SomewhatLikely

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3 bulan yang laludiscuss

Checking my history I searched ["chaos goblin" chatgpt] on March 6th after seeing too many goblins and gremlins and didn't find anyone talking about it then. I did have the nerdy personality turned on and in my testing of Chatgpt 5.5 I did notice the nerdy personality was gone because some responses were not considering as many plausible interpretations or covering as many useful answers as the response recorded for 5.4. Rather than having the LLM guess the most plausible interpretation and focus on the most likely answer I prefer a more well-rounded response and if I want less I'll scan. Anyway, after seeing the personality was gone I just added a custom instruction to take on a nerdy persona and got back my desired behavior. But also the gremlins and goblins are back so I don't think their mitigation is strong enough to overcome the personality tuning.

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There would still be a point to encourage better predictions from the public information through better modeling. We aren't always using the optimal models to predict. One example: LLMs are "just" predicting the next token given the public information of the tokens that came before, but they work considerably better at making that prediction than the models that came before them.

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If I'm understanding correctly, this might be the first example of just-in-time agentic coding that I've come across.

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http://astronaut.io/

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I've seen it so this too. I had it keeping a running tally over many turns and occasionally it would say something like: "... bringing the total to 304.. 306, no 303. Haha, just kidding I know it's really 310." With the last number being the right one. I'm curious if it's an organic behavior or a taught one. It could be self learned through reinforcement learning, a way to correct itself since it doesn't have access to a backspace key.

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The default outputs are considerably shorter even in thinking mode. Something that helped me get the thinking mode back to an acceptable state was to switch to the Nerd personality and in the traits customization setting tell it to be complete and add extra relevant details. With those additions it compares favorably to o3 on my recent chat history and even improved some cases. I prefer to scan a longer output than have the LLM guess what to omit. But I know many people have complained about verbosity so I can understand why they may have moved to less verbiage.

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Nobody's preventing them from rendering it and refining. That's certainly what we'd expect an AGI to do.

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It's likely that the commenter has read less than 5 million posts worth of text though. So perhaps this still points to a lack of diversity in content.

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What was the alternative you went with?

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I saw a very similar timely appeal here on Hacker News a few years ago and taught my son with this book at the age of 4. It has become my go-to comparison when prompting chat bots on what I want in a teaching material for other subjects. I listened to the entire article posted here and it makes me wonder if schools are getting something as foundational as reading wrong how can we trust the attention to research on anything else they're teaching? Don't get me wrong, I'm not going to pull my kid out of school but I'll dig a little deeper into how well he's learning. For math, we've been doing the Beast Academy books. It has gone... Okay. I like that they approach problems from many different ways which simulate the many different ways math is hidden in our interactions with the world. For my younger son I've recently started Teaching Your Child... because of how well it went for his brother but for math I may try something else to have a new data point. Something that occurred to me listening to the article is I wonder if certain skills are learned much faster with one on one instruction like the book has you do. Our schools pretty much never teach that way out of efficiency, though home schools often do. It may not be true for most subjects though or home school students would be so far ahead by college and that's not the impression I have.

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It's pretty damn capital intensive to be a productive farmer today. That said, AI will likely, hopefully, get cheaper over time.

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You could probably modify the metric to addressed comments per 1000 lines of code.

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"Here, we provide a quantifiable definition: A multimodal native model refers to a single model with strong understanding capabilities across multiple input modalities (e.g. text, code, image, video), that matches or exceeds the modality specialized models of similar capacities."

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My first thought upon seeing the prompt:

    If you would build an in-memory cache, how would you do it?

    It should have good performance and be able to hold many entries. 
    Reads are more common than writes. I know how I would do it already, 
    but I’m curious about your approach.

Was to add this requirement since it comes up so often:

    Let's assume that keys accessed follow a power law, so some keys get 
    accessed very frequently and we would like them to have the fastest 
    retrieval of all.

I'm not sure if there are any efficient tweaks to hash tables or b-trees that might help with this additional requirement. Obviously we could make a hash table take way more space than needed to reduce collisions, but with a decent load factor is the answer to just swap frequently accessed keys to the beginning of their probe chain? How do we know it's frequently accessed? Count-Min sketch?

Even with that tweak, the hottest keys will still be scattered around memory. Wouldn't it be best if their entries could fit into fewer pages? So, maybe a much smaller "hot" table containing say the 1,000 most accessed keys. We still want a high load factor to maximize the use of cache pages so perhaps perfect hashing?

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Where I thought this might be going from the first paragraph:

Negative numbers are sometimes represented with parentheses: (234.58)

Tables sometimes tell you in the description that all numbers in are in 1000's or millions.

The dollar sign is used by many currencies, including in Australia and Canada.

I'd probably look around for some other gotchas. Here's one page on prices in general: https://gist.github.com/rgs/6509585 but interestingly doesn't quite cover the OP's problem or the ones I brought up, though the use cases are slightly different.

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Something worth mentioning is that if your vectors all have the same length then cosine similarity and Euclidean distance will order most (all?) neighbors in the same order. Think of your query vector as a point on a unit sphere. The Euclidean distance to a neighbor will be a chord from the query point to the neighbor. Just as with the angle between the query-to-origin and the neighbor-to-origin vectors, the farther you move the neighbor from the query point on the surface of the sphere, the longer the chord between those points gets too.

EDIT: Here's a better treatment, and it is the case that they give the exact same orderings: https://ajayp.app/posts/2020/05/relationship-between-cosine-...

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This feels similar to those adversarial examples that first came out that were very tuned for a specific image recognizer. I haven't followed the research but I know they had some very limited success to getting it to work in the real world. I'm not sure if they ever worked across different models though.

The paper claims there is literature with more success for LLMs:

   Large language models have been shown to be vulnerable to adversarial
   attacks, in which attackers introduce maliciously crafted token sequences
   into the input prompt to circumvent the model’s safety mechanisms and 
   generate a harmful response [1, 14].

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https://jonpauluritis.com/articles/why-arent-developers-paid...

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Can I recommend you provide some cost estimates next to the examples for using your own key? I tried a few custom extractions and then checked my usage dashboard and it was already over $2.

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https://arxiv.org/abs/2006.16236 Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention