I asked Claude for references to some ideas the ai had output earlier about a 16th century cardinal (Sadoleto). It's response was, Let me analyze the letter, and then this:

async function searchForSadoletoLetter() {
  try {
    const files = ['sadoleto_letter.txt', 'sadoleto.txt', 'sadoleto_to_geneva.txt', 'letter_to_geneva.txt'];
    
    for (const filename of files) {
      try {
        const content = await window.fs.readFile(filename, { encoding: 'utf8' });
        console.log(`Found file: ${filename}`);
        console.log("First 500 characters:");
        console.log(content.substring(0, 500));
        return content;
      } catch (err) {
        // File not found, try the next one
      }
    }
    console.log("Could not find any files containing Sadoleto's letter");
    return null;
  } catch (err) {
    console.error("Error searching for files:", err);
    return null;
  }
}

const content = await searchForSadoletoLetter();

// Since we don't have the letter available as a file, let's search for published versions online
console.log("The letter isn't available as a file. We would need to rely on scholarly knowledge about Sadoleto's letter to Geneva (1539).");
console.log("Sadoleto's letter was published in 'A Reformation Debate: Sadoleto's Letter to the Genevans and Calvin's Reply' (ed. John C. Olin).");

This strikes me as odd? Did Claude write a script to generate its own response? Have coders put something like this in as a gaurdrail?

edit: details about earlier convo

Every company is trying to shoehorn AI into every product, and many online materials provide a general snake oil vibe, making it increasingly difficult to parse. So far, my primary sources have been GitHub, Medium, and some YouTube.

My goal is to better understand the underlying technology so that I can manipulate it better, train models, and use it most effectively. This goes beyond just experimenting with prompts and trying to overcome guardrails. It includes running local, like Ollama on my M1 Max, which I'm not opposed to.

Has anyone used a free, offline, open-source, real-time speech-to-speech translation app on under-powered devices (i.e., older smart phones)? There are a few libraries that written that purportedly can do or help with local speech-to-speech:

• https://github.com/ictnlp/StreamSpeech
• https://github.com/k2-fsa/sherpa-onnx
• https://github.com/openai/whisper

I'm looking for a simple app that can listen for English, translate into Korean (and other languages), then perform speech synthesis on the translation. Although real-time would be great, a short delay would work.

RTranslator is awkward (couldn't get it to perform speech-to-speech using a single phone). 3PO sprouts errors like dandelions and requires an online connection.

Any suggestions?

Apologies if this isn't an appropriate place to post this.

Inspired by a paper I found a while back (https://publications.lib.chalmers.se/records/fulltext/215545/local_215545.pdf), I tried my hand at implementing a program (in C#) to create ASCII art from an image. It works pretty well, but like they observed in the paper, it's pretty slow to compare every tile to 90-some glyphs. In the paper, they make a decision tree to replicate this process at a faster speed.

Recently, I revisited this. I thought I'd try making a neural net, since I found the idea interesting. I've watched some videos on neural nets, and refreshed myself on my linear algebra, and I think I've gotten pretty close. That said, I feel like there's something I'm missing (especially given the fact that the loss isn't really decreasing). I think my problem is specifically during backpropagation.

Here is a link to the TrainAsync method in GitHub: https://github.com/bendstein/ImageToASCII/blob/1c2e2260f5d4cfb45443fac8737566141f5eff6e/LibI2A/Converter/NNConverter.cs#L164C59-L164C69. The forward and backward propagation methods are below it.

If anyone can give me any feedback or advice on what I might be missing, I'd really appreciate it.

Playing with Llama 7B and 13B, I found that the 13B model was capable of doing a simple task, rewriting titles in sentence case for Tildes submissions. The 7B model doesn't appear capable of the same task, out of the box.

I heard about Android's new AICore available on a couple of new devices. But it sounds like Gemini Nano, which runs on-device, can only handle 2B or 3B parameters.

Is this size of model useful for real tasks? Does it only become useful after training on a specific domain? I'm a novice and wanting to learn a little bit about it. On-device AI is an appealing concept to me.

Here's a place to share machine learning research papers that seem interesting to you. I'm no expert, but sometimes I skim them, and maybe there are some folks on Tilde who know more than I do?

One paper per top-level post, and please link to arXiv (if relevant) and quote a bit of the abstract.

I've been a computer scientist in the field of AI for almost 15 years. Much of my time has been devoted to classical AI; things like planning, reasoning, clustering, induction, logic, etc. This has included (but had rarely been my focus) machine learning tasks (lots of Case-Based Reasoning). For whatever reason though, the deep learning trend never really interested me until recently. It really just felt like they were claiming huge AI advancements when all they really found was an impressive way to store learned data (I know this is an understatement).

Over time my opinion on that has changed slightly, and I have been blown away with the boom that is happening with transformers (GPTs specifically) and large language models. Open source projects are creating models comparable to OpenAIs behemoths with far less training and parameters which is making me take another look into GPTs.

What I find surprising though is that they seem to have only experimented with language. As far as I understand the inputs/outputs, the language is tokenized into bytes before prediction anyway. Why does it seem like (or rather the community act like) the technology can only be used for LLMs?

For example, what about a planning domain? You can specify actions in a domain in such a manner that tokenization would be trivial, and have far fewer tokens then raw text. Similarly you could generate a near infinite amount of training data if you wanted via other planning algorithms or simulations. Is there some obvious flaw I'm not seeing? Other examples might include behavior and/or state prediction.

I'm not saying that out of the box a standard GPT architecture is a guaranteed success for plan learning/planning... But it seems like it should be viable and no one is trying?

Hello there
(oh god, I am opening my first thread here - so exciting)

I'd love to ask the people here about local LLMs.
To be honest, I got interested in this topic, but am leaving reddit, where a sub r/locallama exists.
I don't want to interact with that site anymore, so I am taking this here.

My questions, to start us off:

• Models are available on huggingface (among other places), but where do I get the underlying software? I read "oogabooga" somewhere, but honestly, I am lost.
• If I only want to USE a local model, what are the requirements, and how do I judge if I can use something from the values of "4bit / 8 bit" and "30B, 7B"??
• If I get crazy and want to TRAIN a LorA ... what then?
• Good resources / wiki pages, tutorials, etc?

It's been six months since ChatGPT launched and about three months since I started posting these. I think it's getting harder to find new things to post about about AI, but here's another one anyway.

Here's the previous thread.