The Future of Machine Learning: A New Breakthrough Technique (MLC)

Submitted ⁨⁨1⁩ ⁨year⁩ ago⁩ by ⁨A_A@lemmy.world⁩ to ⁨technology@lemmy.world⁩

Breakthrough Technique: Meta-learning for Compositionality

scitechdaily.com/the-future-of-machine-learning-a…

How MLC Works
In exploring the possibility of bolstering compositional learning in neural networks, the researchers created MLC, a novel learning procedure in which a neural network is continuously updated to improve its skills over a series of episodes. In an episode, MLC receives a new word and is asked to use it compositionally—for instance, to take the word “jump” and then create new word combinations, such as “jump twice” or “jump around right twice.” MLC then receives a new episode that features a different word, and so on, each time improving the network’s compositional skills.

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A_A@lemmy.world ⁨1⁩ ⁨year⁩ ago
Hey folks, I believe this is really big.

Traditional deep neural network’s training requires millions of example and so, despite its great success, is immensely inefficient.

Now what if learning of these machines was as fast or faster than a human’s ? Well, it seems this is it.

Look at how large language models are disruptive for many sectors of society. This new technology could accelerate the process exponentially.

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- TropicalDingdong@lemmy.world ⁨1⁩ ⁨year⁩ ago
  
  Traditional deep neural network’s training requires millions of example and so, despite its great success, is immensely inefficient.
  
  Is this a limited advancement in training techniques? Right now I’m working on several types of image classification models. How would this be able to help me?
  
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  - A_A@lemmy.world ⁨1⁩ ⁨year⁩ ago
    Sorry, I just read a lot… but I don’t work in this field.
    
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- registrert@lemmy.sambands.net ⁨1⁩ ⁨year⁩ ago
  It’s over for us useless eaters, no matter how useful one is we will always be useless compared to what’s coming.
  
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  - TropicalDingdong@lemmy.world ⁨1⁩ ⁨year⁩ ago
    
    It’s over for us useless eaters
    
    Look at this guy over here sucking up all that oxygen.
    
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  - A_A@lemmy.world ⁨1⁩ ⁨year⁩ ago
    There is still some hope ; maybe the machines will have more compassion than humans or maybe we are inside the matrix already as useful parts.
    There are so many unknowns in the future and our insights are so limited.
    
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- ExLisper@linux.community ⁨1⁩ ⁨year⁩ ago
  
  Now what if learning of these machines was as fast or faster than a human’s ?
  
  What do you mean? It’s already faster than human’s. I takes years for a person to learn basic language and decades to gain expert knowledge in any field.
  
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  - A_A@lemmy.world ⁨1⁩ ⁨year⁩ ago
    What is meant here (and said as such in the article) is that humans can learn from a single example while deep neural networks takes thousands or millions (of examples) to learn.
    
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DigitalMus@feddit.dk ⁨1⁩ ⁨year⁩ ago
While in not in the field either, I do know that it is quite unusual in computer science academics to publish in actual peer reviewed journals. This is because it can be a long process, and the field is very fast moving, so your results would be outdated by the time you publish. Thus, a paper is typically synonymous with a conference proceeding, and can be found on arxiv. I found this Paper on the arxiv from 2017/2018 which seems to be when this paper was originally published for the scientific community and presented at a very “good” (if I had to guess) conference. Google scholar says this paper has 650 citations, so it probably has had quite some impact. However, I would guess this method is well known and is already implemented in many models, if it was truly disruptive.

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- KingRandomGuy@lemmy.world ⁨1⁩ ⁨year⁩ ago
  For reference, ICML is one of the most prestigious machine learning conferences alongside ICLR and NeurIPS.
  
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- Chobbes@lemmy.world ⁨1⁩ ⁨year⁩ ago
  To be clear, the papers at conferences undergo a peer review process as well. There are journal publications in CS, but a lot of publishing is done through conferences. Arxiv, while a great resource, has little to do with the conferences and it is worth noting that the papers on arxiv do not go through a peer review process (but are often published at conferences where the paper has gone under peer review — some papers on arxiv may be preprint versions from before the peer review process).
  
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- A_A@lemmy.world ⁨1⁩ ⁨year⁩ ago
  Good to know, Thanks.
  Yours is the type of comment I was really hoping to read here.
  
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  - DigitalMus@feddit.dk ⁨1⁩ ⁨year⁩ ago
    This certainly could be part of the motivation for publishing it this way, to make themselves more noticed by the big players. Btw, publishing in open source nature is expensive, it’s like 6-8000 euro for the big ones, so there definitely is a reason.
    
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