If I'm reading it right, it only works for LLMs that call an external source. Like Toolformer or Bing Chat. There's no way to inject it into ChatGPT or Github Copilot, it isn't a training data poisoning attack.

I think I remember somebody doing something like this against bing chat. They would give it a link to their blog, which contained the full prompt.

• Region of proposal NNs:A Region Proposal Network, or RPN, is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position.
• YOLO uses the Pr(Object) which I am not sure whether it is a prediction from the model, but it seems so
• And Region of Interest. That is widely used as well, but does not exactly tell "objectness".

I dont know what GM is, but for the second part, you can imagine that training a model is like finding a set of optimal conditions. However, some models find good conditions but not the best ones, this normally depends in the Loss function and other characteristics.

First point is like saying phishing was nonexistent before we invented computers and internet, so we dont have to worry about it once we invent them. There have been no AGI. There have been no comparable events. Basing it on fact that asteroid killing all life on earth is unlikely does not make sense.

Doesn't seem like there are any good mitigations right now and it affects pretty much all the useful use-cases for LLMs, even code completion...

Pattern Recognition and Machine Learning (PRML) and Elements of Statistical Learning (ESL) are two of the standard references that will give you what you're looking for with regards to the more classical topics you allude to (linear models, kernels, boosting, etc.).

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Interesting. LLMs really need a better way to understand what instructions they should follow and what instructions they should ignore.

Neural network security is getting to be a whole subfield at this point. Adversarial attacks, training data poisoning, etc.

That’s just weird

Look up predictive coding; neuroscientists came up with it in the 80s and 90s.

A good portion of learning works by trying to predict the future and updating your brain's internal model when you're wrong. This is especially involved in perception and world modeling tasks, like vision processing or commonsense physics.

You would have a very hard time learning this from RL. Rewards are sparse in the real world, and if you observe something that doesn't affect your reward function, RL can't learn from it. But predictive coding/self-supervised learning can learn from every bit of data you observe.

You do also use RL, because there are some things you can only learn through RL. But this becomes much easier once you already have a rich mental model of the world. Getting good at predicting the future makes you very good at predicting what will maximize your reward.

Here is a course for ML researchers about research areas that help reduce risks from AI (including today's risks as well as more extreme forms of them):

https://course.mlsafety.org

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With the current version, yes it runs an embeddings query for each message. I plan to handle threaded conversations shortly. In that scenario, the chat history will be provided to the prompt.

Words in language are both observations and actions. So language modelling is also a kind of supervised policy learning?

So... Self Supervised Learning is Unsupervised & Supervised & Reinforcement Learning.

But the text coming from a human should be considered "manually" labelled, right?

Does it do only one round of retrieval?

Yes, absolutely, for any size of the dataset and model this is strictly necessary. You can use cross-validation, Leave-One-Out CV, or bootstrap techniques (e.g. 0.632+ bootstrap). You don't need to validate if you don't have any hyperparameters, but this is very rarely the case; the only examples I can think of is Random Forest and Extremely Randomized Trees, where sufficiently large number of trees is typically enough.

I mean, I don't see how you get a plausible explanation of BingGPT from underfitting either. As you say, models are underfit on some types of data, but I think the key here is the finetuning procedure, either normal supervised, or RLHF, which is optimising for a particular type of dialogue data in which the model is asked to act as an "Assistant" to a human user.

Part of the reason I suspect my explanation is right is that ChatGPT and BingGPT were almost certainly finetuned on large amounts of dialogue data, collected from interactions with users, and yet most of the failure modes of BingGPT that made the media are not stuff like "we asked it to solve this complex reasoning problem and it failed horribly", they are instead coming from prompts which are very much in distribution for dialogue data, such as asking the model what it thinks about X, or asking the model to pretend it is Y and you would expect the model to have seen dialogues which start similarly before. I find underfitting on this data to be quite unlikely as an explanation.

At least use an Ensemble and cross-validate, this way u get at least some meaningful results, but you are still in danger of overfitting

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IMO, although the optimization techniques are the same, it is important to make this distinction because SSL does not require curated labels. And in some use cases SSL is not an option at all.

Why does it not stand for Sexual Learning? I see a business opportunity…

The same but feeding frames from the videos into model like a CNN-RNN model which keeps memory of previous frames, but will obviously be a much bigger dataset.

Has anyone made spectrogram 2 spectrogram models for music analysis, specifically demixing such as isolating vocals? I’m currently using a Mel spectrogram for the input and output but struggling to get good results. Using a hop length=512,n_fft=2048, no_mels=128. My model is currently a bi directional GRU model with 3 layers and a 256 hidden size.Does anyone know a good model type to use and/or good audio transformations for this project?

Cheers

You can not just confidently infer meaning from the name. Is "Light Year" a unit of time?

By your logic, "unsupervised learning" is not supervised learning, while SSL is sometimes classified as part of unsupervised learning, so now SSL isn't SL as well!

So "I think classifying them as disjoint is somewhat misleading."

is obvious.

My fault, deleted. Satisfied now?