Every day a new Einstein is born, and their life and choices are dictated by the level of wealth and opportunity they are born into.
We would see stories like this every week if wealth and opportunities were equally distributed.
High school student uses AI to reveal 1.5 million previously unknown objects in space.
Submitted 2 days ago by Tea@programming.dev to technology@lemmy.world
https://www.caltech.edu/about/news/exploring-space-with-AI
Comments
SoftestSapphic@lemmy.world 2 days ago
corsicanguppy@lemmy.ca 2 days ago
I didn’t see where the article was about capitalism. Did you comment the right post? It seems off-topic.
Nurse_Robot@lemmy.world 2 days ago
This doesn’t seem off topic to me. A smart person had access to the tools and support system to enable them to do something incredible, but thousands of people equally capable didn’t have the opportunity. Seems pretty easy to follow the logic
Cocodapuf@lemmy.world 1 day ago
You might not be that new Einstein…
brucethemoose@lemmy.world 2 days ago
The model was run (and I think trained?) on very modest hardware:
The computer used for this paper contains an NVIDIA Quadro RTX 6000 with 22 GB of VRAM, 200 GB of RAM, and a 32-core Xeon CPU, courtesy of Caltech.
That’s a double VRAM Nvidia RTX 2080 TI + a Skylake Intel CPU, a relative potato these days. With room for a batch size of 4096, nonetheless! Though they did run into some preprocessing bottleneck in CPU/RAM.
The primary concern is the clustering step. Given the sheer magnitude of data present in the catalog, without question the task will need to be spatially divided in some way, and parallelized over potentially several machines
MonkderVierte@lemmy.ml 1 day ago
That’s not modest. AI hardware requirements are just crazy.
fuckwit_mcbumcrumble@lemmy.dbzer0.com 1 day ago
For an individual yes. But for an institution? No.
brucethemoose@lemmy.world 1 day ago
I mean, “modest” may be too strong a word, but a 2080 TI-ish workstation is not particularly exorbitant in the research space.
Also that’s not always true. Some “AI” models, especially oldschool ones, function fine on old CPUs. There are also efforts (like bitnet) to get larger ones fast cheaply.
Cort@lemmy.world 1 day ago
So a 5090, 5950x3d & 192gb of RAM would run it on “consumer” hardware?
brucethemoose@lemmy.world 1 day ago
That’s even overkill. A 3090 is pretty standard in the sanely priced ML research space. It’s the same architecture as the A100, so very widely supported.
5090 is actually a mixed bag because it’s too new, and architecture support for it is hit and miss miss. And also because it’s ridiculously priced for a 32G card.
And most CPUs with tons of RAM are fine, depending on the workload, but the constraint is usually “does my dataset fit in RAM” more than core speed (since just waiting 2X or 4X longer is not that big a deal).
echodot@feddit.uk 1 day ago
I’ve managed to run AI on hardware even older than that. The issue is it’s just painfully slow. I have no idea if it has any impact on the actual results though. I have a very high spec AI machine on order, so it’ll be interesting to run the same tests again and see if they’re any better, or if they’re simply quicker.
brucethemoose@lemmy.world 1 day ago
I have no idea if it has any impact on the actual results though.
Is it a PyTorch experiment? Other than maybe different default data types on CPU, the results should be the same.
Endmaker@ani.social 2 days ago
I was hoping the article tell us more about the technique he developed
The model I implemented can be used for other time domain studies in astronomy, and potentially anything else that comes in a temporal format
All I gathered from it is that it is a time-series model
Deebster@programming.dev 2 days ago
I found his paper: iopscience.iop.org/article/10.3847/…/ad7fe6 (no paywall 😃)
From the intro:
VARnet leverages a one-dimensional wavelet decomposition in order to minimize the impact of spurious data on the analysis, and a novel modification to the discrete Fourier transform (DFT) to quickly detect periodicity and extract features of the time series. VARnet integrates these analyses into a type prediction for the source by leveraging machine learning, primarily CNN.
So they start with some good old fashioned signal processing, before feeding the result into a convolution neutral net. The CNN was trained on synthetic data.
FC = Fully Connected layer, so I assume they mix FC and convolution layers in their NN. I need to read the whole paper, which won’t happen right now.
freely1333@reddthat.com 2 days ago
My mans look like he about to be voted most likely to agent 47 a health insurance ceo
singletona@lemmy.world 2 days ago
Let them live in fear.
