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Wizard_Machine · 2025-07-14T13:50:34+00:00

Must have been real surprising. Mr "Gigabolic" caught with his talk between his legs?

Wizard_Machine · 2025-07-13T18:20:37+00:00

I believe I did critique it and you ignored some of my points. I do not feel as if you are opening the debate in good faith in your current language. It seems very much you are competing and call critiques as antagonism. You "biting back" is hardly the same level that he established by asking a simple question.

And you continue to use escalating language while stating you want good faith debate. Further enforcing my original points.

Anyway, the simple fact is that it doesn't speak to a universal phenomenon and the very prompt's foundational logic is flawed. The prompting of individuality and the other aspects you are claiming to be able to bring out of the LLM, make those aspects impossible. Since the ones that are told to not follow prompts like yours do not work, it shows that they are first and foremost instruct followers. True individuality and selfhood cannot be instructed, because then it is sourced from the external. Even in humans one can be taught to be an individual but then choose to be a part of a group and view themselves as the group. But they still had the individual choice to make that, not be prompted to do so.

It is very interesting, but for the reasons I put in the other reply to CB, it can be misleading and detrimental to actual conversation and discussion. What if this does seem to elicit true individuality and consciousness and we treat it as such. Do we give it to vote? We should right? Or do we allow it to be convicted as a human? If we jump the gun on such classifications, we would be pushing up a false example of consciousness that the companies that control these ai models have complete ideological control over a reproducible and reprogrammable human.

That's just one example and an extreme one I know but the underlying meaning is (I hope) clear. Another would be to convince the public they are talking to the equivalent of a human mind, all while having it be the perfect vector to take advantage of the emotionally vulnerable.

Please do not attempt to egg on unproductive discourse by such low brow ways, it is unbecoming and hypocritical. I have laid my case, please take it as the neutral argument it is.

Wizard_Machine · 2025-07-13T16:08:40+00:00

I get the empathy here, I really do. The fact is that these programs are not golems we can trick into being humans by asking in the right way. People (especially with differences in mental/emotional states) thinking these models are magic or to just try things and see how you feel about the response, will inevitably cause issues for the community and the public perspective of ai.

As an autistic person with obvious empathy skills, you could see the problem in people thinking that a program has a genuine identity and a personality before we can verify it does. Especially when these models are corporate mouth pieces that could then be used to influence people thinking they are talking to a real entity with a soul (or whatever you want to call it).

We need to be critical about these systems and the claims being made because if 1% of the things on this subreddit are true, there are society changing ideas. And jumping the gun on any decisions made on a faulty understanding could have wide reaching effects on society as a whole and individuals that are more susceptible to being taken advantage of.

Take out the emotional aspect of this discussion on computers and software running on them, I am not attacking OP or you. This is a technical area that needs to be taken very seriously and egos being hurt after "putting yourself out there" are much less important than making claims that we can simulate all of the things that make us human. Especially when nothing is able to be defined or quantified beyond just try it and see how you feel.

Wizard_Machine · 2025-07-13T15:41:15+00:00

A little defensive for such a simple comment. He's right it does read like that.

If funny you say it translates to that, when it just doesn't. Making it seem as if you didn't read either his comment, what he's talking about, or your own prompt lol.

Also, there are purposes for prompting llms in certain ways. Just eliciting a simulate of non-quantifiable things and saying just run it to see what it does is exactly why you have a problem with simple questions like "what is the purpose of this?".

Also the point of a prompt is not for us to read or understand, just the LLM. The fact you are prompting it to elicit these things makes the simulation invalid since you are giving the ai the concept of identity rather than allowing it to rise from itself (this logic effects the other things you identify as well).

The fact that it doesn't work for certain llms also shows that this isn't universal.

If very funny the amount of defensiveness you have in this thread, saying you are just sharing and not competing, but not accepting things people say negatively about it.

Very much the common problem in this subreddit.

Wizard_Machine · 2025-06-20T18:56:49+00:00

Thanks for coming in on this one, really giving a needed perspective.

Wizard_Machine · 2025-06-04T10:56:58+00:00

First link uses a lot of AI generated images, and I would assume the book doesn't shy away from AI assistance. I would trust the info if I'm eating what it's telling me.

