While it may seem that way correct answers are always expected but never delivered everything works within a margin of error with humans it's pretty large and not easy to fix. Also "correct" is subjective. LLMs are language models use the knowlede embedded in their wieghts combined with the context provided by the prompt to do their best. The positive thing here is that that the margin of error is actively being reduced withn LLMs and not so with however we did this before.

I've now done some further research and read the comments.

By far, my conclusion is that, SSL is indeed, a type of SL. It contains features and corresponding label(s). From wikipedia:

>Supervised learning (SL) is a machine learning paradigm for problems where the available data consists of labeled examples, meaning that each data point contains features (covariates) and an associated label.

Since this is not a debate, I do not want to dwell on the definition. And indeed, *self-*supervised means that it does not require extra resource-consuming labelling from human, making training with huge datasets possible, like GPT-3.

And I disagree that seeing SSL as a kind of SL is the "wrong level" as a comment suggestted. What I originally intended to confirm was that, language modeling, which gives rise to GPT-3/ChatGPT... Is a kind of supervised learning with a large quantity (and sometimes good quality) of data. Strong model with simple, old methods.

People are rushing to deploy LLMs in search, summarization, virtual assistants, question answering and countless other applications where correct answers are expected.

The reason they want to get to the latent space close to the answer is because they want the LLM to output the correct answer.

Ok I saw that not entirely sure what you think prompting is but its not about getting exact answers or anything like that. As I understand it (however limited) it is about bringing attention to a part of the models latent space closest to where your soluton may fall.

The source I linked in the comment you linked and then deleted.

I updated my comment. Not sure what you mean here "You would have realized that if you had checked the source I linked"? what source

I think it's not suitable because it isn't really related to the process of a machine learning anything. It seems to me to belong to the field of human computer interaction.

That comment is very over the top sarcasm. You would have realized that if you had checked the source I linked.

>Not so constructive.

It's not much I am aware. However, what I mean that names of both training paradigm already told you a part of the answer. The last paragraph of mine is to refer two other comments to create a more sufficient answer.

Moreover, the names of both already pointed it's somewhat related. Therefore, this line

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

is obvious. I don't know who have said "classifying them as disjoint" to you. Clearly they didn't pay attention to the names.

It's clear that human and other animals must learn with reinforcement -- requiring the agent to act and recevive feedback/reward. This is an important part and I don't think it's proper to classify it as SSL. Moreover, psychology on learning points out that problem-solving and immediate feedback is very important for learning outcomes -- these feedbacks are typically human labels or reward signal.

Why do you feel that?

So you want to argue that the name of the post is trivally true so not worth mentioning, and problematic(as your last paragraph suggest)? Not so constructive.

>Isn't self-supervised learning(SSL) simply a kind of SL?

Don't their names already tell that? Self-supervised learning... supervised learning...

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

Who said this?

The ways of determining labels of both paradigms are different (as u/cthorrez said). Moreover, the objectives are different (as u/currentscurrents said).

Well, in terms of "out-of-the-box," I'm not sure what else could be better. AWS, Azure or Google provide empty units basically, and you'd have to configure all the "Ops" stuff like network, security, load balancing, etc. That's not that difficult if you do it once in a while, but for a "test-it-and-forget-it" project it might be too difficult.

That's a good question! I'm not sure if there are any pretrained language models with fewer parameters, but you could always try training your own model from scratch and see how small you can get it.

Thank you it is slightly unrelated to my question about large inputs to the network but still very useful.

may be an unpopular opinion these days but I don't think prompt engineering is a suitable topic for /r/MachineLearning

You're looking at the wrong level. SSL is a different training objective. Everything else about the model and optimizer is the same, but you're training it on a different problem.

Also SSL has other advantages beyond being cheaper. SL can only teach you ideas humans already know, while SSL learns from the data directly. It would be fundamentally impossible to create labels for every single concept a large model like GPT-3 knows.

Yann Lecun is almost certainly right that most human learning is SSL. Very little of our input data is labeled - and for animals, possibly none.

Not certain where banning AI came into the discussion. It’s just not going to happen and I don’t see anyone proposing it. However, it shouldn’t be the other extreme either where everyone is running a nuclear plant in their backyard.

To draw parallels from your example, AI needs a lot of regulation, industry standards and careful handling. The current technology is still immature but if the right structures are not put in place now, it will be too late to put the genie back in the bottle later.

How are Siamese networks used for spend classification?

AI has a great many positive uses. Guns not so much. It’s not a good comparison. Nuclear technology might be better, and I’m not for banning nuclear either.

What does it meant by this statement: “GM (Gaussian mixture) on its own is not much of use because it converges too fast to a non-optimal solution”

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Like I said this is similar to the guns argument. Banning guns does not stop people from Killing each other but easy access to guns amplifies the problem.

AI as a tool of automation is a force multiplier that is going to be indistinguishable from human action.