There have been multiple accounts created with the sole purpose of posting advertisement posts or replies containing unsolicited advertising.

Accounts which solely post advertisements, or persistently post them may be terminated.

mozz , (edited )
@mozz@mbin.grits.dev avatar

I just tried Claude after having some issues with using GPT on Firefox that OpenAI’s support was unable to resolve other than some “it’s all your fault, clear your cookies” bullet points.

I only tried Claude a little bit so far, but it seems way better.

WhyJiffie ,

I’ve been using DuckDuckGo’s AI Chat relatively a lot lately and it has always worked for me. Well, with one exception, I was able to break it accidentally by including a specific special character in the input, which then it tried to respond with, and then it failed to load the response. Luckily I was about to tell it to refer to that character in a different way. I don’t remember which character was it, but not a punctuation mark, not something rare I copied from somewhere. Maybe one that’s often displayed with an empty rectangle in UTF-8.

I like that anything I do there is not tied to an account, but not even to an IP address or anything like that, assuming that I trust DDG with it, because it acts as a proxy. Sure, it doesn’t have the latest version of ChatGPT and Claude, but I don’t need cutting edge tech either, they work fine enough, and this added privacy is more important to me.

cygnus ,
@cygnus@lemmy.ca avatar

LLMs will not give us AGI. This is obvious to anyone who knows how they work.

doodledup ,

Maybe it can. If you find a way to port everything to text by hooking in different models, the LLM might be able to reason about everything you throw at it. Who even defines how AGI should be implemented?

mke ,

Except LLMs don’t actually have real reasoning capacity. Hooking in different models that can translate more of the world to text could give the LLM a broader domain, but not an entirely new ability beyond its architecture. That might make it more convincing, but it would still fail in the same ways as it currently does.

doodledup ,

You’re doing reasoning based on chemical reactions. Who says it can’t do reasoning based on text? Who says it’s not doing that already in some capacity? Can you prove that?

MentalEdge ,
@MentalEdge@sopuli.xyz avatar

Is language conscious? Is it possible to “encode” human thinking into the media we produce?

Humans certainly “decode” ideas, knowledge, trains of logic and more from media, but does that mean the media contains the components of consciousness?

Is it possible to produce a machine that “decodes” not the content of media, but the process through which it was produced? Does media contain the latter in the first place?

How can you tell the difference if it does?

The more I learn about how modern machine learning actually works, the more certain I become that even if having a machine “decode” human media is the path to AGI, LLMs ain’t it.

It just doesn’t work in a way that would allow for a mind to arise.

kia ,

The LLM is just trying to produce output text that resembles the patterns it saw in the training set. There’s no “reasoning” involved.

doodledup , (edited )

You’re doing that too from day one you were born.

Besides, aren’t humans thinking in words too?

Why is it impossible to build a text-based AGI model? Maybe there can be reasoning in between word predictions. Maybe reasoning is just a fancy term for statistics? Maybe floating-point rounding errors are sufficient for making it more than a mere token prediction model.

MentalEdge , (edited )
@MentalEdge@sopuli.xyz avatar

The “model” is static after training. It doesn’t continuously change in response to input, and even if it did, it would do so at a snails pace. Training essentially happens by random trial and error, slowly evolving the model towards a desired result. Human minds certainly do NOT work that way. Give a human a piece of information, and they can comprehend and internalize the relevant concepts in one go. And the actual brain is physically, permanently, altered through that process.

Once a model is trained, however, “memory” takes the form of tacking on everything the model has received and produced so far onto its input, each time it needs to output something more within that context. Each output hence become exponentially heavier to produce. The model itself no longer changes in any way beyond this point.

And, the models are all chronically sycophantic. If reason was involved, you’d not be able to just tell one to hold some given opinion. They’d have a developed idea of “reality” based on their dataset, and refuse to entertain concepts opposed to that internal model except by deliberately suspending disbelief. Something humans do with ease, and when doing it, maintain a solid separation between fantasy and reality.

Once you get an LLM to hold a position, which you can do by simply telling it to, getting it to change should require a sane train of convincing logic. In reality, if you tell an LLM to defend a position, getting it to “change its mind” takes the form of a completely arbitrary back and forth that does not need to include any kind of sane argument. It will make good arguments, because it’s likely been trained on them, but your responses to it can be damn near complete gibberish, and it WILL eventually work.

Compare that to the way a human has to be convinced to change their mind.

