Post #75564

2025-12-17 23:52 UTC

Every time I see a headline like this I’m reminded of the time I heard someone describe the modern state of AI research as equivalent to the practice of alchemy. Long before anyone knew about atoms, molecules, atomic weights, or electron bonds, there were dudes who would just mix random chemicals together in an attempt to turn lead to gold, or create the elixir of life or whatever. Their methods were haphazard, their objectives impossible, and most probably poisoned themselves in the process, but those early stumbling steps eventually gave rise to the modern science of chemistry and all that came with it. AI researchers are modern alchemists. They have no idea how anything really works and their experiments result in disaster as often as not. There’s great potential but no clear path to it. We can only hope that we’ll make it out of the alchemy phase before society succumbs to the digital equivalent of mercury poisoning because it’s just so fun to play with.

Replies (2)

@Grace_Schlick@lemmynsfw.com 2025-12-18 00:29

People confuse alchemy with transmutation. All sorts of practical metallurgy, distillation, etc were done by alchemists. Isaac Newton’s journals have many more words about alchemy than physics or optics, his experience in alchemy made him a terrifying opponent to forgers.

Open ##75675
@KingRandomGuy@lemmy.world 2025-12-18 02:54

Every time I see a headline like this I’m reminded of the time I heard someone describe the modern state of AI research as equivalent to the practice of alchemy. Not sure if you’re referencing the same thing, but this actually came from a presentation at NeurIPS 2017 (the largest and most prestigious machine learning/AI conference) for the “Test of Time Award.” The presentation is available here for anyone interested. It’s a good watch. The presenter/awardee, Ali Rahimi, talks about how over time, rigor and fundamental knowledge in the field of machine learning has taken a backseat compared to empirical work that we continue to build upon, yet don’t fully understand. Some of that sentiment is definitely still true today, and unfortunately, understanding the fundamentals is only going to get harder as empirical methods get more complex, and it’s much easier to iterate on empirical things by just throwing more compute at a problem than it is to analyze something mathematically.

Open ##76053