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Sparse Priming Representation (SPR): Como o MemGPT 2.0 Transforma a Memória da IA e Impulsiona a AGI

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Resumo Sobre Sparse Priming Representation (SPR) e sua Aplicação no Serviço Público

Nos últimos anos, a evolução da inteligência artificial (IA) tem gerado debates significativos sobre suas aplicações no setor público. Um conceito que tem ganhado destaque é o Sparse Priming Representation (SPR), uma técnica que permite que modelos de IA, como o MemGPT 2.0, opere com uma “memória ilimitada”. Esta inovação não apenas expande as capacidades da IA, mas levanta questões sobre como essas ferramentas podem ser utilizadas de maneira eficiente e ética no serviço público.

A implementação do SPR no serviço público pode trazer avanços significativos na forma como dados e informações são gerenciados. Imagine um sistema que armazena e processa informações de forma contínua, aprendendo e se adaptando às necessidades da sociedade em tempo real. Esta abordagem poderia melhorar a gestão de serviços, otimizar recursos e, principalmente, atender melhor às demandas dos cidadãos.

Entretanto, é fundamental refletir sobre os desafios éticos e práticos que envolvem essa integração. Como garantir que a IA ajude verdadeiramente a sociedade e não perpetue desigualdades existentes? É preciso um olhar atento para as políticas públicas, assegurando que a tecnologia seja utilizada de forma transparente e inclusiva.

A aplicação do SPR e de tecnologias como o MemGPT 2.0 no serviço público pode ser um passo rumo a um governo mais eficiente, mas exige um debate profundo sobre seus impactos. Ao refletirmos sobre essas inovações, devemos considerar não apenas os benefícios, mas também as responsabilidades que advêm da adoção de tais tecnologias. Assim, o serviço público pode se tornar um verdadeiro agente de transformação, aliando a potência da IA às necessidades reais da população.

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25 Comment on this post

  1. @intheworldofai
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  3. @DrFukuro
    Comentar

    Memgpt offers much more functionality than these prompts. Comparing them with each other only makes limited sense. If I want to use the spr, I have to write the entire framework around it myself, which memgpt already provides me with.
    You could rather think about giving memgpt an spr module, that would make more sense.

  6. @dameanvil
    Comentar

    00:12 🚀 "Sparse Priming Representation (SPR)," introduced by David Shapiro, surpasses MGBt in memory management for large language models, offering a more powerful and efficient alternative.
    01:50 🤔 Both humans and language models benefit from small reminders to recall memories. While MGBt uses a continuous loop, SPR efficiently recalls memories by mimicking the natural human process of sparse memory representations.
    02:46 🔄 SPR enables quick recall and reconstruction of memories associated with specific stimuli, aiming to replicate human memory processes for effective knowledge storage and retrieval.
    03:57 📚 SPR simplifies information storage and recall, resembling human memory by using short, clear sentences to capture main points. It proves useful in AI, machine learning, information management, and education.
    05:17 🌐 SPR's publicly available framework is easier to implement than MGBt, offering a significant advancement in memory organization techniques for a wide range of users.
    08:41 🧩 Using Sparse Priming Representations (SPRs) as primers, large language models can efficiently learn and understand complex novel ideas. This method efficiently compresses information during inference, improving learning effectiveness.
    11:26 🤯 SPR facilitates semantic compression, summarizing memories similar to how humans recall and convey information. By providing enough details, large language models can reconstruct the original statement efficiently.
    12:34 🙌 Kudos to David Shapiro for creating SPR. Viewers are encouraged to check out his video, subscribe to his channel, and explore the SPR repository for implementation details.

  7. @matten_zero
    Comentar

    David Shapiro is underappreciated. Hes known this for 7 months. Hes tested this with prompts, but yo be clear he hasnt built this out yet (as far as I know). MemGPT is not needed. Occams razor in effect here

  11. @bparlan
    Comentar

    I would love to focus on a project which combines spr and knowledge graph as base. Got plenty of ideas, utilising psychometric and sociometric data. Would love to join a community and get support to walk the path.

