This is a strange way to count, the three Turing award winners are not the ones that "created the tech". Many thousands of people did that.

It's common to call them godfathers of AI. Surely Oppenheimer didn't create the bomb alone but you get the point.

The point is wrong, they don't have any claim to having created deep learning and so their perspective can't be used to argue for the reality of "superintelligence". Even if they did create it, the question of whether "superintelligence" is a meaningful concept or not isn't really related to what they would have had to go through in order to make deep learning work.

So in computer science nobody can claim that they are the godfather of X. In physics too because everyone was based on mathcfrom ancient Greece. I get your point. Let's focus on the important stuff. 2 out of 3 Turing award winners related to this tech put up huge warnings. You think a) ignore or b) take seriously?

I don't think you can do this, it will just make things up. Language models don't have this type of reflection. Google would need to indicate this out of band, like on the page itself, in order for you to be confident about what model you're using.

Agreed. I'm not entirely sure that the person you're replying to is not joking

I'm pretty sure they're trying to suggest that LLMs in general are not useful because they can't do this type of thing. It's just the next iteration of goal post moving and should effectively be ignored.

Many artists and such that I've spoken to about AI work have similar comments about these systems because of the disdain for their existence.

The number of times I hear an argument like "well, they can never taste the tartness of a kiwi and feel the heat of the sun while at the beach" gets quite exhausting. For some reason, many people have this weird notion that this is what AGI means - exactly what humans do, and specifically within the same data domains of humans, but they don't consider working solely outside those domains as a possibility for AGI.

Just wait as the multimodal LLM’s roll out! People will be shoving kiwis into their computer and taking their laptops out for a few rays before you know it.

I tried asking it "what is the difference between the palm language model and the bard language model?" and its reply started off "The main difference between the Palm language model and the Bard language model is the size of the dataset they are trained on. Palm is trained on a dataset of 400 billion parameters, while Bard is trained on a dataset of 540 billion parameters." Which to me is even more interesting that what the OP commenter asserted.

It makes up those numbers, I asked about the difference between the small and large PaLM 2 data set size, and it asserted the small model was trained on 540 billion and the large model was trained on 540 trillion. A different draft instead specified 1.4 trillion for the large.

It even gave me a table with a whole bunch of differences. All thats made up?

Here is a table that summarizes the key differences between the two language models:

Feature Palm Bard Number of parameters 400 billion 540 billion Vocabulary size 137 billion words 1.5 trillion words

I didn't mean to argue that everything generated is incorrect. But in my experience, the numbers it generates seem closer to random guesses. If you ask it enough times, it sometimes converges on a number, but I don't think that means it's an accurate value. I was able to make it generate a similar table for the different PaLM 2 sizes, and laMDA, and it listed, PaLM 2 Gecko 137 billion, PaLM 2 Otter 540 billion, PaLM 2 Bison 1.8 trillion, PaLM 2 Unicorn 5.4 trillion, LaMDA 137 billion. For Unicorn, it also lists "Still under development."

Edit: Playing around with it more and it listed WuDao 2.0 1.75 Trillion, Chinchilla 175B, Codex 175B, Dalle2 1.3B, GPT4 1.75T, GPT3.5 540B, GPT3 175B, GPT2 1.37B, GPT 1.3B.

But in the previous question it listed GPT4 540 billion and Codex 5.4 trillion among other contradictions.

Using 8-bit still runs out of RAM for both the 3B and 7B models. It's unclear if it's because it still uses more than the available RAM, or if it's just quietly not using 8-bit since it's not implemented.

Amazon did release a 20B transformer called Alexa last year: https://www.amazon.science/blog/20b-parameter-alexa-model-se...

bing cannot do anything besides read some of microsoft's scrape of the web. it can't make a GET or POST request, it can't click buttons, etc

But there's nothing preventing you from writing an interface that allows it to do a GET or POST, or to do almost anything on the WWW.

CLIP Interrogator uses BLIP, an image captioning model, as well as trying a bunch of prompts with CLIP. I guess you mean that this model uses the captioning model to generate the complete prompt? Is the code for this one available?

Ah yes, this model treats this purely as image captioning. The model isn't open source yet.

OpenAI made GPT, not Google. Did you mean "Attention is all you need", the paper that introduced transformers?

Yes.

Google did not "threaten their own business" by publishing basic research in machine learning. Products threaten businesses, not research.

But research does attract funding, and top tier employees.

Okay, and that funding and top tier talent was attracted to Google, right? I'm not sure what you meant by that.

did you tell chatgpt to act like jar jar binks or what?

transformer-xl is a totally different net. the thing to keep in mind is that gpt will play along with anything unless they explicitly prevent it

It's very different because it will answer the question you asked, rather than answering a question that matches a substring of the question you asked like Google will.

Google apparently uses BERT to actually answer the question you asked … and an obvious incarnation for this sort of tech is probably going to be further integration into google . Makes sense doesn’t it .

BERT is a simple model that is not capable of answering questions in this manner. For very simple things it might help with that answer box at the top, but that's not what I meant.