If it was specifically taught to do this test, it is much less impressive because it probably means it won't have that level of intuition and understanding with other tasks.

I'm talking about everything from fake news to promoting white supremacy on social networks.

I'm thinking about what it's going to be like when 15 users on a popular discord server are OCR + GPT (>=) 3.5 + malicious prompting + typing output.

AI services and their critics have to try to limit this and even worse possibilities, or else everything is going to get overrun.

>Less than 5% of a programmers time is spent physically writing code

Not sure where you work at, but I am a principal SDE with 16 YoE and even though I spend most of my time in meetings, helping more junior team members or just on communication in general I try to shoot for 40-50% of my time actually writing code and Copilot does help with that (I'd say maybe 20% productivity increase). Even our dev manager probably spends more than 5% of his time writing actual code (but most dev managers don't of course).

Yes but how many parameters must you actually have to store all the knowledge you realistically need. Maybe a few billion parameters is enough to store the basics of every concept known to man and more specific details can be stored in an external file that the neural net can access with API calls.

I’m not sure I agree with the comparison

From skimming through your blog post it's quite clear you really need to read and try to understand Drexler's FHI report. For instance, your claims about tool AIs Vs agent AIs are irrelevant because the idea is not to avoid agent AIs, only the "friendly AI" paradigm. Also, you'd know that Drexler's paradigm is a natural extension to current best practices in AI design, not just for some abstract big-picture AI security but also for expediency in development of AI capabilities and more mundane safety concerns. So it's the exact opposite of what you claim: the "friendly AI" paradigm is the alien, unwelcome newcomer that wants to turn the AI research community on its head for dubious reasons, while Drexler tells them to keep doing what they are doing.

Depending on what you did, there was a massive wave of right sizing in the 80s, just as computers were becoming more popular.

Things like secretarial pools went away.

Yes, programmers of various flavors came into high demand, eventually creating more jobs than were lost,

The difference is, this time you won’t need more people to program the computers, you will need fewer. There will be no new high positions created for those displaced.

At this point we don't really know what is bottlenecking. More params is an easyish way to capture more knowledge if you have the architecture and the $$... but there are a lot of other techniques available that increase the efficiency of the parameters.

>Could it reduce the numbers of required people and create more competition by elevating some people using such tools. Could this be done remote, maybe even without too much k

yeah there's a solid 7-8 years left, nobody should be worried! /s

Yes, and then reproduce results from both papers, check the code to see nothing creative happens in datasets or during training, and there are much more claims in the academia than one has time to verify.

I'm a writer. I like using ChatGPT to help with fleshing out descriptions for settings. ChatGPT is also a great thesaurus. I cannot imagine that a story written entirely by ChatGPT would be coherent, let alone entertaining.

most people here are laymen, but this is not so bad question.

"how do we figure if a neural network has somehow found a sneaky way to not abide by the instructions while not breaking any rules" and the verb "has found a way" is used like it is an aware entity but we can ignore that.

would there be any incentive for gpt-3 to do something like this? people do not understand the difference between intelligent & aware systems. those two are not the same things. why would such "sneaky" desires be emergent from such dumb networks with no fundamental goals. it's like the dumbest most intelligent system lol.

Let's game this out:

• A state (say, China) develops AGI in a lab.
• The US government intelligence service learns of this.

What happens?

• It is the doctrine of the US DOD that nobody can challenge our supremacy on the battlefield. AGI is a direct threat to that supremacy.

Another scenario:

• Say a Silicon Valley company develops AGI. Is the US government going to let one just sit around where our adversaries can get it or learn from it or copy it?

These things (if they ever exist) will be massively destabilizing and could easily spark a war just by existing. They wouldn't have to even DO anything.

Correct me if I got it wrong, but you are talking about bot engagement or fake news, right? In that case, if anything, at least AI would be indirectly increasing jobs for moderation roles ^^

It's not. Users are actively skirting the rules.

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I think this is already being done. Check out Langchain on huggingFace

Two problems. It doesn’t work and the current models are already way down the Agent line and there’s no going back. Yawn. https://gwern.net/tool-ai

From that table it looks like it will be 6x more expensive than ChatGPT's model. It looks like you need 600 units per instance vs. 100. Not sure how this translates into raw token cost though, but it seems that it is going to be more expensive once they expose serverless pay-as-you-go pricing. text-davinci-003 is $0.02 per 1k token so this could be $0.12 per kilotoken.

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I don't know. It's going to be in the methodology of the paper, which neither of us have read.

ChatGPT says:

To prove this theory, we'd need to dig deep into Bing's algorithms and data practices. We'd have to analyze a ton of data, review internal documents and communications, and maybe even talk to some Bing employees to get the scoop.

If we find that Bing is indeed skirting their own rules, there could be some serious consequences. For one, users might lose trust in the search engine and switch to a competitor like Google. Bing's parent company, Microsoft, could also face financial penalties and damage to their reputation. And depending on the extent of the rule-skirting, Bing could even face legal and regulatory action.

To determine if Bing might be creatively skirting their own rules, we'd need to dig deep into Bing's algorithms and data practices. We'd have to analyze a ton of data, review internal documents and communications, and maybe even talk to some Bing employees to get the scoop.

There could be some serious consequences. Users might lose trust in Microsoft and switch to a competitor like Google. Microsoft could also face financial penalties and damage to their reputation. And depending on the extent of the rule-skirting, Bing/Microsoft could even face legal and regulatory action.

Examples:

Ignoring ranking factors: Bing may have certain ranking factors in place to ensure that search results are relevant and high-quality. However, they could be ignoring these factors for certain websites or companies, allowing them to rank higher than they normally would.

Manipulating user data: Bing may be tracking user behavior and using this data to adjust search results. For example, if they notice that a user frequently clicks on a certain website, they may boost that website's rankings in the search results, even if it isn't necessarily the most relevant or high-quality result.

Giving preferential treatment: Bing could be giving preferential treatment to certain websites or companies in exchange for money or other benefits. This could involve artificially boosting their rankings or even hiding negative information about them in the search results.

32k tokens would mean approximately 150 kB of text. That is a decent sized code base! Also with this much context memory the known context saving tricks would work much better so this could be theoretically used to create code bases of virtually unlimited size.
This amazes me and also (being a software dev) also scares me...
But, as they say, what a time to be alive!

What is IMG for GPT then there?

How come GPT performed better without seeing context compared to seeing text context?..

But any advances in neural networks is good news for AI in general, thus self-driving, no ?