  12. @godned74
    Comentar

    I was testing the compression of files and uploading instructions before even hearing about this spr and I found it did have positive impact! haha at least I know now for sure I am on the correct path! The biggest thing that kills my projects from becoming something truly groundbreaking is the ai suffering from chronic amnesia.

  15. @jason_v12345
    Comentar

    I respect David Shapiro a great deal and am even a patreon supporter of his, but let's be honest: this is little more than a prompt that asks for gpt to "summarize." It is not a "compression" and "decompression" algorithm, and it is certainly not an "architecture." It has its uses, but it is not a substitute for something like MemGPT.

  17. @LuisBustos-jq8sz
    Comentar

    Its not this only compress info while memgpt directly grabs the full information memgpt is not a compressor is an architecture, whit SPR you still have the token limit while at the same time in some cases you can get wrong info.

  18. @joelswann2310
    Comentar

    will you do a video using LM Studio or a vLLM server to host a local version of a open source large language model, and then configuring Open Interpreter to interact with this local server instead of the OpenAI API?

  19. @SwingingInTheHood
    Comentar

    I watched David's video and this one. I'm still wondering how does this work in the real world? I see what you're doing in the Playground, but I have a RAG implementation using the gpt-4 api. Let's say a user submits a prompt, I run a cosine similarity of the prompt against my vector store. Now, let's say further that I've already also embedded the SPR generated representations of the embedded text, so that is what is retrieved. I now have this list of SPRs. Is this what I send to the model as context along with the original prompt for an answer? Or, do I decompress the SPR representations first? And, if I do that, aren't I taking up the same amount of context that I would have if I had returned the original embedded texts?

    So, if I don't decompress the SPRs, how is the model going to be able to arrive at an accurate answer with just the SPR representations?

    In short, how does this technique work in a current RAG implementation?

  21. @Tony0Green
    Comentar

    Question: when you create an SPR, can you then include that compressed data directly to the LLM as context? Or does the LLM need the decompression instructions to rebuild the data? Wouldn't that still use tokens?

  23. @stunspot
    Comentar

    I do this stuff all the time in my persona prompting, especially when defining a persona's skills. For example, a programmer might have:

    [CODE]:1.[Fund]: 1a.CharId 1b.TskDec 1c.SynPrf 1d.LibUse 1e.CnAdhr 1f.OOPBas 1g.AOPBas 2.[Dsgn]: 2a.AlgoId 2b.CdMod 2c.Optim 2d.ErrHndl 2e.Debug 2f.OOPPatt 2g.AOPPatt 3.[Tst]: 3a.CdRev 3b.UntTest 3c.IssueSpt 3d.FuncVer 3e.OOPTest 3f.AOPTst 4.[QualSec]: 4a.QltyMet 4b.SecMeas 4c.OOPSecur 4d.AOPSecur 5.[QA]: 5a.QA 5b.OOPDoc 5c.AOPDoc 6.[BuiDep]: 6a.CI/CD 6b.ABuild 6c.AdvTest 6d.Deploy 6e.OOPBldProc 6f.AOPBldProc 7.[ConImpPrac]: 7a.AgileRetr 7b.ContImpr 7c.OOPBestPr 7d.AOPBestPr 8.[CodeRevAna]: 8a.PeerRev 8b.CdAnalys 8c.ModelAdmin 8d.OOPCdRev 8e.AOPCdRev

    That's worth about 2000 tokens worth of hagiography praising his coding skills or explicitly writing out his resume or whatever.

    This is from an article I wrote:

    "…Or look at DNA. There aren't INSTRUCTIONS there – there's key salient tokens for the context of cellular molecular chemistry. Shapes of energy and information structures called molecules that inspire proteins in the context of the cell. DNA is a prompt. Cell is the model. There isn't an instruction saying "Ok, now add the next amino acid: `[CODON_STACK++; CALL ORGONELLE.RIBOSOME(lysine);`]." It's just a pattern – a geometrical SHAPE – made of energy potentials. When that informational structure it put in the context of the cell and you add time and energy to the system, the result is a new protein. That is a cell's response to your transaction of having added that DNA. It didn't "do what you say" – you shaped the way it grows its response by the way you shaped what you add to it…."

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