How To Make Tomato Ketchup in Factory | How To Make Tomato Ketchup Process ▶ 5 #Shorts
How To Make Tomato Ketchup in Factory | How To Make Tomato Ketchup Process ▶ 5 #Shorts
December 5, 2023
Vacuum Sealer for Food Savers Automatic Food Sealer Machine
Vacuum Sealer for Food Savers Automatic Food Sealer Machine
December 5, 2023

Liquid Neural Networks, A New Idea That Allows AI To Learn Even After Training

December 5, 2023

Liquid Neural Networks, A New Idea That Allows AI To Learn Even After Training

December 5, 2023

Liquid Neural Networks, A New Idea That Allows AI To Learn Even After Training

Daniela Rus currently serves as the Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. Rus is a renowned Andrew (1956) and Erna Viterbi professor at CSAIL. With a passion for advancing the field of robotics, Rus has made significant contributions to areas such as autonomous vehicles, swarm robotics, and distributed algorithms. Her research and leadership have earned her numerous accolades, establishing her as a prominent figure in the world of robotics and artificial intelligence.

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  1. I'm no expert, just curious, but it seems to me like (1) a form of continous self-reflexivity regarding the specific neuronal changes produced within the liquid network in relation to the produced output effects; and (2) a mapping of the causal relationships of the temporalized relations between these internal neuronal reconfigurations and the specific output effects produced by them.

  2. When is this going to be used for NLP and AGI?

  3. Isn't this from ~4 years ago?

  4. yeah well basically they mimic focusing like we do and are robust to context change … at last !

  5. Мне пару млн долларов нужно было а не эти гниды

  6. @web3global says:

    WOW! Amazing, thanks for sharing Forbes! 🚀

  7. @guten5221 says:

    Please make something like skynet

  8. This is huge.
    Robust under data distribution variance. By targeting more task relevant features. This means less data necessary for continuous learning which is the only and super costly way to keep a model in production.

  9. I was wondering a few days ago about black boxes and now we have liquid neural networks. Amazing 😍

  10. I hate to burst your bubble, but this isn’t a new idea. It’s just the first time someone in academia wrote about it.

  11. @Jukau says:

    Isn't that another huge step in the direction of AGI?

  12. @yogiwp_ says:

    This seems like a bigger breakthrough than whatever else on the AI news in the past couple of months?

  13. I for one welcome our AI overloads

  14. @sc-uk5xg says:

    Just remember MIT was already working on this last year maybe longer. Don't let them have your biometrics. They will own you and everything you think you own. It will be as easy as hitting delete on a keyboard to totally erase you.

  15. @testales says:

    It's a little bit of cheating to give one NN a noisy camera input stream and the other a clear stream, isn't it? 😉 Either way, I'm looking forward to some implementations of this.

  16. @fpgamachine says:

    Ok if this is true is one big advance like CNN were in their time.

  17. very cool. I look forward to the many applications this can be used in. Thanks for sharing.

  18. Umm we are witnessing the next best thing in AI doing real things, wait til this becomes main stream. Props to this team.

  19. @NeonTooth says:

    This is cool and all but I recommend watching the original talk by Ramin Hasani. The salience map she shows for the traditional neuron is being made intentionally bad by introducing noise into the input image, whereas the liquid neuron example is not affected by the noise. Slightly dishonest representation of the results

  20. @azoor5881 says:

    They always start off with AIs potential in healthcare. After trying to work with AI that’s made for radiology and ophtho, I can tell you that it’s currently incompetent. Nowhere near what chatgpt can do.

  21. Hey, Any link to the paper or git repository?

  22. But, just because that first car is focused on e.g. the side of the road (which is perhaps a heuristic visualization anyway), that's not bad. What if, for example, there's a kid on the side of the road, I'd want the network to be on the lookout for that!

  23. @ItzGanked says:

    good for alignment if the arch works well

  24. that's the most impressive thing i have ever seen,kudos to the researcher

  25. @kipling1957 says:

    Has Elon seen this?

  26. @kipling1957 says:

    Relevance realization

  27. this is really cool, but damn AVs are so annoying and such a waste of money. just build public transit. we don’t need even more reasons for car usage.

  28. The entire field of AI is at risk with companies now pay-walling off their data and/or charging obscene amounts to use it. That's a major short-term spectre that will need to be dealt with.

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