i like that openai has partnered with select companies to make sure that they have plugins from the getgo, and then also put development of plugins behind a waitlist, ensuring that select hand-chosen companies can corner their market. very cool, very open and ethical of them

I dont think so. OpenAI has overtaken any research done on LLMs by a long shot.

What language/suite are you using? You can take a look at profilers in your language. I know Tensorflow has some profiling tools and you can look at what operations are running on what device. Probably Torch has some as well. If its more esoteric, just use general language profilers and take a look at what your code is doing most of the time.

General Purpose Technologies (from the Jobs Paper), General Artificial Intelligence. The skirting around the word is really funny. They've figured it out but no one wants to call a spade a spade yet.

From what i have heard, it should be possible. But only with the 7B model. Unless you own a few A/H 100s.

Maybe, maybe not, we cannot say for sure. Even if they haven't, I think that there is nothing you can do that could really help your case. But this is only my personal opinion.

I am in a similar position: no response from any of the three reviewers. But I think that it does not have to be a bad news: maybe they do not need more details to take their decisions.

You have to remember that maybe they are not that interested by you research so they will just ask enough question to have a global view and not more.

No dv-1 is GP3 and dv2 is GPT3.5

Im trying to train yolov7 to detect football(european one) players and the ball. In a typical frame there are lots of players and only one ball. After training it only detects the players. My guess is that it learned to ignore guessing the ball since its statistically irrelevant. Is this assumption correct, and if so how would I go about changing it?

Did this paper really call Copy.ai a chatbot of its own?

No more

If they ask 5+ questions and don't reply or change review or confidence score, doesn't that imply that they simply haven't read it yet?

GPT-4 is potentially missing a vital feature to take this one step further: Visual Grounding - the ability to say where inside an image a specific element is, e.g. if the model wants to click a button, what X,Y position on the screen does that translate to?

Other MLLMs have it though, e.g. One-For-All. I guess it's only a matter of time before we can get MLLMs to provide a layer of automation over desktop applications...

At last it can teach me math like I'm 5 or in banana terms lol

The new plugins can be/ are created by just documenting the api and feeding it to gpt4 aren't they? no actual coding . So it seems at least plausible that the other approach would be as you say, let it interpret the ui visually.

This looks incredible. This is going to save hours of training. Amazing!

Let's agree to "some".

lots of humans too have an uncanny ability to generate output that looks intelligent. most if not all do.

Regardless of whether this is AGI or not seems irrelevant as long as it can demonstrate the capabilities or simulate intelligent behaviour. Also what is AGI if not "artificial" intelligence not real or true intelligence per se. We are trying to compare human intelligence with AI. But if two things demonstrate similar intelligent properties regardless of how, it can still be called sorta intelligent. Intelligence itself is a very subjective and philosophical term. At this point in technology, my opinion is that it shouldn't matter what and what is not AGI coz there's no way to measure that right now that everyone agrees on, as long as it demonstrates some form of "artificial" intelligence.

Thanks but that only clarifies from the UX side, we don't know know if OpenAI does save them and could decide to include past sessions in some form, as a context even with the current model, do we?

HAL-9000 and KITT... here we come!

I tried it yesterday and it worked fairly well but described some details that didn't exist.

Right, emergent properties are the key and they cannot be predicted from what NLM are supposed to do or how they work, this why they are emergent. The only way to find out what properties well trained NLM have is to test experimentally as this paper did and other papers that are doing the same, as this one:
https://arxiv.org/abs/2302.02083#:~:text=Theory%20of%20Mind%20May%20Have%20Spontaneously%20Emerged%20in%20Large%20Language%20Models,-Michal%20Kosinski

There is theory of mind, at the level of a 9 years old human:
https://www.discovermagazine.com/mind/ai-chatbot-spontaneously-develops-a-theory-of-mind

Someone's already taking care of that - Flan-Alpaca

Were you using ChatGPT 3.5 or 4? 3.5 only seems to combine unlikely and unrelated elements, but 4 combines unrelated and incompatible concepts. I think 4 does much better at this task!