real value of mathematical research often lies in the discoveries made along the way. More importantly, I explore a potentially devastating consequence of AI’s growing capabilities: what happens when the next generation of talented students no longer sees a reason to become mathematicians? The implications could extend far beyond mathematics and affect the future of scientific progress itself.