I’m really curious how that would work. It seems very constraining to watermark text. Any existing solutions? For audio and pictures it seems pretty straightforward but for text?

Can chatgpt understand science? I heard it was given science papers, but can it help scientists in their work? Can it give scientific hints?

I think ideas are cheap (“benchmark of AGI-like capabilities”), and this particular execution of the idea (closing a window in a browser?) isn’t really good in any way

I will upload the data and accompanying website soon. What do you think about the idea?

Hi everyone! This is not a technical question, but does anyone know how to find the accepted papers list for AAAI23? (or a reliable way for any ML/AI conferences)

I work in an academic research unit and finding any accepted papers list is a mess, unless it’s readily available from a conference or on open review! I catalogue all our papers by funding sources, individual projects, authors, conferences, and about 10 other data points. Any advice is greatly appreciated! Have an awesome day everyone!

Not really, I tried Chat GPT a few days ago. Thus I gave it a theme in which I had written an Essay before and asked it to rewrite it. I sent both texts to my father, who knows my writing style, and he was unable to differentiate who wrote which one. To be fair, you can tell the AI to give you a whole paragraph in other words, which often improves the language.

That’s actually a really good idea. Would you be willing to pay for such a feature? Something like 1$ per paper? That would cover the cost for the GPT tokens

A lot of folks here already mentioned online learning and the resources for it. However I am going to offer a very hacky solution inspired by idea of boosting. Suppose you had a regression model already trained. Make prediction for the new training batch and calculate the errors. Now train a new random forest model for the residual errors. For inference pass the features into the first model. For inference just pass the features to both models and sum the results.

shoot the hostage

The whole "benchmark" is just a Readme? What is this nonsense

Does this not already exist in permutation_importance() in sklearn? That is also calculating the feature importance based on permutated data. I think the technique was first described in Olden et Al. 2004, so I would be surprised if there aren't even more implementations by now.

Or am I missing something different here?

Help. Can't install sklearn?

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can i upload papers to use its wizardy?

> (12c/kWh)

I wish. It is 6x more expensive here...

Search for "online random forests" there quite a few papers and articles on the subject. I assume they aren't simple usage of normal RF-s, it wouldn't worth publishing a paper on the subject.

This is called Online Learning, as opposed to Batch Learning. It’s a somewhat neglected topic in terms of available packages, but there is one here (it has decision trees, not RF):

https://github.com/online-ml/river

There is a nice interview with the author on the ML Podcast

https://podcasts.apple.com/us/podcast/the-machine-learning-podcast/id1626358243?i=1000577393019

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"legendary"..? really..?

One application I can think of is learning on edge. There is an industry fashion to embed AI inference capabilities in the newer ARM chips. The so called NPUs. Which are simplified GPUs optimized only for inference (forward passes). Such an algorithm would enable them to learn using only forward passes, hence without requiring backpropagation.

Another possibility I think is ability to train one layer at a time, which diminishes GPU memory requirements.

And probably more important it opens the gates for all kind of not yet seen network architectures, topologies and training methods that do not require fully differentiable pathways.

edit: regarding the brain inspired part.. well you can dismiss it as AI's reversed cargo cult - if it imitates some properties of the brain it should act like the brain, but I would be cautious to attribute Hinton this kind of thinking. Brains are very different from ANNs and trying to emulate their properties could provide insights on how they work.

Do you think is better to specialize in just in one area in ML like recommendation system or try to study other ML areas (NLP, time series, etc) for freelance ?

Hinton's original paper discusses the computational advantages this algorithm can provide. See page 14 of this PDF: https://www.cs.toronto.edu/~hinton/FFA13.pdf . Today though there isn't hardware readily available that will exploit these advantages so they'll remain theoretical for now. If there seems to be enough promise that such an algorithm will converge anywhere close to backprop, we'll see some attempts there, probably starting with some FPGAs and extra circuits.

What everyone should be asking is why MNIST? It's a toy dataset at this point, it should be trivial for someone like Hinton to scale these experiments to something closer to reality. MNIST is like mouse experiments of machine learning, maybe it's an early necessity , but it hardly says anything about actual viability.

Hello,
it depends on the problem but it is also possible to train over many graphs.
I am also a beginner, especially in the area of graph neural networks, and found it very confusing that in all the examples only one graph was trained on at a time.
But it seems to be no problem. I am currently training a model and have 200k+ example graphs and I do predict node features.
I collected the dataset myself though. But I think there are also datasets with many graphs in the field of biology / medicine.

Feel free to reach out if you need help :)

Is it okay to call RLHF as a synonym to Active Learning in RL?

Do you mind telling your educational background? I am an undergraduate, and none of the previous residents that I came across held only a bachelors. All of them held atleast a masters degree, mostly a PhD.