Yeah OpenAI was founded to be... Well... open.

It's the most closed ai company in existence probably

Any chance you could explain how to use Step 1 a little clearer? I understood the premise, but I'm not quite sure how that would translate to the instruction in step 1. As an example, I'm trying to perform a content analysis of a document with 7 chapters and identify 10-15 core themes in each chapter.

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GPT is largely built on Google research

It's smart by Google to wait until Microsoft burns the 10 billion, then easily surpass it.

The hype is so painful at the moment, non technical people and sales idiots are way overselling chatgpt.

Maybe they come to their senses and put it back. I wanted to use it to find references for my random ideas, see what results they have.

I tried perplexity.ai for first time yesterday, and was impressed by it. While it uses GPT 3.5 it's not exactly comparable to ChatGPT since it's really an integration of Bing search with GPT 3.5, as you can tell by asking it about current events (and also by asking it about itself!). I'm not sure exactly how they've done the integration, but the gist of it seems to be more that GPT/chat is being used as an interface to search, rather than ChatGPT where the content itself is being generated by GPT.

Microsoft seem to be following a similar approach per the Bing/Chat verson that popped up and disappeared a couple of days ago. It was able to cite sources, which isn't possible for GPT-generated content which has no source as such.

>I've seen a big push from Fast.ai for Swift (they claim it's the future, etc)

You've seen some dated stuff, from before S4TF became dead in the water.

The indisputable most useful language for ML is Python. The ecosystem is by far the strongest, and the language more-or-less stays out of your way while you interact with specific libraries that do what you want. Those libraries, are written in highly optimised compiled languages like C/C++, so are extremely efficient. As long as you keep them fed, you'll see very little of the "python-slow-interpreted-bad".

Y'all need to stop stretching definitions of words past the breaking point.

I am not "acting like" anything. I simply understand the vast difference between a human brain and a highly specialized machine learning algorithm.

Diffusion models are not minds and do not have them.

You only need a very basic understanding of machine learning VS human cognition to be aware of this.

AI =|= Actual Intelligence;

Stable Diffusion =|= Sentient Device.

Is a "mind" a blob of flesh or is it the combination of chemical interactions that happen in that blob of flesh.

Could a perfect simulation of those chemical interactions be considered a "mind?"

What about a slightly simplified model?

How far down that path do you have to go before it's no longer considered a "mind?"

You act like there are obvious answers to these questions, but I don't think you would have much luck if you had to get everyone to agree with you.

Chat GPT is built on this: https://arxiv.org/abs/1706.03762

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I am looking at parametric search, where I can highlight in a graph-database style way, the mistakes with the results, by reassigning weights or links, to redo the search, until I get answers that are more correct, based off things like 'water isn't useful for cleaning dried paint, acetone or paint thinners may be more useful'. Is it possible to build such features into any of the open source tools here, or are lacking any gui for the feedback, beyond text and a thumb up or down as one sees in the commercial packages?

Eh, so like humans

Poorly contained? What do you mean?

Learn python and then a few frameworks around it. Airflow for pipelines, Pandas/Dask/Vaex/Modin[ray]/PySpark for feature engineering, and then get familiar with ML libraries like tensorflow and scipy. For everything you learn, make a quick document in something like Hugo as a cheat sheet. Keep learning and documenting and you’ll be a pretty good ML engineer in no time. And if you want an easy foray into modeling, maybe start with linear regression and move onto weak ensemble like xgboost.

Which tech?

As if the rest of this whole thread isn't opinions, or opinions acting like facts about law that has not yet been explored.

> given the volume of false information that chatGPT generates

It actually generates mostly accurate information. The longer you have the conversation the more it starts to hallucinate, but it is considerably more accurate than most people.

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Major corporations using ML to generate images instead of hiring artists purely in the goal of increasing their profits. Helping to make the richest guy to get even more rich. How does that help humanity?

$4.50.

if you go to www.wrotescan.com and upload a pdf, you can see estimated cost at the bottom for a map-reduce job.

FB was too scared of the bad PR. OpenAI wasn't. People tried to trash chatGPT millions of times, Galactica just a few times. I think chatGPT handled the adversarial attacks pretty well.

Google is another scared company, their models haven't seen any attacks yet, so they are unknown. I don't care how nice their screenshots look, what I want to see is how people hack it. Then I can form an opinion. People are the true test set.

How much did you pay for that single prompt?

You're prepping music for sale and you don't know how to describe it or what genre it is?