When I first came across this project on HN (early last year), I was taken aback by how impossible the project looked and how smart were people working on this. Despite seeing a few intelligent names behind the project, I subconsciously believed that this would at least take 5-10 years before a breakthrough.
Today I sit with the same amazement, taken aback again, appreciating how ridiculously awesome this is. Congratulations to the winners and everyone involved!
So many things that look insane are becoming a reality. You look at those scrolls burnt to a crisp and the idea of reading them is nonsense.
The fact I have a computer writing flowery alt text descriptions of my photos with unnerving accuracy is something I would not have predicted for another 20 years. But, here we are...
Right? Imagine trying to explain some of it to one of the ancients - so, you have this quartz sand, see?...
That would be like explaining a baking recipe by starting with protons and electrons.
State-of-the-art machine learning architectures aren't actually that complex. Diffusion models and transformers can be explained to a bright high schooler. I'm sure Archimedes and Euclid would have no problem understanding them.
What they might have a problem understanding (or even imagining) is the mind-boggling amount of computation required to make those systems do anything useful. Getting Llama to produce a single token of text takes more calculations than all of humanity did by hand during all of Classical Antiquity.
I think the quartz sand metaphor was to illustrate how advanced our silicon-based technology has become, not just the ML parts.
Imagine all the stuff... transistors, Turing/Von Neumann machines, lithography, theoretical computer science, OS and compilers, the Internet... and lastly there's modern day machine learning that builds on top of all the above.
The base level stuff isn't exactly protons and electrons, but given the nanometer scale of our chips, it's not that far away from the truth, and we (humanity) has somehow built amazing stuff on top of that.
The basic of concept how such a thing is powered (electricity) is so far removed from anything people did in ancient times that it would be hard to get them to understand that neither gods nor magic are invovled.
Smart "intellectual" people would certainly be willing to challenge basically everything they assume about nature, but I don't think your run of the mill farmer would be able to do that.
Electricity isn't much different from water. The concept of a water mill has been around a long time. So understanding the functional ideas wouldn't be all that difficult. As far as Gods I think you will have a tough case minimizing their involvement even today.
Well we are wizards who speak in arcane languages to thinking rocks to convince them to do our bidding. We speak to golems.
It would be like explaining a baking recipe just talking about wheat and flour and heat. The point is that from first principles, ML is a huge jump. From first principles, baking is not.
Sufficiently advanced technology is indestinguishable from magic...
Kudos for caring about alt tags. Blind user here. While we are at it. I was also thinking how I could make good use of the new vision models. And after a while...
https://github.com/mlang/tracktales
The fact I have a computer generate spoken narration for my MPD playlist with descriptions of album art included just blows my mind. 2023 was indeed a fucking milestone.
That's awesome. GPT vision is unreal, to me. It's neat that you can finally get the computer to describe the album covers, something that no-one would have thought to bother with previously as it seems so trivial. A lot of album covers are surreal, so you're really going to put GPT to work there!
My grandma was blind, and I just spent 6 months looking after a guy who was blind (but has now had surgery and beat me in the eye test at the doctor's!), so I think about blindness a lot when I design.
It's all about incentives. $1 million is a lot of money. The vast majority of hard problems don't have much brainpower dedicated to them, because the bang/buck ratio doesn't work out. Machine learning, math, and adjacent fields already have many careers that pay very well, so getting top-notch experts to dedicate their attention to what might be a futile endeavor is difficult.
And this isn't only about the monetary value itself, but also the fact that a large cash prize attached to a challenge boosts the prestige of finding a solution. Nobel Prizes come with about a million bucks on top of them, after all.
I'm quite confident that if someone offered $100 million for deciphering the Voynich manuscript or Linear A, we'd have a solution within 3 years.
Not to be argumentative, but $1M isn’t very much money, certainly not for a project of this scope. It’s a testament to the creativity, competence, and dedication of those involved they’ve gotten this far with such little funding. Hopefully their early success will attract more resources to this very worthy project.
it's $700k divided three ways, too. $234k is well within FAANG compensation range, but you get to work on such an awesome project.
FAANG compensation range but no benefits and 100x the risk
Make it $8m or $12m and FAANG employees can actually start to justify working on it seriously from a money perspective
Most of them are not good enough to make a dent in this task.
Most of the developers are. They might not have the right background and so it might take an extra year or two to get up to speed with the difficult areas of this project. However most developers I know have the "smarts" to switch to a different complex area and figure it out. Sure most of them are just doing standard CRUD apps that move a bit of data - but that is because that is what we need a lot of not because they can't do something else.
What do you mean by "of this scope"? The winning solution was produced by students and interns who coordinated over the Internet, in less than a year. The problem isn't scope, the problem is attracting lots of bright individuals to work on such a task (for free). And offering a substantial monetary incentive to the winner is probably the best way to do that.
And yes, $1 million is very substantial for an individual. And the cool thing about offering it as a prize (from the point of view of the organizers, that is) is they only have to pay one person or team, although potentially thousands ultimately contribute to the solution, directly or indirectly.
That's not at all what they did. They explicitly made endpoints to doll out the prizes, to ensentivize collaboration. That tactical aspect of the the whole project and how they set it up is worth highlighting on its own.
I think most of all this is a testament to just how much raw talent and intellectual potential is locked up in the winner-takes-all dynamics and shortsightedness of the stock market. Imagine the exploits and results in a world where everyone had the baseline resources and opportunities for extra funding for pursuing niche interests.
You discount passion as motivator.
But it's not clear what current tech could help with. Machine learning can't be applied to something you don't have training data for.
I'm 90% sure the people that did this project did it because they got nerd sniped by it and got to hang out with nat while earning a reasonable salary