Brotha_Jaufrey@lemmy.world 1 day ago
AI accomplishing something useful for once?!
SkyeStarfall@lemmy.blahaj.zone 1 day ago
AI has been used for tons of useful stuff for ages, you just never heard about it unless you were in the space until LLMs came around
13igTyme@lemmy.world 1 day ago
Also AI is a buzzword. Before it was called Machine Learning (ML) and has been in use for the past two decades.
sentinel@lemmitor.com 2 days ago
How many are hallucinations
Voyajer@lemmy.world 2 days ago
Not every AI is a diffusion or LLM
AbidanYre@lemmy.world 2 days ago
Not every algorithm is AI either.
bradd@lemmy.world 1 day ago
Not every hallucination is generated by an LLM.
sentinel@lemmitor.com 2 days ago
Yeah but they are all complicated non-human readable collections of machine learning and neural networks
HappySkullsplitter@lemmy.world 2 days ago
Zorsith@lemmy.blahaj.zone 2 days ago
Anything but the fuckin’ metric system…
singletona@lemmy.world 2 days ago
Begging your pardon Sir but it’s a bigass sky to search.
shalafi@lemmy.world 2 days ago
Been wanting that gif and been too lazy to record it!
anzo@programming.dev 1 day ago
I havent read the paper and surely he did a great job. Regardless of that, and in principle, anyone can do this in less than hour. The trick is to get an external confirmstion for all the discoveries you’ve made.
henfredemars@infosec.pub 2 days ago
Very cool work! I read the abstract of the paper. I don’t think it needs to use the “AI” buzzword because his work is already impressive and stands on its own, though.
Deebster@programming.dev 2 days ago
It uses a neutral net that he designed and trained, so it is AI. The public’s view of “AI” seems mostly the generation stuff like chatbots and image gen, but deep learning is perfect for science and medical fields.
disguy_ovahea@lemmy.world 2 days ago
Exactly. Artificial intelligence is the parent category.
AI
shalafi@lemmy.world 2 days ago
AI is far more than LLMs. Why does everyone on lemmy think AI is nothing but?!
SpaceNoodle@lemmy.world 2 days ago
Because that’s what the buzzword has come to mean. It’s not Lemmings’ fault, it’s the shitty capitalists pushing this slop.
RememberTheApollo_@lemmy.world 1 day ago
Because actual presentation of analytical and practical AI here is rare. AI conducting analysis of medical imaging to catch tumors early isn’t something we discuss very much here, for example.
What we do get is the marketing hype, AI images, crappy AI search results, ridiculous investment in AI to get rid of human workers, AI’s wasteful power requirements, and everything else under the sun with corporations basically trying to make a buck off AI while screwing over workers. This is the AI we see day to day. Not the ones making interesting and useful scientific discoveries.
hihi24522@lemm.ee 2 days ago
The term “artificial intelligence” is supposed to refer to a computer simulating the actions/behavior of a human.
LLMs can mimic human communication and therefore fits the AI definition.
Generative AI for images is a much looser fit but it still fulfills a purpose that was until recently something most or thought only humans could do, so some people think it counts as AI
However some of the earliest AI’s in computer programs were just NPCs in video games, looong before deep learning became a widespread thing.
Enemies in video games (typically referring to the algorithms used for their pathfinding) are AI whether they use neural networks or not.
Deep learning neural networks are predictive mathematic models that can be tuned from data like in linear regression. This, in itself, is not AI.
Transformers are a special structure that can be implemented in a neural network to attenuate certain inputs. (This is how ChatGPT can act like it has object permanence or any sort of memory when it doesn’t) Again, this kind of predictive model is not AI any more than using Simpson’s Rule to calculate a missing coordinate in a dataset would be AI.
Neural networks can be used to mimic human actions, and when they do, that fits the definition. But the techniques and math behind the models is not AI.
The only people who refer to non-AI things as AI are people who don’t know what they’re talking about, or people who are using it as a buzzword for financial gain (in the case of most corporate executives and tech-bros it is both)
PoopMonster@lemmy.world 2 days ago
I don’t know why but reading this is hilarious to me, picturing the high schoolers log into chat gpt and ask it “how many unknown objects are there in space” and presenting the response as their result.