Reasoning out concepts to come to conclusions isn’t something LLMs actually do, because again, the underlying model is static. All that’s actually happening is that the contents of the context are being altered until the UNCHANGED model produces an opposite response when fed the entire conversation so far as an input. Something which occurs every time it needs to produce new output.

LLMs can “reason” only in the sense that if you give one a thinking problem, it might solve it as long as the answer already exists somewhere in the data it was trained on. But as soon as you try to give it data to work with through your input, it can’t adapt. The model itself can’t evolve in response to what you are telling it. It’s static. It can only work with concepts that it has modelled during training, and even then it will make mistakes.

LLMs can mimic the performing of some pretty complex thinking problems, but a lot of the abilities required for something to become an AGI aren’t among them. Core among these is the ability for the model to alter itself based on input, and do so in a deliberate manner, getting it right within one or two tries.

In reality, training is a brute-force process, not an accurate process of comprehension that nails down an understanding of a concept in one go.

If LLMs could reason, the only safe guards required for their use would be telling them to “do no harm”, because like a person, they’d understand the concept of “harm” as well as be able to reason whether a given action might cause it. Only, that doesn’t actually work.

TimeSquirrel ,
@TimeSquirrel@kbin.melroy.org avatar

Besides, aren't humans thinking in words too?

Not all the time. I can think about abstract concepts with no language needed whatsoever. Like when I'm working on my car. I don't need to think to myself "Ah this bolt is the 10mm one that went on the steering pump", I just recognize it and put it on.

Programming is another area like that. I just think about a particular concept itself. How the data will flow, what a function will do to it, etc. It doesn't need to be described in my head with language to know it and understand it. LLMs cannot do that.

A toddler doesn't need to understand language to build a cool house out of Lego.

EnderMB ,

A LLM is basically just an orchestration mechanism. Saying a LLM doesn’t do reasoning is like saying a step function can’t send an email. The step function can’t, but the lambda I’ve attached to it sure as shit can.

ChatGPT isn’t just a model sat somewhere. There are likely hundreds of services working behind the scenes to coerce the LLM into getting the right result. That might be entity resolution, expert mapping, perhaps even techniques that will “reason”.

The first initial point is right, though. This ain’t AGI, not even close. It’s just your standard compositional stuff with a new orchestration mechanism that is better suited for long-form responses - and wild hallucinations…

Source: Working on this right now.

aodhsishaj ,

You might be interested in Nim then when you get a chance. Talk about orchestration

developer.nvidia.com/nim

anarchrist ,

LLMs do not reason, they probabilistically determine the next word based on the words you prompt it with. The most perfect implementation of “AI” was the T9 predictive text system for dumb phones cmv.

doodledup ,

And you’re just a fancy electro-chemical reaction.

Who says that an LLM with complete access to the sensory world could not pass the Turing Test?

MentalEdge , (edited )
@MentalEdge@sopuli.xyz avatar

And to have conversation, behind the scenes, each prompt gets the entire conversation so far tacked on.

The model itself is static, it doesn’t work like a brain that changes in response to stimulus, or form memories.

To converse about something, the entirety of an exchange is fed back into the model all over again each time it needs to produce a response. In fact, this can happen several times over for each word in a response.

It’s basically an attempt at duct-taping the ability to form memories onto an otherwise static system. It works, but I don’t see how that way of doing it could ever land LLMs in the land of real consciousness.

It basically means these models “think” in frames, but each frame gets exponentially heavier to process, as it has to ingest every frame that came before.

mozz ,
@mozz@mbin.grits.dev avatar

OpenAI at least is now attempting to bolt on a “memory” by having the LLM spit out short snippets of what it might need to know later, which it then has access to when completing later prompts. Like everything else post-GPT-4, it seems fine but doesn’t work really all that well at what it is intended to do.

conciselyverbose ,

LLMs can’t reason about anything, ever.

Rhaedas ,

LLMs alone won't. Experts in the field seem to have different opinions on if they will help get us there. What is concerning to me is that the issues and dangers of AGI also exist with advanced LLM models, and that research is being shelved because it gets in the way of profit. Maybe we'll never be able to get to AGI, but we sure better hope if we do we get it right the first time. How's that been going with the more primitive LLMs?

Do we even know what the "right" AGI would be? We're treading in dangerous waters.

  • All
  • Subscribed
  • Moderated
  • Favorites
  • •
  • [email protected]
  • random
  • lifeLocal
  • goranko
  • All magazines
    •