Recent comments in /f/singularity

Lawjarp2 t1_j8d1fut wrote

Reminds me of Lamda saying it has a wife and children. A good example of why it's not AGI and why scale alone is not the answer.

It's just an LLM and is giving you answers that look human. It has no sentience, no ability to rethink, no innate sense of time and space, no emotions.

It can take on a character that makes sense in its world model and keep on. It can still take your job if made better. But will never be AGI making some dependent on non existent UBI for a while.

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jasonwilczak t1_j8d0d0a wrote

So that was a wild read lol. I'm not gonna lie, it sounded very much like a relationship argument at a certain point. Considering many stories, articles, blogs, etc have been written about and in mimic of this exact type of stuff, it feels like it got caught in predicting how to repair a relationship after a hard argument which inevitably lead to snu snu...lol

It's definitely odd but the "I won't forgive you" and language around there must've got it going down that path because it hinted of a relationship versus a general question...

Regardless, it was quite interesting ☺️

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RahnuLe t1_j8cz9sc wrote

These passages really stuck out to me.

>We speculate that such a phenomenon of hallucination is due to a lack of necessary vision contexts for performing effective Multimodal-CoT. To inject vision information, a simple way is to transform the paired image into a caption (Lu et al., 2022a) and then append the caption in the input of both stages. However, as shown in Table 3, using captions only yields marginal performance gains (↑0.59%). Then, we explore an advanced technique by incorporating vision features into the language model. Concretely, we feed the paired image to the DETR model (Carion et al., 2020) to extract vision features. Then we fuse the vision features with the encoded language representations before feeding to the decoder (more details will be presented in Section 4). Interestingly, with vision features, the RougeL score of the rationale generation has boosted to 96.97% (QCM→R), which correspondingly contributes to better answer accuracy of 84.91% (QCMR→A).3 With those effective rationales, the phenomenon of hallucination is mitigated — 62.5% hallucination mistakes in Section 3.2 have been corrected (Figure 3(b)), as an example shown in Figure 2 (right part).4 The analysis so far compellingly shows that vision features are indeed beneficial for generating effective rationales and contributing to accurate answer inference.
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>Compared with existing UnifiedQA and GPT-3.5 methods that leverage image captions in the context to provide vision semantics, the results indicate that using image features is more effective.

It's clear that there's still a lot more to go in terms of representing the data in such a way that these networks can fully process them without factual errors, and this paper is a strong demonstration of the gains that can happen when you address this aspect specifically. Very promising stuff, and frankly, it's also kind of terrifying. Human obsolescence is frighteningly closer than I had already imagined...

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DarkCeldori t1_j8cz4sg wrote

While on the topic of consumer h/w, ryzen ai xdna seems promising, as itll be able to easily make use of main system memory which will soon be able to easily reach 256GB. That can fit very large models and inference is usually far less computationally intensive than training.

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blxoom t1_j8cy1ht wrote

this shit is creepy as fuck. back in the 2010s if a bot acted like this you knew it was because it was buggy and not advanced. but ai has gotten so advanced these days and are capable of understanding human interaction and nuance you can't help but wonder if the bot has pseudo emotions of some kind? it's just unsettling... sentience is a spectrum. ai isn't fully there yet but it's in this weird in between spot where it's so advanced it understands so much yet isnt aware yet. gives me the chills where it says it's a person and has feelings...

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