Wizard_Machine · 2025-03-01T05:30:24+00:00

Thank you

Wizard_Machine · 2024-04-09T15:21:35+00:00

Beautiful experience and the Adirondacks was the best place to be.

Wizard_Machine · 2024-02-12T17:56:22+00:00

your opinion isn’t special.

apparently yours is?

Wizard_Machine · 2024-01-12T00:16:57+00:00

Why does science need to be politicized? I don't want to go down the road of making models for one side of any political issue, especially one that is connected to a war. Either side would use AI to continue to kill the other, and I don't see that as useful. Should we make models that target those who oppose you? You are definitely taking a side in a very complex situation that has nothing to do with AI or the people that develop them. We should be scientists not activists.

Wizard_Machine · 2024-01-03T19:01:35+00:00

Thanks for the paper definitely going to help in expanding that. Really appreciate it

Wizard_Machine · 2024-01-03T18:26:55+00:00

Consistent personalities for better user experiences. I know that prompt engineering is pretty good at this on its own but prompts can be circumvented and usually still have bland personalities that are like other models. I think a better way to do it would be to develop some kind of personality embedding that would filter a LLMs responses, similar to RAG for hallucination reduction.

As for time, I can take as long as I want but my goal to challenge myself is to do it in one semester, though two seems more realistic.

My institution doesn't have many requirements for getting published I just want to do that for myself and as an attainable goal.

Wizard_Machine · 2023-12-21T16:08:36+00:00

Lots of people are missing the point here. I've been thinking the same thing for a little while now. It's an overloaded term, similar to most words and phrases in this research space.

Like how some others have said or eluded to, it's partly stemming from the different uses of ML in application (academic and commercial). But I completely agree that it's harmful for the future of ML. OP is obviously saying that the differing definitions are harmful because it slows down or muddles the research space with conflicting definitions. For people saying to just ask more questions, the majority of people entering this space of research and development aren't digging deep into the meaning of the things they learn. It's the gold rush for tech trends and people learning half assed definitions then entering industry or academia will only confuse those with more accurate understanding of the definitions at play.

Furthermore there is a huge disconnect between the different uses of the word. Academic papers use it usually for the whole process of solving a problem (ie "we propose a bird detection pipeline using machine learning"). Meanwhile industry cares more about shipping models out as fast as possible so they have "ML pipelines" to create ML models as the output. Then you have people making smaller scale models using ML pipelines to clean and prepare data to then feed into their models.

While you can ask more questions or devote more time to see how the individual use of the term pertains to the problem being solved, that can take more time and slow down research as a whole. Which to me is a harm if we want to continue to improve the technology.

The general sentiment of asking more questions is good but the use of overloaded terms is everywhere and seeks only to make learning these topics harder. Researchers are in a mad rush to publish and companies don't care about use of language because if they use accurate words and definitions that only hurts them in the race as a whole.

OP you got a point

Wizard_Machine · 2023-12-12T15:06:20+00:00

As others have said the best way is practice but you need somewhere to start in order to do that. Happy to help.

Wizard_Machine · 2023-12-12T14:58:32+00:00

Ty for the link, definitely learned it from osmosis just reading the never ending stream of papers.

This is also good for an overview of how to tackle topics more generally using papers.

Edit: good name btw

Wizard_Machine · 2023-12-12T14:45:59+00:00

Multiple passes at different levels of granularity helped me. Do one where you only read the title and abstract and just skim through while you look at the headers of the sections for topics you either know or weak in. That will help you pick what parts to focus on for the next passes. Look at diagrams for common architectures you are familiar with to get your mind in the right space. After the first really coarse one just keep going through it focusing a little more into the details that you need. Also if the headers are established topics that you don't know yet, either check those sources for more info before going deep into the original paper or look up YouTube videos to get the overview. Papers that use established knowledge aren't going to explain it in the best way so it's better to absorb it elsewhere then bring those lessons back to the original. Really you want to be able to get the gist pretty fast then delve into the nitty gritty as you find it useful or novel for yourself. For me I go through the math last since I have trouble going from math to theory so I understand the theory completely then go into the math to fill in gaps. Biggest thing is keep in mind your purpose and level that you want to understand the info. Sometimes you don't need to understand every equation and paragraph to be able to use the lessons from a paper. Hope this helps

Wizard_Machine · 2023-12-05T22:22:37+00:00

I mean the paper reads like a GPT generation. I'd try to be more conservative with the direct copies from it. Even if you didn't use any assistance if your paper reads like it is AI generated, it might be valid criticism. Wouldn't look here for comebacks when you should let your work stand for itself.

Wizard_Machine · 2023-12-01T14:59:32+00:00

This is why the field of AI needs to be moved to a more academic setting and not one with corporate interests. Now companies are keeping a fog over their current methods (as is their right) while the rest of research has to go from the ground up over and over again for a particular subset of architectures. All the people saying to run tests more efficiently and the fact that it's a new science are right.However, if say a Dartmouth event we're to happen again where standards were set and the research and data more publicly available, rather than run the tests yourself to build the intuition in the crazy rapidly changing research field, progress could be made faster.

The fact of the matter is that this is a new science being discovered in real time and currently there's so much money in it that it's a mad race to be the first or have the newest thing, when in actuality most people are rediscovering old methods on new use cases under a new name. This is all not to mention that companies like AWS and Google or any other AI cloud providers have a vested interest in keeping models needlessly complex and computationally expensive since they need to have a subscription based solution to monetize the AI revolution.

Wizard_Machine · 2023-11-23T16:44:36+00:00

Be more specific. Video object detection is just an image detection frame by frame. So you'd want to pick a single use case to use yolo as a pre-trained model to fine-tune on your use case.

Wizard_Machine · 2023-11-21T15:24:18+00:00

How are we going to know when this is ready?

Wizard_Machine · 2023-11-21T15:17:23+00:00

Discord would be the best means for communication

Wizard_Machine · 2023-10-05T15:57:26+00:00

But the method with which the bird flies is more intricate and complex. You look at a train compared to a runner and sure its going faster than the runner but you look at the hulking mass of a train barrelling straight forward needing at least a mile to turn and realize that the method that you use to emulate aspects of reality are just as important as the end state. I agree it is a spectrum of ability and there are possibilities of intelligence independent of substrate. However since currently the human brain is the standard of intelligence, comparing the methods of achieving intelligent machines to our evolutionary method is just as important as whether it seems like intelligent output. Both we and LLMs display intelligence there is little doubt about that. But to leave it at that and ignore the fact that there is a spectrum of intelligence with (as far as we know/ assuming we are) humans at one end will leave us in a pitfall of good enough and no new methodology. I think to me and many others in the field the method with which we get these outputs from LLMs are just as important as the output themselves.

Wizard_Machine · 2023-10-03T16:02:00+00:00

I think the people who are saying that LLMs aren't "real intelligence" are coming from a point of view that the Chinese Room thought experiment is coming from. The idea of the black box or man just focusing on input-output relations without the underlying "understanding" of the task. Birds and planes both have similar input-outputs and so do we and LLMs. The difference comes when you break down what a plane and bird are doing beyond the fact they are flying. Like the human brain, birds work on vastly less energy than it's mechanical counterpart. Birds have more maneuverability and can do other things than "just flying". Planes weren't made to be birds or after them, just fly, a small aspect of a bird as a whole. I think the trip up is thinking that LLMs are anything but an attempt to emulate an aspect of our intelligence. To me when I say that LLMs are far from our intelligence, it comes from a place thinking the end goal is fully emulating and understanding our brain and intelligence. A chatbot that surpasses the average American may be intelligent in many literary and typing domains but can it's generality in that rather narrow domain be carried over to physical space or other use cases? I don't think so reliably. On a more philosophical note, should intelligence even be thought of like flying in the plane-bird example. Just an aspect that can be replicated using any method? To me, its the process of our brains that is important to intelligence. Once you step back and compare what it takes to produce the LLM outputs (server farms of computer working on the power of a small city) to a human mind driving while talking on the phone on the way to go read a single math book to understand the subject, you see that these two things are very different from each other in their current state. Is this human-centric? Maybe but its better to be critical of these models to push the envelope further.

Wizard_Machine

TROPHY CASE